Method, device and equipment for intelligent division of flood storage area and storage medium

Abstract

The application relates to the technical field of flood control and disaster reduction, in particular to a method and device for intelligently dividing a flood storage and detention area, computer equipment and a storage medium. The method comprises the following steps: obtaining basic data of the flood storage and detention area; creating a vector grid set covering the boundary range of the flood storage and detention area based on a GIS platform, performing spatial superposition analysis and fusion on the basic data, and obtaining the population number, GDP value and cultivated land area of each cell; calculating the economic and social vulnerability value of each cell based on the entropy weight method and the population number, GDP value and cultivated land area of each cell; constructing a plurality of candidate partition dike and partition flood diversion point positions, generating an initialization population based thereon, iteratively solving based on a particle swarm algorithm, the initialization population, a hydrodynamic model and a comprehensive loss objective function, obtaining a global optimal particle, and determining an optimal partition scheme according to the global optimal particle. The method can realize accurate, efficient and scientific decision-making of the partition scheme of the flood storage and detention area.

Description

Technical Field

[0001] This application relates to the field of flood control and disaster reduction technology, and in particular to a method for intelligently dividing flood storage and detention areas, a device for intelligently dividing flood storage and detention areas, a computer device, and a computer-readable storage medium. Background Technology

[0002] Flood storage and detention areas refer to low-lying areas and lakes outside the backwater surface of river dikes, including flood diversion outlets, that temporarily store floodwater. They are an important component of river flood control systems. Their function is to be activated according to plan when floodwaters exceed the safe discharge capacity of the river channel to store excess floodwaters, sacrificing local interests to ensure the safety of key downstream areas, cities, and important facilities.

[0003] Traditional flood storage and detention area management typically treats them as a single unit for activation decisions. However, with regional economic and social development and increasing population and asset density, the socio-economic losses caused by the overall activation model are becoming increasingly significant. To minimize flood diversion losses, modern flood control concepts emphasize the sequential use of internally zoned flood storage and detention areas: that is, dividing large flood storage and detention areas into several sub-areas by constructing internal dikes and flood diversion gates; when floods arrive, based on the flood magnitude and characteristics, priority is given to activating sub-areas with low terrain and low population and asset density for flood storage, thereby delaying or avoiding the inundation of high-value sub-areas and achieving the goal of refined scheduling.

[0004] However, for a known flood storage and detention area, scientifically determining the optimal location of internal dikes and the optimal design parameters of flood diversion gates is a challenge rarely addressed in current technologies. Currently, most methods for setting up internal dikes and flood diversion gates rely on human experience and qualitative analysis, meaning they largely depend on the experience of experts in the field. This approach is highly subjective, lacks quantitative decision support, and struggles to comprehensively and accurately weigh complex spatial geographic information and socioeconomic data, potentially leading to zoning schemes that are not globally optimal.

[0005] In summary, there is an urgent need in this field for an intelligent method or device for dividing flood storage and detention areas, in order to automatically and efficiently search for the globally optimal layout of the dikes and the design parameters of the flood diversion gates. Summary of the Invention

[0006] Based on this, it is necessary to provide a method for intelligent zoning of flood storage and detention areas, a device for intelligent zoning of flood storage and detention areas, a computer device, and a computer-readable storage medium to address the above-mentioned technical problems.

[0007] Firstly, this application provides a method for intelligent zoning of flood storage and detention areas, the method comprising:

[0008] Acquire basic data for flood storage and detention areas; the basic data includes the boundary range of flood storage and detention areas, land spatial data, economic and social statistical data, DEM digital elevation data, water conservancy project data, and hydrological data;

[0009] Based on the GIS platform, a vector grid set covering the boundary of the flood storage and detention area is created. The land space data, the economic and social statistics data and the DEM digital elevation data are spatially overlaid and analyzed, and then merged into each cell of the vector grid set to obtain the population, GDP value and cultivated land area of ​​each cell.

[0010] Based on the entropy weight method and the population, GDP and arable land area of ​​each cell, calculate the economic and social vulnerability value of each cell;

[0011] Multiple control points are set at a first preset interval on the boundary of the flood storage and detention area. The line connecting any two control points constitutes a candidate partition dike. Flood diversion points are set at a second preset interval on each candidate partition dike.

[0012] Based on the two control points of each candidate dike, the flood diversion points on the candidate dike, and the flood diversion level and flow rate of the flood diversion points, an initial population is generated. Each particle in the initial population is traversed, and a two-dimensional flood evolution simulation is performed by calling the hydrodynamic model to obtain the maximum inundation depth and inundation duration of each cell under the particle. The hydrodynamic model is constructed based on the hydrological data and water conservancy engineering data.

[0013] Based on the area, socio-economic vulnerability value, maximum inundation depth, and inundation duration of each cell, the comprehensive loss objective function value for that particle is calculated. With the goal of minimizing the comprehensive loss value, and in accordance with the flow constraints determined based on the hydrological data, the water level constraints determined based on the water conservancy engineering data, and the capacity constraints, the particle swarm algorithm is driven to iterate and update until convergence to obtain the globally optimal particle.

[0014] Connect the two control points in the global optimal particle to determine the optimal zone boundary; determine the flood diversion gate location on the boundary of the zone boundary by identifying the flood diversion point in the global optimal particle; determine the activation water level and design flow of the flood diversion gate based on the flood diversion water level and flow rate in the global optimal particle.

[0015] In one embodiment, the comprehensive loss objective function The calculation formula is:

[0016] ;

[0017] in, Indicates the first The area of ​​each cell; Indicates the first The economic and social vulnerability value of each cell; Indicates the first The maximum flood depth of each cell; Indicates based on the first The flooding duration of each cell Calculated correction factor The rules for determining the value are as follows:

[0018] when Timing, =1;

[0019] when Timing, =1.3;

[0020] when hour, ;in, The first preset duration, greater than The second preset duration.

[0021] In one embodiment, each cell in the vector grid set is a 100m × 100m grid;

[0022] And / or, both the first preset interval and the second preset interval are 10m;

[0023] And / or, the hydrodynamic model is a two-dimensional hydrodynamic model constructed based on MIKE.

[0024] In one embodiment, the step of calculating the socio-economic vulnerability value of each cell based on the entropy weight method and the population, GDP, and arable land area of ​​each cell includes:

[0025] Construct an m×3 indicator matrix; m is the number of cells, and the three columns correspond to the population, GDP, and arable land area, respectively.

[0026] The extreme value normalization process is performed on each column of the index matrix to obtain the standardized matrix;

[0027] Calculate the weight of each indicator in each cell; the weight of each indicator is the ratio of the standardized value of that indicator in each cell to the sum of the standardized values ​​of that indicator in all cells.

[0028] Based on the weight of the aforementioned indicators, the information entropy of each indicator is calculated;

[0029] Based on the information entropy, calculate the difference coefficient for each indicator;

[0030] Based on the difference coefficient, calculate the entropy weight of each indicator;

[0031] The socioeconomic vulnerability value of each cell is calculated based on the standardized matrix and the entropy weight.

[0032] In one embodiment, the flow constraint is that the inflow rate during the flood process is not greater than the safe discharge capacity of the upstream river channel, and the outflow rate during the flood discharge process is not greater than the safe discharge capacity of the downstream river channel; the safe discharge capacity of the upstream river channel and the safe discharge capacity of the downstream river channel are both determined based on the hydrological data.

[0033] In one embodiment, the water level constraint is that the water level activated in the zone is not greater than the design elevation of the dike at that location; the design elevation is determined based on the water conservancy project data.

[0034] In one embodiment, the capacity constraint is that the flood storage capacity of the first activated zone is not greater than its maximum effective flood storage capacity.

[0035] Secondly, this application also provides an intelligent zoning device for flood storage and detention areas, the device comprising:

[0036] The data acquisition module is used to acquire basic data of the flood storage and detention area; the basic data includes the boundary range of the flood storage and detention area, land spatial data, economic and social statistical data, DEM digital elevation data, water conservancy project data and hydrological data;

[0037] The interpolation and fusion module is used to create a vector grid set covering the boundary range of the flood storage and detention area based on the GIS platform, perform spatial overlay analysis on the land space data, the economic and social statistics data and the DEM digital elevation data, and fuse them into each cell of the vector grid set to obtain the population, GDP value and cultivated land area of ​​each cell;

[0038] The vulnerability calculation module is used to calculate the socio-economic vulnerability value of each cell based on the entropy weight method and the population, GDP value and cultivated land area of ​​each cell.

[0039] The point setting module is used to set multiple control points at a first preset interval on the boundary of the flood storage and detention area, and the line connecting any two control points constitutes a candidate partition dike, and set the flood diversion point on each candidate partition dike at a second preset interval.

[0040] The simulation optimization module is used to generate an initial population based on the two control points of each candidate dike, the flood diversion points on the candidate dike, and the flood diversion level and flow rate of the flood diversion points. It iterates through each particle in the initial population, calls the hydrodynamic model to perform a two-dimensional flood evolution simulation, and obtains the maximum inundation depth and inundation duration of each cell under that particle. The hydrodynamic model is constructed based on the hydrological data and water conservancy engineering data. Based on the area, socio-economic vulnerability value, maximum inundation depth, and inundation duration of each cell, the comprehensive loss objective function value under that particle is calculated. With the goal of minimizing the comprehensive loss value, and satisfying the flow constraints of safe discharge capacity of upstream and downstream channels determined based on the hydrological data, the water level constraint of the zone's activation water level being lower than the dike's design elevation determined based on the water conservancy engineering data, and the capacity constraint of the zone's flood storage capacity being less than its effective capacity, the particle swarm optimization algorithm is driven to iteratively update until convergence to obtain the globally optimal particle.

[0041] The zoning decision module is used to connect two control points in the global optimal particle to determine the optimal zoning boundary; to determine the flood diversion point in the global optimal particle as the location of the flood diversion gate on the boundary of the zoning boundary; and to determine the activation water level and design flow of the flood diversion gate based on the flood diversion water level and flood diversion flow in the global optimal particle.

[0042] Thirdly, embodiments of this application provide a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of a method for intelligent zoning of flood storage and detention areas.

[0043] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of a method for intelligently dividing flood storage and detention areas.

[0044] The above technical solution has at least the following advantages or beneficial effects: By acquiring multi-source basic data, including the boundary range of flood storage and detention areas, land spatial data, economic and social statistical data, DEM digital elevation data, water conservancy project data, and hydrological data, and by performing gridded fusion of the boundary range of flood storage and detention areas based on land spatial data, the economic and social statistical data, and the DEM digital elevation data, the economic and social vulnerability of each cell is quantified; by automatically constructing candidate zoning dikes and diversion points through a preset algorithm, multiple candidate zoning schemes are generated, and iterative optimization is performed by coupling a hydrodynamic model and a particle swarm optimization algorithm, ultimately automatically determining the optimal zoning dike boundary and flood diversion gate parameters. This method transforms the traditional zoning process, which relies on manual experience, into a quantitative optimization problem based on a mathematical model, ultimately determining the globally optimal solution with the minimum comprehensive loss value. The analysis process is more objective, achieving accurate, efficient, and scientific decision-making for flood storage and detention area zoning schemes. Attached Figure Description

[0045] Figure 1 This is a flowchart illustrating an intelligent zoning method for flood storage and detention areas in one embodiment;

[0046] Figure 2 This is a schematic diagram showing the positional relationship between candidate dikes and flood diversion points in one embodiment;

[0047] Figure 3 This is a structural block diagram of an intelligent zoning device for flood storage and detention areas in one embodiment;

[0048] Figure 4 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0050] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0051] In one embodiment, a method for intelligently dividing flood storage and detention areas is provided. This method can automatically search for the globally optimal layout of dikes and flood diversion gates by quantifying spatial geographic information and socioeconomic data, combined with intelligent optimization algorithms and hydrodynamic simulation, overcoming the limitations of traditional methods that rely on manual experience. Figure 1 As shown, the method specifically includes the following steps:

[0052] S102, Obtain basic data of flood storage and detention areas; basic data includes the boundary range of flood storage and detention areas, land space data, economic and social statistics data, DEM digital elevation data, water conservancy project data and hydrological data.

[0053] The boundary range of a flood storage and detention area can be understood as the boundary range of a known flood storage and detention area. This data can be obtained from flood control planning or construction management planning. More specifically, it can be understood as the outer boundary of an area that can be used for temporary flood storage, as determined by flood control planning or flood storage and detention area construction management planning. This range is usually stored in the computer in vector polygon format, such as Shapefile or GeoJSON. This data defines the spatial computational domain for partitioning optimization in this method; subsequent mesh generation, data fusion, and simulation analysis are all performed based on this boundary range.

[0054] Territorial spatial data can be understood as spatial data reflecting the current status and planned uses of land resources within flood storage and detention areas, primarily derived from the national land survey results. The core content of this data includes the spatial distribution, extent, and area attributes of land use or cover types, such as cultivated land, settlements, forest land, grassland, water areas, construction land, and unused land. In this application, this data can be used to accurately identify the spatial location of key land types such as cultivated land and provide a weighted basis for the spatial interpolation of economic and social statistical data. This data can be provided in raster or vector format.

[0055] Economic and social statistical data can be understood as data that reflects the level of social development and economic activities within a region, using administrative divisions (such as townships and subdistricts) as statistical units. The indicators used in this application include population size and gross domestic product (GDP), which can be obtained from statistical yearbooks or publicly available data, typically in tabular form. In this application, by spatially linking and interpolating these data with residential areas and industrial / commercial land in the land spatial data, and rationally allocating these statistical values ​​to each grid cell, the abstract statistical values ​​are transformed into spatially relevant information, which is then used to calculate economic and social vulnerability.

[0056] Digital elevation model (DEM) data can be understood as data used to characterize topographic relief. Specifically, it is a digital model representing the spatial distribution of ground elevation, typically stored in the form of a regular grid, where the value of each grid cell represents the average elevation of the area. This application preferably uses DEM data with a resolution of 30 meters or higher, which can be downloaded from NASASRTM, ALOS, or similar open-source platforms. This data provides the topographic information necessary for calculating water flow direction, velocity, inundation extent, and water depth, and can be used to construct hydrodynamic models and simulate flood evolution.

[0057] Water conservancy project data can be extracted from the engineering archives of the water conservancy department. This data can be understood as describing the attributes and spatial location of existing water conservancy facilities within and around the flood storage and detention area. Specifically, it may include the location, length, and design elevation of existing dikes, as well as the location, type, bottom elevation, orifice size, and design flow rate of existing flood diversion gates, flood discharge gates, and pumping stations. The design elevation of existing dikes can serve as the basis for the water level constraints in this application, ensuring that the optimized activation water level of the new zone will not cause the project to overflow and fail.

[0058] Hydrological data can be understood as observational or design data related to flood characteristics. Specifically, it can include design flood hydrographs, safe discharge capacity, and historical flood data. The design flood hydrograph represents the change in flood flow over time for different return periods (e.g., 50-year or 100-year return periods). Safe discharge capacity refers to the maximum flow that can safely pass through a downstream channel, divided into upstream and downstream safe discharge capacities. Historical flood data can be understood as data on historical flood flow, water level, and inundation extent. This data can be used to drive hydrodynamic models to simulate flood evolution and to provide crucial flow constraints, such as ensuring that the optimized inflow and outflow of new flood zones does not exceed the safe discharge capacity. Specific data can be obtained from hydrological statistical yearbooks or measured data.

[0059] S104, based on the GIS platform, creates a vector grid set covering the boundary of the flood storage and detention area, performs spatial overlay analysis on land spatial data, economic and social statistical data and DEM digital elevation data, and integrates them into each cell of the vector grid set to obtain the population, GDP value and cultivated land area of ​​each cell.

[0060] In this step, the GIS platform can be understood as a Geographic Information System (GIS) platform, such as commercial or open-source software like ArcGIS, QGIS, and SuperMap. This platform can uniformly import and manage various spatial and attribute data involved in this application, including but not limited to the boundaries of flood storage and detention areas, DEM digital elevation data, and locations of water conservancy projects. It displays these data in an overlay format, providing users with an intuitive visualization of spatial relationships.

[0061] In this step, a vector grid set with a preset size and covering the entire study area can be quickly created using the powerful vector grid generation tools and the boundary range of the flood storage and detention area provided by the GIS platform. The preset size vector grid set can be a 100m×100m square grid. At this point, each grid is an independent vector polygon unit with a unique identifier, i.e., cell ID. Then, based on the spatial interpolation algorithm and spatial analysis tools of the GIS platform, spatial interpolation, spatial overlay analysis, and data fusion are performed on data from different sources and formats. This accurately correlates socio-economic and other statistical attributes with specific geographical locations, realizing the gridding of the data and laying the foundation for calculating the vulnerability value of each grid.

[0062] In one embodiment, spatial overlay analysis is performed on land spatial data, socio-economic statistical data, and DEM digital elevation data, and the data is then integrated into each cell of the vector grid set. Specifically, the operation can be as follows:

[0063] For land space data, the vector polygon layer and vector grid set can be overlaid and intersected to accurately calculate the area of ​​various land uses in each grid, thereby directly obtaining the cultivated land area of ​​each cell.

[0064] For DEM digital elevation data, a bilinear interpolation resampling method can be used to sample the original resolution DEM digital elevation data to match the grid center point, and assign an average elevation value to each grid.

[0065] For economic and social statistical data, spatial weighted interpolation can be used. Specifically, the distribution of residential construction land extracted from the land spatial data can be used as the weight surface, that is, the weight of the area where the residential area is located is 1, and the weight of the non-residential area is 0. Then, the statistical total value of the administrative division unit is allocated to each grid cell according to the weight, so as to obtain the population and GDP value of each grid.

[0066] Based on the spatial overlay analysis and data fusion methods described above, different types of source data, namely discrete classification polygons, continuous elevation surfaces, and macroscopic statistical summaries, can be scientifically and rationally unified into standardized grid cells, thereby achieving true data fusion and obtaining the population, GDP value, and cultivated land area of ​​each cell.

[0067] S106, based on the entropy weight method and the population, GDP value and cultivated land area of ​​each cell, calculate the economic and social vulnerability value of each cell.

[0068] It should be noted that this step aims to transform the relatively abstract population, GDP, and arable land area in each cell into an indicator value that can comprehensively and quantitatively represent the relative social and economic losses that the cell may suffer when flooded. This indicator value is the aforementioned socioeconomic vulnerability value. The socioeconomic vulnerability value can be understood as a dimensionless numerical value, usually ranging from 0 to 1 or within a relative numerical range. It is not a specific amount of loss or population number, but a comprehensive index. Population, GDP, and arable land area correspond to the social, economic, and ecological production attributes of the socioeconomic vulnerability value, respectively. The level of this value reflects the sensitivity and lack of resilience of the socioeconomic system of that cell to flood disasters. Understandably, a higher socioeconomic vulnerability value indicates a denser population, more active economic activity, or more important arable land resources within the grid cell, meaning the cell is more likely to suffer socioeconomic losses and impacts when flooded. Conversely, a lower socioeconomic vulnerability value indicates a relatively sparse or underdeveloped population, economic assets, and arable land resources within the grid cell, meaning the cell is less likely to suffer losses during flooding. Cells with higher vulnerability values ​​are priority areas that should be protected or have their flooding delayed as much as possible when developing zoning plans.

[0069] Entropy weighting, as an objective weighting method, determines weights entirely based on the distribution characteristics of the data itself, avoiding subjective bias. Specifically, the weighting is based on the following: if the numerical differences of an indicator (such as population, GDP, and arable land area) between different cells are greater, the data is more chaotic, or its information entropy is lower, then the indicator has a stronger ability to distinguish the importance of different cells, and therefore can be assigned a higher weight. Conversely, if the values ​​of an indicator are relatively close in all cells, it indicates low distinguishability, and the weight should be lower.

[0070] S108, multiple control points are set at a first preset interval on the boundary of the flood storage and detention area, and the line connecting any two control points constitutes a candidate partition dike, and the partition flood point is set at a second preset interval on each candidate partition dike.

[0071] The boundary of the flood storage and detention area can be determined based on the boundary range of the flood storage and detention area obtained in S102 above. For example... Figure 2 As shown, the solid line schematically indicates the boundary of the flood storage and detention area, and the area within the solid line can be understood as the boundary range of the flood storage and detention area; the dashed line is the candidate boundary of the zone, which represents the candidate zone dike. The two control points corresponding to the candidate zone dike are zone control point 1 and zone control point 2 in the figure. The zone flood control points on the candidate zone dike are the aforementioned zone flood control points. The direction of the arrow in the figure represents the direction of water flow. This step aims to construct the candidate zone dike and zone flood control points according to the preset rules.

[0072] Specifically, multiple control points can be set at first preset intervals along the boundary of the flood storage and detention area, and the line connecting any two control points constitutes a candidate diversion dike. Diversion flood points can be set at second preset intervals on each candidate diversion dike, and each diversion flood point can be associated with corresponding flood level and flood discharge parameters. By adjusting the size of the first and second preset intervals, computational efficiency and scheme accuracy can be balanced. Smaller intervals result in more candidate schemes, more refined optimization results, but also a larger computational load and lower efficiency. Preferably, both the first and second preset intervals can be set to approximately 10 meters.

[0073] S110 generates an initial population based on two control points of each candidate dike, the flood diversion points on the candidate dike, and the flood diversion level and flow rate of the flood diversion points. Iterates through each particle in the initial population, calls the hydrodynamic model to perform a two-dimensional flood evolution simulation, and obtains the maximum inundation depth and inundation duration of each cell under the particle. The hydrodynamic model is constructed based on hydrological data and water conservancy engineering data.

[0074] For this step, the particle swarm population can be initialized first. Each particle represents a partitioning scheme, and each partitioning scheme consists of five parameters: the coordinates of two control points of each candidate partition dike, the flood diversion point location, the flood diversion level, and the flood diversion discharge. Each particle is iterated through, and a hydrodynamic model is invoked to simulate flood evolution. The hydrodynamic model can be constructed based on hydrological and hydraulic engineering data. After simulation, the maximum inundation depth and inundation duration of each cell can be output, which are used in S112 to calculate the comprehensive loss objective function value for each partitioning scheme.

[0075] S112: Calculate the comprehensive loss objective function value for each particle based on the area, socio-economic vulnerability value, maximum inundation depth, and inundation duration of each cell. With the goal of minimizing the comprehensive loss value, and subject to the constraints of flow rate determined by hydrological data, water level, and capacity determined by water conservancy engineering data, drive the particle swarm algorithm to iteratively update until convergence to obtain the globally optimal particle.

[0076] In this step, the comprehensive loss objective function value for each partitioning scheme is first calculated based on the maximum inundation depth and inundation duration obtained from S110. Then, with the goal of minimizing the comprehensive loss value and under the premise of satisfying constraints (flow constraints, water level constraints, and capacity constraints), the particle swarm optimization algorithm is driven to iteratively update the particle positions until convergence to the globally optimal particle. This step automatically searches for the optimal partitioning parameters by coupling the intelligent optimization algorithm with the hydrodynamic model, thereby minimizing the comprehensive loss while satisfying engineering safety constraints and overcoming the blindness of traditional empirical methods.

[0077] It should be noted that the flow constraint can be determined based on hydrological data. This constraint ensures that the throughput of the flood storage area does not pose additional risks to the main river channel. Specifically, it can include inflow and outflow constraints. The water level constraint can be determined based on hydraulic engineering data to ensure the structural safety of the dikes themselves and prevent dike breaches before flood storage. The capacity constraint ensures that the planned flood volume is within the physical carrying capacity of the zone. These three constraints together constitute the safety boundary of the zone optimization scheme. Specifically, the flow constraint ensures the safety of the overall flood control system of the basin, the water level constraint ensures the local structural safety of the dikes, and the capacity constraint ensures the reliability of the zone's flood storage function. This application ensures that each candidate solution found is low-loss and safe and feasible by embedding these three key constraints into the iterative process of the intelligent optimization algorithm. The final output global optimal solution is not only economically optimal but also reliable in engineering practice.

[0078] S114. Connect the two control points in the global optimal particle to determine the optimal zone boundary; determine the flood diversion point in the global optimal particle as the location of the flood diversion gate on the boundary of the zone boundary; determine the activation water level and design flow of the flood diversion gate based on the flood diversion water level and flood diversion flow in the global optimal particle.

[0079] By connecting two control points in the global optimal particle, the optimal zoning boundary of the dike is formed; the flood diversion point of the zoning is determined as the location of the flood diversion gate on the dike; based on the flood diversion level and flood diversion flow, the activation water level and design flow of the flood diversion gate are set, thereby outputting the optimal zoning scheme to directly guide the construction and scheduling decisions of the dike project.

[0080] In the above embodiments of this application, the executing entity is generally a computer device with a certain computing capability.

[0081] In the above-described intelligent zoning method for flood storage and detention areas, multi-source basic data is acquired, including the boundary range of flood storage and detention areas, land spatial data, socio-economic statistics, DEM digital elevation data, water conservancy project data, and hydrological data. Based on the land spatial data, the socio-economic statistics, and the DEM digital elevation data, the boundary range of the flood storage and detention areas is gridded and fused, quantifying the socio-economic vulnerability of each cell. A preset algorithm automatically constructs candidate zoning dikes and diversion points, generating multiple candidate zoning schemes. These schemes are then iteratively optimized using a coupled hydrodynamic model and particle swarm optimization algorithm, ultimately automatically determining the optimal dike boundaries and flood diversion gate parameters. This method transforms the traditional zoning process, which relies on manual experience, into a quantitative optimization problem based on mathematical models. Ultimately, it determines the globally optimal solution with the minimum comprehensive loss value, making the analysis process more objective and achieving precise, efficient, and scientific decision-making for flood storage and detention area zoning schemes.

[0082] In some embodiments, the comprehensive loss objective function The calculation formula is:

[0083] ;

[0084] in, Indicates the first The area of ​​each cell; Indicates the first The economic and social vulnerability value of each cell; Indicates the first The maximum flood depth of each cell; Indicates based on the first The flooding duration of each cell The calculated correction factor follows the logic that the longer the flooding time, the greater the loss. Specifically, The rules for determining the value are as follows:

[0085] when Timing, =1;

[0086] when Timing, =1.3;

[0087] when hour, ;in, The first preset duration, greater than The second preset duration. Specifically, and The value can be configured according to actual needs. A more specific implementation is as follows:

[0088] when Timing, =1;

[0089] when Timing, =1.3;

[0090] When 10 days < <60 days, 50 can be obtained by subtracting 10 from 60.

[0091] It should be pointed out that, This represents the total foreseeable comprehensive loss of the entire flood storage and detention area under a certain candidate partitioning scheme (i.e., particle), after simulating flood evolution. This loss needs to be obtained by summing the losses of all cells. The optimization objective is to achieve this. Minimize the value. As a fundamental multiplier, it ensures that the loss calculation is based on spatial extent; the larger the flooded area, the greater the fundamental amount of potential loss. This reflects the relative importance of the population, GDP, and arable land area within the cell. The higher the value, the more important and vulnerable the socio-economic attributes of the cell are, and the greater the losses after it is submerged. The greater the depth of flooding, the more severe the physical damage to assets and the greater the losses. The impact of time on loss is introduced, meaning that the longer the inundation time, the greater the loss. This comprehensive loss objective function can accurately weigh the total loss that may be caused by different partitioning schemes. The particle swarm optimization algorithm searches for the minimum of this function value, and the final solution found is the optimal partitioning scheme that minimizes the global comprehensive loss after comprehensively considering all key factors such as inundation range, socio-economic value, water depth, and duration.

[0092] In some embodiments, the hydrodynamic model can be a two-dimensional hydrodynamic model built based on MIKE. It should be noted that certain software, components, models, and other existing solutions in the industry may be mentioned in the embodiments of this application. These should be considered as exemplary and are intended only to illustrate the feasibility of implementing the technical solution of this application. They do not imply that the applicant has used or necessarily used the solution, and do not involve software licensing or other intellectual property issues.

[0093] In some embodiments, S106 may specifically include: constructing an m×3 indicator matrix; m is the number of cells, and the three columns correspond to the population, GDP, and cultivated land area, respectively; performing extreme value normalization on each column of the indicator matrix to obtain a standardized matrix; calculating the indicator weight of each indicator in each cell; the indicator weight is the ratio of the standardized value of the indicator in each cell to the sum of the standardized values ​​of the indicator in all cells; calculating the information entropy of each indicator based on the indicator weight; calculating the difference coefficient of each indicator based on the information entropy; calculating the entropy weight of each indicator based on the difference coefficient; and calculating the socioeconomic vulnerability value of each cell based on the standardized matrix and the entropy weight.

[0094] One specific implementation involves performing extreme value normalization on each column of the indicator matrix to obtain a standardized matrix; calculating the indicator weight of each indicator in each cell; the indicator weight is the ratio of the standardized value of that indicator in each cell to the sum of the standardized values ​​of that indicator in all cells; calculating the information entropy of each indicator based on the indicator weight; calculating the difference coefficient of each indicator based on the information entropy; calculating the entropy weight of each indicator based on the difference coefficient; and calculating the socioeconomic vulnerability value of each cell based on the standardized matrix and the entropy weight, which can be obtained using the following formula:

[0095] ;in, The value of the j-th indicator in the i-th row (population, GDP or arable land area) is normalized.

[0096] ; The weight of the indicator in the i-th row and j-th indicator.

[0097] ; The information entropy representing the j-th indicator is m, which is the number of cells mentioned above.

[0098] ;in The coefficient of variation characterizing the j-th indicator The entropy weight represents the j-th index. At this point, the weight vector W can be obtained. These are the entropy weights for population, GDP, and arable land area, respectively.

[0099] Based on the above entropy weight The economic and social vulnerability value of each cell can be calculated using the following formula:

[0100] ;in, The value representing the economic and social vulnerability of the cell in the i-th row. These represent the specific values ​​of population, GDP, and arable land area corresponding to the i-th cell in the standardized matrix.

[0101] In some embodiments, the flow constraints are that the inflow process is not greater than the safe discharge of the upstream channel, and the outflow process is not greater than the safe discharge of the downstream channel; the safe discharge of the upstream channel and the safe discharge of the downstream channel are both determined based on hydrological data.

[0102] In some embodiments, the water level constraint is that the water level enabled for a zone is not greater than the design elevation of the dike at that location; the design elevation is determined based on hydraulic engineering data.

[0103] In some embodiments, the capacity constraint is that the flood storage capacity of the first enabled zone is not greater than its maximum effective flood storage capacity.

[0104] The deficiencies of the above solutions and the proposed solutions are the result of the inventor's practice and careful research. Therefore, the discovery process of the above problems and the solutions proposed in this disclosure below should be considered as the inventor's contribution to this disclosure.

[0105] In summary, the beneficial effect of this application lies in transforming empirical zoning into a spatiotemporal joint optimization problem based on mathematical optimization. By quantifying the spatial distribution of economic and social vulnerability, it optimizes the spatial zoning and activation timing of flood diversion. It also uses intelligent optimization algorithms to automatically generate a complete strategy that includes spatial boundaries and temporal triggering conditions, overcoming the drawbacks of spatiotemporal decision-making disconnect in traditional methods and achieving optimal overall benefits.

[0106] It should be understood that, for the foregoing method embodiments, although the steps in the flowcharts are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the method embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0107] Based on the same inventive concept, this application also provides an intelligent flood storage and detention area zoning device for implementing the above-mentioned intelligent zoning method for flood storage and detention areas. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations of one or more embodiments of the intelligent flood storage and detention area zoning device provided below can be found in the limitations of the intelligent flood storage and detention area zoning method described above, and will not be repeated here.

[0108] In one embodiment, such as Figure 3 As shown, a smart zoning device for flood storage and detention areas is provided, comprising: a data acquisition module 301, an interpolation and fusion module 302, a vulnerability calculation module 303, a point setting module 304, a simulation optimization module 305, and a zoning decision module 306, wherein:

[0109] The data acquisition module 301 is used to acquire basic data of the flood storage and detention area; the basic data includes the boundary range of the flood storage and detention area, land and space data, economic and social statistics data, DEM digital elevation data, water conservancy project data and hydrological data;

[0110] The interpolation and fusion module 302 is used to create a vector grid set covering the boundary of the flood storage and detention area based on the GIS platform. It performs spatial overlay analysis on land spatial data, economic and social statistical data and DEM digital elevation data, and merges them into each cell of the vector grid set to obtain the population, GDP value and cultivated land area of ​​each cell.

[0111] Vulnerability calculation module 303 is used to calculate the socio-economic vulnerability value of each cell based on the entropy weight method and the population, GDP value and cultivated land area of ​​each cell;

[0112] The point setting module 304 is used to set multiple control points at a first preset interval on the boundary of the flood storage and detention area, and the line connecting any two control points constitutes a candidate partition dike, and set the flood diversion point on each candidate partition dike at a second preset interval.

[0113] The simulation optimization module 305 is used to generate an initial population based on two control points of each candidate dike, the flood diversion points on the candidate dike, and the flood diversion level and flow rate of the flood diversion points. It iterates through each particle in the initial population, calls the hydrodynamic model to perform a two-dimensional flood evolution simulation, and obtains the maximum inundation depth and inundation duration of each cell under the particle. The hydrodynamic model is constructed based on hydrological data and water conservancy engineering data. According to the area, socio-economic vulnerability value, maximum inundation depth and inundation duration of each cell, the comprehensive loss objective function value under the particle is calculated. With the goal of minimizing the comprehensive loss value, and with the conditions of satisfying the flow constraints of the safe discharge of upstream and downstream channels determined by hydrological data, the water level constraints of the zone activation water level being lower than the dike design elevation determined by water conservancy engineering data, and the capacity constraints of the zone flood storage being less than its effective capacity, the particle swarm algorithm is driven to iteratively update until convergence to obtain the globally optimal particle.

[0114] The zoning decision module 306 is used to connect two control points in the global optimal particle to determine the optimal zoning boundary; to determine the flood diversion point in the global optimal particle as the location of the flood diversion gate on the boundary of the zoning boundary; and to determine the activation water level and design flow of the flood diversion gate based on the flood diversion level and flood diversion flow in the global optimal particle.

[0115] In some embodiments, the comprehensive loss objective function The calculation formula is:

[0116] ;

[0117] in, Indicates the first The area of ​​each cell; Indicates the first The economic and social vulnerability value of each cell; Indicates the first The maximum flood depth of each cell; Indicates based on the first The flooding duration of each cell Calculated correction factor The rules for determining the value are as follows:

[0118] when Timing, =1;

[0119] when Timing, =1.3;

[0120] when hour, ;in, The first preset duration, greater than The second preset duration.

[0121] In some embodiments, each cell in the vector grid set is a 100m × 100m grid.

[0122] In some embodiments, both the first preset interval and the second preset interval are 10m.

[0123] In some embodiments, the hydrodynamic model is a two-dimensional hydrodynamic model constructed based on MIKE.

[0124] In some embodiments, the vulnerability calculation module 303 described above can be used to: construct an m×3 indicator matrix; m is the number of cells, and the three columns correspond to the population, GDP, and cultivated land area, respectively; perform extreme value normalization on each column of the indicator matrix to obtain a standardized matrix; calculate the indicator weight of each indicator in each cell; the indicator weight is the ratio of the standardized value of the indicator in each cell to the sum of the standardized values ​​of the indicator in all cells; calculate the information entropy of each indicator based on the indicator weight; calculate the difference coefficient of each indicator based on the information entropy; calculate the entropy weight of each indicator based on the difference coefficient; and calculate the economic and social vulnerability value of each cell according to the standardized matrix and the entropy weight.

[0125] In some embodiments, the flow constraints are that the inflow process is not greater than the safe discharge of the upstream channel, and the outflow process is not greater than the safe discharge of the downstream channel; the safe discharge of the upstream channel and the safe discharge of the downstream channel are both determined based on hydrological data.

[0126] In some embodiments, the water level constraint is that the water level enabled for a zone is not greater than the design elevation of the dike at that location; the design elevation is determined based on hydraulic engineering data.

[0127] In some embodiments, the capacity constraint is that the flood storage capacity of the first enabled zone is not greater than its maximum effective flood storage capacity.

[0128] Specific limitations regarding the intelligent zoning device for flood storage and detention areas can be found in the limitations of the intelligent zoning method for flood storage and detention areas described above, and will not be repeated here. Each module in the aforementioned intelligent zoning device for flood storage and detention areas can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0129] Furthermore, in the above-described implementation of the intelligent flood storage and detention area zoning device, the logical division of each program module is merely illustrative. In practical applications, the above functions can be assigned to different program modules as needed, for example, for the sake of corresponding hardware configuration requirements or the convenience of software implementation. That is, the internal structure of the intelligent flood storage and detention area zoning device can be divided into different program modules to complete all or part of the functions described above.

[0130] In one embodiment, a computer device is provided, the internal structure of which can be as follows: Figure 4 As shown, the computer device includes a processor, memory, input / output interfaces, and a communication interface. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements a method for intelligent zoning of flood storage and detention areas.

[0131] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0132] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0133] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0134] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification. In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0135] The terms “comprising” and “having”, and any variations thereof, in the embodiments herein are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or (module) units is not limited to the steps or units listed, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus.

[0136] In this document, "multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. It should be noted that A and / or B should not be interpreted as A and B necessarily having some potential relationship, hence the use of "and / or" to connect them. Rather, "and / or" in this application simply means that A and B can appear alone to form an independent solution, or they can appear simultaneously to form a combined solution. The character " / " indicates that the preceding and following related objects have an "or" relationship.

[0137] The terms "first" and "second" used herein are merely to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first" and "second" can be interchanged in a specific order or sequence where permissible. It should be understood that the objects distinguished by "first" and "second" can be interchanged where appropriate so that the embodiments described herein can be implemented in an order other than those illustrated or described herein.

[0138] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for intelligently dividing flood storage and detention areas, characterized in that, The method includes: Acquire basic data for flood storage and detention areas; the basic data includes the boundary range of flood storage and detention areas, land spatial data, economic and social statistical data, DEM digital elevation data, water conservancy project data, and hydrological data; Based on the GIS platform, a vector grid set covering the boundary of the flood storage and detention area is created. The land space data, the economic and social statistics data and the DEM digital elevation data are spatially overlaid and analyzed, and then merged into each cell of the vector grid set to obtain the population, GDP value and cultivated land area of ​​each cell. Based on the entropy weight method and the population, GDP and arable land area of ​​each cell, calculate the economic and social vulnerability value of each cell; Multiple control points are set at a first preset interval on the boundary of the flood storage and detention area. The line connecting any two control points constitutes a candidate partition dike. Flood diversion points are set at a second preset interval on each candidate partition dike. Based on the two control points of each candidate dike, the flood diversion points on the candidate dike, and the flood diversion level and flow rate of the flood diversion points, an initial population is generated. Each particle in the initial population is traversed, and a two-dimensional flood evolution simulation is performed by calling the hydrodynamic model to obtain the maximum inundation depth and inundation duration of each cell under the particle. The hydrodynamic model is constructed based on the hydrological data and water conservancy engineering data. Based on the area, socio-economic vulnerability value, maximum inundation depth, and inundation duration of each cell, the comprehensive loss objective function value for that particle is calculated. With the goal of minimizing the comprehensive loss value, and in accordance with the flow constraints determined based on the hydrological data, the water level constraints determined based on the water conservancy engineering data, and the capacity constraints, the particle swarm algorithm is driven to iterate and update until convergence to obtain the globally optimal particle. Connect the two control points in the global optimal particle to determine the optimal zone boundary; determine the flood diversion gate location on the boundary of the zone boundary by identifying the flood diversion point in the global optimal particle; determine the activation water level and design flow of the flood diversion gate based on the flood diversion water level and flow rate in the global optimal particle.

2. The method according to claim 1, characterized in that, The comprehensive loss objective function The calculation formula is: ； in, Indicates the first The area of ​​each cell; Indicates the first The economic and social vulnerability value of each cell; Indicates the first The maximum flood depth of each cell; Indicates based on the first The flooding duration of each cell Calculated correction factor The rules for determining the value are as follows: when Timing, =1; when Timing, =1.3; when hour, ;in, The first preset duration, greater than The second preset duration.

3. The method according to claim 1 or 2, characterized in that, Each cell in the vector grid set is a 100m × 100m grid; And / or, both the first preset interval and the second preset interval are 10m; And / or, the hydrodynamic model is a two-dimensional hydrodynamic model constructed based on MIKE.

4. The method according to claim 1 or 2, characterized in that, The steps for calculating the socioeconomic vulnerability value of each cell based on the entropy weight method and the population, GDP, and arable land area of ​​each cell include: Construct an m×3 indicator matrix; m is the number of cells, and the three columns correspond to the population, GDP, and arable land area, respectively. The extreme value normalization process is performed on each column of the index matrix to obtain the standardized matrix; Calculate the weight of each indicator in each cell; the weight of each indicator is the ratio of the standardized value of that indicator in each cell to the sum of the standardized values ​​of that indicator in all cells. Based on the weight of the aforementioned indicators, the information entropy of each indicator is calculated; Based on the information entropy, calculate the difference coefficient for each indicator; Based on the difference coefficient, calculate the entropy weight of each indicator; The socioeconomic vulnerability value of each cell is calculated based on the standardized matrix and the entropy weight.

5. The method according to claim 1 or 2, characterized in that, The flow constraints are that the inflow rate during the flood process is not greater than the safe discharge capacity of the upstream river channel, and the outflow rate during the flood discharge process is not greater than the safe discharge capacity of the downstream river channel; the safe discharge capacity of the upstream river channel and the safe discharge capacity of the downstream river channel are both determined based on the hydrological data.

6. The method according to claim 1 or 2, characterized in that, The water level constraint is that the activated water level of the zone shall not exceed the design elevation of the dike at that location; the design elevation is determined based on the water conservancy project data.

7. The method according to claim 1 or 2, characterized in that, The capacity constraint is that the flood storage capacity of the first activated zone is not greater than its maximum effective flood storage capacity.

8. A smart zoning device for flood storage and detention areas, characterized in that, The device includes: The data acquisition module is used to acquire basic data of the flood storage and detention area; the basic data includes the boundary range of the flood storage and detention area, land spatial data, economic and social statistical data, DEM digital elevation data, water conservancy project data and hydrological data; The interpolation and fusion module is used to create a vector grid set covering the boundary range of the flood storage and detention area based on the GIS platform, perform spatial overlay analysis on the land space data, the economic and social statistics data and the DEM digital elevation data, and fuse them into each cell of the vector grid set to obtain the population, GDP value and cultivated land area of ​​each cell; The vulnerability calculation module is used to calculate the socio-economic vulnerability value of each cell based on the entropy weight method and the population, GDP value and cultivated land area of ​​each cell. The point setting module is used to set multiple control points at a first preset interval on the boundary of the flood storage and detention area, and the line connecting any two control points constitutes a candidate partition dike, and set the flood diversion point on each candidate partition dike at a second preset interval. The simulation optimization module is used to generate an initial population based on the two control points of each candidate dike, the flood diversion points on the candidate dike, and the flood diversion level and flow rate of the flood diversion points. It iterates through each particle in the initial population, calls the hydrodynamic model to perform a two-dimensional flood evolution simulation, and obtains the maximum inundation depth and inundation duration of each cell under that particle. The hydrodynamic model is constructed based on the hydrological data and water conservancy engineering data. Based on the area, socio-economic vulnerability value, maximum inundation depth, and inundation duration of each cell, the comprehensive loss objective function value under that particle is calculated. With the goal of minimizing the comprehensive loss value, and satisfying the flow constraints of safe discharge capacity of upstream and downstream channels determined based on the hydrological data, the water level constraint of the zone's activation water level being lower than the dike's design elevation determined based on the water conservancy engineering data, and the capacity constraint of the zone's flood storage capacity being less than its effective capacity, the particle swarm optimization algorithm is driven to iteratively update until convergence to obtain the globally optimal particle. The zoning decision module is used to connect two control points in the global optimal particle to determine the optimal zoning boundary; to determine the flood diversion point in the global optimal particle as the location of the flood diversion gate on the boundary of the zoning boundary; and to determine the activation water level and design flow of the flood diversion gate based on the flood diversion water level and flood diversion flow in the global optimal particle.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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