Rural waterlogging area waterlogging sheet risk grade evaluation and early warning method based on multi-source data

Abstract

The invention relates to the technical field of data evaluation processing, in particular to a rural waterlogging area waterlogging risk grade evaluation and early warning method based on multi-source data. The method comprises the following steps: acquiring geographic information, hydrological and flood data and flood control and drainage engineering data of a rural waterlogging area, and performing multi-source data fusion to obtain rural flood multi-source data; and performing river flood overflow calculation on the rural flood multi-source data to obtain river flood overflow data. According to the invention, through a data processing technology, a mode identification technology and a deep learning technology, rural multi-source feature data is subjected to fusion analysis, and risk level evaluation of a waterlogging area and a waterlogging sheet in a rural area is realized; a flood real-time forecasting and early warning model is constructed, when rainstorm occurs or a meteorological department issues rainstorm early warning, the rural flood flooding range is quickly predicted, and a risk avoiding transfer route is planned.

Description

Technical Field

[0001] The present invention relates to the technical field of data evaluation and processing, and in particular to a method for evaluating and warning the risk level of rural waterlogged areas based on multi-source data. Background Art

[0002] Early rural waterlogging risk assessment mainly relied on empirical judgment and simple statistical analysis, lacking systematic theories and methods. With the development of science and technology, hydrological models and geographic information system (GIS) technology have been gradually introduced to improve the scientific nature of the assessment. With the continuous advancement of remote sensing technology, meteorological forecasting technology and computer technology, rural waterlogging risk assessment technology has developed rapidly. For example, remote sensing technology is used to monitor rainfall and water level changes, and short-term and medium-term waterlogging forecasts are made in combination with meteorological forecast models. At the same time, the application of GIS technology enables risk assessment results to be more intuitively displayed on maps, which is convenient for decision makers to make intuitive analysis and decisions. However, the existing rural waterlogging risk assessment technology fails to combine rural multi-source characteristic data, resulting in low accuracy in the assessment of rural waterlogging risk levels; and when heavy rain occurs or the meteorological department issues a heavy rain warning, it is difficult to quickly predict the scope of rural flooding. Summary of the invention

[0003] Based on this, it is necessary to provide a method for assessing and warning the risk level of waterlogging in rural waterlogged areas based on multi-source data to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a method for assessing and warning the risk level of waterlogging in rural waterlogged areas based on multi-source data is provided, the method comprising the following steps: Step S1: Obtaining rural waterlogged area geographic information, hydrological and flood data, and flood control and drainage engineering data, and fusing multi-source data to obtain rural flood multi-source data; Step S2: Calculate river flood overflow for rural flood multi-source data to obtain river flood overflow data; perform local two-dimensional hydrological and hydrodynamic quantitative assessment of waterlogging risk points for rural flood multi-source data based on river flood overflow data to obtain local waterlogging risk data; construct a flood overflow and waterlogging risk model based on river flood overflow data and local waterlogging risk data; Step S3: simulate the rural flood inundation process through the flood overflow and waterlogging risk model to obtain the flood inundation simulation results; divide the flood inundation simulation results into watershed waterlogging areas to obtain watershed waterlogging area data; detect the rainstorm flood risk area on the watershed waterlogging area data, and evaluate the rural waterlogging risk level to obtain waterlogging risk level data; Step S4: Determine the rural flood risk warning index according to the waterlogging risk level data and generate a rural flood warning index system; construct a flood real-time forecast and warning model through the rural flood warning index system, and predict the rural flood risk area based on the flood real-time forecast and warning model, plan the risk avoidance and transfer route, so as to obtain a rural flood warning planning report.

[0005] The present invention obtains rural flood multi-source data by acquiring geographical information, hydrological and flood data, and flood control and drainage engineering data of rural waterlogging areas, and performs multi-source data fusion. This multi-source data fusion method can effectively integrate data from different sources and improve the integrity and accuracy of the data. This provides a solid data foundation for subsequent flood overflow calculation and waterlogging risk assessment, and ensures the reliability and effectiveness of model construction. River flood overflow calculation is performed on rural flood multi-source data to obtain river flood overflow data. Based on river flood overflow data, local two-dimensional hydrological and hydrodynamic quantitative assessment of waterlogging risk points is performed on rural flood multi-source data to obtain local waterlogging risk data. This process can accurately quantify waterlogging risk points and provide detailed data support for the construction of flood overflow and waterlogging risk models. Through this quantitative assessment, waterlogging risk areas can be more accurately identified and assessed, and the accuracy of risk warning can be improved. The rural flood inundation process is simulated through the flood overflow and waterlogging risk model to obtain flood inundation simulation results. The flood inundation simulation results are divided into watershed waterlogging areas to obtain watershed waterlogging area data. This simulation process can dynamically display the scope and process of flood inundation, providing an intuitive basis for the detection of rainstorm flood risk areas and the assessment of rural waterlogging risk levels. By dividing the watershed waterlogging area, the risk area can be more clearly defined, providing a clear regional division for subsequent risk management and early warning. The watershed waterlogging area data is used to detect the rainstorm flood risk area, and the rural waterlogging risk level is assessed to obtain waterlogging risk level data. According to the waterlogging risk level data, the rural flood risk warning indicators are determined to generate a rural flood warning indicator system. This evaluation and indicator system construction process can systematically evaluate the waterlogging risk level, providing a scientific basis for the construction of a real-time flood forecasting and early warning model. Through a clear risk level division and early warning indicator system, flood risk warning and management can be carried out more effectively. A real-time flood forecasting and early warning model is constructed through the rural flood early warning indicator system, and the rural flood risk area is predicted based on the real-time flood forecasting and early warning model, and the risk avoidance transfer route is planned to obtain a rural flood early warning planning report. This process can predict the flood risk area in real time and provide scientific route planning for risk avoidance transfer. Therefore, the present invention uses data processing technology, pattern recognition technology and deep learning technology to fuse and analyze rural multi-source feature data to achieve risk level assessment of rural waterlogged areas; construct a real-time flood forecast and warning model to quickly predict the scope of rural flooding and plan risk avoidance routes when heavy rain occurs or the meteorological department issues a heavy rain warning.

[0006] Preferably, step S1 comprises the following steps: Step S11: Determine the rural flood area to be monitored; Step S12: Collecting geographic information of the rural flood-prone areas to be monitored to obtain rural geographic information data; Step S13: Perform hydrological and flood monitoring on the rural flood-prone area to be monitored to obtain hydrological and flood monitoring data; Step S14: obtaining flood control and drainage engineering data of the rural flood-prone area to be monitored, and performing structural digital conversion to generate flood control and drainage engineering data; Step S15: preprocessing the rural geographic information data, hydrological and flood monitoring data, and flood drainage engineering data to obtain rural flood processing data, wherein the data preprocessing includes data cleaning, data format standardization, and feature extraction and conversion; Step S16: aligning the rural flood processing data through data mapping and merging the multi-source data to obtain rural flood multi-source data.

[0007] The present invention clarifies the monitoring scope by determining the rural flood area to be monitored, provides a clear target area for subsequent data collection and processing, and ensures the pertinence and effectiveness of data collection. Geographic information collection for the rural flood area to be monitored can accurately obtain the geographical features in the area, and improve the geographical adaptability of model construction. Hydrological and flood monitoring of the rural flood area to be monitored can timely grasp the changes in water conditions in the area, provide real-time data support for flood overflow calculation and waterlogging risk assessment, and enhance the timeliness of risk warning. Obtain flood control and drainage engineering data in the rural flood area to be monitored, and perform structural digital conversion to convert the engineering data into digital form, which is convenient for integration with geographic information and hydrological data, and improves the operability and analysis efficiency of the data. Data preprocessing of rural geographic information data, hydrological and flood monitoring data, and flood drainage engineering data can effectively remove noise and redundant information in the data, unify the data format, extract key features, and improve the quality and availability of the data. Mapping and aligning rural flood processing data and merging multi-source data can organically integrate data from different sources to form complete rural flood multi-source data, providing a comprehensive data foundation for subsequent flood inundation simulation and risk assessment, and ensuring the integrity and accuracy of model construction.

[0008] Preferably, step S2 comprises the following steps: Step S21: determining river hydrological divisions for rural flood multi-source data, and calculating river flood overflows to obtain river flood overflow data; Step S22: performing a local two-dimensional hydrological and hydrodynamic quantitative assessment of waterlogging risk points on the rural flood multi-source data based on the river flood overflow data to obtain local waterlogging risk data; Step S23: constructing flood overflow and waterlogging risk according to river flood overflow data and local waterlogging risk data to obtain a flood overflow and waterlogging risk pre-model; training the flood overflow and waterlogging risk pre-model based on river flood overflow data and local waterlogging risk data to obtain a flood overflow and waterlogging risk training model; Step S24: performing model cross-validation evaluation on the flood overflow and waterlogging risk training model to obtain training model evaluation data; adjusting model parameters of the flood overflow and waterlogging risk training model using the training model evaluation data to obtain the flood overflow and waterlogging risk model.

[0009] The present invention determines river hydrological zoning for multi-source data of rural floods and performs river flood overflow calculations, and can accurately divide river hydrological areas, provide a clear zoning basis for flood overflow calculations, and improve the accuracy and reliability of flood overflow data. Based on river flood overflow data, a local two-dimensional hydrological and hydrodynamic quantitative assessment of waterlogging risk points is performed on multi-source data of rural floods, and waterlogging risk points can be accurately identified and quantified, providing detailed data support for subsequent risk model construction, and enhancing the scientific nature and accuracy of risk assessment. Flood overflow and waterlogging risk are constructed based on river flood overflow data and local waterlogging risk data, and model training is performed on flood overflow and waterlogging risk pre-models based on river flood overflow data and local waterlogging risk data, which can effectively construct and train flood overflow and waterlogging risk models, improve the adaptability and prediction ability of the model, and provide a reliable model basis for flood inundation simulation and risk assessment. A model cross-validation evaluation was carried out on the flood overflow and waterlogging risk training model, and the model parameters of the flood overflow and waterlogging risk training model were adjusted through the training model evaluation data. Through cross-validation and parameter adjustment, the model performance can be effectively evaluated and optimized, the stability and accuracy of the model are improved, and the reliability and effectiveness of the model in practical applications are ensured.

[0010] Preferably, step S21 includes the following steps: Step S211: extracting topographic and geomorphic features from multi-source data of rural floods to obtain rural topographic and geomorphic features; determining river system distribution based on the rural topographic and geomorphic features to generate river system distribution data; Step S212: performing hydrological similarity recognition on the river system distribution data to obtain hydrological similarity; partitioning the river system distribution data into river hydrologically similar areas according to the hydrological similarity to generate river hydrological partitioning data; Step S213: extracting the mountain rainfall characteristics from the rural flood multi-source data to obtain the mountain rainfall characteristics; determining the reservoir location according to the river hydrological division data, and calculating the reservoir water storage at the reservoir location based on the mountain rainfall characteristics to generate reservoir rainfall-water storage relationship data; Step S214: Calculate the river network water flow direction, water flow velocity and water level change based on the river hydrological division data according to the reservoir rainfall-water storage relationship data to obtain the river network hydrodynamic data; Step S215: Calculate the surface water infiltration intensity, propagation parameters and turbulent viscosity coefficient for the rural topography and geomorphology characteristics according to the river network hydrodynamic data to obtain surface hydrodynamic data; Step S216: Calculate the river flood and surface waterlogging forces based on the river network hydrodynamic data and the surface hydrodynamic data to obtain river flood overflow data.

[0011] The present invention extracts topographic and geomorphic features from multi-source data of rural floods and determines the distribution of river systems based on rural topographic and geomorphic features, can accurately extract topographic and geomorphic features, and determine the distribution of river systems, providing basic data support for subsequent hydrological zoning and flood overflow calculations, and improving the geographical adaptability and data accuracy of model construction. Hydrological similarity recognition is performed on river system distribution data, and river hydrological similarity regions are partitioned according to hydrological similarity. Through hydrological similarity recognition and partitioning, hydrological similarity regions can be effectively divided, providing a clear partition basis for flood overflow calculations, and improving the accuracy and reliability of flood overflow data. Mountain rainfall features are extracted from multi-source data of rural floods and the location of reservoirs is determined based on river hydrological partition data, which can accurately extract mountain rainfall features and perform reservoir water storage calculations, providing detailed rainfall and water storage data support for subsequent river network hydrodynamic calculations, and enhancing the hydrological adaptability and prediction ability of the model. According to the rainfall-storage relationship data of the reservoir, the flow direction, flow velocity and water level change of the river network are calculated for the river hydrological division data, which can accurately simulate the hydrodynamic changes of the river network. According to the river network hydrodynamic data, the surface water inflow infiltration intensity, propagation parameters and turbulent viscosity coefficient of the rural topography and geomorphic characteristics are calculated, which can accurately calculate the surface hydrodynamic parameters and enhance the model's waterlogging risk assessment ability. Based on the river network hydrodynamic data and surface hydrodynamic data, the river flood and surface waterlogging forces are calculated, which can accurately assess the river flood and surface waterlogging forces, provide a scientific basis for the generation of flood overflow data, improve the accuracy and reliability of flood overflow data, and provide a solid data foundation for subsequent risk assessment and early warning.

[0012] Preferably, step S22 includes the following steps: Step S221: marking the flood overflow area of ​​the river flood overflow data to generate the flood overflow area; extracting the terrain elevation, slope and distance from the river channel of the overflow area from the rural flood multi-source data according to the flood overflow area to obtain the spatial characteristic data of the overflow area; Step S222: quantifying the waterlogging risk index of the overflow area spatial feature data, and calculating the waterlogging risk index in combination with the terrain elevation, slope and distance from the river in the overflow area to obtain a waterlogging risk quantification index; Step S223: measuring the local two-dimensional hydrological and hydrodynamic parameters of the waterlogging risk quantification index, and calculating the water depth and flow velocity hydrodynamics of the local area to obtain the local waterlogging hydrodynamic parameters; Step S224: Evaluate the waterlogging risk change characteristics of local waterlogging hydrodynamic parameters to obtain local waterlogging risk data.

[0013] The present invention marks the flood overflow area of ​​river flood overflow data, extracts the terrain elevation, slope and distance from the river channel of the overflow area of ​​rural flood multi-source data according to the flood overflow area, can accurately mark the flood overflow area, and extract relevant spatial feature data, which provides detailed spatial information for subsequent waterlogging risk assessment and improves the geographical accuracy of risk assessment. The waterlogging risk index is quantified for the spatial feature data of the overflow area, and the waterlogging risk index is calculated in combination with the terrain elevation, slope and distance from the river channel of the overflow area, which can convert the spatial feature data into a specific waterlogging risk index. The local two-dimensional hydrological and hydrodynamic parameters of the waterlogging risk quantification index are measured, and the water depth and flow velocity hydrodynamics of the local area are calculated, which can accurately measure the water depth and flow velocity and other hydrodynamic parameters of the local area, provide detailed data support for the subsequent waterlogging risk change characteristic assessment, and improve the hydrodynamic simulation accuracy of waterlogging risk assessment. The waterlogging risk change characteristic assessment of the local waterlogging hydrodynamic parameters can dynamically monitor the changing trend of waterlogging risk and enhance the timeliness and accuracy of risk warning.

[0014] Preferably, step S3 comprises the following steps: Step S31: setting initial conditions for flood simulation according to multi-source data of rural floods, including setting initial rainfall intensity, initial water level and topographic features, and generating simulation initial condition data; Step S32: Based on the simulation initial condition data, the rural flood multi-source data is input into the flood overflow and waterlogging risk model to simulate the rural flood inundation process and obtain the flood inundation simulation result; Step S33: determining the flood depth of the flood simulation result to obtain flood depth simulation data; identifying the flood area boundary of the flood depth simulation data to obtain flood area boundary data; Step S34: performing watershed mapping on the flooded area boundary data to obtain flooded watershed mapping data; performing waterlogging area division on the flooded watershed division data to obtain watershed waterlogging area data; Step S35: Detect the rainstorm and flood risk areas on the watershed waterlogging area data, and evaluate the rural waterlogging risk level to obtain waterlogging risk level data.

[0015] The present invention sets the initial conditions for flood inundation simulation: the initial conditions for flood inundation simulation process are set according to the multi-source data of rural floods, including setting the initial rainfall intensity, initial water level and topographic undulation characteristics, which can accurately set the initial conditions for flood inundation simulation, provide accurate starting parameters for subsequent simulations, and improve the reliability and accuracy of simulation results. Based on the simulation initial condition data, the multi-source data of rural floods are input into the flood overflow and waterlogging risk model to simulate the rural flood inundation process, which can comprehensively consider various influencing factors, improve the comprehensiveness and accuracy of flood inundation simulation, and provide detailed data support for subsequent risk assessment. The flood inundation depth is determined for the flood inundation simulation results to obtain the flood inundation depth simulation data; the flood inundation area boundary is identified for the flood inundation depth simulation data, which can accurately determine the flood inundation depth and area boundary. The watershed mapping of the flooded area boundary data can clearly define the risk area, provide clear regional division for subsequent risk management and early warning, and enhance the geographical adaptability and management efficiency of risk assessment. The rainstorm flood risk area detection is performed on the watershed waterlogging area data, and the rural waterlogging risk level assessment is performed, which can dynamically monitor and assess the waterlogging risk.

[0016] Preferably, step S35 includes the following steps: Step S351: performing multi-dimensional feature correlation analysis on flood depth, flood range and flood propagation speed of watershed waterlogging area data to obtain multi-dimensional flood correlation data; Step S352: locating the rainstorm range of the multi-dimensional flood-related data to obtain rainstorm range data; identifying the rainstorm risk area of ​​the multi-dimensional flood-related data according to the rainstorm range data to generate the rainstorm risk area; Step S353: Dynamically simulate the risk area of ​​the rainstorm and flood risk area, and analyze the changes of the rainstorm and flood risk area at different time points to obtain dynamic change data of the risk area; Step S354: Calculate the risk assessment index for the dynamic change data of the risk area to generate risk assessment index data; classify the risk assessment index data into waterlogging risk levels to obtain waterlogging risk level data.

[0017] The present invention performs multi-dimensional feature correlation analysis on the flood depth, flood range and flood propagation speed of the watershed waterlogging area data, and can comprehensively analyze the multi-dimensional features of flood events. The rainstorm range is located for the multi-dimensional flood-related data, and the rainstorm and flood risk area is identified for the multi-dimensional flood-related data based on the rainstorm range data, which can accurately define the rainstorm and flood risk areas, provide clear regional divisions for subsequent risk assessments, and enhance the geographical accuracy of risk assessments. The risk areas of rainstorm and flood risk areas are dynamically simulated, and the changes in the rainstorm and flood risk areas at different time points are analyzed, which can monitor the changing trends of the risk areas in real time, provide dynamic data support for subsequent risk assessments, and enhance the timeliness and dynamism of risk assessments. The risk assessment index is calculated for the dynamically changing data of the risk area, and the waterlogging risk level can be scientifically assessed through the calculation of risk assessment indexes and the division of waterlogging risk levels.

[0018] Preferably, step S4 comprises the following steps: Step S41: Arrange the waterlogging risk level data in reverse order according to the risk level gradient to obtain risk level gradient data; map low, medium and high risk rural waterlogging areas according to the risk level gradient data to obtain waterlogging risk mapping areas; Step S42: Preliminary selection of flood risk warning indicators for the waterlogging risk mapping area to obtain preliminary selected data of warning indicators; performing correlation analysis on the preliminary selected data of warning indicators to obtain correlation data of warning indicators; performing sensitivity analysis on the preliminary selected data of warning indicators to obtain sensitivity data of warning indicators; Step S43: screening flood risk warning indicators for the preliminary warning indicator data according to the warning indicator correlation data and the warning indicator sensitivity data, and constructing a warning indicator system to generate a rural flood warning indicator system; Step S44: constructing a flood warning model through the rural flood warning indicator system to generate a real-time flood forecast warning model; Step S45: Predict rural flood risk areas based on the real-time flood forecast and warning model to obtain rural flood risk area prediction data; plan risk avoidance and transfer routes based on the rural flood risk area prediction data to generate a rural flood warning planning report.

[0019] The present invention arranges the waterlogging risk level data in reverse order of the risk level gradient, and maps the low, medium and high risk rural waterlogging areas according to the risk level gradient data, which can systematically arrange the risk levels and perform regional mapping, providing a clear regional division for the subsequent preliminary selection of early warning indicators, and enhancing the geographical adaptability and management efficiency of risk warning. The flood risk early warning indicators are preliminarily selected for the waterlogging risk mapping area, and the correlation analysis is performed on the preliminary selection data of the early warning indicators; through the preliminary selection, correlation analysis and sensitivity analysis, effective early warning indicators can be scientifically screened out, providing scientific data support for the subsequent construction of the early warning indicator system, and improving the scientificity and reliability of the early warning indicator system. The flood risk early warning indicators are screened for the preliminary selection data of the early warning indicators according to the early warning indicator correlation data and the early warning indicator sensitivity data, and an early warning indicator system is constructed; through screening and system construction, the early warning indicators can be effectively integrated to form a complete early warning indicator system, providing a solid foundation for the subsequent construction of the flood early warning model, and enhancing the scientificity and operability of the early warning model. The construction of flood warning model through the rural flood warning indicator system can build a real-time forecast warning model based on the warning indicator system, which improves the real-time and prediction capabilities of the model, provides scientific model support for subsequent risk area prediction and risk avoidance transfer route planning, and enhances the timeliness and accuracy of risk warning. Based on the real-time flood forecast warning model, the rural flood risk area is predicted, and through risk area prediction and risk avoidance transfer route planning, it can scientifically predict the flood risk area and provide a clear risk avoidance transfer route.

[0020] Preferably, step S44 includes the following steps: Step S441: determining meteorological conditions for rural flood multi-source data to obtain meteorological condition performance data; performing flood warning demand analysis on the meteorological condition performance data to generate flood warning demand data; Step S442: constructing a flood warning framework according to the rural flood warning indicator system to obtain flood warning framework information; calibrating the flood warning framework information based on the flood warning demand data to obtain a warning calibration framework; Step S443: constructing a flood warning model for the warning calibration framework to generate an initial flood warning model; performing model warning verification on the initial flood warning model to obtain warning performance verification data; optimizing response parameters of the initial flood warning model according to the warning performance verification data to obtain a flood warning response model; Step S444: Acquire real-time meteorological monitoring data and real-time water level monitoring data to obtain real-time monitoring data; input the real-time monitoring data into the flood warning response model, and conduct real-time flood forecast and warning training to generate a real-time flood forecast and warning model.

[0021] The present invention determines the meteorological conditions of rural flood multi-source data, and analyzes the flood warning demand of the meteorological condition performance data, can accurately determine the meteorological conditions, and analyze the flood warning demand, provide clear demand guidance for the subsequent warning framework construction, and improve the pertinence and adaptability of the warning system; construct a flood warning framework according to the rural flood warning indicator system, and calibrate the flood warning framework information based on the flood warning demand data, which can ensure that the warning framework matches the actual demand, and improve the practicality and reliability of the warning framework. The warning calibration framework is used to construct a flood warning model to generate an initial flood warning model; the initial flood warning model is model-verified to obtain warning performance verification data; the initial flood warning model is optimized according to the warning performance verification data to obtain a flood warning response model. Through model construction and parameter optimization, the performance and response capability of the warning model can be improved. Real-time meteorological monitoring data and real-time water level monitoring data are obtained, and the real-time monitoring data are input into the flood warning response model, and real-time flood forecast warning training is performed to generate a real-time flood forecast warning model. Through the acquisition of real-time monitoring data and model training, real-time forecasting and warning of flood events can be achieved, which improves the real-time and dynamic nature of the early warning system, provides scientific decision-making support for the management and early warning of rural flood disasters, and enhances the practicality and operability of risk warning.

[0022] Preferably, step S45 includes the following steps: Step S451: Based on the real-time flood forecasting and warning model, the real-time monitoring data is used to predict rural flood scenarios to obtain rural flood scenario prediction data; the rural flood scenario prediction data is used to delineate the flooding range to obtain flooding range delineation data; Step S452: performing spatiotemporal analysis of flood ranges on the flood range demarcation data, analyzing the spatial distribution characteristics of each flood range at different time points, and obtaining spatiotemporal prediction data of flood ranges; Step S453: determining the flood range change trend of the spatiotemporal prediction data of the flood range to obtain flood range change trend information; performing multi-path planning of evasion routes according to the flood range change trend information, planning multiple evasion routes for each flood range, and obtaining multiple evasion route data; Step S454: performing route feasibility evaluation on the plurality of risk avoidance route data to obtain risk avoidance route feasibility data; determining the optimal risk avoidance route for the plurality of risk avoidance route data according to the risk avoidance route feasibility data to generate the optimal risk avoidance route data; Step S455: compile an early warning planning report based on the optimal risk avoidance route data to obtain a rural flood early warning planning report.

[0023] The present invention predicts rural flood scenes based on real-time monitoring data based on a real-time flood forecast and warning model, and delimits flooding ranges for rural flood scene prediction data, so as to predict rural flood scenes in real time and accurately delimit flooding ranges. The flooding range delimitation data is subjected to spatiotemporal analysis of the flooding range, and the spatial distribution characteristics of each flooding range at different time points are analyzed, so that the changing trend of the flooding range can be dynamically monitored, and scientific spatiotemporal data support is provided for subsequent risk avoidance transfer route planning, thereby enhancing the dynamic and adaptability of early warning. The spatiotemporal prediction data of the flooding range is subjected to the flooding range changing trend determination, and multiple risk avoidance transfer routes are planned for each flooding range. Through the determination of the changing trend and multi-path planning, the risk avoidance transfer routes can be scientifically planned, and multiple route options are provided for subsequent route feasibility evaluation, thereby improving the flexibility and reliability of risk avoidance transfer. The feasibility of multiple risk avoidance route data is evaluated, and the optimal risk avoidance route is determined for multiple risk avoidance route data according to the risk avoidance route feasibility data, so that the most feasible risk avoidance route can be selected. The optimal risk avoidance route data is subjected to early warning planning report compilation, and a rural flood early warning planning report is obtained. By compiling early warning planning reports, we can systematically integrate the data on the optimal risk avoidance routes, provide comprehensive planning support for the management and early warning of rural flood disasters, and enhance the practicality and operability of risk warnings. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a schematic diagram of the steps of a method for risk level assessment and early warning of rural waterlogged areas based on multi-source data; Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart; Figure 3 for Figure 1 Detailed implementation steps of step S4 in FIG. 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

[0025] 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.

[0026] 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.

[0027] 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.

[0028] To achieve this, please refer to Figures 1 to 3 A method for assessing and warning the risk level of waterlogging in rural waterlogged areas based on multi-source data, the method comprising the following steps: Step S1: Obtaining rural waterlogged area geographic information, hydrological and flood data, and flood control and drainage engineering data, and fusing multi-source data to obtain rural flood multi-source data; Step S2: Calculate river flood overflow for rural flood multi-source data to obtain river flood overflow data; perform local two-dimensional hydrological and hydrodynamic quantitative assessment of waterlogging risk points for rural flood multi-source data based on river flood overflow data to obtain local waterlogging risk data; construct a flood overflow and waterlogging risk model based on river flood overflow data and local waterlogging risk data; Step S3: simulate the rural flood inundation process through the flood overflow and waterlogging risk model to obtain the flood inundation simulation results; divide the flood inundation simulation results into watershed waterlogging areas to obtain watershed waterlogging area data; detect the rainstorm flood risk area on the watershed waterlogging area data, and evaluate the rural waterlogging risk level to obtain waterlogging risk level data; Step S4: Determine the rural flood risk warning index according to the waterlogging risk level data and generate a rural flood warning index system; construct a flood real-time forecast and warning model through the rural flood warning index system, and predict the rural flood risk area based on the flood real-time forecast and warning model, plan the risk avoidance and transfer route, so as to obtain a rural flood warning planning report.

[0029] The present invention obtains rural flood multi-source data by acquiring geographical information, hydrological and flood data, and flood control and drainage engineering data of rural waterlogging areas, and performs multi-source data fusion. This multi-source data fusion method can effectively integrate data from different sources and improve the integrity and accuracy of the data. This provides a solid data foundation for subsequent flood overflow calculation and waterlogging risk assessment, and ensures the reliability and effectiveness of model construction. River flood overflow calculation is performed on rural flood multi-source data to obtain river flood overflow data. Based on river flood overflow data, local two-dimensional hydrological and hydrodynamic quantitative assessment of waterlogging risk points is performed on rural flood multi-source data to obtain local waterlogging risk data. This process can accurately quantify waterlogging risk points and provide detailed data support for the construction of flood overflow and waterlogging risk models. Through this quantitative assessment, waterlogging risk areas can be more accurately identified and assessed, and the accuracy of risk warning can be improved. The rural flood inundation process is simulated through the flood overflow and waterlogging risk model to obtain flood inundation simulation results. The flood inundation simulation results are divided into watershed waterlogging areas to obtain watershed waterlogging area data. This simulation process can dynamically display the scope and process of flood inundation, providing an intuitive basis for the detection of rainstorm flood risk areas and the assessment of rural waterlogging risk levels. By dividing the watershed waterlogging area, the risk area can be more clearly defined, providing a clear regional division for subsequent risk management and early warning. The watershed waterlogging area data is used to detect the rainstorm flood risk area, and the rural waterlogging risk level is assessed to obtain waterlogging risk level data. According to the waterlogging risk level data, the rural flood risk warning indicators are determined to generate a rural flood warning indicator system. This evaluation and indicator system construction process can systematically evaluate the waterlogging risk level, providing a scientific basis for the construction of a real-time flood forecasting and early warning model. Through a clear risk level division and early warning indicator system, flood risk warning and management can be carried out more effectively. A real-time flood forecasting and early warning model is constructed through the rural flood early warning indicator system, and the rural flood risk area is predicted based on the real-time flood forecasting and early warning model, and the risk avoidance transfer route is planned to obtain a rural flood early warning planning report. This process can predict the flood risk area in real time and provide scientific route planning for risk avoidance transfer. Therefore, the present invention uses data processing technology, pattern recognition technology and deep learning technology to fuse and analyze rural multi-source feature data to achieve risk level assessment of rural waterlogged areas; construct a real-time flood forecast and warning model to quickly predict the scope of rural flooding and plan risk avoidance routes when heavy rain occurs or the meteorological department issues a heavy rain warning.

[0030] In the embodiment of the present invention, reference Figure 1 As shown, it is a schematic diagram of the steps of the method for assessing and warning the risk level of waterlogged areas in rural areas based on multi-source data of the present invention. In this example, the method for assessing and warning the risk level of waterlogged areas in rural areas based on multi-source data includes the following steps: Step S1: Obtaining rural waterlogged area geographic information, hydrological and flood data, and flood control and drainage engineering data, and fusing multi-source data to obtain rural flood multi-source data; In the embodiment of the present invention, geographic information system (GIS) technology is used to obtain topographic data of rural waterlogged areas from the National Qinghai-Tibet Plateau Science Data Center, including elevation, slope, land use type, etc. ArcGIS software is used to preprocess the acquired geographic information data, including data format conversion, coordinate system one, data clipping, etc., to ensure the accuracy and consistency of the data. Meteorological data such as rainfall and evaporation in rural waterlogged areas are obtained from the regional ground meteorological element driven data set (1979-2018); real-time hydrological data, including river water level and flow, are obtained from data sources such as hydrological networks. Python is used to clean, convert and analyze data, remove outliers and missing values ​​in the data, and uniformly process data of different time scales; obtain existing flood control (drainage) standards and corresponding peak flow, peak water level and other data from the Guidelines for the Preparation of Flood Control Evaluation Reports for Construction Projects within the River Management Scope (Trial); obtain basic information such as the location, scale, and design standards of water conservancy (flood control) projects such as rivers, embankments, reservoirs, culverts, and pumping stations from reports on existing water conservancy projects and other facilities. Use Excel or database software to organize and classify flood control and drainage engineering data, and generate structured data tables for subsequent analysis and fusion. Use multi-source data fusion technology, such as multi-source information fusion method based on Bayesian network, to fuse the acquired geographic information, hydrological and flood data, and flood control and drainage engineering data. First, standardize the data from different sources and convert the data into a unified format and dimension; then, use the Bayesian network model to perform data fusion based on the correlation and dependency between the data to generate rural flood multi-source data. The fused rural flood multi-source data includes comprehensive information of geographic information, hydrological and flood data, and flood control and drainage engineering data, which can fully reflect the flood risk characteristics of rural waterlogged areas and provide data support for subsequent risk level assessment and early warning.

[0031] Step S2: Calculate river flood overflow for rural flood multi-source data to obtain river flood overflow data; perform local two-dimensional hydrological and hydrodynamic quantitative assessment of waterlogging risk points for rural flood multi-source data based on river flood overflow data to obtain local waterlogging risk data; construct a flood overflow and waterlogging risk model based on river flood overflow data and local waterlogging risk data; In the embodiment of the present invention, the multi-source data fusion technology is used to fuse geographic information, hydrological and flood data, and flood control and drainage engineering data to generate rural flood multi-source data. A flood overflow calculation model based on the water level-flow relationship is adopted to calculate the river flood overflow range and water depth according to the river water level and flow data, combined with the river section morphology and flood control engineering parameters. Parameter setting: The time step of flood overflow calculation is set to 10 minutes, the spatial resolution is 10 meters × 10 meters, and the flood overflow under different recurrence periods (such as 10 years, 20 years, and 50 years) is considered. Result output: Generate river flood overflow data, including overflow range, water depth distribution and other information, to provide basic data for subsequent waterlogging risk assessment. Based on the river flood overflow data, combined with the geographic information and flood control and drainage engineering data in the rural flood multi-source data, a local two-dimensional hydrological and hydrodynamic quantitative assessment of waterlogging risk points is carried out. Using a two-dimensional hydrological and hydrodynamic model, such as SWMM (Storm Water Management Model) or MIKE21, simulate the hydrological and hydrodynamic parameters such as water accumulation depth and flow velocity at waterlogging risk points. Parameter setting: Set the time step of the model to 5 minutes and the spatial resolution to 5 meters × 5 meters, considering the waterlogging risk under different rainfall intensities (such as 10 mm / hour, 20 mm / hour) and drainage system conditions (such as normal operation, partial blockage). Output results: Obtain local waterlogging risk data, including water accumulation depth and flow velocity distribution at waterlogging risk points, to provide data support for the construction of flood overflow and waterlogging risk model. The river flood overflow data and local waterlogging risk data are fused, and multi-source data fusion technology such as Bayesian network is used to establish the correlation between flood overflow and waterlogging risk. Based on the fused data, a flood overflow and waterlogging risk model is constructed. The model considers factors such as flood overflow range, water depth, flow velocity, and water accumulation depth and flow velocity at waterlogging risk points to comprehensively evaluate the flood risk in rural waterlogged areas. Parameter optimization: Optimize the model parameters through historical flood event data, and use grid search and other methods to determine the best parameter combination of the model to improve the prediction accuracy of the model. Model verification: Use independent flood event data to verify the constructed model, evaluate the accuracy and reliability of the model, and ensure that the model can effectively reflect the flood risk characteristics of rural waterlogged areas.

[0032] Step S3: simulate the rural flood inundation process through the flood overflow and waterlogging risk model to obtain the flood inundation simulation results; divide the flood inundation simulation results into watershed waterlogging areas to obtain watershed waterlogging area data; detect the rainstorm flood risk area on the watershed waterlogging area data, and evaluate the rural waterlogging risk level to obtain waterlogging risk level data; In the embodiment of the present invention, the constructed flood overflow and waterlogging risk model is used, combined with rural flood multi-source data, including geographic information, hydrological and flood data, and flood control and drainage engineering data. The GIS-based flood inundation simulation technology is used, combined with a one-dimensional hydraulic model (such as HecRAS) and a two-dimensional hydrological and hydrodynamic model (such as SWMM) to simulate the rural flood inundation process. Parameter setting: The simulation time step is set to 5 minutes, the spatial resolution is 10 meters × 10 meters, and the flood inundation under different recurrence periods (such as 10 years, 20 years, 50 years) and different rainfall intensities (such as 10 mm / hour, 20 mm / hour) are considered. Result output: Generate flood inundation simulation results, including information such as inundation range, water depth distribution, and flow velocity distribution, providing basic data for subsequent watershed waterlogging area division. Based on the flood inundation simulation results, combined with information such as topography and land use type in geographic information data, the watershed waterlogging area division is carried out. Division method: The DEM-based overland flow accumulation method (D8 algorithm) is used. According to the gravity of surface runoff under the terrain, the water flow direction of each grid in the DEM is calculated to determine the cumulative flow of the downstream grid, so as to extract the river network of the basin and determine the catchment area. Parameter setting: The minimum cumulative flow threshold for the catchment area division is set to 1000 grids to ensure the rationality and accuracy of the division results. Result output: The watershed waterlogging area data of the basin is obtained, including the boundary, area, average water depth and other information of each waterlogging area, which provides data support for the subsequent detection of rainstorm and flood risk areas. Based on the watershed waterlogging area data of the basin, combined with the water depth, flow velocity and other information in the flood inundation simulation results, the rainstorm and flood risk area detection and waterlogging risk level assessment are carried out. The normalized difference water index (NDWI) is used to extract the flood inundation area from the satellite remote sensing image, and the rainstorm and flood risk area is detected in combination with the historical flood event data. The waterlogging risk level assessment technology is adopted, and the waterlogging depth, flow velocity and hazard parameters are considered as the assessment criteria. The core calculation formula is RH=d×(v+0.5)+df, where RH is the risk level index, d is the waterlogging depth, v is the flow velocity, and df is the water depth hazard parameter. Parameter setting: When the waterlogging depth d≤0.2m, df=0.5; when d>0.2m, df=1.0. According to the risk level index RH, the waterlogging risk level is divided into low risk (0~10), medium risk (10~20), high risk (20~30) and other levels. Result output: The waterlogging risk level data is obtained, including the waterlogging risk level of each waterlogging area, the distribution of risk areas and other information, which provides a scientific basis for the risk level assessment and early warning of waterlogging areas in rural waterlogging areas.

[0033] Step S4: Determine the rural flood risk warning index according to the waterlogging risk level data and generate a rural flood warning index system; construct a flood real-time forecast and warning model through the rural flood warning index system, and predict the rural flood risk area based on the flood real-time forecast and warning model, plan the risk avoidance and transfer route, so as to obtain a rural flood warning planning report.

[0034] In an embodiment of the present invention, waterlogging risk level data is used in combination with geographic information, hydrological and flood data, and flood control and drainage engineering data in rural flood multi-source data. According to the waterlogging risk level data, rural flood risk warning indicators are determined, including water accumulation depth, flow velocity, flooding time, rainfall intensity, etc.; specifically, water accumulation depth: water accumulation depth less than 15 cm is a slight water accumulation point, and greater than or equal to 15 cm is a waterlogging point. Flow velocity: areas with a flow velocity exceeding 0.5 m / s are considered high-risk areas. Flooding time: areas with a flooding time exceeding 24 hours are considered high-risk areas. Rainfall intensity: areas with a rainfall intensity exceeding 20 mm / hour are considered high-risk areas. Result output: A rural flood warning indicator system is generated, including the above-mentioned indicators and their corresponding thresholds. Based on the rural flood warning indicator system, real-time meteorological data, hydrological data and geographic information data are combined. A real-time flood forecasting and warning model based on neural networks, such as BP neural network, convolutional neural network (CNN) and recurrent neural network (RNN), is used, and optimized by stochastic gradient descent (SGD) algorithm, adaptive learning rate optimization method (such as Adam), L2 regularization and Dropout technology. Parameter setting: The time step of the model is set to 5 minutes, the spatial resolution is set to 10 meters × 10 meters, and the flood situation under different recurrence periods (such as 10 years, 20 years, 50 years) and different rainfall intensities (such as 10 mm / hour, 20 mm / hour) is considered. Generate a real-time flood forecasting and warning model, which can predict the flooding range, water depth, flow rate and other information of rural flood risk areas in real time. Use the real-time flood forecasting and warning model, combined with real-time meteorological data, hydrological data and geographic information data. Use the model to predict rural flood risk areas, and predict the flooding range, water depth, flow rate and other information at different time points in the future. Generate the prediction results of rural flood risk areas, including the prediction time point, flooding range, water depth distribution, flow rate distribution and other information, to provide data support for the planning of risk avoidance and transfer routes. Based on the prediction results of rural flood risk areas, combined with information such as road networks and settlement distribution in geographic information data. Using the Network Analysis function of GIS, based on the road network data in the study area, analyze and determine the two best flood avoidance transfer routes with the shortest time and the shortest distance. Set the division principle of risk avoidance transfer units, such as the transfer units of flood storage and detention areas and floodplains are not larger than natural villages, and the transfer units of protection and protection areas can be at the township level. Generate a risk avoidance transfer route planning map, including information such as transfer units, transfer routes, and resettlement sites, to provide specific content for the rural flood warning planning report. Integrate the rural flood risk warning indicator system, real-time flood forecasting and warning model, rural flood risk area prediction results, and risk avoidance transfer route planning results. Prepare a rural flood warning planning report, including the warning indicator system, model construction method, risk area prediction results, risk avoidance transfer route planning, etc., to provide a scientific basis for the early warning and response of rural flood disasters.

[0035] Preferably, step S1 comprises the following steps: Step S11: Determine the rural flood area to be monitored; Step S12: Collecting geographic information of the rural flood-prone areas to be monitored to obtain rural geographic information data; Step S13: Perform hydrological and flood monitoring on the rural flood-prone area to be monitored to obtain hydrological and flood monitoring data; Step S14: obtaining flood control and drainage engineering data of the rural flood-prone area to be monitored, and performing structural digital conversion to generate flood control and drainage engineering data; Step S15: preprocessing the rural geographic information data, hydrological and flood monitoring data, and flood drainage engineering data to obtain rural flood processing data, wherein the data preprocessing includes data cleaning, data format standardization, and feature extraction and conversion; Step S16: aligning the rural flood processing data through data mapping and merging the multi-source data to obtain rural flood multi-source data.

[0036] In the embodiment of the present invention, the rural flood area to be monitored is determined based on the geographic information system (GIS) technology, combined with historical flood data and topographic information. The specific operation includes using GIS software (such as ArcGIS) to load historical flood event data and topographic data, and determining the area prone to flooding through the spatial analysis function. Using unmanned aerial vehicle (UAV) aerial survey technology, equipped with sensors such as high-definition cameras and laser radars, aerial photogrammetry and remote sensing data acquisition are performed on the determined rural flood area. The collected data includes orthophotos and oblique photography real-life three-dimensional models for subsequent geographic information analysis. A hydrological monitoring system based on the Internet of Things is used to monitor the water level, flow, rainfall and other hydrological data of the area to be monitored in real time using a sensor network. The specific operation includes deploying water level sensors, rainfall sensors and flow rate sensors, and transmitting data to the monitoring center through a wireless network for real-time analysis. Collect flood control and drainage engineering data in the area to be monitored, including information such as the location, scale and design standards of projects such as embankments, reservoirs, and pumping stations. Use GIS software to digitize these data to generate structured flood control and drainage engineering data for subsequent analysis and application. Use data cleaning tools (such as the Pandas library) to clean the collected rural geographic information data, hydrological and flood monitoring data, and flood drainage engineering data to remove duplicate data and outliers. Then, standardize the data format and unify the data format and dimension. Finally, extract features related to flood risk, such as water depth, flow rate, rainfall intensity, etc. through feature extraction and transformation. Use GIS multi-source data fusion technology to map and align the pre-processed rural geographic information data, hydrological and flood monitoring data, and flood drainage engineering data. The specific operation includes unifying the spatial coordinates of different data sources, and then merging the multi-source data through data merging tools (such as ArcGIS's Merge tool) to generate rural flood multi-source data, providing basic data for subsequent risk assessment and early warning.

[0037] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes: Step S21: determining river hydrological divisions for rural flood multi-source data, and calculating river flood overflows to obtain river flood overflow data; Step S22: performing a local two-dimensional hydrological and hydrodynamic quantitative assessment of waterlogging risk points on the rural flood multi-source data based on the river flood overflow data to obtain local waterlogging risk data; Step S23: constructing flood overflow and waterlogging risk according to river flood overflow data and local waterlogging risk data to obtain a flood overflow and waterlogging risk pre-model; training the flood overflow and waterlogging risk pre-model based on river flood overflow data and local waterlogging risk data to obtain a flood overflow and waterlogging risk training model; Step S24: performing model cross-validation evaluation on the flood overflow and waterlogging risk training model to obtain training model evaluation data; adjusting model parameters of the flood overflow and waterlogging risk training model using the training model evaluation data to obtain the flood overflow and waterlogging risk model.

[0038] In the embodiment of the present invention, multi-source data of rural floods are used, including geographic information data, hydrological and flood monitoring data, and flood control and drainage engineering data. A clustering partitioning method based on hydrological similarity theory is used to combine basin characteristics and hydrological variables to perform hydrological partitioning on the study area. Flood frequency distribution curve parameters, runoff characteristics, soil characteristics, etc. are selected as partitioning indicators, and fuzzy clustering method is used for partitioning. Through consistency test and uniformity test, a river hydrological partition map is generated to clarify the boundaries and hydrological characteristics of each partition. The MIKE 11 model is used to calculate river flood overflow, and the MIKE 21 model is combined to simulate two-dimensional flood propagation; the time step of the model is set to 5 minutes, the spatial resolution is 10 meters × 10 meters, and the flood overflow under different recurrence periods (such as 10 years, 20 years, 50 years) and different rainfall intensities (such as 10 mm / hour, 20 mm / hour) is considered to generate river flood overflow data, including overflow range, water depth distribution, flow velocity distribution and other information. Based on river flood overflow data, combined with geographic information in rural flood multi-source data and flood control and drainage engineering data; the SWMM model is used to conduct a quantitative assessment of local two-dimensional hydrological and hydrodynamics at waterlogging risk points, simulating hydrological and hydrodynamic parameters such as waterlogging depth and flow velocity at waterlogging risk points; the model time step is set to 5 minutes, the spatial resolution is 5 meters × 5 meters, and the waterlogging risk under different rainfall intensities (such as 10 mm / hour, 20 mm / hour) and drainage system conditions (such as normal operation, partial blockage) are considered to obtain local waterlogging risk data, including waterlogging depth, flow velocity distribution and other information at waterlogging risk points. The river flood overflow data and local waterlogging risk data are fused, and multi-source data fusion technologies such as Bayesian networks are used to establish the correlation between flood overflow and waterlogging risk. Based on the fused data, a flood overflow and waterlogging risk pre-model is constructed. The model considers factors such as flood overflow range, water depth, flow velocity, and waterlogging depth and flow velocity at waterlogging risk points to conduct a comprehensive assessment of flood risk in rural waterlogged areas. The pre-model was trained using historical flood event data to ensure that the model could effectively reflect the flood risk characteristics of rural waterlogged areas. The model was optimized using the stochastic gradient descent (SGD) algorithm, adaptive learning rate optimization method (such as Adam), L2 regularization and Dropout technology to obtain a flood overtopping and waterlogging risk training model, which can accurately predict the flood risk in rural waterlogged areas. The cross-validation method was used to evaluate the flood overtopping and waterlogging risk training model. The evaluation indicators included mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and determination coefficient (R²). Training model evaluation data was generated, including the values ​​of the above evaluation indicators, to evaluate the accuracy and generalization ability of the model. According to the training model evaluation data, the model parameters were adjusted to optimize the performance of the model.Specific adjustment methods include adjusting the learning rate, increasing the number of iterations, adjusting the regularization parameters, etc., to obtain the optimized flood overburden and waterlogging risk model. The model shows good prediction performance in the training set, cross-validation set and test set.

[0039] Preferably, step S21 includes the following steps: Step S211: extracting topographic and geomorphic features from multi-source data of rural floods to obtain rural topographic and geomorphic features; determining river system distribution based on the rural topographic and geomorphic features to generate river system distribution data; Step S212: performing hydrological similarity recognition on the river system distribution data to obtain hydrological similarity; partitioning the river system distribution data into river hydrologically similar areas according to the hydrological similarity to generate river hydrological partitioning data; Step S213: extracting the mountain rainfall characteristics from the rural flood multi-source data to obtain the mountain rainfall characteristics; determining the reservoir location according to the river hydrological division data, and calculating the reservoir water storage at the reservoir location based on the mountain rainfall characteristics to generate reservoir rainfall-water storage relationship data; Step S214: Calculate the river network water flow direction, water flow velocity and water level change based on the river hydrological division data according to the reservoir rainfall-water storage relationship data to obtain the river network hydrodynamic data; Step S215: Calculate the surface water infiltration intensity, propagation parameters and turbulent viscosity coefficient for the rural topography and geomorphology characteristics according to the river network hydrodynamic data to obtain surface hydrodynamic data; Step S216: Calculate the river flood and surface waterlogging forces based on the river network hydrodynamic data and the surface hydrodynamic data to obtain river flood overflow data.

[0040] In the embodiment of the present invention, rural flood multi-source data are used, including geographic information data, hydrological and flood monitoring data, and flood control and drainage engineering data. GIS technology is used to extract the topographic and geomorphic features of rural areas, such as elevation, slope, and slope direction, in combination with digital elevation model (DEM) data. The resolution of the DEM data is set to a spatial resolution of 10 meters × 10 meters, and features such as elevation, slope, and slope direction are extracted to generate rural topographic and geomorphic feature data, including elevation maps, slope maps, and slope direction maps. Based on the extracted rural topographic and geomorphic feature data, a DEM-based water flow accumulation analysis method is used to determine the distribution of river systems, and the threshold of water flow accumulation is set to 1000 grids to determine the river network, and generate river system distribution data, including river network maps, river flow direction maps, etc. The river system distribution data is used in combination with hydrological monitoring data; a cluster analysis method based on hydrological characteristics is used to identify the hydrological similarity of river systems. Flood frequency distribution curve parameters, runoff characteristics, soil characteristics, etc. are selected as clustering indicators, and fuzzy clustering method is used for similarity identification to obtain hydrological similarity data, including the similarity matrix of each river system. Based on the hydrological similarity data, the river system distribution data is partitioned according to the hydrological similarity to generate river hydrological partition data. The partition threshold is set to ensure the consistency and uniformity of the partition results; river hydrological partition data is generated, including partition maps, partition feature tables, etc. The meteorological data in the rural flood multi-source data, including rainfall, rainfall intensity, etc., are used. The method based on time series analysis is used to extract the characteristics of rainfall in mountainous areas, such as rainfall intensity, rainfall duration, etc.; the time step is set to 5 minutes, and the characteristics of rainfall intensity, rainfall duration, etc. are extracted to obtain rainfall characteristic data in mountainous areas, including rainfall intensity map, rainfall duration map, etc. Based on the river hydrological division data, combined with the rainfall characteristic data in the mountainous area; according to the river hydrological division data, determine the location of the reservoir; based on the rainfall characteristics in the mountainous area, use the reservoir water storage model to calculate the water storage and generate the reservoir rainfall-water storage relationship data; set the initial reservoir water level, control target water level and other parameters of the reservoir, perform water storage calculations, and generate the reservoir rainfall-water storage relationship data, including the reservoir water storage curve, rainfall-water storage relationship table, etc. Using the reservoir rainfall-water storage relationship data, combined with the river hydrological division data, the calculation method based on the hydrodynamic model, such as the MIKE 11 model, calculates the flow direction, flow velocity and water level changes of the river network; set the model time step to 5 minutes, the spatial resolution to 10 meters × 10 meters, and consider the hydrodynamic changes under different rainfall intensities; obtain the river network hydrodynamic data, including the flow direction map, the flow velocity map, the water level change map, etc.Using river network hydrodynamic data, combined with rural topographic and geomorphic data, the calculation method based on the surface hydrodynamic model is used to calculate the inflow and infiltration intensity, propagation parameters and turbulent viscosity coefficient of surface water; the time step of the model is set to 5 minutes, the spatial resolution is 5 meters × 5 meters, and the surface hydrodynamic parameters under different terrain conditions are considered to obtain surface hydrodynamic data, including inflow and infiltration intensity maps, propagation parameter maps, turbulent viscosity coefficient maps, etc. Using river network hydrodynamic data and surface hydrodynamic data, the calculation method based on the coupling model, such as the MIKE 11 and SWMM coupling model, is used to calculate the forces of river floods and surface waterlogging; the time step of the model is set to 5 minutes, the spatial resolution is 10 meters × 10 meters, and the forces under different rainfall intensities and terrain conditions are considered to obtain river flood overflow data, including overflow range maps, water depth distribution maps, flow velocity distribution maps, etc.

[0041] Preferably, step S22 includes the following steps: Step S221: marking the flood overflow area of ​​the river flood overflow data to generate the flood overflow area; extracting the terrain elevation, slope and distance from the river channel of the overflow area from the rural flood multi-source data according to the flood overflow area to obtain the spatial characteristic data of the overflow area; Step S222: quantifying the waterlogging risk index of the overflow area spatial feature data, and calculating the waterlogging risk index in combination with the terrain elevation, slope and distance from the river in the overflow area to obtain a waterlogging risk quantification index; Step S223: measuring the local two-dimensional hydrological and hydrodynamic parameters of the waterlogging risk quantification index, and calculating the water depth and flow velocity hydrodynamics of the local area to obtain the local waterlogging hydrodynamic parameters; Step S224: Evaluate the waterlogging risk change characteristics of local waterlogging hydrodynamic parameters to obtain local waterlogging risk data.

[0042] In the embodiment of the present invention, river flood overflow data is used in combination with geographic information data, and GIS technology is used to perform spatial analysis on river flood overflow data, and flood overflow areas are marked; thresholds of overflow areas are set, such as areas with a water depth greater than 0.2 meters are marked as overflow areas, and flood overflow area maps are generated to clarify the boundaries and locations of overflow areas; based on the marked flood overflow areas, geographic information data in multi-source data of rural floods are combined. GIS extraction tools are used to extract spatial feature data such as terrain elevation, slope, and distance from the river channel in the overflow area, and the extraction resolution is set to a spatial resolution of 10 meters × 10 meters. Features such as terrain elevation, slope, and distance from the river channel are extracted to obtain spatial feature data of the overflow area, including terrain elevation maps, slope maps, and distance from the river channel maps. The waterlogging risk index is quantified for the spatial characteristic data of the overflow area, and the waterlogging risk index is calculated in combination with the terrain elevation, slope and distance from the river in the overflow area to obtain the waterlogging risk quantification index; the waterlogging risk level assessment model is used to quantify the waterlogging risk index by combining the terrain elevation, slope and distance from the river using the spatial characteristic data of the overflow area, including the terrain elevation, slope and distance from the river; the parameters of the waterlogging risk level assessment model are set, such as the waterlogging depth, flow velocity and water depth hazard parameters, to obtain the waterlogging risk quantification index, including the risk level index. Based on the waterlogging risk quantification index, combined with the characteristics of the overflow area terrain elevation, slope and distance from the river; the core calculation formula of the waterlogging risk level assessment model is used, such as RH=d×(v+0.5)+df, where RH is the risk level index, d is the waterlogging depth, v is the flow velocity, and df is the water depth hazard parameter. Parameter setting: When the water depth is set to d≤0.2m, df=0.5; when d>0.2m, df=1.0; the local two-dimensional hydrological and hydrodynamic parameters of the waterlogging risk quantification index are measured, and the water depth and flow velocity hydrodynamics of the local area are calculated to obtain the local waterlogging hydrodynamic parameters; the waterlogging risk quantification index is used, combined with geographic information data and hydrological monitoring data. The SWMM model is used to measure the local two-dimensional hydrological and hydrodynamic parameters and simulate the water depth and flow velocity hydrodynamics of the local area. The time step of the model is set to 5 minutes, the spatial resolution is 5 meters × 5 meters, and the hydrodynamic changes under different rainfall intensities and drainage system conditions are considered. The local waterlogging hydrodynamic parameters, including the water depth and flow velocity distribution map of the local area, are obtained. Based on the results of the local two-dimensional hydrological and hydrodynamic parameters, the SWMM model is used to calculate and obtain the water depth and flow velocity hydrodynamics of the local area. Parameter setting: The model's time step is set to 5 minutes and the spatial resolution is set to 5 meters × 5 meters. The hydrodynamic changes under different rainfall intensities and drainage system conditions are considered to obtain local waterlogging hydrodynamic parameters, including the water depth and flow velocity distribution map of the local area.Using local waterlogging hydrodynamic parameters, including the water depth and flow velocity distribution map of the local area, and adopting the waterlogging risk change characteristic assessment model, combined with local waterlogging hydrodynamic parameters, the changing characteristics of waterlogging risk are assessed, and the parameters of the assessment model are set, such as the range of change of the risk level index, to obtain local waterlogging risk data, including waterlogging risk change characteristic maps.

[0043] Preferably, step S3 comprises the following steps: Step S31: setting initial conditions for flood simulation according to multi-source data of rural floods, including setting initial rainfall intensity, initial water level and topographic features, and generating simulation initial condition data; Step S32: Based on the simulation initial condition data, the rural flood multi-source data is input into the flood overflow and waterlogging risk model to simulate the rural flood inundation process and obtain the flood inundation simulation result; Step S33: determining the flood depth of the flood simulation result to obtain flood depth simulation data; identifying the flood area boundary of the flood depth simulation data to obtain flood area boundary data; Step S34: performing watershed mapping on the flooded area boundary data to obtain flooded watershed mapping data; performing waterlogging area division on the flooded watershed division data to obtain watershed waterlogging area data; Step S35: Detect the rainstorm and flood risk areas on the watershed waterlogging area data, and evaluate the rural waterlogging risk level to obtain waterlogging risk level data.

[0044] In the embodiment of the present invention, rural flood multi-source data, including geographic information data, hydrological and flood monitoring data, and flood control and drainage engineering data, are used in combination with historical flood event data and topographic data to set initial rainfall intensity, initial water level, and topographic fluctuation characteristics. Parameter setting, initial rainfall intensity: set to 10 mm / hour. Initial water level: set to the average value of river water level monitoring data. Topographic fluctuation characteristics: using digital elevation model (DEM) data, extract terrain elevation, slope and slope aspect and other features; generate simulation initial condition data, including initial rainfall intensity, initial water level, and topographic fluctuation characteristics and other information. Input the simulation initial condition data and rural flood multi-source data into the flood overflow and waterlogging risk model; adopt GIS-based flood inundation simulation technology, combined with a one-dimensional hydraulic model (such as Hec-RAS) and a two-dimensional hydrological and hydrodynamic model (such as SWMM); parameter setting, time step: set to 5 minutes. Spatial resolution: set to 10 meters × 10 meters. Consider different recurrence periods: such as 10 years, 20 years, and 50 years; obtain flood inundation simulation results, including inundation range, water depth distribution, flow velocity distribution and other information. Use the flood inundation simulation results and GIS technology to perform spatial analysis on the flood inundation simulation results, extract inundation depth information, and obtain inundation depth simulation data, including inundation depth distribution map. Based on the inundation depth simulation data, a threshold-based boundary recognition method is used to set the area with an inundation depth greater than 0.2 meters as the inundation area, and obtain the inundation area boundary data, including the inundation area boundary map. Perform basin mapping on the inundation area boundary data to obtain inundation basin mapping data; perform waterlogging area division on the inundation basin division data to obtain basin waterlogging area data. Use the inundation area boundary data and the GIS watershed division tool to map the inundation area boundary data onto the watershed map to obtain the inundation basin mapping data, including the correspondence between the watershed map and the inundation area. Based on the inundation basin mapping data. The waterlogging area division was carried out by using the zoning method based on topography and hydrological characteristics, combined with the cluster analysis tool of GIS, and the waterlogging area data of the basin was obtained, including the boundary map and feature table of the waterlogging area. The waterlogging area data of the basin was used, and the rainstorm flood risk area detection method based on the hydrological model was used, combined with the overlay analysis tool of GIS, to detect the rainstorm flood risk area, and the rainstorm flood risk area data was obtained, including the boundary map and risk level of the risk area. Based on the rainstorm flood risk area data, the waterlogging risk level assessment model was used, combined with the multi-source data fusion technology of GIS, to assess the rural waterlogging risk level. Parameter setting, waterlogging depth: waterlogging depth less than 15 cm is a slight waterlogging point, and greater than or equal to 15 cm is a waterlogging point. Flow velocity: areas with a flow velocity exceeding 0.5 m / s are considered high-risk areas. Inundation time: areas with an inundation time of more than 24 hours are considered high-risk areas. Inundation risk level data were obtained, including the distribution map of waterlogging risk levels and the risk area table.

[0045] Preferably, step S35 includes the following steps: Step S351: performing multi-dimensional feature correlation analysis on flood depth, flood range and flood propagation speed of watershed waterlogging area data to obtain multi-dimensional flood correlation data; Step S352: locating the rainstorm range of the multi-dimensional flood-related data to obtain rainstorm range data; identifying the rainstorm risk area of ​​the multi-dimensional flood-related data according to the rainstorm range data to generate the rainstorm risk area; Step S353: Dynamically simulate the risk area of ​​the rainstorm and flood risk area, and analyze the changes of the rainstorm and flood risk area at different time points to obtain dynamic change data of the risk area; Step S354: Calculate the risk assessment index for the dynamic change data of the risk area to generate risk assessment index data; classify the risk assessment index data into waterlogging risk levels to obtain waterlogging risk level data.

[0046] In the embodiment of the present invention, watershed waterlogging area data is used, including information such as flooding depth, flooding range, and flood propagation speed. GIS technology is used in combination with a multi-source data fusion method to perform correlation analysis on flooding depth, flooding range, and flood propagation speed. Parameter setting: flooding depth: set the threshold to 0.2 meters, and areas greater than 0.2 meters are considered as waterlogged areas. Flooding range: set the threshold to 100 square meters, and areas greater than 100 square meters are considered as waterlogged areas. Flood propagation speed: set the threshold to 0.5 meters per second, and areas greater than 0.5 meters per second are considered as high-risk areas. Multi-dimensional flood-related data is obtained, including a correlation map of flooding depth, flooding range, and flood propagation speed. Using multi-dimensional flood-related data, a GIS-based rainstorm range positioning method is used, combined with meteorological data and topographic data, to determine the rainstorm range. Parameter setting: set the rainstorm intensity threshold to 10 mm / hour, and areas greater than 10 mm / hour are considered as rainstorm ranges; obtain rainstorm range data, including rainstorm range maps. Based on rainstorm range data, combined with multi-dimensional flood-related data. The GIS-based rainstorm and flood risk area identification method is used to identify the rainstorm and flood risk areas in combination with the characteristics of inundation depth, inundation range and flood propagation speed. The rainstorm and flood risk areas are generated, including the risk area boundary map and feature table. Using the rainstorm and flood risk area data, the GIS-based dynamic simulation method is used, combined with time series analysis, to dynamically simulate the rainstorm and flood risk areas. Parameter setting: Set the time step to 5 minutes to simulate the changes in the risk areas at different time points. The dynamic change data of the risk area are obtained, including the risk area change map at different time points. Based on the dynamic change data of the risk area, the GIS-based spatiotemporal analysis method is used to analyze the changes in the rainstorm and flood risk areas at different time points. The dynamic change data of the risk area are obtained, including the risk area change trend map and change feature table. Using the dynamic change data of the risk area, the GIS-based risk assessment index calculation method is used, combined with the characteristics of inundation depth, inundation range and flood propagation speed, to calculate the risk assessment index. Parameter setting, water accumulation depth: water accumulation depth less than 15 cm is a slight water accumulation point, and greater than or equal to 15 cm is a waterlogging point. Flow velocity: Areas with a flow velocity exceeding 0.5 m / s are considered high-risk areas. Inundation time: Areas with an inundation time exceeding 24 hours are considered high-risk areas; Generate risk assessment indicator data, including risk assessment indicator maps and indicator tables. Based on the risk assessment indicator data, the GIS-based waterlogging risk level classification method is used, combined with risk assessment indicators, to classify waterlogging risk levels. Parameter setting: Risk level index: 0~10 is low risk, 10~20 is medium risk, and 20~30 is high risk. Obtain waterlogging risk level data, including waterlogging risk level distribution maps and risk area tables.

[0047] As an example of the present invention, refer to Figure 3 As shown, in this example, step S4 includes: Step S41: Arrange the waterlogging risk level data in reverse order according to the risk level gradient to obtain risk level gradient data; map low, medium and high risk rural waterlogging areas according to the risk level gradient data to obtain waterlogging risk mapping areas; Step S42: Preliminary selection of flood risk warning indicators for the waterlogging risk mapping area to obtain preliminary selected data of warning indicators; performing correlation analysis on the preliminary selected data of warning indicators to obtain correlation data of warning indicators; performing sensitivity analysis on the preliminary selected data of warning indicators to obtain sensitivity data of warning indicators; Step S43: screening flood risk warning indicators for the preliminary warning indicator data according to the warning indicator correlation data and the warning indicator sensitivity data, and constructing a warning indicator system to generate a rural flood warning indicator system; Step S44: constructing a flood warning model through the rural flood warning indicator system to generate a real-time flood forecast warning model; Step S45: Predict rural flood risk areas based on the real-time flood forecast and warning model to obtain rural flood risk area prediction data; plan risk avoidance and transfer routes based on the rural flood risk area prediction data to generate a rural flood warning planning report.

[0048] In the embodiment of the present invention, waterlogging risk level data is used, and GIS technology is used to arrange the waterlogging risk level data in reverse order to generate risk level gradient data. Parameter setting: The risk levels are set from high to low as high risk (3), medium risk (2), and low risk (1). Risk level gradient data are obtained, including a risk level gradient map. Low, medium, and high risk rural waterlogging area mapping: Based on the risk level gradient data, the risk level gradient data is mapped onto the rural waterlogging area map using the GIS mapping tool to generate a waterlogging risk mapping area. The waterlogging risk mapping area is obtained, including a distribution map of low, medium, and high risk areas. The waterlogging risk mapping area data is used, including information such as flooding depth, flooding range, and flood propagation speed. Combined with historical flood data and expert experience, flood risk warning indicators are preliminarily selected. Common warning indicators include water accumulation depth, flow rate, rainfall intensity, flooding time, terrain elevation, slope, etc. Preliminary warning indicator data is obtained, including preliminarily selected warning indicators and their data. Based on the preliminary data of early warning indicators, statistical analysis methods such as Pearson correlation coefficient analysis are used to calculate the correlation between each early warning indicator. The details are as follows: Data standardization: Standardize the data of each early warning indicator to eliminate the dimension effect. Calculate the correlation coefficient: Use the Pearson correlation coefficient formula to calculate the correlation coefficient between each indicator. Result output: Obtain the correlation data of the early warning indicators, including the correlation coefficient matrix between each early warning indicator. Based on the preliminary data of the early warning indicators, sensitivity analysis methods such as single factor sensitivity analysis are used to calculate the sensitivity of each early warning indicator to the risk of waterlogging. The details are as follows: Set a baseline scenario: Select a baseline scenario, such as the current rainfall intensity and terrain conditions. Change factors one by one: Change each early warning indicator one by one, keep other factors unchanged, and observe the changes in the risk of waterlogging. Calculate the sensitivity index: According to the change range of the risk of waterlogging, calculate the sensitivity index of each early warning indicator. Obtain the sensitivity data of the early warning indicators, including the sensitivity index of each early warning indicator. Use the early warning indicator correlation data and the early warning indicator sensitivity data, combined with the results of correlation analysis and sensitivity analysis, to screen out early warning indicators with high correlation and strong sensitivity. Obtain the screened early warning indicator data, including the screened early warning indicators and their data. Based on the screened warning indicator data, the analytic hierarchy process (AHP) or principal component analysis (PCA) is used to construct a rural flood warning indicator system. Generate a rural flood warning indicator system, including the weights of each warning indicator and a comprehensive evaluation formula. Use the rural flood warning indicator system and use machine learning models, such as random forest (RF) or support vector machine (SVM), to train the model in combination with historical flood data. Parameter setting: Set the training parameters of the model, such as the number of trees in the random forest, the kernel function of the support vector machine, etc. Generate a real-time flood forecast and warning model that can predict rural flood risks in real time. Use the real-time flood forecast and warning model, use the model for real-time prediction, and combine real-time meteorological data and hydrological data to predict rural flood risk areas.Obtain the forecast data of rural flood risk areas, including forecast time point, risk area scope, risk level and other information. Based on the forecast data of rural flood risk areas, use GIS network analysis tools, combined with road network and settlement distribution, to plan risk avoidance transfer routes. Generate risk avoidance transfer route planning map, including transfer routes, resettlement sites and other information. Integrate the rural flood risk area forecast data and risk avoidance transfer route planning results, and compile a rural flood early warning planning report, including early warning indicator system, model construction method, risk area forecast results, risk avoidance transfer route planning, etc., to provide a scientific basis for early warning and response to rural flood disasters.

[0049] Preferably, step S44 includes the following steps: Step S441: determining meteorological conditions for rural flood multi-source data to obtain meteorological condition performance data; performing flood warning demand analysis on the meteorological condition performance data to generate flood warning demand data; Step S442: constructing a flood warning framework according to the rural flood warning indicator system to obtain flood warning framework information; calibrating the flood warning framework information based on the flood warning demand data to obtain a warning calibration framework; Step S443: constructing a flood warning model for the warning calibration framework to generate an initial flood warning model; performing model warning verification on the initial flood warning model to obtain warning performance verification data; optimizing response parameters of the initial flood warning model according to the warning performance verification data to obtain a flood warning response model; Step S444: Acquire real-time meteorological monitoring data and real-time water level monitoring data to obtain real-time monitoring data; input the real-time monitoring data into the flood warning response model, and conduct real-time flood forecast and warning training to generate a real-time flood forecast and warning model.

[0050] In the embodiment of the present invention, multi-source data of rural floods are used, including meteorological data, topographic data, hydrological monitoring data, etc. Combined with the data of the meteorological monitoring station, the current meteorological conditions, such as rainfall intensity, rainfall duration, wind speed, wind direction, etc. are determined. Meteorological condition performance data are obtained, including rainfall intensity map, rainfall duration map, wind speed map, wind direction map, etc. Based on the meteorological condition performance data, combined with historical flood data and topographic data, the flood risk that may be caused under the current meteorological conditions is analyzed to determine the demand for flood warning. Flood warning demand data is generated, including warning level, warning area, warning time, etc. A rural flood warning indicator system is used, including indicators such as water accumulation depth, flow rate, rainfall intensity, and flooding time. The hierarchical analysis method (AHP) or the principal component analysis method (PCA) is used to construct a flood warning framework, and the weights and comprehensive evaluation formulas of each warning indicator are clarified. Flood warning framework information is obtained, including the weights, comprehensive evaluation formulas, and warning thresholds of each warning indicator. Based on the flood warning demand data, combined with the flood warning demand data, the flood warning framework is calibrated, the weights and thresholds of the warning indicators are adjusted, and the accuracy and reliability of the warning framework are ensured. The warning calibration framework is obtained, including the calibrated warning indicator weights, comprehensive evaluation formulas and warning thresholds. Using the warning calibration framework, including the calibrated warning indicator weights, comprehensive evaluation formulas and warning thresholds; using machine learning models such as random forests (RF) or support vector machines (SVM), combined with historical flood data for model training. Generate an initial flood warning model, which can make a preliminary prediction of rural flood risks. Based on the initial flood warning model, the cross-validation method is used to verify the initial flood warning model to evaluate the accuracy and generalization ability of the model. The warning performance verification data is obtained, including evaluation indicators such as mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and determination coefficient (R²). Based on the early warning performance verification data, the response parameters of the initial flood warning model are optimized according to the early warning performance verification data, such as adjusting the learning rate, increasing the number of iterations, adjusting the regularization parameters, etc. The flood warning response model is obtained, and the model shows good prediction performance on the training set, cross-validation set and test set. Using the real-time data of the meteorological monitoring station and the water level monitoring station, the meteorological monitoring data and the water level monitoring data are obtained in real time through the Internet of Things technology, including rainfall intensity, rainfall duration, wind speed, wind direction, water level, etc. Real-time monitoring data are obtained, including real-time rainfall intensity map, real-time water level map, etc. Based on the real-time monitoring data, the real-time monitoring data is input into the flood warning response model, and real-time flood forecast and warning training is carried out to optimize the parameters of the model and improve the real-time prediction ability of the model. The real-time flood forecast and warning model is generated, which can predict the rural flood risk in real time and provide a scientific basis for the early warning and response of flood disasters.

[0051] Preferably, step S45 includes the following steps: Step S451: Based on the real-time flood forecasting and warning model, the real-time monitoring data is used to predict rural flood scenarios to obtain rural flood scenario prediction data; the rural flood scenario prediction data is used to delineate the flooding range to obtain flooding range delineation data; Step S452: performing spatiotemporal analysis of flood ranges on the flood range demarcation data, analyzing the spatial distribution characteristics of each flood range at different time points, and obtaining spatiotemporal prediction data of flood ranges; Step S453: determining the flood range change trend of the spatiotemporal prediction data of the flood range to obtain flood range change trend information; performing multi-path planning of evasion routes according to the flood range change trend information, planning multiple evasion routes for each flood range, and obtaining multiple evasion route data; Step S454: performing route feasibility evaluation on the plurality of risk avoidance route data to obtain risk avoidance route feasibility data; determining the optimal risk avoidance route for the plurality of risk avoidance route data according to the risk avoidance route feasibility data to generate the optimal risk avoidance route data; Step S455: compile an early warning planning report based on the optimal risk avoidance route data to obtain a rural flood early warning planning report.

[0052] In the embodiment of the present invention, a real-time flood forecasting and warning model and real-time monitoring data are used, including meteorological data, water level data, etc. The real-time flood forecasting and warning model is used in combination with real-time monitoring data to predict rural flood scenarios. Rural flood scenario prediction data are obtained, including prediction time point, flooding range, water depth distribution and other information. Based on the rural flood scenario prediction data, GIS technology is used in combination with topographic data to delineate the flooding range. Flooding range delineation data are obtained, including a flooding range map. Using the flooding range delineation data, the spatial distribution characteristics of each flooding range at different time points are analyzed using the spatiotemporal analysis tool of GIS. Flooding range spatiotemporal prediction data are obtained, including a flooding range distribution map at different time points. Using the spatiotemporal prediction data of the flooding range, a trend analysis method is used to determine the change trend of the flooding range. Flooding range change trend information is obtained, including the expansion or contraction trend of the flooding range. Based on the flooding range change trend information, the network analysis tool of GIS is used in combination with the road network and the distribution of settlements to plan multiple risk avoidance transfer routes for each flooding range. Obtain data on multiple evacuation routes, including multiple evacuation route maps. Using multiple evacuation route data, adopt GIS overlay analysis tools, combine topography, road conditions and settlement distribution to evaluate the feasibility of evacuation routes. Obtain feasibility data on evacuation routes, including feasibility assessment reports for each evacuation route. Based on the feasibility data of evacuation routes, select the optimal evacuation route according to the feasibility data of evacuation routes. Generate optimal evacuation route data, including optimal evacuation route maps. Use optimal evacuation route data, combine GIS technology and text editing tools, and compile a rural flood warning planning report, including warning indicator system, model building method, risk area prediction results, evacuation route planning and other contents. Obtain a rural flood warning planning report to provide a scientific basis for early warning and response to rural flood disasters.

[0053] 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.

[0054] 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 method for assessing and warning the risk level of waterlogging in rural waterlogged areas based on multi-source data, characterized in that: The following steps are involved: Step S1: Obtaining rural waterlogged area geographic information, hydrological and flood data, and flood control and drainage engineering data, and fusing multi-source data to obtain rural flood multi-source data; Step S2: Calculate river flood overflow for rural flood multi-source data to obtain river flood overflow data; perform local two-dimensional hydrological and hydrodynamic quantitative assessment of waterlogging risk points for rural flood multi-source data based on river flood overflow data to obtain local waterlogging risk data; construct a flood overflow and waterlogging risk model based on river flood overflow data and local waterlogging risk data; Step S3: simulate the rural flood inundation process through the flood overflow and waterlogging risk model to obtain the flood inundation simulation results; divide the flood inundation simulation results into watershed waterlogging areas to obtain watershed waterlogging area data; detect the rainstorm flood risk area on the watershed waterlogging area data, and evaluate the rural waterlogging risk level to obtain waterlogging risk level data; Step S4: Determine the rural flood risk warning index according to the waterlogging risk level data and generate a rural flood warning index system; construct a flood real-time forecast and warning model through the rural flood warning index system, and predict the rural flood risk area based on the flood real-time forecast and warning model, plan the risk avoidance and transfer route, so as to obtain a rural flood warning planning report.

2. The method for assessing and warning the risk level of waterlogging in rural waterlogged areas based on multi-source data according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Determine the rural flood area to be monitored; Step S12: Collecting geographic information of the rural flood-prone areas to be monitored to obtain rural geographic information data; Step S13: Perform hydrological and flood monitoring on the rural flood-prone area to be monitored to obtain hydrological and flood monitoring data; Step S14: obtaining flood control and drainage engineering data of the rural flood-prone area to be monitored, and performing structural digital conversion to generate flood control and drainage engineering data; Step S15: preprocessing the rural geographic information data, hydrological and flood monitoring data, and flood drainage engineering data to obtain rural flood processing data, wherein the data preprocessing includes data cleaning, data format standardization, and feature extraction and conversion; Step S16: aligning the rural flood processing data through data mapping and merging the multi-source data to obtain rural flood multi-source data.

3. The method for assessing and warning the risk level of waterlogging in rural waterlogged areas based on multi-source data according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: determining river hydrological divisions for rural flood multi-source data, and performing river flood overflow calculations to obtain river flood overflow data; Step S22: performing a local two-dimensional hydrological and hydrodynamic quantitative assessment of waterlogging risk points on the rural flood multi-source data based on the river flood overflow data to obtain local waterlogging risk data; Step S23: constructing flood overflow and waterlogging risk according to river flood overflow data and local waterlogging risk data to obtain a flood overflow and waterlogging risk pre-model; training the flood overflow and waterlogging risk pre-model based on river flood overflow data and local waterlogging risk data to obtain a flood overflow and waterlogging risk training model; Step S24: performing model cross-validation evaluation on the flood overflow and waterlogging risk training model to obtain training model evaluation data; adjusting model parameters of the flood overflow and waterlogging risk training model using the training model evaluation data to obtain the flood overflow and waterlogging risk model.

4. The method for assessing and warning the risk level of waterlogging in rural waterlogged areas based on multi-source data according to claim 3 is characterized in that: Step S21 includes the following steps: Step S211: extracting topographic and geomorphic features from multi-source data of rural floods to obtain rural topographic and geomorphic features; determining river system distribution based on the rural topographic and geomorphic features to generate river system distribution data; Step S212: performing hydrological similarity recognition on the river system distribution data to obtain hydrological similarity; partitioning the river system distribution data into river hydrologically similar areas according to the hydrological similarity to generate river hydrological partitioning data; Step S213: extracting the mountain rainfall characteristics from the rural flood multi-source data to obtain the mountain rainfall characteristics; determining the reservoir location according to the river hydrological division data, and calculating the reservoir water storage at the reservoir location based on the mountain rainfall characteristics to generate reservoir rainfall-water storage relationship data; Step S214: Calculate the river network water flow direction, water flow velocity and water level change based on the river hydrological division data according to the reservoir rainfall-water storage relationship data to obtain the river network hydrodynamic data; Step S215: Calculate the surface water infiltration intensity, propagation parameters and turbulent viscosity coefficient for the rural topography and geomorphology characteristics according to the river network hydrodynamic data to obtain surface hydrodynamic data; Step S216: Calculate the river flood and surface waterlogging forces based on the river network hydrodynamic data and the surface hydrodynamic data to obtain river flood overflow data.

5. The method for assessing and warning the risk level of waterlogging in rural waterlogged areas based on multi-source data according to claim 3 is characterized in that: Step S22 includes the following steps: Step S221: marking the flood overflow area of ​​the river flood overflow data to generate the flood overflow area; extracting the terrain elevation, slope and distance from the river channel of the overflow area from the rural flood multi-source data according to the flood overflow area to obtain the spatial characteristic data of the overflow area; Step S222: quantifying the waterlogging risk index of the overflow area spatial feature data, and calculating the waterlogging risk index in combination with the terrain elevation, slope and distance from the river in the overflow area to obtain a waterlogging risk quantification index; Step S223: measuring the local two-dimensional hydrological and hydrodynamic parameters of the waterlogging risk quantification index, and calculating the water depth and flow velocity hydrodynamics of the local area to obtain the local waterlogging hydrodynamic parameters; Step S224: Evaluate the waterlogging risk change characteristics of local waterlogging hydrodynamic parameters to obtain local waterlogging risk data.

6. The method for assessing and warning the risk level of waterlogging in rural waterlogged areas based on multi-source data according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: setting initial conditions for flood simulation according to multi-source data of rural floods, including setting initial rainfall intensity, initial water level and topographic features, and generating simulation initial condition data; Step S32: Based on the simulation initial condition data, the rural flood multi-source data is input into the flood overflow and waterlogging risk model to simulate the rural flood inundation process and obtain the flood inundation simulation result; Step S33: determining the flood depth of the flood simulation result to obtain flood depth simulation data; identifying the flood area boundary of the flood depth simulation data to obtain flood area boundary data; Step S34: performing watershed mapping on the flooded area boundary data to obtain flooded watershed mapping data; performing waterlogging area division on the flooded watershed division data to obtain watershed waterlogging area data; Step S35: Detect the rainstorm and flood risk areas on the watershed waterlogging area data, and evaluate the rural waterlogging risk level to obtain waterlogging risk level data.

7. The method for assessing and warning the risk level of waterlogging in rural waterlogged areas based on multi-source data according to claim 6 is characterized in that: Step S35 includes the following steps: Step S351: performing multi-dimensional feature correlation analysis on flood depth, flood range and flood propagation speed of watershed waterlogging area data to obtain multi-dimensional flood correlation data; Step S352: locating the rainstorm range of the multi-dimensional flood-related data to obtain rainstorm range data; identifying the rainstorm risk area of ​​the multi-dimensional flood-related data according to the rainstorm range data to generate the rainstorm risk area; Step S353: Dynamically simulate the risk area of ​​the rainstorm and flood risk area, and analyze the changes of the rainstorm and flood risk area at different time points to obtain dynamic change data of the risk area; Step S354: Calculate the risk assessment index for the dynamic change data of the risk area to generate risk assessment index data; classify the risk assessment index data into waterlogging risk levels to obtain waterlogging risk level data.

8. The method for assessing and warning the risk level of waterlogging in rural waterlogged areas based on multi-source data according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: Arrange the waterlogging risk level data in reverse order according to the risk level gradient to obtain risk level gradient data; map low, medium and high risk rural waterlogging areas according to the risk level gradient data to obtain waterlogging risk mapping areas; Step S42: Preliminary selection of flood risk warning indicators for the waterlogging risk mapping area to obtain preliminary selected data of warning indicators; performing correlation analysis on the preliminary selected data of warning indicators to obtain correlation data of warning indicators; performing sensitivity analysis on the preliminary selected data of warning indicators to obtain sensitivity data of warning indicators; Step S43: screening flood risk warning indicators for the preliminary warning indicator data according to the warning indicator correlation data and the warning indicator sensitivity data, and constructing a warning indicator system to generate a rural flood warning indicator system; Step S44: constructing a flood warning model through the rural flood warning indicator system to generate a real-time flood forecast warning model; Step S45: Predict rural flood risk areas based on the real-time flood forecast and warning model to obtain rural flood risk area prediction data; plan risk avoidance and transfer routes based on the rural flood risk area prediction data to generate a rural flood warning planning report.

9. The method for assessing and warning the risk level of waterlogging in rural waterlogged areas based on multi-source data according to claim 8 is characterized in that: Step S44 includes the following steps: Step S441: determining meteorological conditions for rural flood multi-source data to obtain meteorological condition performance data; performing flood warning demand analysis on the meteorological condition performance data to generate flood warning demand data; Step S442: constructing a flood warning framework according to the rural flood warning indicator system to obtain flood warning framework information; calibrating the flood warning framework information based on the flood warning demand data to obtain a warning calibration framework; Step S443: constructing a flood warning model for the warning calibration framework to generate an initial flood warning model; performing model warning verification on the initial flood warning model to obtain warning performance verification data; optimizing response parameters of the initial flood warning model according to the warning performance verification data to obtain a flood warning response model; Step S444: Acquire real-time meteorological monitoring data and real-time water level monitoring data to obtain real-time monitoring data; input the real-time monitoring data into the flood warning response model, and conduct real-time flood forecast and warning training to generate a real-time flood forecast and warning model.

10. The method for assessing and warning the risk level of waterlogging in rural waterlogged areas based on multi-source data according to claim 8, characterized in that: Step S45 includes the following steps: Step S451: Based on the real-time flood forecasting and warning model, the real-time monitoring data is used to predict rural flood scenarios to obtain rural flood scenario prediction data; the rural flood scenario prediction data is used to delineate the flooding range to obtain flooding range delineation data; Step S452: performing spatiotemporal analysis of flood ranges on the flood range demarcation data, analyzing the spatial distribution characteristics of each flood range at different time points, and obtaining spatiotemporal prediction data of flood ranges; Step S453: determining the flood range change trend of the spatiotemporal prediction data of the flood range to obtain flood range change trend information; performing multi-path planning of evasion routes according to the flood range change trend information, planning multiple evasion routes for each flood range, and obtaining multiple evasion route data; Step S454: performing route feasibility evaluation on the plurality of risk avoidance route data to obtain risk avoidance route feasibility data; determining the optimal risk avoidance route for the plurality of risk avoidance route data according to the risk avoidance route feasibility data to generate the optimal risk avoidance route data; Step S455: compile an early warning planning report based on the optimal risk avoidance route data to obtain a rural flood early warning planning report.

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