A flood diversion gate control management method and system for digital flood storage analysis

Abstract

The present invention relates to the technical field of digital flood storage analysis, and in particular to a method and system for regulating and managing flood diversion gates for digital flood storage analysis. The method comprises the following steps: constructing a terrain model of a flood storage area; obtaining historical flood storage hydrological data of the flood storage area; conducting spatial hydrological characteristic coupling analysis of the flood storage mode on the historical flood storage hydrological data according to the flood storage area terrain model, and generating spatial hydrological characteristic data of the flood storage mode; constructing a flood storage dynamic generalization model based on the flood storage area terrain model and the flood storage mode spatial hydrological characteristic data; conducting flood storage dynamic generalization model based on the flood storage dynamic generalization model to simulate and analyze flood characteristics in the flood storage embankment, and generating flood characteristic simulation data in the flood storage embankment; analyzing simulated land planning data in the embankment area based on the flood characteristic simulation data in the flood storage embankment; and designing intelligent regulation and decision making of the flood diversion gate based on the simulated land planning data in the embankment area and the flood characteristic simulation data in the flood storage embankment. The present invention realizes accurate analysis of flood characteristics in the flood storage embankment.

Description

Technical Field

[0001] The invention relates to the technical field of digital flood storage analysis, and in particular to a flood diversion gate control and management method and system for digital flood storage analysis. Background Art

[0002] In flood management and flood control, the role of flood storage areas is to relieve river pressure, temporarily store flood water in designated areas, reduce downstream flood control pressure, and protect the safety of coastal areas. Flood storage areas are usually enclosed by embankment areas, and flood diversion gates are used to regulate the entry and exit of flood water inside the embankment areas. This area is usually low-lying and has a large flood storage capacity. When the flood reaches a certain water level or regulation needs, the diversion gates are opened and the flood enters the embankment for temporary detention. The area within the flood storage embankment can effectively relieve flood pressure and protect downstream cities and important infrastructure from flood damage. In addition, the land, buildings, and population resettlement in the embankment area need to be scientifically planned to minimize the damage caused by floods to the area. When responding to floods, how to effectively manage and regulate the water level, flow rate, and flood evolution within the flood storage embankment is the key to achieving flood control and disaster reduction. However, the existing digital flood storage analysis and diversion gate control and management methods rely on static hydrological information and simple mathematical models, which are difficult to adapt to the rapidly changing dynamic characteristics of floods. The analysis accuracy of flood distribution within the embankment is poor, and it is impossible to achieve accurate regional planning within the embankment and intelligent control of diversion gates. Summary of the invention

[0003] Based on this, the present invention provides a method and system for regulating and managing flood diversion gates for digital flood storage analysis to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a flood diversion gate control and management method for digital flood storage analysis includes the following steps:

[0005] Step S1: obtaining measured terrain data of the flood storage area in the geographic information system; using unmanned aerial vehicle detection equipment to monitor the terrain of the flood storage area and generate terrain monitoring data of the flood storage area; performing flood area terrain modeling based on the measured terrain data of the flood storage area and the terrain monitoring data of the flood storage area and generating a terrain model of the flood storage area;

[0006] Step S2: Acquire historical flood storage hydrological data of the flood storage area; perform spatial hydrological characteristic coupling analysis of the flood storage mode on the historical flood storage hydrological data according to the flood storage area terrain model to generate spatial hydrological characteristic data of the flood storage mode;

[0007] Step S3: performing a flood storage dynamic boundary condition analysis based on the hydrological spatial distribution characteristic data to generate flood storage dynamic boundary condition data; performing a flood storage dynamic operating condition analysis based on the flood storage dynamic boundary condition data to generate flood storage dynamic operating condition data; transmitting the flood storage dynamic operating condition data as a flood storage dynamic generalization coefficient to the flood storage terrain model to perform flood storage dynamic generalization coefficient mapping to construct a flood storage dynamic generalization model;

[0008] Step S4: Based on the flood storage dynamic generalization model, simulate and analyze the flow rate trend of the diversion gate regulated inflow to the embankment, and generate simulated data of the flow rate trend of the diversion gate regulated inflow to the embankment; based on the flow rate trend data of the diversion gate regulated inflow to the embankment, simulate and analyze the flood characteristics in the flood storage embankment, and generate simulated data of the flood characteristics in the flood storage embankment;

[0009] Step S5: Based on the flood characteristic simulation data in the flood storage embankment, simulated land planning processing is performed in the embankment area to generate simulated land planning data in the embankment area, and the simulated land planning data in the embankment area is transmitted to the terminal to perform the zoning land planning in the flood storage embankment;

[0010] Step S6: Based on the simulated land planning data of the area within the embankment and the simulated data of flood characteristics in the flood storage embankment, the intelligent regulation and control analysis of the flood diversion gate is performed to design the intelligent regulation and control decision of the flood diversion gate, and the intelligent regulation and control decision of the flood diversion gate is transmitted to the terminal to execute the intelligent regulation and control strategy of the flood diversion gate.

[0011] Further, step S1 includes the following steps:

[0012] Step S11: obtaining measured topographic data of the flood storage area in the geographic information system;

[0013] Step S12: performing digital feature analysis of the measured terrain according to the measured terrain data of the flood storage area to generate digital feature data of the measured terrain; and establishing a preliminary flood storage area terrain model based on the digital feature data of the measured terrain;

[0014] Step S13: using drone detection equipment to perform terrain monitoring processing in the flood storage area to generate terrain monitoring data for the flood storage area;

[0015] Step S14: performing terrain monitoring elevation feature analysis based on the terrain monitoring data of the flood storage area to generate terrain monitoring elevation feature data;

[0016] Step S15: performing flood diversion gate location monitoring processing based on the flood storage area terrain monitoring data to generate flood diversion gate location monitoring data;

[0017] Step S16: The relative position of the diversion gate in the preliminary flood storage area terrain model is marked by the diversion gate positioning monitoring data, and the elevation accuracy of the marked preliminary flood storage area terrain model is optimized based on the terrain monitoring elevation feature data to generate a flood storage area terrain model.

[0018] Further, step S2 includes the following steps:

[0019] Step S21: Acquire historical flood storage hydrological data of the flood storage area;

[0020] Step S22: Analyze historical flood storage events based on historical flood storage hydrological data to generate historical flood storage event data;

[0021] Step S23: using historical flood storage event data as flood storage event clustering labels; performing flood storage hydrology clustering analysis on historical flood storage hydrology data according to the flood storage event clustering labels to generate historical flood storage hydrology clustering data;

[0022] Step S24: mining and processing the hydrological characteristics of historical flood storage modes according to the historical flood storage hydrological clustering data to generate hydrological characteristic data of the flood storage mode;

[0023] Step S25: Analyze the spatial characteristics of the flood storage area according to the flood storage area terrain model to generate spatial characteristic data of the flood storage area;

[0024] Step S26: performing spatial hydrological characteristic coupling analysis of the flood storage mode on the hydrological characteristic data of the flood storage mode according to the spatial characteristic data of the flood storage area, and generating spatial hydrological characteristic data of the flood storage mode.

[0025] Further, step S3 includes the following steps:

[0026] Step S31: analyzing the flood storage dynamic boundary conditions based on the hydrological spatial distribution characteristic data to generate flood storage dynamic boundary condition data;

[0027] Step S32: performing finite element meshing processing on the flood storage dynamic boundary condition data to generate mesh flood storage dynamic boundary condition data;

[0028] Step S33: performing flood storage dynamic finite element numerical parameter analysis according to the grid flood storage dynamic boundary condition data to generate flood storage dynamic finite element numerical parameters;

[0029] Step S34: Calculating the flood storage power condition according to the flood storage power finite element data to generate flood storage power condition data;

[0030] Step S35: transmitting the flood storage dynamic condition data as the flood storage dynamic generalization coefficient to the flood storage terrain model for flood storage dynamic generalization coefficient mapping to construct the flood storage dynamic generalization model.

[0031] Further, step S31 includes the following steps:

[0032] Step S311: performing water depth distribution boundary condition analysis according to the hydrological spatial distribution characteristic data to generate water depth distribution boundary condition data;

[0033] Step S312: performing flood storage dynamic basin characteristic analysis based on the hydrological spatial distribution characteristic data and the water depth distribution boundary condition data to generate flood storage dynamic basin characteristic data, wherein the flood storage dynamic basin characteristic data includes flood storage dynamic flow velocity data and flood storage dynamic flow direction data;

[0034] Step S313: Perform flood storage dynamic boundary condition analysis based on the water depth distribution boundary condition data and the flood storage dynamic basin characteristic data to generate flood storage dynamic boundary condition data.

[0035] Further, step S4 includes the following steps:

[0036] Step S41: performing simulation analysis on the flow rate trend of the dike regulated by the diversion gate based on the flood storage dynamic generalization model, and generating simulation data on the flow rate trend of the diversion gate regulated by the dike;

[0037] Step S42: Selecting the characteristic point data and the characteristic section data of the digitized embankment through the flood diversion gate positioning monitoring data in the flood storage area terrain model;

[0038] Step S43: performing time series correlation characteristic analysis of flow velocity and water depth at characteristic points within the embankment according to the digital characteristic point data within the embankment on the flow trend data of the diversion gate regulating the embankment flow, and generating time series correlation characteristic data of flow velocity and water depth at characteristic points within the embankment;

[0039] Step S44: performing a time series correlation characteristic analysis of flow velocity and water depth at characteristic sections within the embankment on the trend data of the flow into the embankment regulated by the flood diversion gate according to the digital characteristic section data within the embankment, and generating time series correlation characteristic data of flow velocity and water depth at characteristic sections within the embankment;

[0040] Step S45: Perform flood characteristic simulation analysis in the flood storage embankment according to the time series correlation characteristic data of flow velocity and water depth at characteristic points in the embankment and the time series correlation characteristic data of flow velocity and water depth at characteristic sections in the embankment to generate flood characteristic simulation data in the flood storage embankment.

[0041] Further, step S41 includes the following steps:

[0042] Step S411: performing simulation analysis on the dynamic evolution process of flood storage according to the flood storage dynamic generalization model, and generating simulation data on the dynamic evolution process of flood storage;

[0043] Step S412: performing a flood storage dynamics evolution trend analysis on the flood storage dynamics evolution process simulation data according to a preset hydrodynamics evolution time step, and generating flood storage dynamics evolution trend data;

[0044] Step S413: Perform simulation analysis on the flow rate trend of the diversion gate regulated inflow according to the dynamic evolution trend data of flood storage power, and generate simulation data on the flow rate trend of the diversion gate regulated inflow.

[0045] Further, step S5 includes the following steps:

[0046] Step S51: performing flood impact zoning analysis within the flood storage embankment according to flood characteristic simulation data within the flood storage embankment, and generating flood impact zoning data within the embankment;

[0047] Step S52: simulate land planning processing in the area within the embankment through the flood impact zoning data within the embankment, generate simulated land planning data in the area within the embankment, and transmit the simulated land planning data in the area within the embankment to the terminal to execute the zoning land planning operation in the flood storage embankment.

[0048] Further, step S6 includes the following steps:

[0049] Step S61: analyzing the characteristics of flood control demand in the flood storage embankment according to the simulated land planning data in the embankment area, and generating the characteristics of flood control demand in the flood storage embankment;

[0050] Step S62: Dynamically monitor and process the flood characteristic simulation data in the flood storage embankment to generate dynamic flood characteristic simulation data in the flood storage embankment; perform intelligent control analysis of the flood diversion gate based on the dynamic flood characteristic simulation data in the flood storage embankment and the flood control demand characteristic data in the flood storage embankment to design the intelligent control decision of the flood diversion gate, and transmit the intelligent control decision of the flood diversion gate to the terminal to execute the intelligent control strategy of the flood diversion gate.

[0051] This specification provides a digital flood storage analysis flood diversion gate control and management system, which is used to execute the digital flood storage analysis flood diversion gate control and management method as described above. The digital flood storage analysis flood diversion gate control and management system includes:

[0052] The flood storage area terrain modeling module is used to obtain the measured terrain data of the flood storage area in the geographic information system; use the drone detection equipment to monitor the terrain of the flood storage area and generate the terrain monitoring data of the flood storage area; perform flood area terrain modeling based on the measured terrain data of the flood storage area and the terrain monitoring data of the flood storage area to generate the terrain model of the flood storage area;

[0053] The spatial hydrological characteristic analysis module is used to obtain the historical flood storage hydrological data of the flood storage area; according to the terrain model of the flood storage area, the spatial hydrological characteristic coupling analysis of the flood storage mode is carried out on the historical flood storage hydrological data to generate the spatial hydrological characteristic data of the flood storage mode;

[0054] The hydrodynamic generalization model construction module is used to analyze the flood storage dynamic boundary conditions based on the hydrological spatial distribution characteristic data and generate the flood storage dynamic boundary condition data; analyze the flood storage dynamic operating conditions based on the flood storage dynamic boundary condition data and generate the flood storage dynamic operating conditions data; transmit the flood storage dynamic operating conditions data as the flood storage dynamic generalization coefficient to the flood storage terrain model to map the flood storage dynamic generalization coefficient, so as to construct the flood storage dynamic generalization model;

[0055] The flood analysis module in the flood storage embankment is used to simulate and analyze the flow trend of the flood diversion gate regulated inflow based on the flood storage dynamic generalization model, and generate the simulated data of the flow trend of the flood diversion gate regulated inflow; simulate and analyze the flood characteristics in the flood storage embankment based on the flow trend data of the flood diversion gate regulated inflow, and generate the simulated data of the flood characteristics in the flood storage embankment;

[0056] The land planning module of the area within the embankment is used to perform simulated land planning processing of the area within the embankment based on the flood characteristic simulation data within the flood storage embankment, generate simulated land planning data of the area within the embankment, and transmit the simulated land planning data of the area within the embankment to the terminal to execute the zoning land planning of the flood storage embankment;

[0057] The intelligent control module of flood storage and diversion gates is used to perform intelligent control analysis of flood diversion gates based on simulated land planning data in the area within the embankment and simulated data of flood characteristics in the flood storage embankment, so as to design intelligent control decisions of flood diversion gates, and transmit the intelligent control decisions of flood diversion gates to the terminal to execute the intelligent control strategy of flood diversion gates.

[0058] The beneficial effect of this application is that by using geographic information systems (GIS) and drone detection equipment, it is possible to obtain high-precision measured data on the topography of the flood storage area, monitor terrain changes in real time, and generate a dynamically updated terrain model of the flood storage area. The improved accuracy of the terrain model makes the hydrological analysis and flood diversion regulation of the flood storage area more accurate. Through the coupling analysis of historical hydrological data and the terrain model of the flood storage area, it is possible to extract the spatial hydrological characteristics of the flood storage mode, realize the fine simulation of the flood evolution process, and provide a scientific basis for predicting flood evolution trends and flood diversion regulation, making the regulation more targeted and effective. The hydrodynamic boundary conditions and working conditions are analyzed using the hydrological spatial distribution characteristics, and a generalized model of flood storage dynamics is constructed. The model accurately reflects information such as flood velocity, flow direction, and inundation range, thereby supporting the simulation of inflow flow trends and flood characteristics of flood diversion gate regulation, and improving the hydrodynamic analysis capabilities of the overall system. Based on the flood storage dynamic generalization model, the inflow flow trend simulation of the diversion gate regulation is carried out. By simulating the inflow flow trend under different diversion gate regulation, according to the time series correlation analysis of the flow velocity and water depth of the characteristic points and sections in the digital diversion gate, the changes in flood characteristics in different characteristic areas can be dynamically tracked. This helps to fully understand the flood changes at various locations within the flood storage diversion gate area, ensure that the diversion gate regulation strategy can adapt to the actual hydrological conditions at different locations in the diversion gate, and effectively prevent the occurrence of flood disasters in local areas. Through the simulation and analysis of flood characteristics in the diversion gate, the key characteristics of the flow velocity, water depth and duration of the flood in the diversion gate can be accurately simulated, and combined with the terrain characteristics of different regions, more accurate data support is provided for the diversion gate regulation decision. This simulation analysis can help managers scientifically formulate regulation strategies and reduce the risk of blind operation. Through the simulation data of flood characteristics, the land in the embankment is scientifically planned, and combined with the flood impact zoning analysis, the use and safety layout of the land in the embankment are optimized to reduce the impact of floods on the population, agriculture and economic activities in the flood storage area, protect the lives and property of residents, and provide strong support for the subsequent restoration work after the flood recedes. Based on the intelligent regulation and analysis of the dynamic characteristics of floods in the embankment, the opening and closing status of the flood diversion gate can be automatically adjusted according to the real-time flood evolution and regulation needs, which improves the regulation efficiency of the flood diversion gate, realizes the automation and intelligence of regulation, reduces the lag and error of manual operation, ensures that flood control measures can be implemented at the optimal time, significantly enhances the emergency response capability to floods, and can quickly adjust the operation of the flood diversion gate when the flood breaks out or changes, minimizing the disaster losses. Therefore, the diversion gate regulation and management method for digital flood storage analysis of the present invention constructs a digital flood storage dynamic generalization model by analyzing the terrain model of the flood storage area and the hydrological spatial distribution characteristics. It can accurately simulate the changes in flow velocity, flow direction and water depth of the flood in the embankment when the diversion gate is regulated, thereby improving the accuracy of flood evolution analysis and helping to deeply understand the distribution of floods in the embankment, so as to improve the accuracy of dynamic characteristic evolution analysis of rapidly changing floods in the embankment.By analyzing the time-series correlation characteristics of flow velocity and water depth at characteristic points and sections within the embankment, the flood dynamics of each characteristic area can be accurately grasped. This detailed time-series correlation analysis improves the accuracy of flood distribution analysis within the embankment, making the control strategy more accurate, ensuring that each area within the embankment can obtain scientific and reasonable flood protection, so as to achieve more accurate land planning and processing of the areas within the flood storage embankment, combine the flood risks of different regions, reasonably zone, optimize land use, and reduce the impact of floods on important facilities and densely populated areas. And automatically adjust the opening and closing status of the flood diversion gate to achieve accurate flow control of floods within the embankment, reduce lags and errors in manual operations, and significantly improve the efficiency of control and the ability to respond to sudden floods. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 A schematic diagram of the steps of a flood diversion gate control and management method for digital flood storage analysis according to the present invention;

[0060] Figure 2 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0061] Figure 3 for Figure 1 Detailed implementation steps of step S4 in FIG.

[0062] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0063] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0064] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0065] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0066] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for controlling and managing flood diversion gates based on digital flood storage analysis. In the embodiments of the present invention, please refer to Figure 1 As shown, it is a schematic diagram of the steps of a method for regulating and managing a flood diversion gate based on digital flood storage analysis of the present invention. The method for regulating and managing a flood diversion gate based on digital flood storage analysis comprises the following steps:

[0067] To achieve the above purpose, a flood diversion gate control and management method for digital flood storage analysis includes the following steps:

[0068] Step S1: obtaining measured terrain data of the flood storage area in the geographic information system; using unmanned aerial vehicle detection equipment to monitor the terrain of the flood storage area and generate terrain monitoring data of the flood storage area; performing flood area terrain modeling based on the measured terrain data of the flood storage area and the terrain monitoring data of the flood storage area and generating a terrain model of the flood storage area;

[0069] In the embodiment of the present invention, the measured terrain data of the flood storage area in the geographic information system is obtained, and the measured terrain data of the flood storage area is retrieved through the geographic information system (GIS) platform. These data are usually obtained by surveying and mapping units or other specialized institutions through ground survey equipment, including but not limited to high-precision terrain measurement data generated by methods such as laser radar (LiDAR) scanning and total station measurement. After obtaining the measured terrain data, the digital features of the regional terrain are analyzed according to these data to generate digital feature data of the measured terrain, and the measured terrain data are digitized using special terrain analysis software (such as ArcGIS or QGIS). The terrain height, slope, geomorphic features and other information of each point are converted into digital features to form a digital elevation model (DEM) to establish a preliminary flood storage area terrain model, which covers key terrain information such as terrain undulations and slope trends in the region. Since the measured terrain data of the flood storage area in the geographic information system has information lag, in order to improve the accuracy of the terrain model, drone detection equipment is used to monitor the terrain of the flood storage area in real time to generate higher resolution and real-time flood storage area terrain monitoring data. According to the terrain monitoring data obtained by the drone, the terrain monitoring elevation feature analysis is carried out. Through the dedicated elevation analysis software (such as Global Mapper or Pix4D), the data collected by the drone is refined, the elevation features of each point are extracted, and an accurate elevation data set is generated. The elevation fitting algorithm is applied to filter and correct the noise in the original data to ensure the accuracy of the elevation feature data. At the same time, the elevation data of important terrain features such as local height difference, mountains, and river valleys are analyzed to supplement the deficiencies of traditional measurement data. The terrain monitoring data obtained by the above-mentioned drone detection is used for the positioning and monitoring of the diversion gate. It is necessary to combine the existing location information of the diversion gate infrastructure and recalibrate the precise geographic coordinates of the diversion gate. The specific location of the diversion gate is identified through the GIS system and the images taken by the drone, and the location is matched with the reference coordinate system in the terrain model through the coordinate conversion tool. High-precision GPS equipment is used to ensure that the positioning and monitoring data of the diversion gate reaches the sub-meter level of accuracy, thereby providing accurate geometric parameters for subsequent hydrodynamic analysis and flow simulation. After obtaining the positioning monitoring data of the flood diversion gate, the data is applied to the preliminary constructed flood storage area terrain model to identify the relative position of the flood diversion gate. The exact position of the flood diversion gate is superimposed on the terrain model through a software platform (such as AutoCAD Civil 3D or ArcGIS) to ensure that the geometric position of the flood diversion gate fully corresponds to the surrounding terrain features. At the same time, the accuracy of the preliminary terrain model is optimized based on the elevation monitoring feature data, especially the terrain height difference around the flood diversion gate is further refined, and the key parameters such as the slope of the terrain and the riverbed depth in the model are adjusted to make the terrain model of the entire flood storage area more accurate and suitable for subsequent hydrodynamic analysis and flood diversion control decisions.

[0070] Step S2: Acquire historical flood storage hydrological data of the flood storage area; perform spatial hydrological characteristic coupling analysis of the flood storage mode on the historical flood storage hydrological data according to the flood storage area terrain model to generate spatial hydrological characteristic data of the flood storage mode;

[0071] In the embodiment of the present invention, by accessing the historical hydrological monitoring database of the water conservancy department, the historical flood data of the designated flood storage area is extracted, including the peak flow, maximum water level, flow change time series data of each flood, and the inflow and outflow inside and outside the flood storage area. Each flood event in the historical hydrological data is segmented by an analysis tool (such as MATLAB or Python), and the key features of each flood (such as peak water level, maximum flow, flow change rate) are extracted from the time series data, and the start time, peak arrival time, flood retreat time and other information of each event are recorded. A complete historical flood storage event data table is generated using these extracted key features. The table takes the time axis as the main line, records the detailed parameters of each flood event, and classifies and organizes the main hydrological features of the event to generate historical flood storage event data. The main characteristic parameters of each flood storage event are extracted from the historical flood storage event data. These parameters include the peak flow, maximum water level, flood duration, peak arrival time and other key hydrological indicators of the flood event as clustering labels, which represent the characteristic attributes of the event, facilitating subsequent clustering analysis. Perform cluster analysis on historical hydrological data with cluster labels. Standardize the historical flood storage event data and use the K-means clustering algorithm to analyze the cluster labels. Perform similarity analysis by using the peak flow, maximum water level, flood duration, peak arrival time, and other key hydrological indicators as cluster labels. Cluster the similarity analysis results of cluster labels based on the divided K value, set the appropriate number of iterations and initial center points to ensure that the algorithm converges and finds the best classification. Run the clustering algorithm to divide each historical flood storage event into specific categories and generate historical flood storage hydrological cluster data. According to the historical flood storage hydrological cluster data, the hydrological characteristics of the historical flood storage mode are mined and processed, and the clustered historical flood storage data are further analyzed to mine the key hydrological characteristics of each type of flood mode. By using statistical methods (such as mean, variance, correlation analysis, etc.) to analyze the peak water level, flow velocity change, flood duration and other parameters of each cluster category, the common and unique characteristics of different types of flood events are extracted. Through frequency distribution and trend analysis, the hydrological change patterns of various flood events are extracted to generate hydrological characteristic data of flood storage mode. Use GIS software (such as ArcGIS or QGIS) to load the previously generated terrain model of the flood storage area, and use the digital elevation model (DEM) to extract the terrain undulations, drainage paths and low-lying area distribution in the area. The generated spatial characteristic data should include data tables such as slope distribution maps, river channel and watershed distribution, and flood inundation risk levels in low-lying areas.According to the spatial characteristic data of the flood storage area, the spatial hydrological characteristics of the flood storage mode are coupled with the hydrological characteristic data of the flood storage mode. The hydrological characteristic data and the spatial characteristic data are superimposed and analyzed on the GIS platform. The hydrological characteristics are coupled with the terrain spatial characteristics using spatial analysis tools (such as the "spatial connection" or "interpolation analysis" function in ArcGIS). This includes correlation analysis of the flow and water level of different flood modes with the slope and low-lying areas of the region to determine the flood inundation range and flow velocity distribution under various flood modes. Through spatial analysis, the flow path, inundation depth and duration of floods under different terrain conditions are clarified to form the spatial hydrological characteristic data of the flood storage mode.

[0072] Step S3: performing a flood storage dynamic boundary condition analysis based on the hydrological spatial distribution characteristic data to generate flood storage dynamic boundary condition data; performing a flood storage dynamic operating condition analysis based on the flood storage dynamic boundary condition data to generate flood storage dynamic operating condition data; transmitting the flood storage dynamic operating condition data as a flood storage dynamic generalization coefficient to the flood storage terrain model to perform flood storage dynamic generalization coefficient mapping to construct a flood storage dynamic generalization model;

[0073] In an embodiment of the present invention, the dynamic boundary condition analysis of flood storage is performed based on the hydrological spatial distribution characteristic data, and the acquired hydrological spatial distribution characteristic data, including water level changes, flow changes, flow velocity distribution, river channels and low-lying areas in the flood storage area, etc., are input into the hydrodynamic analysis software (such as HEC-RAS or MIKE 21). A preliminary analysis is performed on each key hydrological parameter to determine the type of boundary conditions, such as upstream and downstream flow, river water level, etc. By analyzing the temporal changes of the boundary conditions, a complete data set reflecting the flood flow boundary is generated. The boundary condition data includes the initial conditions of the flood, the flow input and output paths, the flood peak time, etc., which provide a basis for subsequent hydrodynamic calculations. The finite element meshing processing is performed on the dynamic boundary condition data of the flood storage area, and the hydrodynamic boundary condition data of the flood storage area, including water level, flow, and flow velocity distribution, etc., is imported using a dedicated finite element analysis tool (such as ANSYS or OpenFOAM). Before meshing, it is necessary to reasonably select the type and density of the grid according to the terrain complexity and water flow characteristics of the area. For areas with complex and drastic water flow (such as bends in the river or places with large slopes), denser three-dimensional unstructured grids are divided to more accurately capture the changes in water flow; sparser grids are used for areas with relatively stable water flow and relatively flat terrain. The grid data is imported into the hydrodynamic calculation module, and numerical simulation is performed based on hydrodynamic models such as the Navier-Stokes equation or the shallow water equation. First, the boundary conditions are set, such as inflow flow, flood peak time, and time step of flood evolution. Then, the finite element solver is used to calculate the hydrodynamic parameters of each grid node to obtain the flow velocity, pressure, turbulence characteristics, water level changes and other results of each node. Through iterative calculation of the time step, the propagation and evolution of the entire flood process in the flood storage area are simulated. The calculation results include the hydrodynamic parameters of each grid in different time periods. The hydrodynamic working conditions of the flood storage are calculated based on the hydrodynamic parameters of each grid in the same time period. Combined with the preset working conditions, specific boundary conditions are set, such as different inflow flows, flood durations, and flood peak arrival times. At the beginning of the calculation, the dynamic evolution of the flood under different working conditions is simulated by solving the hydrodynamic equations (such as the Navier-Stokes equations). In the working condition calculation, the key parameters of the flood, such as flow velocity, water depth, flow distribution, and pressure changes, are analyzed, and the propagation path, inundation range, and water level changes of the flood are tracked. Through multiple iterative calculations, the evolution law of floods in the flood storage area under different working conditions is obtained. Finally, a detailed data table of the dynamic working conditions of flood storage is generated, which contains the distribution of parameters such as flow velocity, flow, and water level in different time periods and spatial locations for subsequent analysis and optimization. The dynamic working condition data of flood storage is used as input, and the hydrodynamic data of each node is converted into generalization coefficients through interpolation and fitting algorithms (such as linear interpolation or bilinear interpolation).The generalization coefficient reflects the simplified expression of hydrodynamic characteristics in space and time. The generalization coefficient includes unit flow velocity, flow gradient, flood depth change rate, etc. These generalization coefficients are combined with the three-dimensional terrain model of the flood storage area, and each terrain unit is mapped through a geographic information system (GIS) or a hydrodynamic simulation platform (such as HEC-RAS or MIKE FLOOD), and the generalization coefficient is matched with its spatial position. By simplifying complex hydrodynamic calculations, the generalization model can quickly respond to different flood conditions and predict the evolution trend of floods.

[0074] Step S4: Based on the flood storage dynamic generalization model, simulate and analyze the flow rate trend of the diversion gate regulated inflow to the embankment, and generate simulated data of the flow rate trend of the diversion gate regulated inflow to the embankment; based on the flow rate trend data of the diversion gate regulated inflow to the embankment, simulate and analyze the flood characteristics in the flood storage embankment, and generate simulated data of the flood characteristics in the flood storage embankment;

[0075] In the embodiment of the present invention, the flood storage dynamics trend simulation analysis is performed by establishing a flood storage dynamics generalization model, inputting historical hydrological data, including information such as rainfall, flow and water level, and setting specific parameters of the diversion gate, such as opening and control strategy, to analyze the flood storage dynamics trend under different conditions. The model is used for simulation calculation, and the inflow flow trend under different flow and water level conditions is analyzed through step-by-step iteration. During the calculation process, the flow change output by the model is monitored in real time, and the inflow flow data at each time node is recorded. The final generated simulation data of the inflow flow trend of the diversion gate control will include a series of time series of flow changes, which can clearly show the dynamic changes of the inflow flow under different control measures. The diversion gate positioning monitoring data in the flood storage area terrain model is obtained, and the evolution of different total flood storage under different inflow flows and different total flood storage capacities is studied by analyzing the characteristic points and the changes in the flooded water depth and flow velocity of the characteristic sections over time. By analyzing the hydrological monitoring data of the area, such as the data provided by the flow meter and the water level meter, the characteristic point data of the specific digital dike is selected. These characteristic points usually include multiple water level and flow velocity measurement points near the flood diversion gate. In addition to the characteristic point data, it is also necessary to extract the characteristic section data within the digitized embankment, including multiple sections with information such as water depth, flow velocity and flow direction. The generated digital characteristic point data and characteristic section data within the embankment will provide the necessary input data for subsequent flow velocity and water depth analysis. Read the characteristic point data, which usually includes the water depth and flow velocity information of each measurement point, and align it with the inflow flow trend data in time series. Then, evaluate the relationship between flow velocity and water depth through time series analysis methods such as cross-correlation analysis or dynamic time warping (DTW). During the analysis process, focus on observing the dynamic characteristics of flow velocity and water depth over time, and display the relationship between the two by generating scatter plots or time series graphics. At the same time, use statistical analysis methods (such as linear regression or time series regression) to quantify the correlation between flow velocity and water depth. The generated velocity and water depth time series correlation characteristic data of characteristic points in the embankment will contain the correlation information of time, velocity and water depth, providing an in-depth understanding of the change of velocity and water depth over time, and providing data support for subsequent flood simulation analysis; the relationship between the water depth and velocity information corresponding to the characteristic sections in different embankments and time is statistically analyzed, and the velocity and water depth time series correlation characteristic data of characteristic sections in the embankment will contain the correlation information of time, velocity and water depth, providing an in-depth understanding of the change of velocity and water depth over time, and providing data support for subsequent flood simulation analysis. Collect and organize the velocity and water depth time series correlation characteristic data of characteristic points in the embankment and the velocity and water depth time series correlation characteristic data of characteristic sections in the embankment, including the velocity, water depth and their change over time of each characteristic point and section. Import these data into hydrodynamic simulation software (such as HEC-RAS or SWMM), which can perform one-dimensional or two-dimensional water flow simulation. Set simulation parameters in the simulation software, including initial water level, inflow flow trend and the location of characteristic points and characteristic sections.These parameters need to be reasonably configured according to the actual basin conditions and historical data. Use the model's setting function to specify flow velocity and water depth data as boundary conditions to ensure that the simulation process truly reflects the dynamics of floods. Run the model to simulate flood characteristics in the flood storage embankment. The model will calculate the propagation process of the flood in the embankment based on the input flow velocity, water depth and flow data, generate the spatial distribution of the flood, the flow path and its state of change over time, and generate flood characteristic simulation data in the flood storage embankment.

[0076] Step S5: Based on the flood characteristic simulation data in the flood storage embankment, simulated land planning processing is performed in the embankment area to generate simulated land planning data in the embankment area, and the simulated land planning data in the embankment area is transmitted to the terminal to perform the zoning land planning in the flood storage embankment;

[0077] In an embodiment of the present invention, the relationship between the flood depth and flow velocity of each characteristic point of the cross section and time is greatly affected by the elevation of each point and the surrounding terrain. If the flood impact analysis is performed on a village as an area, a comprehensive analysis needs to be performed based on the shape of the village and the relative position of the flood diversion gate and the buildings and terrain in the village. Import the flood characteristic simulation data in the flood storage embankment, including the data of water depth, flow velocity, flow direction and their changes over time. Use geographic information system (GIS) software (such as ArcGIS or QGIS) to process these data. Define different flood impact zones according to preset water depth thresholds and flow velocity standards. These thresholds are usually set based on historical flood data and expert experience. Through spatial analysis tools, spatial interpolation algorithms (such as Kriging interpolation method) are applied to perform spatial distribution analysis on water depth and flow velocity data to identify flood impact areas. The generated flood impact zone data in the embankment is saved as a GIS data file (such as Shapefile) for subsequent analysis and application. Load the generated flood impact zone data in the embankment, and conduct a comprehensive analysis in combination with the existing land use information in the region. Use land use planning software (such as Land F / X or AutoCAD) to simulate land planning in detail for each flood impact zone. According to the characteristics of the flood impact zone, determine the land use type of different areas. For example, areas with less flood impact can be planned as cultivated land or residential areas, while areas with severe flood impact should be planned as ecological protection areas or water areas. By establishing a land use model, simulate the impact of different planning schemes for use in the land space planning of towns and villages in flood storage areas. After completing the land planning, the generated simulated land planning data for the area within the embankment is saved in a structured format (such as Excel or database format) to ensure that the data is easy to analyze and extract. Finally, the generated land planning data is transmitted to the terminal system to implement the land planning operations of the zoning within the flood storage embankment.

[0078] Step S6: Based on the simulated land planning data of the area within the embankment and the simulated data of flood characteristics in the flood storage embankment, the intelligent regulation and control analysis of the flood diversion gate is performed to design the intelligent regulation and control decision of the flood diversion gate, and the intelligent regulation and control decision of the flood diversion gate is transmitted to the terminal to execute the intelligent regulation and control strategy of the flood diversion gate.

[0079] In an embodiment of the present invention, simulated land planning data of the area within the embankment is imported, and the characteristic analysis of flood control demand is performed in combination with the historical flood data within the flood storage embankment. Statistical analysis software (such as the Pandas library of R or Python) is used to cross-analyze the land planning data and the flood historical records to identify the needs of different land use types for flood control, including evaluating the flood carrying capacity of each land use area, the degree of flood impact and its requirements for the ecological environment. By constructing a mathematical model, the water level changes under different flood scenarios are simulated, and then the flood control demand characteristic data of each region is generated. These data include information such as water level control, flow distribution and control strategies required for different regions. Real-time flood characteristic simulation data within the flood storage embankment is collected, including information such as water level, flow rate and rainfall. These data are obtained in real time through sensors and monitoring equipment (such as water level meters and flow meters) and uploaded to a data processing platform. Data processing software (such as MATLAB or Python's NumPy and SciPy libraries) is used to process and analyze real-time data to identify the dynamic characteristics of flood changes. Through time series analysis and prediction models, dynamic flood characteristic simulation data in the flood storage area are generated, including real-time water level change trends, flow rate changes and their impact on regulation strategies. Based on these dynamic data and combined with the characteristic data of flood regulation demand in the flood storage area, intelligent regulation analysis of the diversion gate is carried out. A dynamic regulation decision model for the diversion gate is constructed, and the optimal intelligent regulation decision for the diversion gate is generated by simulating the impact of different regulation strategies on flood management effects. These decisions will be transmitted to the terminal system through the network to execute the corresponding intelligent regulation strategy for the diversion gate, ensure effective water volume regulation and management in flood events, and dynamically manage the regulation parameters.

[0080] Further, step S1 includes the following steps:

[0081] Step S11: obtaining measured topographic data of the flood storage area in the geographic information system;

[0082] In the embodiment of the present invention, the measured terrain data of the flood storage area in the geographic information system is obtained, and the measured terrain data of the flood storage area is retrieved through the geographic information system (GIS) platform. These data are usually obtained by surveying and mapping units or other specialized institutions through ground survey equipment, including but not limited to high-precision terrain measurement data generated by methods such as laser radar (LiDAR) scanning and total station measurement. These data should cover the overall terrain characteristics of the flood storage area and ensure that the data accuracy reaches the centimeter level, thereby laying a foundation for subsequent modeling.

[0083] Step S12: performing digital feature analysis of the measured terrain according to the measured terrain data of the flood storage area to generate digital feature data of the measured terrain; and establishing a preliminary flood storage area terrain model based on the digital feature data of the measured terrain;

[0084] In an embodiment of the present invention, after obtaining the measured terrain data, the digital features of the regional terrain are analyzed based on these data, and the digital feature data of the measured terrain is generated, and the measured terrain data is digitized using a special terrain analysis software (such as ArcGIS or QGIS). First, the terrain height, slope, geomorphic features and other information of each point are converted into digital features to form a digital elevation model (DEM). Through the feature extraction algorithm, a data set reflecting the terrain characteristics is generated, including terrain slope, valley direction, river distribution, etc. Based on these data, a preliminary flood storage area terrain model is established, which covers key terrain information such as terrain undulations and slope trends in the region.

[0085] Step S13: using drone detection equipment to perform terrain monitoring processing in the flood storage area to generate terrain monitoring data for the flood storage area;

[0086] In the embodiment of the present invention, since the measured terrain data of the flood storage area in the geographic information system has information lag, in order to improve the accuracy of the terrain model, unmanned aerial vehicle detection equipment is used to conduct real-time terrain monitoring of the flood storage area, and generate higher resolution and real-time terrain monitoring data of the flood storage area. The unmanned aerial vehicle is equipped with high-precision camera equipment and laser radar, which scans the entire flood storage area during flight and captures the detailed changes of the terrain in real time. The scanned data is converted into a high-precision digital model through the camera and laser scanning system carried by the unmanned aerial vehicle. The data monitored by the unmanned aerial vehicle can achieve centimeter-level accuracy, which is particularly suitable for covering large and inaccessible regional terrain, and supplements the parts that are difficult to capture by traditional ground measurement methods.

[0087] Step S14: performing terrain monitoring elevation feature analysis based on the terrain monitoring data of the flood storage area to generate terrain monitoring elevation feature data;

[0088] In the embodiment of the present invention, terrain monitoring elevation feature analysis is performed based on the terrain monitoring data obtained by the drone, and the data collected by the drone is refined through dedicated elevation analysis software (such as Global Mapper or Pix4D), and the elevation features of each point are extracted to generate an accurate elevation data set. In this process, the elevation fitting algorithm is applied to filter and correct the noise in the original data to ensure the accuracy of the elevation feature data. At the same time, the elevation data of important terrain features such as local height difference, mountains, and river valleys are analyzed to supplement the deficiencies of traditional measurement data.

[0089] Step S15: performing flood diversion gate location monitoring processing based on the flood storage area terrain monitoring data to generate flood diversion gate location monitoring data;

[0090] In the embodiment of the present invention, the terrain monitoring data obtained by the above-mentioned drone detection is used to carry out the positioning monitoring of the diversion gate. It is necessary to combine the existing location information of the diversion gate infrastructure to recalibrate the precise geographic coordinates of the diversion gate. The specific location of the diversion gate is identified through the GIS system and the images taken by the drone, and the location is matched with the reference coordinate system in the terrain model through the coordinate conversion tool. The high-precision GPS equipment is used to ensure that the positioning monitoring data of the diversion gate reaches the sub-meter level of accuracy, thereby providing accurate geometric parameters for subsequent hydrodynamic analysis and flow simulation.

[0091] Step S16: The relative position of the diversion gate in the preliminary flood storage area terrain model is marked by the diversion gate positioning monitoring data, and the elevation accuracy of the marked preliminary flood storage area terrain model is optimized based on the terrain monitoring elevation feature data to generate a flood storage area terrain model.

[0092] In an embodiment of the present invention, after obtaining the positioning monitoring data of the flood diversion gate, the data is applied to the initially constructed flood storage area terrain model to perform identification processing on the relative position of the flood diversion gate. The accurate position of the flood diversion gate is superimposed on the terrain model through a software platform (such as AutoCAD Civil3D or ArcGIS) to ensure that the geometric position of the flood diversion gate completely corresponds to the surrounding terrain features. At the same time, the accuracy of the preliminary terrain model is optimized based on the elevation monitoring feature data, especially the terrain height difference around the flood diversion gate is further refined, and the key parameters such as the slope of the terrain and the riverbed depth in the model are adjusted to make the terrain model of the entire flood storage area more accurate and suitable for subsequent hydrodynamic analysis and flood diversion control decisions.

[0093] Further, step S2 includes the following steps:

[0094] Step S21: Acquire historical flood storage hydrological data of the flood storage area;

[0095] In an embodiment of the present invention, historical flood data of a designated flood storage area is extracted by accessing the historical hydrological monitoring database of the water conservancy department, including the peak flow, maximum water level, time series data of flow changes of each flood, and inflow and outflow inside and outside the flood storage area. Complete historical hydrological data is extracted from the database using a script or data interface (such as SQL query) to ensure that the data covers the entire flood process, including the water accumulation before the flood, the key nodes of the flood evolution, and detailed records of the flood retreat stage. The data is preliminarily cleaned and formatted using a data extraction tool (such as Python's pandas library or R language data processing package) to ensure that the data has no duplicates or abnormal missing values.

[0096] Step S22: Analyze historical flood storage events based on historical flood storage hydrological data to generate historical flood storage event data;

[0097] In the embodiment of the present invention, historical flood storage events are analyzed based on historical flood storage hydrological data, and each flood event in the historical hydrological data is processed in sections through analysis tools (such as MATLAB or Python). First, the key features of each flood (such as flood peak water level, maximum flow, flow change rate) are extracted from the time series data, and the start time, flood peak arrival time, flood retreat time and other information of each event are recorded. These extracted key features are used to generate a complete historical flood storage event data table, which takes the time axis as the main line, records the detailed parameters of each flood event, and classifies and organizes the main hydrological features of the event to generate historical flood storage event data.

[0098] Step S23: using historical flood storage event data as flood storage event clustering labels; performing flood storage hydrology clustering analysis on historical flood storage hydrology data according to the flood storage event clustering labels to generate historical flood storage hydrology clustering data;

[0099] In an embodiment of the present invention, the main characteristic parameters of each flood storage event are extracted from the historical flood storage event data. These parameters include the peak flow, maximum water level, flood duration, peak arrival time and other key hydrological indicators of the flood event as clustering labels, which represent the characteristic attributes of the event and facilitate subsequent clustering analysis. Data analysis software (such as the Scikit-learn library in Python or the clustering analysis package in the R language) is used to perform cluster analysis on the historical hydrological data with clustering labels. The historical flood storage event data is standardized to ensure that all data features (such as flow, water level, etc.) are in the same order of magnitude to avoid certain features from having too much impact on the clustering results. The commonly used standardization method is to normalize the data to a range of 0 to 1, or to use the z-score standardization method. The K-means clustering algorithm is selected to analyze the clustering labels, and similarity analysis is performed by using the peak flow, maximum water level, flood duration, peak arrival time and other key hydrological indicators as clustering labels. The K-means algorithm divides the data into different clusters by minimizing the distance between data points within the cluster, and uses the cluster labels of key hydrological indicators with similar characteristics as cluster features. Before cluster analysis, set the K value, that is, the number of classes into which the data is expected to be divided. The selection of the K value here can be selected by the "elbow method". The number of clusters of cluster labels for key hydrological indicators is determined by the elbow method, that is, the clustering result error graph of different K values ​​is drawn, and the optimal K value at the inflection point of the error is observed. The similarity analysis results of the cluster labels are clustered based on the divided K value, and the appropriate number of iterations and initial center points are set to ensure that the algorithm can converge and find the best classification. Run the clustering algorithm to divide each historical flood storage event into specific categories and generate historical flood storage hydrological cluster data.

[0100] Step S24: mining and processing the hydrological characteristics of historical flood storage modes according to the historical flood storage hydrological clustering data to generate hydrological characteristic data of the flood storage mode;

[0101] In an embodiment of the present invention, the hydrological characteristics of the historical flood storage mode are mined and processed based on the historical flood storage hydrological clustering data. First, the clustered historical flood storage data is further analyzed using a data mining tool (such as the Scikit-learn library in Python or the Caret package in the R language) to mine the key hydrological characteristics of each type of flood mode. By using statistical methods (such as mean, variance, correlation analysis, etc.) to analyze the peak water level, flow velocity change, flood duration and other parameters of each clustering category, the common characteristics and unique characteristics of flood events of different categories are extracted. Through frequency distribution and trend analysis, the hydrological change patterns of various flood events are extracted, and the analysis results are summarized into a flood storage mode hydrological characteristic data table, which records the changing trends of water level, flow and other changes under different flood modes in detail, and generates flood storage mode hydrological characteristic data.

[0102] Step S25: Analyze the spatial characteristics of the flood storage area according to the flood storage area terrain model to generate spatial characteristic data of the flood storage area;

[0103] In an embodiment of the present invention, a spatial characteristic analysis of the flood storage area is performed based on the terrain model of the flood storage area to generate spatial characteristic data of the flood storage area. A previously generated terrain model of the flood storage area is loaded using GIS software (such as ArcGIS or QGIS), and a terrain analysis tool is used to perform a detailed spatial characteristic analysis, including calculations of key geographical features such as terrain elevation, slope, depressions, and valleys. The terrain undulations, drainage paths, and low-lying area distribution in the area are extracted using a digital elevation model (DEM). The generated spatial characteristic data should include data tables such as slope distribution maps, river channel and watershed distribution, and flood inundation risk levels in low-lying areas. These spatial characteristic data provide an accurate terrain basis for the next step of the coupled analysis of hydrological characteristics.

[0104] Step S26: performing spatial hydrological characteristic coupling analysis of the flood storage mode on the hydrological characteristic data of the flood storage mode according to the spatial characteristic data of the flood storage area, and generating spatial hydrological characteristic data of the flood storage mode.

[0105] In the embodiment of the present invention, the spatial hydrological characteristics coupling analysis of the flood storage mode is performed on the hydrological characteristic data of the flood storage mode according to the spatial characteristic data of the flood storage area. First, the hydrological characteristic data and the spatial characteristic data are superimposed and analyzed on the GIS platform, and the hydrological characteristics are coupled with the terrain spatial characteristics using spatial analysis tools (such as the "spatial connection" or "interpolation analysis" function in ArcGIS). It includes correlation analysis of the flow, water level and slope and low-lying area of ​​different flood modes, and determining the flood inundation range and flow velocity distribution under various flood modes. Through spatial analysis, the flow path, inundation depth and duration of floods under different terrain conditions are clarified. For example, the preliminary value of the hydrological characteristic is set according to the clustering mode, and then the deviation range is calculated by the difference between the clustering modes to reduce the data dimension of the hydrological numerical data of the overall flood storage mode, such as assigning the deviation between the hydrological data under different terrain condition modes, thereby reducing the data dimension of the hydrological numerical values ​​between the different terrain condition modes, generating the spatial hydrological characteristic data of the flood storage mode, recording the diffusion path and related characteristics of the flood in different regions, and providing a basis for flood diversion and regulation.

[0106] Further, as an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S3 in the embodiment, step S3 includes the following steps:

[0107] Step S31: analyzing the flood storage dynamic boundary conditions based on the hydrological spatial distribution characteristic data to generate flood storage dynamic boundary condition data;

[0108] In an embodiment of the present invention, the dynamic boundary conditions of flood storage are analyzed based on the hydrological spatial distribution characteristic data, and the acquired hydrological spatial distribution characteristic data, including water level changes, flow changes, flow velocity distribution, river channels and low-lying areas in the flood storage area, etc., are input into the hydrodynamic analysis software (such as HEC-RAS or MIKE 21). A preliminary analysis is performed on each key hydrological parameter to determine the type of boundary conditions, such as upstream and downstream flow, river water level, etc. By analyzing the temporal changes of the boundary conditions, a complete data set reflecting the flood flow boundary is generated. The boundary condition data includes the initial conditions of the flood, the flow input and output paths, the peak time of the flood, etc., which provide a basis for subsequent hydrodynamic calculations.

[0109] Step S32: performing finite element meshing processing on the flood storage dynamic boundary condition data to generate mesh flood storage dynamic boundary condition data;

[0110] In an embodiment of the present invention, the hydrodynamic boundary condition data of the flood storage area is processed by finite element meshing, and the hydrodynamic boundary condition data of the flood storage area, including water level, flow rate and velocity distribution, is imported using a dedicated finite element analysis tool (such as ANSYS or OpenFOAM). Before meshing, it is necessary to reasonably select the type and density of the mesh according to the terrain complexity and water flow characteristics of the area. A denser three-dimensional unstructured mesh is used for areas with complex and drastic water flows (such as bends in the river or places with large slopes) to more accurately capture the changes in water flows; a sparser mesh is used for areas with relatively stable water flows and relatively gentle terrain. The mesh after division should meet the accuracy requirements of hydrodynamic calculations while ensuring the convergence and stability of numerical calculations. Each node of the mesh contains its three-dimensional spatial coordinates and corresponding boundary condition information, and the generated mesh data file will serve as the basis for the next step of numerical calculations.

[0111] Step S33: performing flood storage dynamic finite element numerical parameter analysis according to the grid flood storage dynamic boundary condition data to generate flood storage dynamic finite element numerical parameters;

[0112] In an embodiment of the present invention, a flood storage dynamic finite element numerical parameter analysis is performed based on the grid flood storage dynamic boundary condition data, the grid data is imported into the hydrodynamic calculation module, and a numerical simulation is performed based on a hydrodynamic model such as the Navier-Stokes equation or the shallow water equation. First, boundary conditions are set, such as inflow flow, flood peak time, time step of flood evolution, etc. Then, a finite element solver is used to calculate the hydrodynamic parameters of each grid node to obtain the flow velocity, pressure, turbulence characteristics, water level changes and other results of each node. Through iterative calculation of the time step, the propagation and evolution of the entire flood process in the flood storage area are simulated. The calculation results include the hydrodynamic parameters of each grid in different time periods, which reflect the flow path of the flood, the water level rise process and the flow velocity change trend. All results are stored in the numerical parameter file for subsequent analysis and decision support.

[0113] Step S34: Calculating the flood storage power condition according to the flood storage power finite element data to generate flood storage power condition data;

[0114] In an embodiment of the present invention, the flood storage dynamic condition calculation is performed according to the flood storage dynamic finite element data, and the flood storage dynamic finite element data is used as input using finite element hydrodynamic analysis software (such as ANSYS Fluent, HEC-RAS or MIKE 21). In combination with the preset working conditions, specific boundary conditions are set, such as different inflow flows, flood durations, flood peak arrival times, etc. At the beginning of the calculation, the dynamic evolution process of the flood under different working conditions is simulated by solving the hydrodynamic equation (such as the Navier-Stokes equation). In the working condition calculation, the key parameters of the flood, such as flow velocity, water depth, flow distribution, and pressure changes, are analyzed, and the propagation path, flooding range, and water level changes of the flood are tracked. Through multiple iterative calculations, the evolution law of the flood in the flood storage area under different working conditions is obtained. Finally, a detailed flood storage dynamic condition data table is generated, which contains the distribution of parameters such as flow velocity, flow, and water level in different time periods and spatial positions for subsequent analysis and optimization.

[0115] Step S35: transmitting the flood storage dynamic condition data as the flood storage dynamic generalization coefficient to the flood storage terrain model for flood storage dynamic generalization coefficient mapping to construct the flood storage dynamic generalization model.

[0116] In an embodiment of the present invention, the hydrodynamic condition data of flood storage is used as input, and the hydrodynamic data of each node is converted into a generalization coefficient through interpolation and fitting algorithms (such as linear interpolation or bilinear interpolation). The generalization coefficient reflects the simplified expression of hydrodynamic characteristics in space and time. The generalization coefficient includes unit flow velocity, flow gradient, flood depth change rate, etc. These generalization coefficients are combined with the three-dimensional terrain model of the flood storage area, and each terrain unit is mapped through a geographic information system (GIS) or a hydrodynamic simulation platform (such as HEC-RAS or MIKE FLOOD), and the generalization coefficient is matched with its spatial position. The generalization model can quickly respond to different flood conditions and predict the evolution trend of floods by simplifying complex hydrodynamic calculations.

[0117] Further, step S31 includes the following steps:

[0118] Step S311: performing water depth distribution boundary condition analysis according to the hydrological spatial distribution characteristic data to generate water depth distribution boundary condition data;

[0119] In the embodiment of the present invention, the hydrological spatial distribution characteristic data, including historical hydrological records, real-time water level monitoring data and rainfall data, are obtained, and analyzed through a hydrological model (such as HEC-HMS) to determine the law of water depth variation in the flood storage area, including using a spatial interpolation method (such as Kriging interpolation or inverse distance weighted method) to process the historical hydrological data and generate a water depth distribution map. Subsequently, combined with terrain data (such as digital elevation model, DEM), the water depth variation under different terrain conditions is analyzed to determine the water depth boundary conditions of each measuring point. The generated water depth distribution boundary condition data will contain the water depth information of each monitoring point and the corresponding time variation data for subsequent analysis.

[0120] Step S312: performing flood storage dynamic basin characteristic analysis based on the hydrological spatial distribution characteristic data and the water depth distribution boundary condition data to generate flood storage dynamic basin characteristic data, wherein the flood storage dynamic basin characteristic data includes flood storage dynamic flow velocity data and flood storage dynamic flow direction data;

[0121] In an embodiment of the present invention, a flood storage dynamic basin characteristics analysis is performed based on the hydrological spatial distribution characteristic data and the water depth distribution boundary condition data. The hydrological spatial distribution characteristic data and the water depth distribution boundary condition data are combined using a geographic information system (GIS) tool to partition the hydrological characteristics within the basin and determine the flow velocity and flow direction of each basin unit. A hydrodynamic model (such as a fluid dynamics model) is used to simulate the hydrological data to analyze the flow velocity distribution and flow direction changes within the basin. Through the operation of the model, the calculated flood storage dynamic flow velocity data and flow direction data are organized into a database to form flood storage dynamic basin characteristic data. The final result will be displayed in the form of a vector diagram and parameter information to show the spatial distribution characteristics of the flow velocity and flow direction.

[0122] Step S313: Perform flood storage dynamic boundary condition analysis based on the water depth distribution boundary condition data and the flood storage dynamic basin characteristic data to generate flood storage dynamic boundary condition data.

[0123] In an embodiment of the present invention, a flood storage dynamic boundary condition analysis is performed based on the water depth distribution boundary condition data and the flood storage dynamic basin characteristic data, and the parameter information of the water depth distribution boundary condition data and the flood storage dynamic basin characteristic data are spatially integrated. A numerical calculation method (such as the finite element method or the finite difference method) is used to perform boundary condition analysis on the integrated data. By setting different boundary conditions, the hydrodynamic behavior under different working conditions is simulated, such as flow velocity, pressure distribution, and water level changes. After running the simulation, the flood storage dynamic boundary condition data generated by the analysis results will contain dynamic information such as flow velocity, flow direction, and water level changes at each measuring point in the flood storage area to generate flood storage dynamic boundary condition data. These data provide necessary boundary condition support for subsequent hydrodynamic calculations and model verification.

[0124] Further, as an embodiment of the present invention, refer to Figure 3 As shown, Figure 1 Detailed step flow diagram of step S4 in FIG. 1 , in this embodiment, step S4 includes the following steps:

[0125] Step S41: performing simulation analysis on the flow rate trend of the dike regulated by the diversion gate based on the flood storage dynamic generalization model, and generating simulation data on the flow rate trend of the diversion gate regulated by the dike;

[0126] In the embodiment of the present invention, a flood storage dynamics generalization model is established to simulate and analyze the flood storage dynamics trend, input historical hydrological data, including rainfall, flow, water level and other information, and set specific parameters of the flood diversion gate, such as opening and regulation strategy, to analyze the flood storage dynamics trend under different conditions. Then, the model is used for simulation calculation, and the inflow flow trend under different flow and water level conditions is analyzed through step-by-step iteration. During the calculation process, the flow change output by the model is monitored in real time, and the inflow flow data at each time node is recorded. The final generated simulation data of the trend of inflow flow regulated by the diversion gate will include a series of time series of flow changes, which can clearly show the dynamic changes of inflow flow under different regulation measures and provide a basis for subsequent analysis. For example, by adjusting the inflow flow through the diversion gate, specific parameter examples include: the evolution of flood in the dike after active flood storage for 133 hours at the maximum design flow of 3630 m³ / s (total flood storage volume of about 1.73 billion m³); the evolution of flood in the dike after active flood storage for 240 hours at a flow of 2000 m³ / s until the water level is balanced (total flood storage volume of about 1.73 billion m³). The maximum design flow is 3630 m³ / s, and the flood is actively stored for 75 hours until the water level is balanced and the evolution of the flood in the embankment is analyzed (the total flood storage volume is about 1 billion m³); the flow is 2000 m³ / s, and the flood is actively stored for 138 hours until the water level is balanced and the evolution of the flood in the embankment is analyzed (the total flood storage volume is about 1 billion m³); the flow is 1000 m³ / s, and the flood is actively stored for 138 hours until the water level is balanced and the evolution of the flood in the embankment is analyzed (the total flood storage volume is about 500 million m³).

[0127] Step S42: Selecting the characteristic point data and the characteristic section data of the digitized embankment through the flood diversion gate positioning monitoring data in the flood storage area terrain model;

[0128] In an embodiment of the present invention, the acquisition of the monitoring data of the flood gate location in the terrain model of the flood storage area is usually achieved through GIS (geographic information system) technology. Using GIS software (such as ArcGIS or QGIS), the terrain data of the flood storage area, especially the information containing the location of the flood gate, is extracted. By analyzing the changes in the flooded water depth and flow velocity of the characteristic points and characteristic sections over time, the evolution of different total flood storage volumes under different inflows is studied. By analyzing the hydrological monitoring data of the area, such as the data provided by the flow meter and the water level meter, the specific digital characteristic point data in the embankment are selected. These characteristic points usually include multiple water level and flow velocity measurement points near the flood gate. In addition to the characteristic point data, it is also necessary to extract the characteristic section data in the digitized embankment, including multiple sections with information such as water depth, flow velocity and flow direction, for example, 10 specific locations are selected and the points and section positions with different slopes are presented. By using a three-dimensional modeling tool (such as AutoCAD or SketchUp) in combination with actual measurement data, the selected characteristic points and sections are digitally modeled to ensure the accuracy and integrity of the data. Ultimately, the generated digital feature point data and feature section data within the embankment will provide the necessary input data for subsequent flow velocity and water depth analysis.

[0129] Step S43: performing time series correlation characteristic analysis of flow velocity and water depth at characteristic points within the embankment according to the digital characteristic point data within the embankment on the flow trend data of the diversion gate regulating the embankment flow, and generating time series correlation characteristic data of flow velocity and water depth at characteristic points within the embankment;

[0130] In an embodiment of the present invention, the obtained digital feature point data in the embankment and the trend data of the flow rate into the embankment regulated by the flood diversion gate are used as the basis for analysis. The two sets of data are processed using a data analysis tool (such as Python's Pandas library or R language), and the feature point data are read. These data usually include the water depth and flow rate information of each measuring point, and the flow rate trend data into the embankment are aligned in time series. Then, the relationship between flow rate and water depth is evaluated by time series analysis methods, such as cross-correlation analysis or dynamic time warping (DTW). During the analysis process, the dynamic characteristics of flow rate and water depth changing over time are focused on, and the relationship between the two is displayed graphically by generating a scatter plot or a time series. At the same time, the correlation between flow rate and water depth is quantified using a statistical analysis method (such as linear regression or time series regression). The generated time series correlation feature data of flow rate and water depth at the feature point in the embankment will contain the correlation information of time, flow rate and water depth, providing an in-depth understanding of the change of flow rate and water depth over time, and providing data support for subsequent flood simulation analysis.

[0131] Step S44: performing a time series correlation characteristic analysis of flow velocity and water depth at characteristic sections within the embankment on the trend data of the flow into the embankment regulated by the flood diversion gate according to the digital characteristic section data within the embankment, and generating time series correlation characteristic data of flow velocity and water depth at characteristic sections within the embankment;

[0132] In the embodiment of the present invention, based on the extracted digitized characteristic section data within the embankment and the trend data of the flow rate into the embankment regulated by the diversion gate, an analysis is performed, similar to the implementation result of step S43, and the relationship between the water depth and flow velocity information corresponding to the characteristic sections within the embankment and time is statistically analyzed. The generated characteristic data of flow velocity and water depth time series correlation of the characteristic sections within the embankment will contain the correlation information of time, flow velocity and water depth, providing an in-depth understanding of the changes in flow velocity and water depth over time, and providing data support for subsequent flood simulation analysis.

[0133] Step S45: Perform flood characteristic simulation analysis in the flood storage embankment according to the time series correlation characteristic data of flow velocity and water depth at characteristic points in the embankment and the time series correlation characteristic data of flow velocity and water depth at characteristic sections in the embankment to generate flood characteristic simulation data in the flood storage embankment.

[0134] In the embodiment of the present invention, the characteristic data of flow velocity and water depth of characteristic points in the embankment and the characteristic data of flow velocity and water depth of characteristic sections in the embankment are collected and sorted, including the flow velocity, water depth and their changes over time of each characteristic point and section. These data are imported into a hydrodynamic simulation software (such as HEC-RAS or SWMM), which can perform one-dimensional or two-dimensional water flow simulation. Simulation parameters are set in the simulation software, including the initial water level, the flow trend of the embankment, and the position of the characteristic points and characteristic sections. These parameters need to be reasonably configured according to the actual basin conditions and historical data. The setting function of the model is used to specify the flow velocity and water depth data as boundary conditions to ensure that the simulation process truly reflects the dynamics of the flood. The model is run to simulate the flood characteristics in the flood storage embankment. The model will calculate the propagation process of the flood in the embankment based on the input flow velocity, water depth and flow data, and generate the spatial distribution of the flood, the flow path and its state of change over time. The simulation process usually involves multiple time steps to ensure that the dynamic characteristics of flood changes can be captured. For example, according to the flow velocity and flood depth of 74 feature points on 6 feature surfaces over the flood storage time, the flood depth near the diversion gate increases significantly within 72 hours after the flood arrives, and increases slowly after 72 hours; the flow velocity tends to stabilize 48 hours after the flood arrives. The simulation results are exported as flood characteristic simulation data in the flood storage embankment. The data will contain information such as flood water level changes, flow velocity distribution and flow direction, and generate flood characteristic simulation data in the flood storage embankment.

[0135] Further, step S41 includes the following steps:

[0136] Step S411: performing simulation analysis on the dynamic evolution process of flood storage according to the flood storage dynamic generalization model, and generating simulation data on the dynamic evolution process of flood storage;

[0137] In an embodiment of the present invention, numerical simulation software (such as FLO-2D or SMS) is used to simulate the evolution process of the constructed flood storage dynamic generalization model. During the simulation process, the software gradually calculates the movement, expansion and interaction of the water flow through the time stepping method to form a dynamic evolution process of the flood. The simulation time range needs to cover the possible flood evolution cycle to capture the key stages of flood development. After the simulation is completed, the simulation results are extracted to generate simulation data of the dynamic evolution process of the flood storage. The data contains the time series changes of the flood in the flood storage area, including the water level, flow velocity and flow direction information at different time points. The results are exported as a database file for subsequent analysis and application. The generated data provides basic support for subsequent flood characteristic analysis, inflow flow trend analysis, etc., to ensure scientific decision-making in flood diversion gate regulation.

[0138] Step S412: performing a flood storage dynamics evolution trend analysis on the flood storage dynamics evolution process simulation data according to a preset hydrodynamics evolution time step, and generating flood storage dynamics evolution trend data;

[0139] In an embodiment of the present invention, the time step of the hydrodynamic evolution is set, and the step should be selected based on the actual situation of the hydrological characteristics and dynamic changes in the flood storage area. For example, the time step is set by the parameters of the diversion gate adjustment, such as the diversion gate is fully opened, the step is designed to be 5 minutes, the diversion gate is opened 30%, and the step is designed to be one hour, so as to ensure that the details of the dynamic changes of the flood can be captured. This information is configured through the time series analysis module in the data analysis software (such as MATLAB or Python). The previously generated simulation data of the dynamic evolution process of the flood storage is imported, and the data contains the water level, flow velocity and flow direction information at different time nodes. The cyclic processing of these data is realized by programming, and the data of the key time points are extracted according to the preset time step to form a time series data set. In the process of data extraction, the missing data points are supplemented by data interpolation technology to improve the integrity and continuity of the data. Linear interpolation or spline interpolation and other methods can be used to ensure that the generated data is reasonable in a physical sense and can reflect the real hydrodynamic evolution trend. The extracted data are statistically analyzed, including maximum, minimum, mean and standard deviation, to quantify the trend of dynamic evolution and generate dynamic evolution trend data of flood storage power.

[0140] Step S413: Perform simulation analysis on the flow rate trend of the diversion gate regulated inflow according to the dynamic evolution trend data of flood storage power, and generate simulation data on the flow rate trend of the diversion gate regulated inflow.

[0141] In an embodiment of the present invention, the dynamic evolution trend data of flood storage power is imported, and the data includes water level, flow velocity and flow direction information at different time nodes. The basic parameters of the simulation analysis model for the diversion gate to regulate the inflow flow trend are set, including the design flow of the diversion gate, the flow velocity limit and the boundary conditions of the flood storage area. These parameters are usually derived from historical data and field measurements to ensure that the actual hydrodynamic characteristics can be reflected. At this stage, the specific performance of the diversion gate can be modeled using existing hydrological and water resources models, such as HEC-RAS or MIKE 21, to simulate its performance under different hydrological conditions. Next, based on the dynamic evolution trend data of flood storage power, a numerical calculation method is used to perform a trend simulation analysis of the inflow flow. Specifically, the inflow flow of the diversion gate can be calculated by gradually solving fluid dynamics equations (such as the Navier-Stokes equations), taking into account the water level changes and flow velocity changes in different time periods. In this process, for data of different time steps, the inflow flow at each time point is gradually calculated by numerical integration and interpolation methods. After the simulation is completed, the generated inflow flow trend data will be summarized and statistically analyzed, focusing on key indicators such as peak flow, average flow and rate of change.

[0142] Further, step S5 includes the following steps:

[0143] Step S51: performing flood impact zoning analysis within the flood storage embankment according to flood characteristic simulation data within the flood storage embankment, and generating flood impact zoning data within the embankment;

[0144] In an embodiment of the present invention, the relationship between the flood depth and flow velocity of each characteristic point of the cross section and time is greatly affected by the elevation of each point and the surrounding terrain. If the flood impact analysis is performed on a village as an area, a comprehensive analysis is required based on the shape of the village and the relative position of the flood diversion gate and the buildings and terrain in the village. Import the flood characteristic simulation data in the flood storage embankment, including the data of water depth, flow velocity, flow direction and their changes over time. Use geographic information system (GIS) software (such as ArcGIS or QGIS) to process these data. Define different flood impact zones according to preset water depth thresholds and flow velocity standards. These thresholds are usually set based on historical flood data and expert experience. Through spatial analysis tools, spatial interpolation algorithms (such as Kriging interpolation method) are applied to perform spatial distribution analysis on water depth and flow velocity data to identify flood impact areas. Based on the interpolation results, a zoning map of the flood impact area is generated and converted into a vector data format to clearly identify different flood impact level areas. The generated flood impact zoning data in the embankment is saved as a GIS data file (such as Shapefile) for subsequent analysis and application.

[0145] Step S52: simulate land planning processing in the area within the embankment through the flood impact zoning data within the embankment, generate simulated land planning data in the area within the embankment, and transmit the simulated land planning data in the area within the embankment to the terminal to execute the zoning land planning operation in the flood storage embankment.

[0146] In an embodiment of the present invention, the generated flood impact zoning data within the embankment is loaded, and a comprehensive analysis is performed in combination with the existing land use information of the region. A land use planning software (such as Land F / X or AutoCAD) is used to perform detailed simulated land planning processing on each flood impact zone. According to the characteristics of the flood impact zone, the land use types of different regions are determined. For example, areas with less flood impact can be planned as cultivated land or residential areas, while areas with severe flood impact should be planned as ecological protection areas or waters. By establishing a land use model, the impact of different planning schemes is simulated for use in the land space planning of towns and villages in flood storage areas. After the land planning is completed, the generated simulated land planning data of the embankment area is saved in a structured format (such as Excel or database format) to ensure that the data is easy to analyze and extract. Finally, the generated land planning data is transmitted to the terminal system to implement the land planning operation of the zoning within the flood storage embankment. This process ensures the scientific nature of land use and the effective management of flood risks.

[0147] Further, step S6 includes the following steps:

[0148] Step S61: analyzing the characteristics of flood control demand in the flood storage embankment according to the simulated land planning data in the embankment area, and generating the characteristics of flood control demand in the flood storage embankment;

[0149] In an embodiment of the present invention, simulated land planning data of the area within the embankment is imported, and combined with historical flood data in the flood storage embankment, characteristic analysis of flood control needs is performed. Statistical analysis software (such as R or Python's Pandas library) is used to cross-analyze land planning data and flood historical records to identify the needs of different land use types for flood control, including evaluating the flood carrying capacity of each land use area, the degree of flood impact, and its requirements for the ecological environment. By constructing a mathematical model, the water level changes under different flood scenarios are simulated, and then the characteristic data of flood control needs in each region are generated. These data include information such as water level control, flow distribution and control strategies required for different regions.

[0150] Step S62: Dynamically monitor and process the flood characteristic simulation data in the flood storage embankment to generate dynamic flood characteristic simulation data in the flood storage embankment; perform intelligent control analysis of the flood diversion gate based on the dynamic flood characteristic simulation data in the flood storage embankment and the flood control demand characteristic data in the flood storage embankment to design the intelligent control decision of the flood diversion gate, and transmit the intelligent control decision of the flood diversion gate to the terminal to execute the intelligent control strategy of the flood diversion gate.

[0151] In the embodiment of the present invention, real-time flood characteristic simulation data in the flood storage embankment are collected, including information such as water level, flow rate and rainfall. These data are obtained in real time through sensors and monitoring equipment (such as water level gauges and flow rate meters) and uploaded to the data processing platform. Data processing software (such as MATLAB or Python's NumPy and SciPy libraries) is used to process and analyze the real-time data to identify the dynamic characteristics of flood changes. Through time series analysis and prediction models, dynamic flood characteristic simulation data in the flood storage embankment are generated, including real-time water level change trends, flow rate changes and their impact on the regulation strategy. Based on these dynamic data and combined with the flood regulation demand characteristic data in the flood storage embankment, intelligent regulation analysis of the diversion gate is performed. A dynamic regulation decision model for the diversion gate is constructed, and the optimal intelligent regulation decision for the diversion gate is generated by simulating the impact of different regulation strategies on the flood management effect. These decisions will be transmitted to the terminal system through the network to execute the corresponding intelligent regulation strategy for the diversion gate, ensure effective water volume regulation and management in flood events, and dynamically manage the regulation parameters.

[0152] This specification provides a digital flood storage analysis flood diversion gate control and management system, which is used to execute the digital flood storage analysis flood diversion gate control and management method as described above. The digital flood storage analysis flood diversion gate control and management system includes:

[0153] The flood storage area terrain modeling module is used to obtain the measured terrain data of the flood storage area in the geographic information system; use the drone detection equipment to monitor the terrain of the flood storage area and generate the terrain monitoring data of the flood storage area; perform flood area terrain modeling based on the measured terrain data of the flood storage area and the terrain monitoring data of the flood storage area to generate the terrain model of the flood storage area;

[0154] The spatial hydrological characteristic analysis module is used to obtain the historical flood storage hydrological data of the flood storage area; according to the terrain model of the flood storage area, the spatial hydrological characteristic coupling analysis of the flood storage mode is carried out on the historical flood storage hydrological data to generate the spatial hydrological characteristic data of the flood storage mode;

[0155] The hydrodynamic generalization model construction module is used to analyze the flood storage dynamic boundary conditions based on the hydrological spatial distribution characteristic data and generate the flood storage dynamic boundary condition data; analyze the flood storage dynamic operating conditions based on the flood storage dynamic boundary condition data and generate the flood storage dynamic operating conditions data; transmit the flood storage dynamic operating conditions data as the flood storage dynamic generalization coefficient to the flood storage terrain model to map the flood storage dynamic generalization coefficient, so as to construct the flood storage dynamic generalization model;

[0156] The flood analysis module in the flood storage embankment is used to simulate and analyze the flow trend of the flood diversion gate regulated inflow based on the flood storage dynamic generalization model, and generate the simulated data of the flow trend of the flood diversion gate regulated inflow; simulate and analyze the flood characteristics in the flood storage embankment based on the flow trend data of the flood diversion gate regulated inflow, and generate the simulated data of the flood characteristics in the flood storage embankment;

[0157] The land planning module of the area within the embankment is used to perform simulated land planning processing of the area within the embankment based on the flood characteristic simulation data within the flood storage embankment, generate simulated land planning data of the area within the embankment, and transmit the simulated land planning data of the area within the embankment to the terminal to execute the zoning land planning of the flood storage embankment;

[0158] The intelligent control module of flood storage and diversion gates is used to perform intelligent control analysis of flood diversion gates based on simulated land planning data in the area within the embankment and simulated data of flood characteristics in the flood storage embankment, so as to design intelligent control decisions of flood diversion gates, and transmit the intelligent control decisions of flood diversion gates to the terminal to execute the intelligent control strategy of flood diversion gates.

[0159] The beneficial effect of this application is that by using geographic information systems (GIS) and drone detection equipment, it is possible to obtain high-precision measured data on the topography of the flood storage area, monitor terrain changes in real time, and generate a dynamically updated terrain model of the flood storage area. The improved accuracy of the terrain model makes the hydrological analysis and flood diversion regulation of the flood storage area more accurate. Through the coupling analysis of historical hydrological data and the terrain model of the flood storage area, it is possible to extract the spatial hydrological characteristics of the flood storage mode, realize the fine simulation of the flood evolution process, and provide a scientific basis for predicting flood evolution trends and flood diversion regulation, making the regulation more targeted and effective. The hydrodynamic boundary conditions and working conditions are analyzed using the hydrological spatial distribution characteristics, and a generalized model of flood storage dynamics is constructed. The model accurately reflects information such as flood velocity, flow direction, and inundation range, thereby supporting the simulation of inflow flow trends and flood characteristics of flood diversion gate regulation, and improving the hydrodynamic analysis capabilities of the overall system. Based on the flood storage dynamic generalization model, the inflow flow trend simulation of the diversion gate regulation is carried out. By simulating the inflow flow trend under different diversion gate regulation, according to the time series correlation analysis of the flow velocity and water depth of the characteristic points and sections in the digital diversion gate, the changes in flood characteristics in different characteristic areas can be dynamically tracked. This helps to fully understand the flood changes at various locations within the flood storage diversion gate area, ensure that the diversion gate regulation strategy can adapt to the actual hydrological conditions at different locations in the diversion gate, and effectively prevent the occurrence of flood disasters in local areas. Through the simulation and analysis of flood characteristics in the diversion gate, the key characteristics of the flow velocity, water depth and duration of the flood in the diversion gate can be accurately simulated, and combined with the terrain characteristics of different regions, more accurate data support is provided for the diversion gate regulation decision. This simulation analysis can help managers scientifically formulate regulation strategies and reduce the risk of blind operation. Through the simulation data of flood characteristics, the land in the embankment is scientifically planned, and combined with the flood impact zoning analysis, the use and safety layout of the land in the embankment are optimized to reduce the impact of floods on the population, agriculture and economic activities in the flood storage area, protect the lives and property of residents, and provide strong support for the subsequent restoration work after the flood recedes. Based on the intelligent regulation and analysis of the dynamic characteristics of floods in the embankment, the opening and closing status of the flood diversion gate can be automatically adjusted according to the real-time flood evolution and regulation needs, which improves the regulation efficiency of the flood diversion gate, realizes the automation and intelligence of regulation, reduces the lag and error of manual operation, ensures that flood control measures can be implemented at the optimal time, significantly enhances the emergency response capability to floods, and can quickly adjust the operation of the flood diversion gate when the flood breaks out or changes, minimizing the disaster losses. Therefore, the diversion gate regulation and management method for digital flood storage analysis of the present invention constructs a digital flood storage dynamic generalization model by analyzing the terrain model of the flood storage area and the hydrological spatial distribution characteristics. It can accurately simulate the changes in flow velocity, flow direction and water depth of the flood in the embankment when the diversion gate is regulated, thereby improving the accuracy of flood evolution analysis and helping to deeply understand the distribution of floods in the embankment, so as to improve the accuracy of dynamic characteristic evolution analysis of rapidly changing floods in the embankment.By analyzing the time-series correlation characteristics of flow velocity and water depth at characteristic points and sections within the embankment, the flood dynamics of each characteristic area can be accurately grasped. This detailed time-series correlation analysis improves the accuracy of flood distribution analysis within the embankment, making the control strategy more accurate, ensuring that each area within the embankment can obtain scientific and reasonable flood protection, so as to achieve more accurate land planning and processing of the areas within the flood storage embankment, combine the flood risks of different regions, reasonably zone, optimize land use, and reduce the impact of floods on important facilities and densely populated areas. And automatically adjust the opening and closing status of the flood diversion gate to achieve accurate flow control of floods within the embankment, reduce lags and errors in manual operations, and significantly improve the efficiency of control and the ability to respond to sudden floods.

[0160] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0161] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A flood gate control and management method for digital flood storage analysis, characterized in that: The following steps are involved: Step S1: obtaining measured terrain data of the flood storage area in the geographic information system; using unmanned aerial vehicle detection equipment to monitor the terrain of the flood storage area and generate terrain monitoring data of the flood storage area; performing flood area terrain modeling based on the measured terrain data of the flood storage area and the terrain monitoring data of the flood storage area and generating a terrain model of the flood storage area; Step S2: Acquire historical flood storage hydrological data of the flood storage area; perform spatial hydrological characteristic coupling analysis of the flood storage mode on the historical flood storage hydrological data according to the flood storage area terrain model to generate spatial hydrological characteristic data of the flood storage mode; Wherein, step S2 comprises the following steps: Step S21: Acquire historical flood storage hydrological data of the flood storage area; Step S22: Analyze historical flood storage events based on historical flood storage hydrological data to generate historical flood storage event data; Step S23: using historical flood storage event data as flood storage event clustering labels, including: extracting characteristic attributes of the flood peak flow, maximum water level, flood duration and flood peak arrival time of each flood storage event from the historical flood storage event data as the flood storage event clustering labels; performing flood storage hydrology clustering analysis on the historical flood storage hydrology data according to the flood storage event clustering labels to generate historical flood storage hydrology clustering data; Step S24: mining and processing the hydrological characteristics of historical flood storage modes according to the historical flood storage hydrological clustering data to generate hydrological characteristic data of the flood storage mode; Step S25: Analyze the spatial characteristics of the flood storage area according to the flood storage area terrain model to generate spatial characteristic data of the flood storage area; Step S26: performing spatial hydrological characteristic coupling analysis of the flood storage mode on the hydrological characteristic data of the flood storage mode according to the spatial characteristic data of the flood storage area, and generating spatial hydrological characteristic data of the flood storage mode; Step S3: performing a flood storage dynamic boundary condition analysis based on the hydrological spatial distribution characteristic data to generate flood storage dynamic boundary condition data; performing a flood storage dynamic operating condition analysis based on the flood storage dynamic boundary condition data to generate flood storage dynamic operating condition data; transmitting the flood storage dynamic operating condition data as a flood storage dynamic generalization coefficient to the flood storage area terrain model to perform flood storage dynamic generalization coefficient mapping to construct a flood storage dynamic generalization model; Step S4: Based on the flood storage dynamic generalization model, simulate and analyze the flow rate trend of the diversion gate regulated inflow to the embankment, and generate simulated data of the flow rate trend of the diversion gate regulated inflow to the embankment; based on the flow rate trend data of the diversion gate regulated inflow to the embankment, simulate and analyze the flood characteristics in the flood storage embankment, and generate simulated data of the flood characteristics in the flood storage embankment; Step S5: Based on the flood characteristic simulation data in the flood storage embankment, simulated land planning processing is performed in the embankment area to generate simulated land planning data in the embankment area, and the simulated land planning data in the embankment area is transmitted to the terminal to perform the zoning land planning operation in the flood storage embankment; Step S6: Based on the simulated land planning data of the area within the embankment and the simulated data of flood characteristics within the flood storage embankment, the intelligent regulation and control analysis of the flood diversion gate is performed to design the intelligent regulation and control decision of the flood diversion gate, and the intelligent regulation and control decision of the flood diversion gate is transmitted to the terminal to execute the intelligent regulation and control strategy of the flood diversion gate; Wherein, step S6 comprises the following steps: Step S61: analyzing the characteristics of flood control demand in the flood storage embankment according to the simulated land planning data in the embankment area, and generating the characteristics of flood control demand in the flood storage embankment; Step S62: Dynamically monitor and process the flood characteristic simulation data in the flood storage embankment to generate dynamic flood characteristic simulation data in the flood storage embankment; perform intelligent control analysis of the flood diversion gate based on the dynamic flood characteristic simulation data in the flood storage embankment and the flood control demand characteristic data in the flood storage embankment to design the intelligent control decision of the flood diversion gate, and transmit the intelligent control decision of the flood diversion gate to the terminal to execute the intelligent control strategy of the flood diversion gate.

2. The method for controlling and managing flood diversion gates based on digital flood storage analysis according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: obtaining measured topographic data of the flood storage area in the geographic information system; Step S12: performing digital feature analysis of the measured terrain according to the measured terrain data of the flood storage area to generate digital feature data of the measured terrain; and establishing a preliminary flood storage area terrain model based on the digital feature data of the measured terrain; Step S13: using drone detection equipment to perform terrain monitoring processing in the flood storage area to generate terrain monitoring data for the flood storage area; Step S14: performing terrain monitoring elevation feature analysis based on the terrain monitoring data of the flood storage area to generate terrain monitoring elevation feature data; Step S15: performing flood diversion gate location monitoring processing based on the flood storage area terrain monitoring data to generate flood diversion gate location monitoring data; Step S16: The relative position of the diversion gate in the preliminary flood storage area terrain model is marked by the diversion gate positioning monitoring data, and the elevation accuracy of the marked preliminary flood storage area terrain model is optimized based on the terrain monitoring elevation feature data to generate a flood storage area terrain model.

3. The method for controlling and managing flood diversion gates based on digital flood storage analysis according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: analyzing the flood storage dynamic boundary conditions based on the hydrological spatial distribution characteristic data to generate flood storage dynamic boundary condition data; Step S32: performing finite element meshing processing on the flood storage dynamic boundary condition data to generate mesh flood storage dynamic boundary condition data; Step S33: performing flood storage dynamic finite element numerical parameter analysis according to the grid flood storage dynamic boundary condition data to generate flood storage dynamic finite element numerical parameters; Step S34: Calculating the flood storage power condition according to the flood storage power finite element data to generate flood storage power condition data; Step S35: transmitting the flood storage dynamic condition data as the flood storage dynamic generalization coefficient to the flood storage area terrain model for flood storage dynamic generalization coefficient mapping to construct a flood storage dynamic generalization model.

4. The method for controlling and managing flood diversion gates based on digital flood storage analysis according to claim 3 is characterized in that: Step S31 The following steps are involved: Step S311: performing water depth distribution boundary condition analysis according to the hydrological spatial distribution characteristic data to generate water depth distribution boundary condition data; Step S312: performing flood storage dynamic basin characteristic analysis based on the hydrological spatial distribution characteristic data and the water depth distribution boundary condition data to generate flood storage dynamic basin characteristic data, wherein the flood storage dynamic basin characteristic data includes flood storage dynamic flow velocity data and flood storage dynamic flow direction data; Step S313: Perform flood storage dynamic boundary condition analysis based on the water depth distribution boundary condition data and the flood storage dynamic basin characteristic data to generate flood storage dynamic boundary condition data.

5. The method for controlling and managing flood diversion gates based on digital flood storage analysis according to claim 2 is characterized in that: Step S4 includes the following steps: Step S41: performing simulation analysis on the flow rate trend of the dike regulated by the diversion gate based on the flood storage dynamic generalization model, and generating simulation data on the flow rate trend of the diversion gate regulated by the dike; Step S42: Selecting the characteristic point data and the characteristic section data of the digitized embankment through the flood diversion gate positioning monitoring data in the flood storage area terrain model; Step S43: performing time series correlation characteristic analysis of flow velocity and water depth at characteristic points within the embankment according to the digital characteristic point data within the embankment on the flow trend data of the diversion gate regulating the embankment flow, and generating time series correlation characteristic data of flow velocity and water depth at characteristic points within the embankment; Step S44: performing a time series correlation characteristic analysis of flow velocity and water depth at characteristic sections within the embankment on the trend data of the flow into the embankment regulated by the flood diversion gate according to the digital characteristic section data within the embankment, and generating time series correlation characteristic data of flow velocity and water depth at characteristic sections within the embankment; Step S45: Perform flood characteristic simulation analysis in the flood storage embankment according to the time series correlation characteristic data of flow velocity and water depth at characteristic points in the embankment and the time series correlation characteristic data of flow velocity and water depth at characteristic sections in the embankment to generate flood characteristic simulation data in the flood storage embankment.

6. The method for controlling and managing flood diversion gates based on digital flood storage analysis according to claim 5 is characterized in that: Step S41 includes the following steps: Step S411: performing simulation analysis on the dynamic evolution process of flood storage according to the flood storage dynamic generalization model, and generating simulation data on the dynamic evolution process of flood storage; Step S412: performing a flood storage dynamics evolution trend analysis on the flood storage dynamics evolution process simulation data according to a preset hydrodynamics evolution time step, and generating flood storage dynamics evolution trend data; Step S413: Perform simulation analysis on the flow rate trend of the diversion gate regulated inflow according to the dynamic evolution trend data of flood storage power, and generate simulation data on the flow rate trend of the diversion gate regulated inflow.

7. The method for controlling and managing flood diversion gates based on digital flood storage analysis according to claim 1 is characterized in that: Step S5 includes the following steps: Step S51: performing flood impact zoning analysis within the flood storage embankment according to flood characteristic simulation data within the flood storage embankment, and generating flood impact zoning data within the embankment; Step S52: simulate land planning processing in the area within the embankment through the flood impact zoning data within the embankment, generate simulated land planning data in the area within the embankment, and transmit the simulated land planning data in the area within the embankment to the terminal to execute the zoning land planning operation in the flood storage embankment.

8. A flood diversion gate control and management system for digital flood storage analysis, characterized in that: The method for controlling and managing flood diversion gates for executing the digital flood storage analysis according to claim 1, wherein the method comprises: The flood storage area terrain modeling module is used to obtain the measured terrain data of the flood storage area in the geographic information system; use the drone detection equipment to monitor the terrain of the flood storage area and generate the terrain monitoring data of the flood storage area; perform flood area terrain modeling based on the measured terrain data of the flood storage area and the terrain monitoring data of the flood storage area to generate the terrain model of the flood storage area; The spatial hydrological characteristic analysis module is used to obtain the historical flood storage hydrological data of the flood storage area; according to the terrain model of the flood storage area, the spatial hydrological characteristic coupling analysis of the flood storage mode is carried out on the historical flood storage hydrological data to generate the spatial hydrological characteristic data of the flood storage mode; The hydrodynamic generalization model construction module is used to analyze the flood storage dynamic boundary conditions based on the hydrological spatial distribution characteristic data and generate the flood storage dynamic boundary condition data; analyze the flood storage dynamic operating conditions based on the flood storage dynamic boundary condition data and generate the flood storage dynamic operating conditions data; transmit the flood storage dynamic operating conditions data as the flood storage dynamic generalization coefficient to the flood storage area terrain model to map the flood storage dynamic generalization coefficient, so as to construct the flood storage dynamic generalization model; The flood analysis module in the flood storage embankment is used to simulate and analyze the flow trend of the flood diversion gate regulated inflow based on the flood storage dynamic generalization model, and generate the simulated data of the flow trend of the flood diversion gate regulated inflow; simulate and analyze the flood characteristics in the flood storage embankment based on the flow trend data of the flood diversion gate regulated inflow, and generate the simulated data of the flood characteristics in the flood storage embankment; The land planning module of the area within the embankment is used to perform simulated land planning processing of the area within the embankment based on the flood characteristic simulation data within the flood storage embankment, generate simulated land planning data of the area within the embankment, and transmit the simulated land planning data of the area within the embankment to the terminal to perform the zoning land planning operation within the flood storage embankment; The intelligent control module of flood storage and diversion gates is used to perform intelligent control analysis of flood diversion gates based on simulated land planning data in the area within the embankment and simulated data of flood characteristics in the flood storage embankment, so as to design intelligent control decisions of flood diversion gates, and transmit the intelligent control decisions of flood diversion gates to the terminal to execute the intelligent control strategy of flood diversion gates.

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