A method and device for standard flood control configuration in a flood protection zone.

Abstract

This application relates to the field of flood control planning technology, and proposes a method and device for configuring flood control standards in flood protection zones. The method includes acquiring multiple regional units; calculating the comprehensive protection benefits of each regional unit within a preset planning period; constructing a flood control cost function for each regional unit; using the flood control standard level of the flood control cost function as a decision variable, with the optimization objective of maximizing the ratio of comprehensive protection benefits to flood control investment costs, and with spatial continuity constraints and water system correlation constraints as constraints, optimizing the flood control standard level of each regional unit to output a flood control standard configuration scheme for the target flood protection zone. This method transforms the traditional flood control standard determination model of first dividing, then matching, and statically calibrating into an intelligent configuration paradigm driven by planning period benefit prediction, joint cost-benefit optimization, and dual spatial and hydrological constraints. It has strong engineering feasibility and overcomes the technical defects of traditional schemes, such as static configuration, lack of optimization, and neglect of constraints.

Description

Technical Field

[0001] This application relates to the field of flood control planning technology, and in particular to a method and device for standard flood control configuration of a flood protection zone. Background Technology

[0002] A flood protection zone is an area designated according to the basin flood control plan or actual topographic and water system conditions. It is protected by engineering measures such as dikes, flood storage and detention areas, and flood diversion channels, with the main purpose of preventing floods. The flood control standard refers to the size of flood that a flood control project or flood protection zone can withstand. It is usually expressed in terms of flood recurrence interval (such as once in 20 years, once in 50 years, once in 100 years, etc.) or can be characterized by the corresponding peak flow or water level. The level of the flood control standard directly determines the investment cost of the construction of the flood protection zone and the safety level of the protected objects in the area. It is a core decision variable in flood control planning.

[0003] The current determination of flood control standards for flood protection zones is typically based on ex-post empirical matching, conducted after the delineation of flood protection zones, using static indicators such as the current population, economy, and arable land in the area. This ex-post matching mechanism has the following technical shortcomings: First, the determination of flood control standards is based solely on current static indicators, without incorporating dynamic forecast data during the planning period of flood protection zones. This results in the determination of flood control standards failing to reflect the dynamic changes in protection needs due to population growth and economic development during the planning period, leading to a discrepancy between the configuration of flood control standards and future actual protection needs.

[0004] Secondly, the lack of a quantitative correlation between the determination of flood control standards and the cost of flood control investment makes it impossible to compare and select the best among multiple candidate flood control standard levels. This may result in the actual flood control standards being configured causing engineering investment to exceed the actual protection benefits obtained, or the protection benefits not being fully guaranteed.

[0005] Furthermore, the existing ex-post matching mechanism for flood control standards does not take into account the spatial continuity and water system linkage constraints between flood protection zones. The independent determination of flood control standards for each protection zone may lead to abrupt changes in flood control standards between adjacent protection zones, or upstream standards being lower than downstream standards, resulting in projects being unable to be implemented in terms of spatial structure and hydrological logic, or serious safety conflicts.

[0006] Therefore, how to overcome the limitations of the above-mentioned ex-post static matching, establish a flood control standard configuration scheme that can jointly optimize flood control standards with planning period benefits and flood control investment costs, and achieve the optimal balance of input and output under the premise of satisfying spatial continuity and water system correlation constraints, is a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0007] Therefore, it is necessary to provide a flood control standard configuration method and a flood control standard configuration device for flood control protection areas to address the above-mentioned technical problems.

[0008] Firstly, this application provides a method for the standard configuration of flood control in a flood protection zone, the method comprising: Obtain multiple regional units to be configured within the target flood protection zone; Calculate the comprehensive protection benefits of each regional unit within the preset planning period; Construct flood control cost functions for each regional unit; these functions characterize the mapping relationship between flood control standard levels and flood control input costs. The flood control standard level of the flood control cost function is used as the decision variable. The optimization objective is to maximize the ratio of comprehensive protection benefits to flood control investment costs. Spatial continuity constraints and water system correlation constraints are used as constraints. The flood control standard level of each regional unit is optimized and calculated to output the flood control standard configuration scheme of the target flood protection area. The spatial continuity constraint is used to limit the difference in flood control standard levels between spatially adjacent regional units to no more than a preset threshold, and the water system association constraint is used to limit the flood control standard level of regional units in the upstream river section to no less than the flood control standard level of regional units in the downstream river section.

[0009] In one embodiment, acquiring the multiple regional units to be configured within the target flood protection zone includes: The target flood protection area is divided into a set of grids; each grid in the set of grids is configured with a multi-dimensional attribute vector, which includes the type of protected object, population size, economic output and cultivated land area; Based on the population, economic output, and arable land area in each grid, the global objective weight coefficients of each value indicator corresponding to the grid set are determined; each value indicator includes population indicators, economic indicators, and arable land indicators. Based on the multidimensional attribute vector and the global objective weight coefficient, the comprehensive flood control similarity between each adjacent grid is calculated. A spatial relationship network is constructed using grid nodes and the comprehensive flood control similarity as edge weights; The spatial relationship network is divided based on the community detection algorithm to obtain multiple regional units to be configured.

[0010] In one embodiment, determining the global objective weight coefficients of each value indicator corresponding to the grid set based on the population, economic output, and arable land area in each grid includes: Based on the minimum and maximum values ​​of each value indicator in the grid set, the population, economic output, and cultivated land area in each grid are standardized to obtain the standardized values ​​of the corresponding value indicators. Based on the standardized values, calculate the weight of each value indicator; Calculate the information entropy of each value indicator based on its weight; Based on the information entropy, the global objective weight coefficients of each value indicator corresponding to the grid set are determined.

[0011] In one embodiment, calculating the comprehensive flood control similarity between adjacent grids based on the multidimensional attribute vector and the global objective weight coefficient includes: Calculate the sub-similarities of each adjacent grid in terms of protected object type dimension, population index, economic index, and arable land index; By combining the global objective weight coefficient and the spatial attenuation factor, the sub-item similarities are weighted and summed to obtain the comprehensive flood control similarity between adjacent grids; Among them, the comprehensive flood control similarity Calculated using the following formula: ; in, Preset similarity weight coefficients to protect the object type dimension; For grid and grid Sub-item similarity in the dimension of protected object type When the grid and grid When the types of protected objects are the same Select 1 if the value is 1, otherwise select 0. These are the global objective weighting coefficients for population indicators, economic indicators, and arable land indicators, respectively. and grid Sub-item similarity in population indicators, economic indicators, and arable land indicators; For grid and grid The Euclidean distance between them is used as the spatial attenuation factor.

[0012] In one embodiment, a computational grid and grid The sub-items of similarity in population indicators, economic indicators, and arable land indicators include: According to the formula:

[0013]

[0014] in, , They are grids and grid Population size; , They are grids and grid The total economic output; , They are grids and grid The area of ​​cultivated land; and These are the logarithmic transformation values ​​of the population in the grid set, respectively. The maximum and minimum values; and These are the logarithmic transformation values ​​of the total economic output in the grid set, respectively. The maximum and minimum values; and These are the logarithmic transformation values ​​of the cultivated land area in the grid set. The maximum and minimum values; This is a preset positive smoothing constant.

[0015] In one embodiment, the partitioning of the spatial relationship network based on the community detection algorithm includes: To maximize modularity To achieve the objective, the spatial relationship network is iteratively partitioned; wherein the modularity... Calculated using the following formula: ; in, For the grid in the spatial relationship network and grid Edge weights between them; , indicating grid The sum of edge weights of all adjacent grids in the spatial relationship network; , indicating grid The sum of edge weights of all adjacent grids in the spatial relationship network; , representing half of the sum of all edge weights in the spatial relational network; For the Kronecker function, and Representing grids respectively and grid The assigned community identifier, when hour The value is 1 if it is not 1, otherwise the value is 0.

[0016] In one embodiment, calculating the comprehensive protection benefits of each regional unit within a preset planning period includes: Obtain the predicted values ​​of each value indicator of the regional unit at each unit time within a preset planning period; the value indicators include population indicators, economic indicators, and arable land indicators. Based on the type of protected object of the aforementioned regional unit, determine the policy impact coefficient corresponding to each value indicator; Based on the policy impact coefficient, the predicted values ​​of each value indicator of the regional unit at each unit time are weighted and summed to obtain the comprehensive protection benefits of the regional unit within the preset planning period.

[0017] In one embodiment, the comprehensive protection benefits of the regional unit during the preset planning period Calculated using the following formula: ; in, The planning period is the number of years corresponding to the preset planning period; , , The respective region units Policy impact coefficients on population, economic, and arable land indicators; , , The respective region units In the Forecast values ​​for population, economic, and arable land indicators for the year.

[0018] In one embodiment, the flood control cost function is an increasing function with the flood control standard level as the independent variable; And / or, the flood control cost function is a step function; wherein, different flood control standard levels correspond to different unit length dike construction costs and / or total regional protection costs.

[0019] Secondly, this application also provides a standard flood control configuration device for a flood protection zone, comprising: The regional unit acquisition module is used to acquire multiple regional units to be configured within the target flood protection zone; The protection benefit calculation module is used to calculate the comprehensive protection benefits of each regional unit within the preset planning period; The cost function construction module is used to construct the flood control cost function for each regional unit; the flood control cost function can characterize the mapping relationship between the flood control standard level and the flood control input cost; The optimization calculation module is used to take the flood control standard level of the flood control cost function as the decision variable, take maximizing the ratio of the comprehensive protection benefits to the flood control investment cost as the optimization objective, and take spatial continuity constraints and water system correlation constraints as constraints to optimize the flood control standard level of each regional unit, so as to output the flood control standard configuration scheme of the target flood protection area. The spatial continuity constraint is used to limit the difference in flood control standard levels between spatially adjacent regional units to no more than a preset threshold, and the water system association constraint is used to limit the flood control standard level of regional units in the upstream river section to no less than the flood control standard level of regional units in the downstream river section.

[0020] One of the above technical solutions has the following advantages or beneficial effects: by predicting the comprehensive protection benefits during the planning period, quantitatively mapping the flood control cost function, and jointly optimizing with dual constraints, the traditional determination mode of flood control standards—first dividing, then matching, and static calibration—is transformed into an intelligent configuration paradigm of dynamic prediction, cost-benefit optimization, and spatial hydrological dual constraint guarantee. This effectively overcomes the technical defects of traditional solutions, such as static lag, lack of optimization benchmarks, and neglect of engineering constraints.

[0021] First, in response to the problem that flood control standards are determined solely based on current static indicators and cannot reflect the dynamic needs of the planning period, this application calculates the comprehensive protection benefits of each regional unit within the preset planning period, incorporating dynamic changes such as population growth and economic development into the decision-making basis for flood control standard configuration, thereby enabling flood control standards to be configured in a forward-looking manner for the planning period.

[0022] Secondly, addressing the lack of quantitative correlation between flood control standards and flood control investment costs, which makes cost-benefit comparison and selection impossible, this application constructs a flood control cost function that characterizes the mapping relationship between flood control standard levels and flood control investment costs. It takes maximizing the ratio of comprehensive protection benefits to flood control investment costs as the optimization objective and uses flood control standard levels as decision variables for optimization calculation. This allows each candidate flood control standard level to be compared for cost-benefit under a unified quantitative framework, thereby achieving the optimal balance between input and output.

[0023] Furthermore, to address the issue that failure to consider spatial continuity and water system correlation constraints may lead to the inability to implement the project or the existence of safety conflicts, this application introduces spatial continuity constraints and water system correlation constraints in the optimization calculation. The former restricts the difference in flood control standard levels between adjacent regional units to not exceed a preset threshold, while the latter ensures that the flood control standard level of upstream regional units is not lower than that of downstream units, thereby ensuring that the optimized flood control standard configuration scheme is feasible in terms of spatial structure and hydrological logic. Attached Figure Description

[0024] Figure 1This is a flowchart illustrating a standard flood control configuration method for a flood protection zone in one embodiment. Figure 2 This is a structural block diagram of a standard flood control configuration device for a flood protection zone in one embodiment; Figure 3 This is a structural block diagram of a computer device in one embodiment. Detailed Implementation

[0025] 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.

[0026] 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.

[0027] In one embodiment, a method for standard flood control configuration of a flood protection zone is provided. This method overcomes the limitations of static ex-post matching in existing technologies by introducing planning-period protection benefit prediction, constructing a flood control cost function, and performing joint optimization with the goal of maximizing the benefit-cost ratio and constraints of spatial continuity and water system correlation. Figure 1 As shown, the method specifically includes the following steps: S102, Obtain multiple regional units to be configured within the target flood protection zone.

[0028] Among them, the target flood protection zone can be understood as an area delineated based on the basin flood control plan or actual topographic and water system conditions, which relies on engineering measures such as dikes, flood storage and detention areas, and flood diversion channels to protect the area with the main purpose of preventing floods; the scope of this area can be obtained from the flood control plan or construction management plan, and is usually stored in the computer in vector polygon format.

[0029] A regional unit can be understood as a basic spatial unit constituting the target flood protection zone. Each regional unit has relatively independent protection value and flood control requirements, and is a configuration object whose flood control standard level needs to be determined separately. Multiple regional units can be pre-delineated sub-protection zones, or they can be a set of regional units automatically divided by the grid division and community detection algorithm described in subsequent embodiments. Furthermore, the multiple regional units to be configured, that is, the set of objects for which this method needs to optimize the flood control standard, can be all the basic spatial units within the target flood protection zone, or it can be a selected part; there are no restrictions here.

[0030] S104, calculate the comprehensive protection benefits of each regional unit within the preset planning period.

[0031] The pre-planning period can be understood as the future time range covered by the flood control plan, such as 10 years, 20 years or 50 years, usually represented by the planning period n.

[0032] Comprehensive protection benefits refer to the total protection value that a regional unit can obtain within the planning period. This can be understood as a core quantitative indicator for measuring whether protection is worthwhile and how much cost is worth investing in it. In some embodiments, comprehensive protection benefits take into account the projected growth of multiple value indicators such as population, economy, and arable land during the planning period, and are adjusted by combining policy impact coefficients for different types of protected objects (such as urban or rural areas), thereby dynamically reflecting the accumulated protection needs of the regional unit at the end of the planning period or during the planning period. Unlike traditional methods that only use current static indicators, this step introduces planning period parameters, enabling the configuration of flood control standards to be forward-looking and possessing a certain degree of foresight.

[0033] S106, Construct the flood control cost function for each regional unit; the flood control cost function can characterize the mapping relationship between the flood control standard level and the flood control input cost.

[0034] Among them, the flood control standard level can be understood as a graded representation of the size of floods that flood control projects or flood protection areas can withstand. It is usually identified by the flood recurrence period (such as once in 20 years, once in 50 years, once in 100 years, etc.) or by the corresponding peak flow or water level.

[0035] Flood control investment costs can be understood as the total cost of engineering construction and maintenance required to bring a regional unit up to a certain flood control standard level. Specifically, it can include the cost of dike construction, flood diversion channel construction, and flood storage and detention area construction.

[0036] Understandably, the flood control cost function establishes a quantitative mapping relationship between standard levels and input costs, enabling comparisons of different flood control standard levels on a unified cost scale. Preferably, the flood control cost function can be an increasing function, meaning that the higher the flood control standard level, the greater the required input cost. Furthermore, the flood control cost function can be a step function, with different flood control standards corresponding to different unit length dike construction costs and / or total regional protection costs. The unit length dike construction cost can be understood as the construction cost required to achieve a certain flood control standard for a unit length of dike (e.g., per kilometer). This cost increases stepwise with the standard level; for example, a 100-year flood standard corresponds to a dike crest elevation and dike cross-sectional dimensions that are greater than a 50-year flood standard, thus resulting in a higher unit length construction cost. The total regional protection cost can be understood as the total engineering costs required to achieve a certain flood control standard for the entire regional unit, typically estimated by multiplying the unit length dike construction cost by the total dike length required for that regional unit. Both functions characterize flood control costs from the perspectives of linear engineering unit price and overall regional investment, respectively. In practical applications, they can be used individually or in combination depending on data availability and engineering assessment accuracy.

[0037] This demonstrates that by constructing a flood control cost function, this step overcomes the current disconnect between flood control standards and input costs, providing a quantitative basis for subsequent joint cost-benefit optimization.

[0038] S108 uses the flood control standard level of the flood control cost function as the decision variable, takes maximizing the ratio of comprehensive protection benefits to flood control investment costs as the optimization objective, and uses spatial continuity constraints and water system correlation constraints as constraints to optimize the flood control standard level of each regional unit, so as to output the flood control standard configuration scheme of the target flood protection area.

[0039] This step is the core optimization step of this method. Specifically, using the flood control standard level as a decision variable means that the flood control standard itself is no longer a fixed value that is passively matched after the fact, but a variable that can be actively adjusted during the optimization process. The optimization objective is to maximize the ratio of comprehensive protection benefits to flood control investment costs, that is, to maximize the protection benefits obtained per unit of flood control investment, achieving the optimal balance between cost and benefit. More specifically, when the protection benefits of a certain regional unit are high, the optimization calculation tends to assign it a higher flood control standard level; conversely, when the protection benefits are low, it tends to assign a lower flood control standard level to avoid over-investment.

[0040] The optimization calculation can be implemented using traversal search, gradient descent, or heuristic optimization algorithms; no limitation is set here. It should be noted that those skilled in the art, based on the optimization objective function and constraints given in this application and combined with the principles of conventional optimization algorithms, can implement this optimization calculation process without any inventive effort.

[0041] In addition, spatial continuity constraints are used to limit the difference in flood control standard levels between spatially adjacent regional units to no more than a preset threshold. This constraint is introduced to ensure a continuous spatial transition of flood control standards, avoiding abrupt changes in flood control standard levels between adjacent regional units (e.g., one region having a 100-year return period while an adjacent region only has a 20-year return period), which would be difficult to implement in actual engineering or create a flood control weakness. River system correlation constraints are used to limit the flood control standard level of regional units in upstream river sections to no lower than that of regional units in downstream river sections. This constraint is introduced to adhere to the hydrological safety principle that upstream standards should not be lower than downstream standards, preventing unreasonable configurations of low upstream standards and high downstream standards, and avoiding situations where upstream levee breaches render downstream high-standard protection projects ineffective. By incorporating these two constraints into the optimization calculation, the engineering rationality of the output flood control standard configuration scheme in terms of spatial structure and hydrological relationships is ensured.

[0042] In the above embodiments of this application, the executing entity is generally a computer device with a certain computing capability. In the flood control standard configuration method for flood protection areas described in the above embodiments, dynamic prediction data is introduced into the configuration decision by calculating the comprehensive protection benefits during the planning period; a quantitative mapping between standards and costs is established by constructing a flood control cost function; and flood control standards are transformed from passively matching after the fact into active optimization decision variables by jointly optimizing with the goal of maximizing the benefit-cost ratio and constrained by spatial continuity and water system correlation, ultimately outputting a flood control standard configuration scheme that is engineering-feasible and has an optimal balance between input and output. This method eliminates the subjectivity and static defects of traditional methods, realizing the scientific and precise configuration of flood control standards.

[0043] In some embodiments, S102 may specifically include: dividing the target flood protection area into a set of grids; each grid in the set of grids is configured with a multi-dimensional attribute vector, the multi-dimensional attribute vector including the type of protected object, population, economic output, and cultivated land area; determining the global objective weight coefficient of each value indicator corresponding to the set of grids based on the population, economic output, and cultivated land area in each grid; each value indicator includes population indicators, economic indicators, and cultivated land indicators; calculating the comprehensive flood control similarity between adjacent grids based on the multi-dimensional attribute vector and the global objective weight coefficient; constructing a spatial relationship network with grids as nodes and comprehensive flood control similarity as edge weights; and dividing the spatial relationship network based on a community detection algorithm to obtain multiple regional units to be configured.

[0044] Here, the grid set can be understood as arranging the target flood protection area according to a preset resolution (e.g., ...). The set of regular grid cells obtained by systematically dividing the entire area. ,in The total number of grid cells in the grid set. Index number of grid cells ， , Indicates that the index is Grid cells. Multidimensional attribute vectors. middle, To protect object type encoding, used to distinguish different types such as urban and rural areas; The number of permanent residents reflects the social dimension of protection needs; GDP represents the total economic output, reflecting the value of protection from an economic perspective. This represents arable land area, reflecting the protection needs of food security. These attribute data can be obtained through spatial overlay analysis, precisely linking population, economic, and arable land data with grid cells before assigning values.

[0045] Global objective weighting coefficients can be understood as a set of unified weighting coefficients applicable to the entire grid set, calculated using the entropy weighting method based on the attribute data of all grids in the grid set. This includes global objective weighting coefficients for population indicators. Global objective weighting coefficients of economic indicators Global objective weighting coefficient of cultivated land index Unlike traditional subjective methods that rely on expert scoring to determine weights, the entropy weight method follows the difference-driven principle: the greater the variation in index values, the lower its information entropy, indicating that the index has a stronger ability to distinguish the importance of different grids and should be given greater weight. The weight coefficients determined in this way are entirely determined by the distribution characteristics of the data itself, making them objective, quantifiable, and reproducible, thus helping to eliminate the influence of subjective factors on the partitioning results.

[0046] Comprehensive flood control similarity can be understood as a quantitative index that comprehensively measures the overall similarity of two adjacent grids in terms of flood control protection characteristics. In some embodiments, comprehensive flood control similarity integrates four dimensions: similarity of protected object type, similarity of population size, similarity of economic scale, and similarity of arable land resources. It is then weighted and combined using global objective weight coefficients determined by the entropy weight method, and further corrected by introducing a spatial attenuation factor (Euclidean distance between grids). This results in grids that are geographically close and have similar protection characteristics having higher similarity values, providing objective and reliable edge weight inputs for subsequent community detection algorithms.

[0047] The spatial relationship network can be understood as an undirected weighted network with grid nodes, spatial adjacency relationships between grids as edges, and comprehensive flood control similarity as edge weights. The edge connection rules can be determined using Delaunay triangulation or the k-nearest neighbor algorithm, and can be adjusted according to actual hydrological connections and geographical features (such as linear land cover separations like rivers and roads) to ensure the spatial continuity and hydrological rationality of the network topology.

[0048] Community detection algorithms can be understood as algorithms used to divide a network into multiple communities (i.e., regional units) with tight internal connections and relatively sparse external connections. Preferably, the Louvain algorithm based on modularity optimization can be used to achieve automatic network partitioning by maximizing modularity. Compared with the traditional method of manually delineating protected area boundaries based on expert experience, the partitioning process based on community detection algorithms is entirely data-driven, the partitioning results are objective and repeatable, and can automatically capture the inherent correlation structure between grids based on multidimensional similarity.

[0049] This embodiment achieves the scientific and automated delineation of flood protection zones through the organic combination of four steps: grid-based modeling, objective weighting using entropy weighting, comprehensive similarity calculation, and community discovery algorithm. This provides an objective and refined spatial unit basis for subsequent optimization of flood control standards.

[0050] In some embodiments, the step of determining the global objective weight coefficients of each value indicator corresponding to the grid set based on the population, economic output, and cultivated land area in each grid may specifically include: standardizing the population, economic output, and cultivated land area in each grid based on the minimum and maximum values ​​of each value indicator in the grid set to obtain standardized values ​​of the corresponding value indicators; calculating the indicator weight of each value indicator based on the standardized values; calculating the information entropy of each value indicator based on the indicator weight; and determining the global objective weight coefficients of each value indicator corresponding to the grid set based on the information entropy.

[0051] One specific implementation method for determining the global objective weight coefficients is to first use the range standardization method to analyze the population. P ,economy E ,arable land F The three continuous value indicators were standardized, and the raw data of each value indicator were transformed to... Interval: ; in, , indicating the type of value indicator currently being processed; It is the first The grid in the first The raw values ​​of the value indicators; for The standardized value obtained after range standardization; and These are the minimum and maximum values ​​of the value indicator across all grids, respectively. ε This can be understood as an extremely small smoothing constant, such as 10.-4 It can be used to prevent the denominator from being zero.

[0052] Then, calculate the weight distribution of each value indicator: ; This represents the total number of grids in the grid set.

[0053] Next, the information entropy of each value indicator is calculated: Among them, when When =0, it is stipulated that =0.

[0054] Finally, the global objective weight coefficients for each value indicator are determined based on information entropy: ; in, .

[0055] Through the above calculation process, the greater the degree of variation in the indicator value, the higher its information entropy. The smaller the coefficient of variation (1- The larger the value, the larger the final objective weight coefficient. After calculation, the rationality of the weight coefficient can be verified through sensitivity analysis; the global objective weight coefficient determined in this way is based on the characteristics of the data itself, making the subsequent similarity calculation and region division results objective and reproducible.

[0056] In some embodiments, adjacent grids are calculated. and grid The specific similarity of the sub-items of population indicators, economic indicators, and arable land indicators can be calculated using the following formula:

[0057]

[0058] in, and grid Sub-item similarity in population indicators, economic indicators, and arable land indicators; , They are grids and grid Population size; , They are grids and grid The total economic output; , They are grids and grid The area of ​​cultivated land; and These are the logarithmic transformation values ​​of the population in the grid set. The maximum and minimum values; and These are the logarithmic transformation values ​​of the total economic output in the grid set. The maximum and minimum values; and These are the logarithmic transformation values ​​of the cultivated land area in the grid set. The maximum and minimum values; This is a preset positive smoothing constant.

[0059] The above formula for calculating the similarity of individual items uses logarithmic transformation and a relative difference measure. The role of the logarithmic transformation ln(1+·) is to compress the magnitude differences in data, making indicators such as population size and economic output, which may have orders of magnitude differences, comparable in similarity calculations, and avoiding excessive compression of similarity between large numerical grids. The denominator... (i.e., the range of the logarithmic transformation values) achieves normalization, making the similarity values ​​fall within the [0,1] interval; This ensures the stability of the numerical calculations. Ultimately, when the index values ​​of two grids are exactly the same, the sub-similarity reaches its maximum value of 1; the greater the difference, the closer the similarity is to 0. This calculation method objectively reflects the degree of similarity between grids in terms of conservation value.

[0060] In some embodiments, after calculating the sub-item similarity of each adjacent grid in terms of protected object type dimension, population index, economic index and cultivated land index, the method further includes: combining the global objective weight coefficient and the spatial attenuation factor to perform a weighted summation of the sub-item similarity to obtain the comprehensive flood control similarity between adjacent grids. Among them, the overall flood control similarity Calculated using the following formula: ; in, The preset similarity weight coefficient for the object type dimension can be preset by the strategic priority of the protected object, such as urban > rural, important infrastructure > general arable land, etc. The specific coefficient value is not uniquely limited here. For grid and grid Sub-item similarity in the dimension of protected object type When the grid and grid When the types of protected objects are the same Select 1 if the value is 1, otherwise select 0. These are the global objective weighting coefficients for population indicators, economic indicators, and arable land indicators, respectively; preferably, , The relative weights calculated by the entropy weight method are calculated according to (1- The normalization process is obtained by scaling up the scale. Of course, this normalization process is only a preferred implementation method. Those skilled in the art can make adaptive adjustments to the weighting coefficients according to actual engineering needs, which does not constitute a limitation on the scope of protection of the claims. For grid and grid The Euclidean distance between them serves as the spatial attenuation factor mentioned above.

[0061] In some embodiments, the spatial relationship network is partitioned based on a community detection algorithm, which may specifically include: To maximize modularity To achieve the goal, the spatial relationship network is iteratively divided; where the modularity... Calculated using the following formula: ; in, For the grid in the spatial relationship network and grid Edge weights between them; , indicating grid The sum of edge weights of all adjacent grids in the spatial relationship network; , indicating grid The sum of edge weights of all adjacent grids in the spatial relationship network; , which represents half of the sum of the weights of all edges in a spatial relational network; For the Kronecker function, and Representing grids respectively and grid The assigned community identifier, when hour The value is 1 if it is not 1, otherwise the value is 0.

[0062] In some embodiments, the specific execution process of the community discovery algorithm may include the following sub-steps: During the initialization phase, each grid node is treated as an independent community, meaning the initial number of communities equals the number of grids. In the local optimization phase, all grid nodes are traversed. For each node, the modularity gain ΔQ resulting from moving it to an adjacent community is calculated, and the move operation with the largest gain is selected for execution. If the maximum gain is... Then move the node to the corresponding community; if If so, the node remains in the original community; In the community aggregation phase, the optimized community is regarded as a new network node, and the edge weights are updated (the edge weights between new nodes are the sum of the edge weights between all the original communities), forming a new network; The algorithm converges when it iterates through local optimization and community aggregation steps until the modularity Q no longer increases significantly (i.e., the increase in modularity after multiple iterations is less than a preset threshold).

[0063] Ultimately, the set of grids within each community constitutes a regional unit. .

[0064] The essence of modularity optimization is to maximize the sum of edge weights within a community after partitioning (i.e., high internal similarity) and minimize the sum of edge weights between communities (i.e., large external differences), thereby ensuring that the protection characteristics within the partitioned regional units are highly consistent and that there are significant differences between units, which meets the actual needs of flood control protection zone partitioning.

[0065] In some embodiments, calculating the comprehensive protection benefits of each regional unit within a preset planning period may specifically include: Obtain the predicted values ​​of various value indicators for each unit of time within the preset planning period for the regional unit; each value indicator includes population indicators, economic indicators, and arable land indicators. Based on the type of protected object in the regional unit, determine the policy impact coefficient corresponding to each value indicator; Based on the policy impact coefficient, the predicted values ​​of various value indicators of the regional unit at each unit time are weighted and summed to obtain the comprehensive protection benefits of the regional unit within the preset planning period.

[0066] The predicted values ​​of each value indicator can be understood as the predicted data of population, economy and arable land in each year of the planning period within the regional unit, based on historical growth trends and planning development goals.

[0067] Forecasting methods can include trend extrapolation, growth rate analysis, or scenario analysis.

[0068] The types of protected objects can be understood as categories such as urban or rural areas. Different types of protected objects have different importance and priority in flood control policies.

[0069] The policy impact coefficient λ can be understood as a weighting coefficient determined based on the type of protected object and the national or local flood control policy orientation. It is used to adjust the relative importance of different protected objects in the calculation of comprehensive protection benefits. For example, the policy impact coefficient for cities may be higher than that for rural areas to reflect the priority of urban protection.

[0070] By weighting and summing the predicted values ​​for each year within the planning period according to the policy impact coefficient, the overall protection benefits are considered. The quantitative analysis reflects the total accumulated protection value of the area unit during the planning period, providing a dynamic and forward-looking basis for subsequent benefit-cost ratio optimization.

[0071] In some embodiments, the comprehensive protection benefits of a regional unit within a predetermined planning period Specifically, it can be calculated using the following formula: in, The planning period is the number of years corresponding to the preset planning period; , , They are regional units Policy impact coefficients on population, economic, and arable land indicators; , , They are regional units In the Forecast values ​​for population, economic, and arable land indicators for the year.

[0072] The formula accumulates regional units year by year. Weighted protection value during the planning period.

[0073] Policy impact coefficient The different standards are set according to different types of protected objects, so that different regional units are treated differently in the benefit calculation due to the differences in the types of protected objects, which reflects the guiding role of policy in the configuration of flood control standards.

[0074] In some embodiments, the flood control cost function is an increasing function with the flood control standard level as the independent variable. That is, the higher the flood control standard level, the higher the required dike design standard and the greater the engineering investment cost, so the flood control cost function must show an increasing trend.

[0075] Furthermore, the flood control cost function can be a step function; where different flood control standard levels correspond to different unit length dike construction costs and / or total regional protection costs. The characteristic of a step function is that flood control standard levels are discrete (e.g., 20-year return period, 50-year return period, 100-year return period), and each increase in level leads to a significant cost jump. This step characteristic realistically reflects the actual situation in flood control engineering construction: different standard levels correspond to different engineering design parameters (e.g., dike crest elevation, dike cross-sectional dimensions), and these parameters do not change continuously but rather undergo a step-like change when switching standard levels. This embodiment constructs the flood control cost function as a step function, enabling optimization calculations to make decisions under a realistic discrete cost structure, avoiding distortions caused by the assumption of a continuous function.

[0076] In some embodiments, the spatial continuity constraint can be specifically expressed as: for any spatially adjacent regional units m1 and m2, the following condition is met. ,in , These are the flood control standard levels for units m1 and m2, respectively. A preset threshold for the difference in flood control standard levels is used; spatial adjacency can be determined through shared boundaries or distance thresholds (e.g., the distance between the center points of two units is less than a preset value). The preset threshold... It can be set to 2 to 3 standard grade differences (such as the standard grade difference between adjacent units not exceeding 2 to 3 grades) to avoid unreasonable standard jumps in engineering.

[0077] The water system association constraint can be specifically expressed as follows: if regional unit m1 is located in the upstream river segment of unit m2, denoted as m1→m2, then the following condition is met: ,in , The flood control standard levels for units m1 and m2 remain the same; the river system relationships can be determined based on the topology of the watershed, including upstream and downstream sections, the confluence of main streams and tributaries, and the correspondence between left and right banks. This constraint reflects the basic principle that upstream protection should not be lower than downstream hydrological safety, ensuring the coordination of the flood control system in terms of hydrological logic.

[0078] The technical solution of this application will be described in full and in detail below with reference to a specific embodiment.

[0079] In this most specific embodiment, the specific implementation methods of the above embodiments can be combined to form the following complete implementation process: Step 1, Regional Gridding and Multi-Dimensional Attribute Assignment: The target flood control protection area is divided into... The resolution is divided into a set of regular grid cells, and a hierarchical grid coding system is established to ensure that each grid cell has a globally unique identifier. Publicly available DEM, population, GDP, and cultivated land unit-kilometer grid data are collected, along with protected object type data (urban and rural categories) obtained through a combination of remote sensing interpretation and field verification. Spatial overlay analysis is used to overlay the protected object type data with the grid boundaries to determine the dominant protected type for each grid. Continuous attribute data such as population, economic (total GDP), and cultivated land are assigned to corresponding grids using area weighting or center point assignment methods. All numerical attributes are standardized to eliminate the influence of units.

[0080] Step 2: Determining objective weights based on the entropy weight method: The three continuous indicators of population, economy, and arable land are processed using the range standardization method, the indicator proportions are calculated, and the information entropy is calculated to finally determine the global objective weight coefficients.

[0081] Step 3, Grid Spatial Relationship Network Construction: Define the center point of each grid cell as a network node, and use Delaunay triangulation to determine the spatial adjacency relationships between grids and establish edge connections. For grids separated by linear features such as rivers and roads, adjust the connection relationships according to the actual connectivity to construct an undirected weighted network.

[0082] Step 4, Calculate the overall flood control similarity: Calculate the overall flood control similarity of adjacent grid pairs.

[0083] Step 5: Implementation of the intelligent community discovery algorithm: Using comprehensive flood control similarity as edge weights, the Louvain algorithm is used for modularity optimization in community discovery: In the initialization phase, each grid is an independent community; in the local optimization phase, all nodes are traversed, the modularity gain ΔQ is calculated, and the move with the largest gain is executed; in the community aggregation phase, the optimized communities are aggregated into new network nodes and the edge weights are updated; iterative execution continues until the modularity Q no longer increases significantly, forming a set of basic units for the flood protection zone.

[0084] Step 6, Calculation of protection benefits during the planning period: Set the planning period parameter n, such as n=20 years. For each regional unit, introduce the corresponding policy impact coefficient according to the different types of protected objects (urban or rural), predict and calculate the comprehensive protection benefits during the planning period year by year and weighted summation.

[0085] Step 7, Constructing the Flood Control Cost Function: Establish the mapping relationship between flood control standard levels and flood control input costs. The flood control cost function is an increasing step function; for example, a 20-year flood event corresponds to a unit length of dike construction cost C1, a 50-year flood event to cost C2, and a 100-year flood event to cost C3, and C1... <C2<C3。

[0086] Step 8, Joint Optimization Calculation of Flood Control Standards: Using the flood control standard level of each regional unit as the decision variable, maximizing the ratio of comprehensive protection benefits to flood control investment costs as the optimization objective function, and spatial continuity constraints and water system correlation constraints as constraints, a joint optimization calculation is performed on the flood control standard level of each regional unit. The optimization calculation employs traversal search or heuristic optimization algorithms.

[0087] Step 9, Result Output and Verification: Output the flood control standard level configuration schemes for each regional unit that meet the constraints and have the best benefit-cost ratio, and verify the spatial continuity and water system correlation. Adjust the results that do not meet the constraints, and finally form the flood protection zone delineation results and corresponding flood control standard configuration schemes for the planning period.

[0088] The most specific embodiment described above includes the entire implementation process from gridding within the target flood protection zone, entropy weight method, similarity calculation, community discovery, planning period benefit prediction, cost function construction to constraint optimization calculation, realizing the intelligent, objective, and quantitative full-process delineation of flood protection zones and configuration of flood control standards.

[0089] 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.

[0090] 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.

[0091] Based on the same inventive concept, this application also provides a flood control standard configuration device for implementing the flood control standard configuration method for flood control protection zones as described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the flood control standard configuration device for flood control protection zones provided below can be found in the limitations of the flood control standard configuration method for flood control protection zones described above, and will not be repeated here.

[0092] In one embodiment, such as Figure 2 As shown, a standard flood control configuration device for a flood protection zone is provided, including: a regional unit acquisition module 210, a protection benefit calculation module 220, a cost function construction module 230, and an optimization calculation module 240, wherein: The regional unit acquisition module 210 is used to acquire multiple regional units to be configured within the target flood protection area; The protection benefit calculation module 220 is used to calculate the comprehensive protection benefits of each regional unit within the preset planning period; The cost function construction module 230 is used to construct the flood control cost function for each regional unit; the flood control cost function can characterize the mapping relationship between the flood control standard level and the flood control input cost; The optimization calculation module 240 is used to take the flood control standard level of the flood control cost function as the decision variable, take maximizing the ratio of comprehensive protection benefits to flood control investment costs as the optimization objective, and take spatial continuity constraints and water system correlation constraints as constraints to optimize the flood control standard level of each regional unit and output the flood control standard configuration scheme of the target flood protection area. Among them, the spatial continuity constraint is used to limit the difference in flood control standard level between spatially adjacent regional units to not exceed a preset threshold, and the water system association constraint is used to limit the flood control standard level of regional units in the upstream river section to not be lower than the flood control standard level of regional units in the downstream river section.

[0093] In some embodiments, the region unit acquisition module 210 specifically includes: The grid processing submodule is used to divide the target flood protection area into a set of grids; each grid in the set of grids is configured with a multi-dimensional attribute vector, which includes the type of protected object, population, economic output and cultivated land area; The weight calculation submodule is used to determine the global objective weight coefficients of each value indicator corresponding to the grid set based on the population, economic output, and cultivated land area in each grid; each value indicator includes population indicators, economic indicators, and cultivated land indicators. The similarity calculation submodule is used to calculate the comprehensive flood control similarity between adjacent grids based on multidimensional attribute vectors and global objective weight coefficients; The network construction submodule is used to construct a spatial relationship network with grid nodes and comprehensive flood control similarity as edge weights. The community segmentation submodule is used to segment the spatial relationship network based on the community discovery algorithm to obtain multiple regional units to be configured.

[0094] In some embodiments, the weight calculation submodule is specifically used to: standardize the population, economic output, and cultivated land area in each grid based on the minimum and maximum values ​​of each value indicator in the grid set, so as to obtain the standardized values ​​of the corresponding value indicators; calculate the indicator weight of each value indicator based on the standardized values; calculate the information entropy of each value indicator based on the indicator weight; and determine the global objective weight coefficient of each value indicator corresponding to the grid set based on the information entropy.

[0095] In some embodiments, the similarity calculation submodule is specifically used to: calculate the sub-item similarity of each adjacent grid in terms of the protected object type dimension, population index, economic index and cultivated land index respectively; and combine the global objective weight coefficient and spatial attenuation factor to perform weighted summation of the sub-item similarity to obtain the comprehensive flood control similarity between adjacent grids; Among them, the overall flood control similarity Calculated using the following formula: ; in, Preset similarity weight coefficients to protect the object type dimension; For grid and grid Sub-item similarity in the dimension of protected object type When the grid and grid When the types of protected objects are the same Select 1 if the value is 1, otherwise select 0. These are the global objective weighting coefficients for population indicators, economic indicators, and arable land indicators, respectively. and grid Sub-item similarity in population indicators, economic indicators, and arable land indicators; For grid and grid The Euclidean distance between them is used as a spatial attenuation factor.

[0096] In some embodiments, computational grid and grid The sub-items of similarity in population indicators, economic indicators, and arable land indicators include: According to the formula:

[0097]

[0098] in, , They are grids and grid Population size; , They are grids and grid The total economic output; , They are grids and grid The area of ​​cultivated land; and These are the logarithmic transformation values ​​of the population in the grid set. The maximum and minimum values; and These are the logarithmic transformation values ​​of the total economic output in the grid set. The maximum and minimum values; and These are the logarithmic transformation values ​​of the cultivated land area in the grid set. The maximum and minimum values; This is a preset positive smoothing constant.

[0099] In some embodiments, the community partitioning submodule is specifically used to: maximize modularity To achieve the goal, the spatial relationship network is iteratively divided; where the modularity... Calculated using the following formula: ; in, For the grid in the spatial relationship network and grid Edge weights between them; , indicating grid The sum of edge weights of all adjacent grids in the spatial relationship network; , indicating grid The sum of edge weights of all adjacent grids in the spatial relationship network; , which represents half of the sum of the weights of all edges in a spatial relational network; For the Kronecker function, and Representing grids respectively and grid The assigned community identifier, when hour The value is 1 if it is not 1, otherwise the value is 0.

[0100] In some embodiments, the protection benefit calculation module 220 is specifically used to: obtain the predicted values ​​of each value indicator of the regional unit at each unit time within a preset planning period; each value indicator includes population indicators, economic indicators and cultivated land indicators; determine the policy impact coefficient corresponding to each value indicator according to the type of protection object of the regional unit; and, based on the policy impact coefficient, perform a weighted summation of the predicted values ​​of each value indicator of the regional unit at each unit time to obtain the comprehensive protection benefit of the regional unit within the preset planning period.

[0101] In some embodiments, the comprehensive protection benefits of a regional unit within a predetermined planning period Calculated using the following formula: ; in, The planning period is the number of years corresponding to the preset planning period; , , They are regional units Policy impact coefficients on population, economic, and arable land indicators; , , They are regional units In the Forecast values ​​for population, economic, and arable land indicators for the year.

[0102] In some embodiments, the flood control cost function is an increasing function with flood control standard level as the independent variable; and / or, the flood control cost function is a step function; wherein, different flood control standard levels correspond to different unit length dike construction costs and / or total regional protection costs.

[0103] Specific limitations regarding the standard flood control configuration devices for flood protection zones can be found in the above-mentioned limitations on the standard flood control configuration methods for flood protection zones, and will not be repeated here. Each module in the aforementioned standard flood control configuration device for flood protection zones 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.

[0104] Furthermore, in the above-described implementation of the flood control standard configuration device for the flood protection zone, the logical division of each program module is merely illustrative. In actual 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 flood control standard configuration device for the flood protection zone can be divided into different program modules to complete all or part of the functions described above.

[0105] In one embodiment, a computer device is provided, such as Figure 3 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 an environment for the operation of the operating system and computer programs stored 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, or other technologies. When the computer program is executed by the processor, it implements a standard flood control configuration method for a flood protection zone.

[0106] Those skilled in the art will understand that the structure shown above 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 figures, or combine certain components, or have different component arrangements.

[0107] 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.

[0108] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] In this article, "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 alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0113] 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 protection of this application. 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 modifications and improvements all fall within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the appended claims.

Claims

1. A method for standard flood control configuration of a flood protection zone, characterized in that, include: Obtain multiple regional units to be configured within the target flood protection zone; Calculate the comprehensive protection benefits of each regional unit within the preset planning period; Construct flood control cost functions for each regional unit; these functions characterize the mapping relationship between flood control standard levels and flood control input costs. The flood control standard level of the flood control cost function is used as the decision variable. The optimization objective is to maximize the ratio of comprehensive protection benefits to flood control investment costs. Spatial continuity constraints and water system correlation constraints are used as constraints. The flood control standard level of each regional unit is optimized and calculated to output the flood control standard configuration scheme of the target flood protection area. The spatial continuity constraint is used to limit the difference in flood control standard levels between spatially adjacent regional units to no more than a preset threshold, and the water system association constraint is used to limit the flood control standard level of regional units in the upstream river section to no less than the flood control standard level of regional units in the downstream river section. The calculation of the comprehensive protection benefits of each regional unit within the preset planning period includes: Obtain the predicted values ​​of each value indicator of the regional unit at each unit time within a preset planning period; the value indicators include population indicators, economic indicators, and arable land indicators. Based on the type of protected object of the aforementioned regional unit, determine the policy impact coefficient corresponding to each value indicator; Based on the policy impact coefficient, the predicted values ​​of each value indicator of the regional unit at each unit time are weighted and summed to obtain the comprehensive protection benefits of the regional unit within the preset planning period. The comprehensive protection benefits of the regional unit during the preset planning period Calculated using the following formula: ； in, The planning period is the number of years corresponding to the preset planning period; , , The respective region units Policy impact coefficients on population, economic, and arable land indicators; , , The respective region units In the Forecast values ​​for population, economic, and arable land indicators for the year.

2. The method according to claim 1, characterized in that, The acquisition of multiple regional units to be configured within the target flood protection zone includes: The target flood protection area is divided into a set of grids; each grid in the set of grids is configured with a multi-dimensional attribute vector, which includes the type of protected object, population size, economic output and cultivated land area; Based on the population, economic output, and arable land area in each grid, the global objective weight coefficients of each value indicator corresponding to the grid set are determined; each value indicator includes population indicators, economic indicators, and arable land indicators. Based on the multidimensional attribute vector and the global objective weight coefficient, the comprehensive flood control similarity between each adjacent grid is calculated. A spatial relationship network is constructed using grid nodes and the comprehensive flood control similarity as edge weights; The spatial relationship network is divided based on the community detection algorithm to obtain multiple regional units to be configured.

3. The method according to claim 2, characterized in that, The determination of global objective weight coefficients for each value indicator corresponding to the grid set based on the population, economic output, and arable land area in each grid includes: Based on the minimum and maximum values ​​of each value indicator in the grid set, the population, economic output, and cultivated land area in each grid are standardized to obtain the standardized values ​​of the corresponding value indicators. Based on the standardized values, calculate the weight of each value indicator; Calculate the information entropy of each value indicator based on its weight; Based on the information entropy, the global objective weight coefficients of each value indicator corresponding to the grid set are determined.

4. The method according to claim 2, characterized in that, The calculation of the comprehensive flood control similarity between adjacent grids based on the multidimensional attribute vector and the global objective weight coefficient includes: Calculate the sub-similarities of each adjacent grid in terms of protected object type dimension, population index, economic index, and arable land index; By combining the global objective weight coefficient and the spatial attenuation factor, the sub-item similarities are weighted and summed to obtain the comprehensive flood control similarity between adjacent grids; Among them, the comprehensive flood control similarity Calculated using the following formula: ； in, Preset similarity weight coefficients to protect the object type dimension; For grid and grid Sub-item similarity in the dimension of protected object type When the grid and grid When the types of protected objects are the same Select 1 if the value is 1, otherwise select 0. These are the global objective weighting coefficients for population indicators, economic indicators, and arable land indicators, respectively. and grid Sub-item similarity in population indicators, economic indicators, and arable land indicators; For grid and grid The Euclidean distance between them is used as the spatial attenuation factor.

5. The method according to claim 4, characterized in that, Computational grid and grid The sub-items of similarity in population indicators, economic indicators, and arable land indicators include: According to the formula: in, , They are grids and grid Population size; , They are grids and grid The total economic output; , They are grids and grid The area of ​​cultivated land; and These are the logarithmic transformation values ​​of the population in the grid set, respectively. The maximum and minimum values; and These are the logarithmic transformation values ​​of the total economic output in the grid set, respectively. The maximum and minimum values; and These are the logarithmic transformation values ​​of the cultivated land area in the grid set. The maximum and minimum values; This is a preset positive smoothing constant.

6. The method according to claim 2, characterized in that, The partitioning of the spatial relationship network based on the community detection algorithm includes: To maximize modularity To achieve the objective, the spatial relationship network is iteratively partitioned; wherein the modularity... Calculated using the following formula: ； in, For the grid in the spatial relationship network and grid Edge weights between them; , indicating grid The sum of edge weights of all adjacent grids in the spatial relationship network; , indicating grid The sum of edge weights of all adjacent grids in the spatial relationship network; , representing half of the sum of all edge weights in the spatial relational network; For the Kronecker function, and Representing grids respectively and grid The assigned community identifier, when hour The value is 1 if it is not 1, otherwise the value is 0.

7. The method according to any one of claims 1 to 6, characterized in that, The flood control cost function is an increasing function with the flood control standard level as the independent variable; And / or, the flood control cost function is a step function; wherein, different flood control standard levels correspond to different unit length dike construction costs and / or total regional protection costs.

8. A standard flood control configuration device for a flood protection zone, characterized in that, include: The regional unit acquisition module is used to acquire multiple regional units to be configured within the target flood protection zone; The protection benefit calculation module is used to calculate the comprehensive protection benefits of each regional unit within the preset planning period; The cost function construction module is used to construct the flood control cost function for each regional unit; the flood control cost function can characterize the mapping relationship between the flood control standard level and the flood control input cost; The optimization calculation module is used to take the flood control standard level of the flood control cost function as the decision variable, take maximizing the ratio of the comprehensive protection benefits to the flood control investment cost as the optimization objective, and take spatial continuity constraints and water system correlation constraints as constraints to optimize the flood control standard level of each regional unit, so as to output the flood control standard configuration scheme of the target flood protection area. The spatial continuity constraint is used to limit the difference in flood control standard levels between spatially adjacent regional units to no more than a preset threshold, and the water system association constraint is used to limit the flood control standard level of regional units in the upstream river section to no less than the flood control standard level of regional units in the downstream river section. The protection benefit calculation module is specifically used to: obtain the predicted values ​​of each value indicator of the regional unit at each unit time within the preset planning period; the value indicators include population indicators, economic indicators and arable land indicators; Based on the type of protected object of the aforementioned regional unit, determine the policy impact coefficient corresponding to each value indicator; Based on the policy impact coefficient, the predicted values ​​of each value indicator of the regional unit at each unit time are weighted and summed to obtain the comprehensive protection benefits of the regional unit within the preset planning period. The comprehensive protection benefits of the regional unit during the preset planning period Calculated using the following formula: ； in, The planning period is the number of years corresponding to the preset planning period; , , The respective region units Policy impact coefficients on population, economic, and arable land indicators; , , The respective region units In the Forecast values ​​for population, economic, and arable land indicators for the year.

9. The apparatus according to claim 8, characterized in that, The region unit acquisition module includes: The grid processing submodule is used to divide the target flood protection area into a set of grids; each grid in the set of grids is configured with a multi-dimensional attribute vector, which includes the type of protected object, population, economic output and cultivated land area; The weight calculation submodule is used to determine the global objective weight coefficients of each value indicator corresponding to the grid set based on the population, economic output, and cultivated land area in each grid; each value indicator includes population indicators, economic indicators, and cultivated land indicators. The similarity calculation submodule is used to calculate the comprehensive flood control similarity between adjacent grids based on multidimensional attribute vectors and global objective weight coefficients; The network construction submodule is used to construct a spatial relationship network with grid nodes and comprehensive flood control similarity as edge weights. The community segmentation submodule is used to segment the spatial relationship network based on the community discovery algorithm to obtain multiple regional units to be configured.

10. The apparatus according to claim 8, characterized in that, The flood control cost function is an increasing function with the flood control standard level as the independent variable; And / or, the flood control cost function is a step function; wherein, different flood control standard levels correspond to different unit length dike construction costs and / or total regional protection costs.

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