Flood risk management method and system, computer and storage medium

Abstract

The invention provides a flood risk management method and system, a computer and a storage medium, and the method comprises the following steps: carrying out the numerical modeling based on ground surface features through employing a digital twin technology, and building a hydrodynamic model based on a shallow water equation; monitoring and obtaining flood risk data of each region; the operation scheme of the hydraulic structure is optimized based on the flood risk data, the hydrodynamic model is transferred, the water network flow evolution process of the optimized operation scheme of the hydraulic structure is simulated, and the water network is monitored and managed. Through combination of the hydrodynamic model and flood risk assessment, when the flood risk is found, an intelligent optimization algorithm can be automatically called to dispatch gates, dams and other hydraulic structures, the flood risk is coped with in real time, the influence of potential disasters is reduced, compared with traditional manual dispatching, delay or misjudgment caused by human intervention can be reduced, and the working efficiency is improved. And the flood control scheduling efficiency and accuracy are improved.

Description

Technical Field

[0001] The present invention relates to the field of flood control technology, and in particular to a flood risk management method, system, computer and storage medium. Background Art

[0002] The existing water network flood control system usually relies on historical hydrological data and historical scenario libraries to predict and manage flood risks. Due to the relatively scattered data acquisition, meteorological data, hydrological data, river network information, etc. come from different sources and lack effective integration methods. At the same time, due to climate change and frequent extreme weather, it is impossible to predict flood risks in real time and accurately, which increases the difficulty of predicting and managing water network flood events. On this basis, due to the lack of overall grasp of the water network, the scheduling of hydraulic structures is often delayed or unreasonable, making it difficult to respond quickly to sudden flood events. Summary of the invention

[0003] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a flood risk management method, system, computer and storage medium, aiming to solve the technical problems in the prior art of delayed scheduling of hydraulic structures and difficulty in quickly responding to sudden flood events.

[0004] In order to achieve the above objectives, in a first aspect, the present invention provides a flood risk management method, comprising the following steps: Preprocessing the digital elevation data of the water network, and using a multi-flow direction algorithm to determine the water flow direction of the water network based on the terrain characteristics, so as to divide the sub-water network by the accumulated flow of each pixel in the digital elevation data; Obtaining runoff data of each sub-water network according to soil data and rainfall intensity of each sub-water network; Digital twin technology is used to perform numerical modeling based on surface features, and based on shallow water equations, a hydrodynamic model simulating shallow water flow under different conditions is established according to the runoff data by discretely solving the shallow water equations; According to the preset flood risk assessment standards, the corresponding flood inundation data of each area and the demographic data are spatially superimposed and calculated in real time during the water flow simulation process to monitor and obtain the flood risk data of each area in real time; Based on the flood risk data and the current hydraulic structure operation plan, the optimal operation plan of the hydraulic structure that reduces the flood risk is finally obtained by setting constraints and iterative optimization based on the population evolution algorithm; The hydrodynamic model is called to simulate the water network flow evolution process of the hydraulic structure optimization operation plan, and the water network is monitored and managed based on the optimized flood risk data.

[0005] According to one aspect of the above technical solution, the steps of preprocessing the digital elevation data of the water network and using a multi-flow direction algorithm to determine the water flow direction of the water network based on the terrain features specifically include: The depressions in the digital elevation are filled and leveled, and then gradually raised to ensure that each grid water flow in the water network has a corresponding water network outlet; The multi-flow direction algorithm is used to calculate the slopes between each grid unit, and the water flow is proportionally allocated based on the weights between different slopes to confirm the water flow direction of the water network.

[0006] According to one aspect of the above technical solution, the expression of the water flow distribution ratio between each grid unit is as follows: ; ; ; In the formula, is the flow ratio of water from the current grid cell to the i-th adjacent cell, is the slope angle from the current grid to the i-th adjacent grid, is the flow distribution function used to adjust for slope effects, is the slope from the current grid to the adjacent grid, and are the minimum and maximum values ​​of all slope values, is the current pixel elevation, is the elevation of the adjacent pixel, is the distance from the current grid cell to the i-th adjacent grid cell.

[0007] According to one aspect of the above technical solution, the step of dividing the sub-water network by the accumulated flow of each pixel in the digital elevation data specifically includes: According to the accumulated flow of each pixel in the digital elevation data, the boundary of the sub-water network is determined based on the direction of the accumulated flow and the location of the outlet section of the water network, thereby dividing the sub-water network; The calculation expression of the cumulative flow is as follows: ; In the formula, is the accumulated flow of the current pixel, is the accumulated flow of adjacent pixels, is the proportion of flow direction allocated to the adjacent pixel.

[0008] According to one aspect of the above technical solution, the step of obtaining the runoff data of each sub-water network according to the soil data and rainfall intensity of each sub-water network specifically includes: The infiltration rate is calculated according to the hydraulic conductivity and suction of the soil by the following calculation formula, so as to obtain the cumulative infiltration amount of each sub-water network according to the rainfall intensity: ; ; In the formula, is the cumulative infiltration at time t, is the saturated hydraulic conductivity of soil, The suction force at the wetting front, is the saturated water content of the soil, is the initial soil moisture content, is the infiltration rate at time t.

[0009] According to one aspect of the above technical solution, the shallow water equation includes a continuity equation and a momentum equation. The expression of the continuity equation is as follows: ; The momentum equation is expressed as follows: ; ; In the formula, For water depth, is the surface elevation, For time, and are the average velocity values ​​along the x and y directions in the Cartesian coordinate system, is the acceleration due to gravity, and is the source term of the equations, including bottom friction, surface wind stress, Coriolis force, and atmospheric pressure. is the turbulent viscous diffusion coefficient, is the gradient symbol, is the divergence symbol.

[0010] According to one aspect of the above technical solution, the steps of finally obtaining an optimized operation plan for hydraulic structures that reduces flood risks by setting constraints and performing cyclic iterative optimization based on a population evolution algorithm specifically include: An optimization model with the goal of minimizing flood losses and flooded population is established based on the following calculation formula: ; ; In the formula, is the flood loss, To assess the value of the r-th asset in the unit at the c-th water depth, is the loss rate of the rth type of assets under the cth level of water depth, For the flooded population, is the flooded area of ​​the bth residential area in the ath regional unit, is the population density of the bth residential area in the ath regional unit; The SCE-UA algorithm is used to perform cyclic iterative optimization while changing the opening of hydraulic structures and the improvement of the objective function meets the preset conditions, so as to search for the global optimal solution of the opening of hydraulic structures, and set the following constraints according to the design data of each hydraulic structure in the water network: ; ; ; In the formula, is the water level of the nth water network section, is the flood control level of the nth water network section, is the flow rate of the mth hydraulic structure, is the maximum design flow of the mth hydraulic structure, is the opening of the mth hydraulic structure, is the minimum opening of the mth hydraulic structure, is the maximum opening of the mth hydraulic structure.

[0011] Secondly, the present application also provides a flood risk management system, including: A water network division module is used to pre-process the digital elevation data of the water network and use a multi-flow algorithm to determine the water flow direction of the water network based on the terrain characteristics, so as to divide the sub-water network by the accumulated flow of each pixel in the digital elevation data; A rainfall runoff module, used to obtain the runoff data of each sub-water network according to the soil data and rainfall intensity of each sub-water network; A hydrodynamic model module is used to perform numerical modeling based on surface features using digital twin technology, and to establish a hydrodynamic model simulating shallow water flow under different conditions based on the runoff data by discretely solving the shallow water equation; The flood risk module is used to perform spatial superposition calculations on the flood inundation data and demographic data corresponding to each region in real time during the water flow simulation according to the preset flood risk assessment standards, so as to monitor and obtain the flood risk data of each region in real time; An optimization module is used to obtain an optimized operation plan for hydraulic structures that reduces flood risks by setting constraints and performing cyclic iterative optimization based on the flood risk data and the current operation plan for hydraulic structures based on a population evolution algorithm; The simulation module is used to call the hydrodynamic model to simulate the water network flow evolution process of the hydraulic structure optimization operation plan, and monitor and manage the water network based on the optimized flood risk data.

[0012] According to one aspect of the above technical solution, the water network division module is specifically used for: The depressions in the digital elevation are filled and leveled, and then gradually raised to ensure that each grid water flow in the water network has a corresponding water network outlet; The multi-flow direction algorithm is used to calculate the slopes between each grid unit, and the water flow is proportionally allocated based on the weights between different slopes to confirm the water flow direction of the water network.

[0013] According to one aspect of the above technical solution, the water network division module is further used for: According to the accumulated flow of each pixel in the digital elevation data, the boundary of the sub-water network is determined based on the direction of the accumulated flow and the location of the outlet section of the water network, thereby dividing the sub-water network; The calculation expression of the cumulative flow is as follows: ; In the formula, is the accumulated flow of the current pixel, is the accumulated flow of adjacent pixels, is the proportion of flow direction allocated to the adjacent pixel.

[0014] According to one aspect of the above technical solution, the rainfall runoff module is specifically used for: The infiltration rate is calculated according to the hydraulic conductivity and suction of the soil by the following calculation formula, so as to obtain the cumulative infiltration amount of each sub-water network according to the rainfall intensity: ; ; In the formula, is the cumulative infiltration at time t, is the saturated hydraulic conductivity of soil, The suction force at the wetting front, is the saturated water content of the soil, is the initial soil moisture content, is the infiltration rate at time t.

[0015] According to one aspect of the above technical solution, the optimization module is specifically used for: An optimization model with the goal of minimizing flood losses and flooded population is established based on the following calculation formula: ; ; In the formula, is the flood loss, To assess the value of the r-th asset in the unit at the c-th water depth, is the loss rate of the rth type of assets under the cth level of water depth, For the flooded population, is the flooded area of ​​the bth residential area in the ath regional unit, is the population density of the bth residential area in the ath regional unit; The SCE-UA algorithm is used to perform cyclic iterative optimization while changing the opening of hydraulic structures and the improvement of the objective function meets the preset conditions, so as to search for the global optimal solution of the opening of hydraulic structures, and set the following constraints according to the design data of each hydraulic structure in the water network: ; ; ; In the formula, is the water level of the nth water network section, is the flood control level of the nth water network section, is the flow rate of the mth hydraulic structure, is the maximum design flow of the mth hydraulic structure, is the opening of the mth hydraulic structure, is the minimum opening of the mth hydraulic structure, is the maximum opening of the mth hydraulic structure.

[0016] In a third aspect, an embodiment of the present application provides a computer, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, a flood risk management method as described in the first aspect is implemented.

[0017] In a fourth aspect, an embodiment of the present application provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements a flood risk management method as described in the first aspect.

[0018] Compared with the existing technology, the beneficial effects of the present invention are: by constructing a dynamic virtual model of the water network through digital twin technology, real-time monitoring, refined simulation and accurate prediction of flood risks in the water network can be realized; by combining the hydrodynamic model with flood risk assessment, when flood risks are discovered, the intelligent optimization algorithm can be automatically called to dispatch hydraulic structures such as gates and dams, so as to respond to flood risks in real time and reduce the impact of potential disasters. Compared with traditional manual scheduling, it can reduce delays or misjudgments caused by human intervention and improve the efficiency and accuracy of flood control scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flow chart of the flood risk management method in the first embodiment of the present invention; Figure 2 It is a structural block diagram of a flood risk management system in a second embodiment of the present invention; Figure 3 is a schematic diagram of the hardware structure of a computer in the third embodiment of the present application; The following specific implementation manner will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0020] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0021] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0023] Embodiment 1 See also Figure 1 , which shows a flood risk management method in a first embodiment of the present invention. As shown in the figure, the method includes the following steps: Step S100, pre-processing the digital elevation data of the water network, and using a multi-flow direction algorithm to confirm the water flow direction of the water network based on the terrain characteristics, so as to divide the sub-water network by the accumulated flow of each pixel in the digital elevation data.

[0024] Preferably, in this embodiment, before preprocessing the digital elevation data, by acquiring and integrating multi-source data including meteorological, hydrological, geographic information (including digital elevation data) and hydraulic structure status used in this scheme, basic data support can be provided for water network flood control monitoring and intelligent scheduling of hydraulic structures.

[0025] In this embodiment, the efficient integration of multi-source data such as meteorological, hydrological, geographic information and hydraulic structure status of the Laohe River water network is achieved through automated tools and API interfaces. The accuracy and consistency of the data are ensured by introducing technical means such as data cleaning and preprocessing. A unified data format is formed through structured processing, and is provided to each sub-module of the system through a standardized interface to support functions such as real-time monitoring, scheduling analysis and model simulation.

[0026] This embodiment specifically describes the present invention for the Laohe River water network project. For the Laohe River water network project, the above data include necessary data such as hydrological monitoring station data and meteorological data in the Laohe River water network area, including timestamp, flow value, water level value and meteorological data. Data sources include hydrological stations, remote sensing satellites, meteorological forecast systems, Internet of Things sensors, etc., providing accurate data support for subsequent modules.

[0027] Data cleaning and preprocessing include: Missing value cleaning: Identify and process missing values, and fill in the missing values ​​with the values ​​from the previous moment.

[0028] Content format cleaning: standardize data formats, such as unifying the timestamp format of flow data, water level data, meteorological data, etc. At the same time, check whether the data is within a reasonable range. If there is obvious unreasonableness, it needs to be marked as abnormal and the outlier needs to be removed.

[0029] Logical error cleaning: Check whether the time series is sorted in chronological order. If there is an abnormal time sequence, it needs to be adjusted.

[0030] Data integration and storage: Merge the cleaned hydrological data with the meteorological data to ensure that the timestamps are aligned, and finally generate a data set containing all necessary fields. Store the cleaned data in a relational database (such as PostgreSQL) and establish a standardized table structure to facilitate subsequent query and analysis.

[0031] Specifically, in this embodiment, in the above step S100, the steps of preprocessing the digital elevation data of the water network and using the multi-flow direction algorithm to determine the water flow direction of the water network based on the terrain features specifically include: Step S110, fill and level the depressions in the digital elevation, and then gradually raise them to ensure that each grid water flow in the water network has a corresponding water network outlet. Step S120, using a multi-flow direction algorithm to calculate the slopes between the grid cells, and based on the weights between the different slopes, allocating the water flow in proportion to confirm the water flow direction of the water network.

[0032] For ease of understanding, the multi-flow direction algorithm (MFD) determines the flow direction by using terrain features that directly reflect local terrain changes to establish a flow distribution function instead of a fixed flow distribution weight. Its advantage is that it allows water to flow to multiple adjacent grid cells at the same time, thereby better simulating the flow path in complex terrain. The multi-flow direction algorithm (MFD) calculates the slope of each grid and its surrounding adjacent grids, while considering the weights between different slopes, and distributes water flow in proportion. The expression of the water flow distribution ratio between each grid cell is as follows: ; ; ; In the formula, is the flow ratio of water from the current grid cell to the i-th adjacent cell, is the slope angle from the current grid to the i-th adjacent grid, is the flow distribution function used to adjust for slope effects, is the slope from the current grid to the adjacent grid, and are the minimum and maximum values ​​of all slope values, is the current pixel elevation, is the elevation of the adjacent pixel, is the distance from the current grid cell to the i-th adjacent grid cell.

[0033] The step of dividing the sub-water network by the accumulated flow of each pixel in the digital elevation data specifically includes: According to the accumulated flow of each pixel in the digital elevation data, the boundary of the sub-water network is determined based on the direction of the accumulated flow and the location of the outlet section of the water network, thereby dividing the sub-water network; The calculation expression of the cumulative flow is as follows: ; In the formula, is the accumulated flow of the current pixel, is the accumulated flow of adjacent pixels, is the proportion of flow direction allocated to the adjacent pixel.

[0034] Step S200, obtaining the runoff data of each sub-water network according to the soil data and rainfall intensity of each sub-water network.

[0035] In this embodiment, the Green-Ampt model is used to calculate the rainfall runoff of the water network, considering the infiltration calculation under the condition of heterogeneous soil in the water network. The model regards the soil infiltration process as a dynamic process moving through the wetting front. The core idea is to assume that the area above the wetting front of the soil is the saturated area, the area below the wetting front is the unsaturated area, and the wetting front advances at a stable rate. The model determines the time and flow of runoff by describing the relationship between the hydraulic conductivity and suction of the soil and the rainfall intensity. The formula is as follows: ; ; In the formula, is the cumulative infiltration at time t, is the saturated hydraulic conductivity of soil, The suction force at the wetting front, is the saturated water content of the soil, is the initial soil moisture content, is the infiltration rate at time t.

[0036] When the rainfall intensity is greater than the instantaneous infiltration rate of the soil Surface runoff begins to occur.

[0037] Specifically, by obtaining the initial soil moisture content of each sub-water network, the saturated soil moisture content, suction at the wetting front, soil saturated hydraulic conductivity and other data of each sub-water network are determined according to the land use type of the water network. Based on the above infiltration rate formula and cumulative infiltration formula and the conditions for generating runoff, it is judged whether runoff will be generated under the rainfall conditions at each moment. As the rainfall process continues, the infiltration rate and cumulative infiltration are continuously updated to determine the runoff generated at each time step, and finally the runoff data corresponding to the complete runoff process at the outlet of each sub-water network under specific rainfall conditions are obtained.

[0038] Furthermore, in this embodiment, for the Laohe River water network project, the multi-flow direction algorithm (MFD) is used to divide the Laohe River sub-water network by integrating the terrain elevation data of the Laohe River water network and the location of the key outlet sections of the water network. The real-time rainfall data is connected, and the rainfall runoff process of each sub-water network is calculated based on the rainfall runoff mechanism. The specific scheme is as follows: First, terrain preprocessing and correction are performed through terrain elevation data, and the boundary of the Laohezi water network is determined using the multi-flow direction algorithm (MFD). After the Laohezi water network is divided, the saturated hydraulic conductivity, water absorption head, porosity, water production capacity and shrinkage point parameter values ​​of each soil type are determined according to the soil types of the Laohezi water network: loam, clay, and sand. The numerical types are shown in Table 1 below:

[0039] Table 1 Among them, K is the saturated hydraulic conductivity; ψ is the water head; φ is the porosity; FC is the water production capacity; WP is the shrinkage point.

[0040] The calculation formula of suction head is: The initial soil moisture loss is calculated as the difference between the soil porosity and the initial soil moisture content.

[0041] After the parameters are determined, the meteorological forecast data including rainfall and runoff data are accessed through the above standardized interface: ; In the formula, is the time to call rainfall runoff data, is the longest forecast range, is the rainfall intensity at the i-th moment.

[0042] The rainfall runoff module calculates the instantaneous soil infiltration rate hour by hour, and combines the rainfall intensity at each moment to determine whether the rainfall conditions meet the runoff generation standards, and dynamically calculates the runoff volume during the rainfall process. The runoff volume changes with time and rainfall as shown below: ; In the formula, represents the change of runoff over time. is the rainfall at the corresponding time point, is the runoff at the corresponding time point.

[0043] Step S300, using digital twin technology to perform numerical modeling based on surface features, and based on the shallow water equation, by discretely solving the shallow water equation, a hydrodynamic model simulating shallow water flow under different conditions is established according to the runoff data.

[0044] Specifically, digital twin technology is used to numerically model the river channels and surface areas of the water network based on information such as river network topology, land use type, water network digital elevation data, river underwater terrain data, and hydraulic structure data. It can simulate the flow evolution process under specific rainfall conditions and the current hydraulic structure operation plan, and provide accurate support for the simulation calculation of the evolution of river channels and surface water flows in the water network.

[0045] The hydrodynamic model of the water network is based on the topological structure of the river network. It discretizes the space of the study area, discretizes the river channel and the surface into several triangular unstructured grid units of appropriate size, and uses local encryption at important river channels and cross-section locations to improve the simulation accuracy of key areas. The model solves the hydrodynamic elements such as water depth, flow rate, and flow velocity of each grid unit through the finite element method. The constructed water network hydrodynamic model predicts the spatiotemporal changes of the hydrodynamic elements of the future water network and the evolution path of water flow on the surface based on the hydrological process entering the water network and the current operation plan of hydraulic structures calculated based on rainfall forecast data.

[0046] The hydrodynamic model of the water network is based on the shallow water equation and supports the use of finite elements to discretely solve the shallow water equation in time and space. The equations discretized by the finite element method are solved using a variety of solution methods such as the conjugate gradient method and Newton's method.

[0047] The shallow water equations it solves include the continuity equation and the momentum equation. The expression of the continuity equation is as follows: ; The momentum equation is expressed as follows: ; ; In the formula, For water depth, is the surface elevation, For time, and are the average velocity values ​​along the x and y directions in the Cartesian coordinate system, is the acceleration due to gravity, and is the source term of the equations, including bottom friction, surface wind stress, Coriolis force, and atmospheric pressure. is the turbulent viscous diffusion coefficient, is the gradient symbol, is the divergence symbol.

[0048] Furthermore, in the present embodiment, for the Laohe River water network project, a numerical simulation hydrodynamic model that can accurately reflect the water flow evolution process of the water network is constructed based on multi-source measurement information such as the hydraulic structure information of the Laohe River water network, river section mapping data, and surface scanning data. The hydrological process of future water networks entering the river network calculated by rainfall runoff data is accessed through the above-mentioned standard interface. At the same time, the moment-by-moment height process of the hydraulic structures in the Laohe River water network is accessed through the above-mentioned standard interface to predict the spatiotemporal changes of the hydrodynamic elements of the water network and the evolution path of the surface water flow.

[0049] Specifically, in this embodiment, the water conservancy projects of the Laohe River network include hydraulic structures such as gates and rubber dams. Based on the design information of these structures, the hydrodynamic model can truly simulate the operation effect of the hydraulic structures. Taking the rubber dam as an example, the following are the dam flow calculation formulas for five dams of different shapes.

[0050] Rectangular cross-section dam: ; Side dam: ; Triangle Dam: ; Trapezoidal dam: ; Rectangular road surface: ; In the formula, is the discharge coefficient of the dam; For the dam chief; The slope of a triangular dam or a trapezoidal dam; is the head difference across the dam; is the flow coefficient through both sides of the trapezoidal weir.

[0051] Step S400, according to the preset flood risk assessment standard, in the process of water flow simulation, the corresponding flood inundation data of each area and the demographic data are spatially superimposed and calculated in real time to monitor and obtain the flood risk data of each area in real time.

[0052] Specifically, this step conducts flood risk assessment on key sections and key areas of the water network based on the hydrodynamic element process of key sections and key areas obtained in the above step S300 and the flood risk assessment standard. The assessment content includes elements such as water level, flow velocity, submerged area, and submerged population to determine the flood risk level of key sections and key areas.

[0053] According to the set flood risk assessment standards and the locations of key sections and key areas, disaster-causing characteristic indicators (flood inundation data) such as flood inundation range, flood depth, inundation history and flow velocity distribution map are spatially superimposed and calculated with the corresponding spatial map layers and demographic data in real time during the water flow evolution simulation. The flood risk level of each key location and area is monitored in real time, the dynamic changes of the flood risk level are quickly analyzed, and the necessity of launching the intelligent optimization algorithm is intelligently judged.

[0054] The flooded population is obtained by multiplying the area of ​​each flooded residential area by its population density and adding them up. The population density of each residential area is obtained through the spatial distribution model of demographic data.

[0055] Furthermore, in this embodiment, for the Laohe River water network project, based on the hydrodynamic element results obtained from the water network hydrodynamic model, combined with the flood risk assessment content and industry standards, in the process of water flow evolution, the key disaster-causing indicators such as flood inundation range, flood depth, flood duration, and flow velocity distribution are calculated and analyzed in real time. And these disaster-causing characteristic indicators are spatially superimposed and analyzed with the spatial map layers, infrastructure distribution, and population density in the geographic information system (GIS) to obtain the population and property losses of the water network at each moment, and comprehensively evaluate the impact of flood disasters on key areas. Through real-time monitoring and evaluation of the flood risk level of each area, intelligent analysis is performed on whether it is necessary to start the optimization scheduling algorithm, and automatic scheduling and optimization operations are performed on hydraulic structures (such as dams, gates, etc.) in the water network to minimize flood risks and ensure the safety of personnel and property.

[0056] The population affected by flooding at each moment in the Laohe River network can be expressed as: ; In the formula, For the flooded population, is the longest weather forecast time range; For the flooded population in key areas, The current time for accessing weather forecast data.

[0057] Step S500, based on the flood risk data and the current hydraulic structure operation plan, by setting constraints and iterative optimization based on the population evolution algorithm, finally obtaining the hydraulic structure optimization operation plan that reduces the flood risk.

[0058] Specifically, the steps of setting constraints and iterative optimization based on the population evolution algorithm in step S500 to finally obtain an optimized operation plan for hydraulic structures that reduce flood risks specifically include: Step S510, establishing a target optimization model with the least flood loss and the least flooded population based on the following calculation formula: ; ; In the formula, is the flood loss, To assess the value of the r-th asset in the unit at the c-th water depth, is the loss rate of the rth type of assets under the cth level of water depth, For the flooded population, is the flooded area of ​​the bth residential area in the ath regional unit, is the population density of the bth residential area in the ath regional unit.

[0059] Step S520, using the SCE-UA algorithm, the opening of the hydraulic structure is changed, and the improvement of the objective function meets the preset conditions, and a loop iterative optimization is performed to search for the global optimal solution of the opening of the hydraulic structure, and the following constraints are set according to the design data of each hydraulic structure of the water network: ; ; ; In the formula, is the water level of the nth water network section, is the flood control level of the nth water network section, is the flow rate of the mth hydraulic structure, is the maximum design flow of the mth hydraulic structure, is the opening of the mth hydraulic structure, is the minimum opening of the mth hydraulic structure, is the maximum opening of the mth hydraulic structure.

[0060] Specifically, this step is based on the water network flood risk assessment results obtained in step S500 and the current hydraulic structure operation plan, and uses the intelligent optimization algorithm to call the water network hydrodynamic model in real time to repeatedly calculate the operation plans of different hydraulic structures. By setting constraints, the results of the intelligent optimization algorithm are restricted to ensure the rationality of the calculation, and through cyclic iterative optimization, the optimal hydraulic structure operation plan that reduces the flood risk of the water network is finally obtained.

[0061] The product of flooded water depth and asset value. The smaller the value, the lower the flood loss caused by the hydraulic structure operation plan, and the more successful the optimization plan. The product of flooded area and population density. The smaller the value, the fewer people are flooded due to the hydraulic structure operation plan, and the more successful the optimization plan.

[0062] The intelligent optimization algorithm module can be used to obtain the operation plan of each hydraulic structure that best meets the weighted objective function. At each time point, the operation status of the hydraulic structure can be represented by a matrix: ; In the formula, It represents the operating status of the nth hydraulic structure at the mth time point. Based on the combination of the operating schemes of the hydraulic structure group, the hydrodynamic elements of a certain section under specific working conditions, such as water level, can be further obtained.

[0063] To make it easier to understand, the SCE-UA algorithm combines the simplex method, random search, and biological competitive evolution methods, regards the global search process as a process similar to biological evolution in nature, and adopts a biologically inspired strategy to optimize the problem solution. In specific operations, the algorithm first randomly generates a number of points to form an initial group, and divides this group into multiple tribes. Independent evolution operations are performed within each tribe. In each tribe, a number of excellent individuals are selected as the parent group for breeding the next generation through a selective strategy. The selection of these parent groups is based on a probability distribution, so that individuals with better performance have a higher probability of being selected to participate in the reproduction of the next generation.

[0064] During the reproduction process, the parent group will generate new offspring individuals, and then replace the worst performing individuals in the original parent group with the best offspring individuals. This iterative evolution process is repeated multiple times within the tribe until a certain number of evolutionary generations is reached or the stopping condition is met. In order to avoid information islands between tribes, the algorithm is set to periodically remix individuals from each tribe to form new groups, thereby maintaining information sharing and diversity, and enhancing the adaptability of the entire group and the global search ability for solutions.

[0065] Preferably, in this embodiment, the iteration is terminated when, after 10 consecutive loop iterations, the change in the opening of the hydraulic structure to be optimized is less than 4e-6, the improvement in the objective function value is less than 10%, and the maximum number of calculations exceeds 1000.

[0066] Step S600, calling the hydrodynamic model to simulate the water network flow evolution process of the hydraulic structure optimization operation plan, and monitoring and managing the water network based on the optimized flood risk data.

[0067] Specifically, in this step, the optimized hydraulic structure operation plan based on step S500 is combined with the hydrodynamic model obtained in step S300 to simulate the optimized water network flow evolution process, and obtain the spatiotemporal distribution fields of the water level, flow, flooded area, etc. of the whole field. At the same time, the flood risk levels of key sections and key areas of the water network are generated. Through these visualization results, a scientific basis is provided for the evaluation of flood risk management of water networks. Visualized data cloud maps of water network depth, water level, flow, flooded area, etc. are displayed, and the flood risk levels of key sections and key areas of the optimized water network are listed, providing a scientific decision-making basis for flood risk management of water networks, and helping to more effectively manage flood risks of water networks.

[0068] In this embodiment, based on the optimized operation plan of hydraulic structures, a visualized data cloud map of water network depth, water level, flow, flooded area, etc. is displayed, and the flood risk levels of key sections and key areas of the optimized water network are listed, providing a scientific decision-making basis for water network flood risk management and helping to manage water network flood risks more effectively.

[0069] Furthermore, in this embodiment, for the Laohe River water network project, the system outputs the spatiotemporal distribution field of the numerical cloud map of the water level, flow and other numerical values ​​of the entire water network every hour. At the same time, flood risk assessment and statistics are conducted on key areas, and key evaluation indicators such as flood depth, flood range, and risk level of key areas are displayed. Through these results, the flood control monitoring effect of the Laohe River water network is intuitively evaluated, the effectiveness of the optimization scheduling plan of hydraulic structures is standardized, and the intelligent development of flood risk management of the Laohe River water network is promoted.

[0070] In summary, the flood risk management method in the above-mentioned embodiment of the present invention constructs a dynamic virtual model of the water network through digital twin technology, so as to realize real-time monitoring, refined simulation and accurate prediction of flood risks in the water network; through the combination of hydrodynamic model and flood risk assessment, when flood risks are discovered, the intelligent optimization algorithm can be automatically called to dispatch hydraulic structures such as gates and dams, so as to respond to flood risks in real time and reduce the impact of potential disasters. Compared with traditional manual dispatching, it can reduce delays or misjudgments caused by human intervention and improve the efficiency and accuracy of flood control dispatching.

[0071] Embodiment 2 The second embodiment of the present application also provides a flood risk management system, which is used to implement the embodiments and preferred implementations, and will not be repeated here. As used below, the terms "module", "unit", "subunit", etc. can be a combination of software and / or hardware that implements a predetermined function. Although the system described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0072] like Figure 2 As shown, the system includes: a water network division module 100, a rainfall runoff module 200, a hydrodynamic model module 300, a flood risk module 400, an optimization module 500 and a simulation module 600; The water network division module 100 is used to pre-process the digital elevation data of the water network, and use a multi-flow direction algorithm to determine the water flow direction of the water network based on the terrain characteristics, so as to divide the sub-water network by the accumulated flow of each pixel in the digital elevation data; The rainfall runoff module 200 is used to obtain the runoff data of each sub-water network according to the soil data and rainfall intensity of each sub-water network; The hydrodynamic model module 300 is used to perform numerical modeling based on surface features using digital twin technology, and based on the shallow water equation, by discretely solving the shallow water equation, so as to establish a hydrodynamic model simulating shallow water flow under different conditions according to the runoff data; The flood risk module 400 is used to perform spatial superposition calculations on the flood inundation data corresponding to each region and the demographic data in real time during the water flow simulation according to the preset flood risk assessment standard, so as to monitor and obtain the flood risk data of each region in real time; The optimization module 500 is used to obtain an optimized operation plan for hydraulic structures that reduces flood risks by setting constraints and performing cyclic iterative optimization based on the flood risk data and the current operation plan for hydraulic structures based on a population evolution algorithm; The simulation module 600 is used to call the hydrodynamic model, simulate the water network flow evolution process of the hydraulic structure optimization operation plan, and monitor and manage the water network based on the optimized flood risk data.

[0073] Preferably, in this embodiment, the water network division module 100 is specifically used for: The depressions in the digital elevation are filled and leveled, and then gradually raised to ensure that each grid water flow in the water network has a corresponding water network outlet; The multi-flow direction algorithm is used to calculate the slopes between each grid unit, and the water flow is proportionally allocated based on the weights between different slopes to confirm the water flow direction of the water network.

[0074] Preferably, in this embodiment, the water network division module 100 is further used for: According to the accumulated flow of each pixel in the digital elevation data, the boundary of the sub-water network is determined based on the direction of the accumulated flow and the location of the outlet section of the water network, thereby dividing the sub-water network; The calculation expression of the cumulative flow is as follows: ; In the formula, is the accumulated flow of the current pixel, is the accumulated flow of adjacent pixels, is the proportion of flow direction allocated to the adjacent pixel.

[0075] Preferably, in this embodiment, the rainfall runoff module 200 is specifically used for: The infiltration rate is calculated according to the hydraulic conductivity and suction of the soil by the following calculation formula, so as to obtain the cumulative infiltration amount of each sub-water network according to the rainfall intensity: ; ; In the formula, is the cumulative infiltration at time t, is the saturated hydraulic conductivity of soil, The suction force at the wetting front, is the saturated water content of the soil, is the initial soil moisture content, is the infiltration rate at time t.

[0076] Preferably, in this embodiment, the optimization module 500 is specifically used for: An optimization model with the goal of minimizing flood losses and flooded population is established based on the following calculation formula: ; ; In the formula, is the flood loss, To assess the value of the r-th asset in the unit at the c-th water depth, is the loss rate of the rth type of assets under the cth level of water depth, For the flooded population, is the flooded area of ​​the bth residential area in the ath regional unit, is the population density of the bth residential area in the ath regional unit; The SCE-UA algorithm is used to perform cyclic iterative optimization while changing the opening of hydraulic structures and the improvement of the objective function meets the preset conditions, so as to search for the global optimal solution of the opening of hydraulic structures, and set the following constraints according to the design data of each hydraulic structure in the water network: ; ; ; In the formula, is the water level of the nth water network section, is the flood control level of the nth water network section, is the flow rate of the mth hydraulic structure, is the maximum design flow of the mth hydraulic structure, is the opening of the mth hydraulic structure, is the minimum opening of the mth hydraulic structure, is the maximum opening of the mth hydraulic structure.

[0077] It should be noted that each module can be a functional module or a program module, and can be implemented by software or hardware. For modules implemented by hardware, each module can be located in the same processor; or each module can be located in different processors in any combination.

[0078] Embodiment 3 The third embodiment of the present application provides a computer. It can be understood that the principles mentioned in the flood risk management system in this embodiment correspond to the flood risk management method in the first embodiment of the present application. The relevant principles not described can be referred to the first embodiment for details, and will not be elaborated here.

[0079] The computer may include a processor 81 and a memory 82 storing computer program commands.

[0080] Specifically, the processor 81 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0081] Among them, the memory 82 may include a large-capacity memory for data or commands. By way of example and not limitation, the memory 82 may include a hard disk drive (HDD), a floppy disk drive, a solid-state drive (SSD), a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 82 may include a removable or non-removable (or fixed) medium. Where appropriate, the memory 82 may be inside or outside the data processing device. In a specific embodiment, the memory 82 is a non-volatile memory. In a specific embodiment, the memory 82 includes a read-only memory (ROM) and a random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM) or a flash memory (FLASH), or a combination of two or more of these. Under appropriate circumstances, the RAM can be a static random access memory (SRAM) or a dynamic random access memory (DRAM), wherein the DRAM can be a fast page mode dynamic random access memory (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0082] The memory 82 may be used to store or cache various data files required for processing and / or communication, as well as possible computer program commands executed by the processor 81 .

[0083] The processor 81 implements any one of the flood risk management methods in the above embodiments by reading and executing computer program commands stored in the memory 82 .

[0084] In some embodiments, the computer may further include a communication interface 83 and a bus 80. Figure 3 As shown, the processor 81, the memory 82, and the communication interface 83 are connected via a bus 80 and communicate with each other.

[0085] The communication interface 83 is used to implement communication between the modules, devices, units and / or equipment in the embodiment of the present application. The communication interface 83 can also implement data communication with other components such as: external devices, image / data acquisition equipment, databases, external storage, and image / data processing workstations.

[0086] The bus 80 includes hardware, software or both, and couples the components of the computer to each other. The bus 80 includes but is not limited to at least one of the following: a data bus, an address bus, a control bus, an expansion bus, and a local bus. By way of example and not limitation, bus 80 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses or a combination of two or more of these. Where appropriate, bus 80 may include one or more buses. Although embodiments of the present application describe and illustrate a particular bus, the present application contemplates any suitable bus or interconnect.

[0087] Embodiment 4 In combination with the flood risk management method in the above embodiments, the fourth embodiment of the present application provides a readable storage medium having computer program commands stored thereon; when the computer program commands are executed by a processor, any one of the flood risk management methods in the above embodiments is implemented.

[0088] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0089] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A flood risk management method, characterized in that: The following steps are involved: Preprocessing the digital elevation data of the water network, and using a multi-flow direction algorithm to determine the water flow direction of the water network based on the terrain characteristics, so as to divide the sub-water network by the accumulated flow of each pixel in the digital elevation data; Obtaining runoff data of each sub-water network according to soil data and rainfall intensity of each sub-water network; Digital twin technology is used to perform numerical modeling based on surface features, and based on shallow water equations, a hydrodynamic model simulating shallow water flow under different conditions is established according to the runoff data by discretely solving the shallow water equations; According to the preset flood risk assessment standards, the corresponding flood inundation data of each area and the demographic data are spatially superimposed and calculated in real time during the water flow simulation process to monitor and obtain the flood risk data of each area in real time; Based on the flood risk data and the current hydraulic structure operation plan, the optimal operation plan of the hydraulic structure that reduces the flood risk is finally obtained by setting constraints and iterative optimization based on the population evolution algorithm; The hydrodynamic model is called to simulate the water network flow evolution process of the hydraulic structure optimization operation plan, and the water network is monitored and managed based on the optimized flood risk data.

2. The flood risk management method according to claim 1, characterized in that: The steps of preprocessing the digital elevation data of the water network and using the multi-flow direction algorithm to determine the water flow direction of the water network based on the terrain characteristics specifically include: The depressions in the digital elevation are filled and leveled, and then gradually raised to ensure that each grid water flow in the water network has a corresponding water network outlet; The multi-flow direction algorithm is used to calculate the slopes between each grid unit, and the water flow is proportionally allocated based on the weights between different slopes to confirm the water flow direction of the water network.

3. The flood risk management method according to claim 2, characterized in that: The expression of the water flow distribution ratio between each grid unit is as follows: ； ； ； In the formula, is the flow ratio of water from the current grid cell to the i-th adjacent cell, is the slope angle from the current grid to the i-th adjacent grid, is the flow distribution function used to adjust for slope effects, is the slope from the current grid to the adjacent grid, and are the minimum and maximum values ​​of all slope values, is the current pixel elevation, is the elevation of the adjacent pixel, is the distance from the current grid cell to the i-th adjacent grid cell.

4. The flood risk management method according to claim 3, characterized in that: The step of dividing the sub-water network by the accumulated flow of each pixel in the digital elevation data specifically includes: According to the accumulated flow of each pixel in the digital elevation data, the boundary of the sub-water network is determined based on the direction of the accumulated flow and the location of the outlet section of the water network, thereby dividing the sub-water network; The calculation expression of the cumulative flow is as follows: ； In the formula, is the accumulated flow of the current pixel, is the accumulated flow of adjacent pixels, is the proportion of flow direction allocated to the adjacent pixel.

5. The flood risk management method according to claim 1, characterized in that: The step of obtaining the runoff data of each sub-water network according to the soil data and rainfall intensity of each sub-water network specifically includes: The infiltration rate is calculated according to the hydraulic conductivity and suction of the soil by the following calculation formula, so as to obtain the cumulative infiltration amount of each sub-water network according to the rainfall intensity: ； ； In the formula, is the cumulative infiltration at time t, is the saturated hydraulic conductivity of soil, The suction force at the wetting front, is the saturated water content of the soil, is the initial soil moisture content, is the infiltration rate at time t.

6. The flood risk management method according to claim 1, characterized in that: The shallow water equations include the continuity equation and the momentum equation. The expression of the continuity equation is as follows: ； The momentum equation is expressed as follows: ； ； In the formula, For water depth, is the surface elevation, For time, and are the average velocity values ​​along the x and y directions in the Cartesian coordinate system, is the acceleration due to gravity, and is the source term of the equations, including bottom friction, surface wind stress, Coriolis force, and atmospheric pressure. is the turbulent viscous diffusion coefficient, is the gradient symbol, is the divergence symbol.

7. The flood risk management method according to claim 1, characterized in that: Based on the population evolution algorithm, by setting constraints and iterative optimization, the steps to finally obtain the optimal operation plan for hydraulic structures to reduce flood risks include: An optimization model with the goal of minimizing flood losses and flooded population is established based on the following calculation formula: ； ； In the formula, is the flood loss, To assess the value of the r-th asset in the unit at the c-th water depth, is the loss rate of the rth type of assets under the cth level of water depth, For the flooded population, is the flooded area of ​​the bth residential area in the ath regional unit, is the population density of the bth residential area in the ath regional unit; The SCE-UA algorithm is used to perform cyclic iterative optimization while changing the opening of hydraulic structures and the improvement of the objective function meets the preset conditions, so as to search for the global optimal solution of the opening of hydraulic structures, and set the following constraints according to the design data of each hydraulic structure in the water network: ； ； ； In the formula, is the water level of the nth water network section, is the flood control level of the nth water network section, is the flow rate of the mth hydraulic structure, is the maximum design flow of the mth hydraulic structure, is the opening of the mth hydraulic structure, is the minimum opening of the mth hydraulic structure, is the maximum opening of the mth hydraulic structure.

8. A flood risk management system, characterized in that: include: A water network division module is used to pre-process the digital elevation data of the water network and use a multi-flow algorithm to determine the water flow direction of the water network based on the terrain characteristics, so as to divide the sub-water network by the accumulated flow of each pixel in the digital elevation data; A rainfall runoff module, used to obtain the runoff data of each sub-water network according to the soil data and rainfall intensity of each sub-water network; A hydrodynamic model module is used to perform numerical modeling based on surface features using digital twin technology, and to establish a hydrodynamic model simulating shallow water flow under different conditions based on the runoff data by discretely solving the shallow water equation; The flood risk module is used to perform spatial superposition calculations on the flood inundation data and demographic data corresponding to each region in real time during the water flow simulation according to the preset flood risk assessment standards, so as to monitor and obtain the flood risk data of each region in real time; An optimization module is used to obtain an optimized operation plan for hydraulic structures that reduces flood risks by setting constraints and performing cyclic iterative optimization based on the flood risk data and the current operation plan for hydraulic structures based on a population evolution algorithm; The simulation module is used to call the hydrodynamic model to simulate the water network flow evolution process of the hydraulic structure optimization operation plan, and monitor and manage the water network based on the optimized flood risk data.

9. A computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the flood risk management method according to any one of claims 1 to 7 is implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the flood risk management method as described in any one of claims 1 to 7 is implemented.

Images