Method for optimizing spatial layout of flexible coastal protection space of synergic ecological potential dike and vegetation

Abstract

The present application provides a coastal flexible protection space layout optimization method for synergizing ecological potential dyke and vegetation, and belongs to the technical field of computer-aided design optimization, comprising: dividing a target coastal area into grid units; taking a single grid unit as a decision carrier, setting the protection type and corresponding attribute parameters of each grid unit as spatial decision variables; determining the vegetation survival habitat condition as a hard constraint; setting the maximum of the nearshore wave height attenuation rate as a single optimization objective; adopting a genetic algorithm to iteratively solve, and outputting a spatial configuration scheme satisfying the hard constraint and making the single optimization objective optimal; the genetic algorithm performs the following operations when solving: adopting a hierarchical real number coding mode, coding the spatial decision variables of each grid unit into a unified gene string; adopting a single-objective fitness function composed of a wave dissipation efficiency component and a habitat constraint violation penalty component to evaluate the gene string; adopting a hierarchical spatial block crossover operator to perform a crossover operation; and adopting a hierarchical adaptive mutation operator to perform a mutation operation.

Description

Technical Field

[0001] This invention belongs to the field of computer-aided design optimization technology, specifically involving a method for optimizing the spatial layout of flexible coastal protection based on the synergistic integration of ecological submerged dikes and vegetation. Background Technology

[0002] Mangroves, as natural defense barriers of coastal systems, effectively attenuate wave energy and reduce the risk of disasters caused by extreme ocean dynamics. They also offer significant ecological benefits in carbon storage and biodiversity, making them an important "nature-based solution" in flexible coastal protection. However, factors such as increased frequency of strong waves and tidal intrusions have led to reduced mangrove survival rates due to high-energy nearshore waves, resulting in ineffective coastal protection. Therefore, auxiliary protection engineering is needed to protect vegetation. Against this backdrop, a combined wave-dissipating model of "ecological submerged dike + vegetation" has emerged, as detailed in the instruction manual. Figure 1 As shown.

[0003] Submerged breakwaters and vegetation are key coastal protection measures, but they differ significantly. Location-wise, ecological breakwaters are situated in the subtidal zone below low tide, while vegetation is located in the intertidal zone. In terms of wave dissipation, ecological breakwaters primarily reduce wave energy by decreasing water depth and inducing wave breaking, while vegetation mainly dissipates wave energy by increasing water flow resistance and dissipating kinetic energy. Ecologically, both mangroves and ecological breakwaters offer excellent ecological benefits. Combining them can effectively enhance overall disaster reduction capabilities and ecological benefits, forming a multi-pronged, full-section wave dissipation model. Existing research indicates that the wave dissipation capacity of submerged breakwaters is related to parameters such as breakwater height, crest width, and porosity; the wave dissipation capacity of vegetation is related to parameters such as vegetation type, density, and width. Furthermore, wave size varies significantly across different regions. Therefore, a reasonable spatial distribution of ecological breakwater and vegetation parameters is crucial for achieving wave dissipation goals.

[0004] Currently, the spatial layout of mangrove forests is mainly based on the natural attributes of the vegetation, using its habitat conditions as the primary basis for spatial distribution. It only considers the wave-dissipating capacity of the vegetation itself, failing to achieve global synergistic optimization with the wave-dissipating performance of ecological submerged dikes. Because dike engineering design and vegetation planting have long been disconnected, how to coordinate vegetation habitat conditions and wave-dissipating capacity to achieve global optimization of the spatial distribution of vegetation and ecological submerged dikes has become a core technical challenge in the application of vegetation-based coastal protection. This fragmented design often results in vegetation failing to survive in high-wave areas and waste of dike resources in low-wave areas, making it difficult to maximize overall protective effectiveness. Summary of the Invention

[0005] In response to the problem that existing technologies often separate the design of submerged dikes from vegetation planting, resulting in mangrove spatial layouts that only consider their own habitat conditions or the wave-dissipating capacity of vegetation, failing to achieve global synergistic optimization of vegetation and ecological submerged dikes in terms of wave dissipation efficiency and habitat conditions, and making it difficult to balance vegetation survival in strong wave areas with maximizing overall protection efficiency, this invention provides a method for optimizing the spatial layout of flexible coastal protection by synergistically integrating ecological submerged dikes and vegetation.

[0006] This method discretizes the target coastal area into multiple grid cells, uses each grid cell as a decision-making carrier, and uniformly sets its protection type and attribute parameters as spatial decision variables; takes the three habitat conditions required for vegetation survival—inundation time, substrate type, and wave height—as hard constraints, and takes maximizing the nearshore wave height attenuation rate as the single optimization objective to construct an optimization configuration model; and uses a genetic algorithm for iterative solution. During the solution process, the genetic algorithm performs the following specific designs: A hierarchical real-number encoding method is used to encode the spatial decision variables of each grid cell into a unified gene string. This gene string contains a protection type encoding layer and a corresponding attribute parameter encoding layer, where the attribute parameter encoding layer for protection types not selected is set to zero. A single-objective fitness function, consisting of a wave-damping effectiveness component and a habitat constraint violation penalty component, is used to evaluate each gene string, achieving simultaneous quantitative evaluation of wave-damping effect and habitat constraint satisfaction. A hierarchical spatial block crossover operator is used to exchange parent gene information in continuous spatial grid blocks. Discrete value exchange is performed on the protection type encoding layer, and real-number linear crossover is used on the attribute parameter encoding layer to ensure the spatial continuity of the generated scheme after crossover. A hierarchical adaptive mutation operator is used to adaptively decrease the mutation probability with the number of iterations. Discrete random mutation is used on the protection type encoding layer, and Gaussian continuous mutation is used on the attribute parameter encoding layer. Through iterative optimization using the above genetic algorithm, the optimal flexible protection spatial configuration scheme for each grid cell is finally output.

[0007] The specific technical solution adopted by this invention to solve its technical problem is as follows:

[0008] This invention provides a method for optimizing the spatial layout of coastal flexible protection by coordinating ecological submerged dikes and vegetation. This method integrates the engineering parameters of ecological submerged dikes and the planting parameters of vegetation into a spatial decision variable system. It constructs an integrated optimization configuration model with vegetation habitat conditions as hard constraints and overall wave dissipation efficiency as the optimization objective, and uses a genetic algorithm adapted to spatial layout characteristics to solve the problem, thereby achieving global synergistic optimization of the two.

[0009] The method includes the following steps:

[0010] S1. Divide the target coastal area into grid cells;

[0011] S2. Using a single grid cell as the decision-making carrier, the protection type and corresponding attribute parameters of each grid cell are set as spatial decision variables;

[0012] S3. Determine the habitat conditions for vegetation survival as a hard constraint;

[0013] S4. Set maximizing the nearshore wave height attenuation rate as the single optimization objective;

[0014] S5. Use a genetic algorithm to iteratively solve the problem and output a spatial configuration scheme that satisfies the hard constraints and makes the single optimization objective optimal.

[0015] In S5, the genetic algorithm performs the following operations during the solution process:

[0016] a) A hierarchical real number encoding method is adopted to encode the spatial decision variables of each grid cell into a unified gene string, wherein the gene string contains a protection type encoding layer and a corresponding attribute parameter encoding layer;

[0017] b) Each gene string is evaluated using a single-objective fitness function consisting of a wave-dissipation efficiency component and a habitat constraint violation penalty component;

[0018] c) A hierarchical spatial block crossover operator is used to perform crossover operations. The crossover operator exchanges parent gene information in units of continuous spatial grid blocks, and performs discrete value exchange on the protection type coding layer and continuous value crossover on the attribute parameter coding layer.

[0019] d) Perform mutation operations using a hierarchical adaptive mutation operator.

[0020] In the above method, grid partitioning discretizes the originally continuous coastal space into a finite number of independent decision units, enabling the spatial layout problem to be quantitatively expressed and processed in matrix form within a computer. The setting of decision variables is crucial; it unifies the parameters of the submersible and vegetation, originally belonging to different engineering disciplines, into a single parameter space for collaborative optimization, breaking down the design barriers between them. Vegetation survival habitat conditions are established as hard constraints prior to the optimization objective, reflecting the principle of "ensuring survival first, then optimizing effectiveness." Maximizing the nearshore wave height attenuation rate is used as the single optimization objective, unifying the measurement of the wave height attenuation effects of the two physical processes—ecological submersible wave dissipation and vegetation wave dissipation—providing a unified evaluation benchmark for collaborative optimization.

[0021] In the execution of the genetic algorithm, hierarchical real-number encoding is the core method for handling mixed-type decision variables. By organizing the protection type and attribute parameters of each grid cell into a unified gene string structure, and setting the corresponding attribute parameter layer to zero when the protection type does not match, this encoding method can simultaneously express multiple spatial configuration modes such as "no protection", "vegetation only", "ecological dike only", and "vegetation + ecological dike" in a single gene string. This allows the genetic algorithm to simultaneously solve discrete type selection and continuous parameter optimization in a unified search space.

[0022] The single-objective fitness function combines the quantitative evaluation of wave dissipation effectiveness with the constraint satisfaction evaluation. The wave dissipation effectiveness component quantifies the physical protection effect of the layout scheme, while the constraint violation penalty component accumulates the degree of violation of the three habitat conditions (inundation time, substrate type, and wave height) by the vegetation grid cells in the scheme. After weighted combination of the two through penalty coefficients, the algorithm can automatically identify and eliminate infeasible schemes that sacrifice vegetation survival conditions for wave dissipation effectiveness, ensuring that the final output scheme maximizes wave dissipation effectiveness while satisfying the hard constraints of vegetation habitat.

[0023] The hierarchical spatial block crossover operator is specifically adapted to the spatial characteristics of coastal protection engineering. Unlike the crossover method of randomly selecting gene loci in traditional genetic algorithms, this operator exchanges information in units of continuous spatial grid blocks, ensuring that the exchanged gene information remains spatially continuous, thereby avoiding fragmented and patchy infeasible spatial layout schemes. Furthermore, discrete value exchange and real-number linear crossover are used for the protection type encoding layer and attribute parameter encoding layer, respectively, taking into account the properties of different types of variables.

[0024] The hierarchical adaptive mutation operator further enhances the algorithm's search performance. For the discrete-valued protection type encoding layer, random replacement is used to achieve type conversion; for the continuously-valued attribute parameter encoding layer, Gaussian mutation is used to achieve small-amplitude perturbations. The adaptive decay mechanism of mutation probability with the number of iterations allows the algorithm to explore a wider solution space with a higher mutation rate in the early stages to avoid getting trapped in local optima, and to perform a fine search with a lower mutation rate in the later stages to accelerate convergence, thus balancing global search capability and convergence stability.

[0025] As a preferred option, the protection types include no protection, vegetation, and ecological submerged dikes; the attribute parameters of the vegetation include vegetation type and planting density; the attribute parameters of the ecological submerged dikes include dike height, dike crest width, and porosity. These parameters cover the main engineering adjustable variables affecting the wave-dissipating effectiveness of flexible coastal protection, giving the optimization results direct engineering guidance value.

[0026] As a preferred option, the vegetation survival habitat conditions include wave height constraints, inundation time constraints, and substrate type constraints. These three constraints ensure the normal survival of vegetation from three dimensions: wave dynamic conditions, tidal hydrological conditions, and substrate physical conditions, respectively, comprehensively covering the key environmental factors affecting the success or failure of vegetation establishment.

[0027] As a preferred embodiment, the formula for calculating the single-objective fitness function is:

[0028] in, To improve wave dissipation efficiency, The severity of punishment for violating habitat constraints This is the penalty coefficient.

[0029] This calculation formula unifies the principle that the greater the wave dissipation effectiveness, the better, and the principle that the smaller the constraint violation, the better, into a single maximum value optimization problem, thereby achieving simultaneous evaluation of both protective effectiveness and ecological feasibility.

[0030] As a preferred embodiment, in step d), the hierarchical adaptive mutation operator adaptively decays the mutation probability with the number of iterations, and uses discrete random mutation for the protection type encoding layer and Gaussian continuous mutation for the attribute parameter encoding layer. This design takes into account both the need for a large-scale exploration of the solution space in the early stage of the genetic algorithm search and the need for fine optimization in the later stage of the search. At the same time, it adopts different mutation strategies for different mathematical properties of discrete and continuous values, ensuring the rationality of the mutation operation on various parameters.

[0031] As a preferred embodiment, the calculation of the wave dissipation efficiency component includes: sequentially calculating the post-dissipation wave height of each grid cell according to the wave propagation direction. The post-dissipation wave height of the ecological submerged dike is calculated using the porous submerged dike wave height transmission formula, while the post-dissipation wave height of vegetation is calculated using the vegetation wave height attenuation formula. This method recursively calculates wave height changes along the wave propagation path, accurately reflecting the secondary attenuation process of waves passing through the submerged dike and vegetation areas, ensuring the physical authenticity of the wave dissipation efficiency quantification.

[0032] As a preferred embodiment, the method further includes the following steps: during the solution process, wave intensity is determined grid by grid; for grid cells with wave heights exceeding the vegetation survival threshold, ecological breakwaters are preferentially deployed for pre-emptive wave dissipation, and the wave boundary conditions of the grid cell are updated. This step embodies the technical concept of improving habitat conditions through engineering measures. By preferentially deploying ecological breakwaters in strong wave areas to reduce wave heights to a range where vegetation can survive, conditions for vegetation survival are created in areas that were previously unsuitable for planting, fundamentally expanding the feasible spatial range for vegetation.

[0033] As a preferred embodiment, in step a), the attribute parameter encoding layer value corresponding to the unselected protection type is set to zero. This design keeps the gene string length fixed, facilitating standard operations of the genetic algorithm. Simultaneously, the zeroing operation does not produce wave-sweeping effects during computation, ensuring the accuracy of the physical meaning during decoding.

[0034] As a preferred embodiment, in step c), the continuous spatial grid block is a rectangular region, and the continuous values ​​of the attribute parameter encoding layer are crossed using a real-number linear crossing method. This limitation further clarifies the specific implementation method of spatial block crossing, the selection of rectangular blocks simplifies the operation, and the real-number linear crossing ensures that the continuous parameters remain within a reasonable value range after crossing.

[0035] Corresponding to the above method, this invention also provides a coastal flexible protection spatial layout optimization system. This system includes: a grid division module for dividing the target coastal area into grid cells, with each grid cell serving as the decision-making carrier; a parameter setting module for setting the protection type and corresponding attribute parameters of each grid cell as spatial decision variables, determining vegetation survival habitat conditions as hard constraints, and setting maximizing nearshore wave height attenuation rate as the single optimization objective; and an algorithm solving module for iteratively solving using a genetic algorithm, outputting a spatial configuration scheme that satisfies the hard constraints and optimizes the single optimization objective. The algorithm solving module performs the operations described in steps a) to d) above during the solution process. This system implements the above method using a modular architecture, with clear functional boundaries for each module, facilitating independent adjustment and adaptation based on parameter differences in different coastal areas.

[0036] Compared to existing technologies, this invention and its preferred solutions achieve global collaborative optimization of ecological submerged dikes and mangrove vegetation, breaking the traditional engineering design convention that the two are independent of each other, and fully leveraging the synergistic protective effect of combined wave dissipation modes. Through improvements to genetic algorithms specifically adapted to spatial layout problems, it fundamentally solves the industry pain points of fragmented generation and infeasible engineering layouts by general optimization algorithms, ensuring that all output solutions can be directly applied to actual projects. It establishes an optimization mechanism that prioritizes habitat constraints, maximizing wave dissipation and protection effectiveness while ensuring the survival conditions of mangrove vegetation, achieving an organic unity of ecological and disaster reduction benefits. It constructs a complete automated optimization process from data input to solution output, significantly improving the efficiency and scientific nature of coastal flexible protection engineering design. At the same time, it provides dual protection of methods and systems, covering multiple implementation forms and has broad application prospects. Attached Figure Description

[0037] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments:

[0038] Figure 1 A schematic diagram of a wave-dissipating method combining ecological submerged dikes and vegetation;

[0039] Figure 2 This is a diagram illustrating the spatial layout optimization configuration and model of the wave dissipation mode combining ecological submerged dikes and vegetation in an embodiment of the present invention.

[0040] Figure 3 This is a schematic diagram of the regional grid division of the coastal flexible protection spatial configuration model according to an embodiment of the present invention;

[0041] Figure 4 This is a schematic diagram of the coastal flexible protection space configuration model framework according to an embodiment of the present invention;

[0042] Figure 5 This is a flowchart illustrating the workflow of the coastal flexible protection space configuration model according to an embodiment of the present invention.

[0043] Figure 6 This is a schematic diagram of mesh division for an application example of the present invention;

[0044] Figure 7 This is a regional elevation and substrate type distribution map of an application example of the present invention;

[0045] Figure 8 The inundation time-elevation relationship curve and regional inundation time distribution map are examples of application examples of this invention.

[0046] Figure 9 This is a distribution map of suitable survival areas for Kandelia candel and Avicennia marina, representing an application example of this invention.

[0047] Figure 10 This is a vegetation minimum density distribution map that meets the wave dissipation and protection requirements in an application example of this invention.

[0048] Figure 11 This is a schematic diagram of a partial solution set of vegetation spatial distribution data for an application example of this invention. Detailed Implementation

[0049] To make the features and advantages of the present invention more apparent and understandable, specific embodiments are described below in detail:

[0050] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0051] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0052] To address the spatial layout optimization problem of the combined wave dissipation mode of "ecological submerged dike + vegetation," this invention considers the key to the spatial layout of flexible coastal protection lies in the rational combination of various elements (vegetation habitat conditions, wave dissipation attributes, and dike parameters). This involves a multi-parameter spatial configuration that coordinates vegetation habitat conditions with wave dissipation performance and the wave dissipation performance of the ecological submerged dike, thereby obtaining a planting scheme that meets wave dissipation and protection requirements. Therefore, this invention constructs a coastal flexible protection spatial configuration model to solve the spatial layout optimization problem of the combined wave dissipation mode of "ecological submerged dike + vegetation." Figure 2 As shown, the coastal flexible protection space configuration technology takes vegetation habitat parameters (topographic elevation, substrate type, vegetation species), vegetation wave dissipation parameters (vegetation density, vegetation type, vegetation width), and ecological submerged body parameters (submerged breakwater height, width, porosity, etc.) as its core. It coordinates the rational configuration of these parameters based on offshore wave conditions to construct a planting scheme that meets wave dissipation and protection requirements. The flexible protection space configuration model is a crucial means of achieving this process. The model consists of three parts: a model initialization module for grid generation and regional attribute assignment; an objective function module for wave dissipation performance based on the ecological submerged breakwater and vegetation disaster reduction and protection capabilities; and constraint conditions based on vegetation habitat construction conditions. Through optimization and iterative solutions, a flexible protection space configuration scheme that meets these conditions is ultimately formed.

[0053] Based on the above design concept, this invention provides a complete design of a flexible coastal protection space configuration model, which will be shown and introduced below through more specific embodiments.

[0054] 1. Model Structure

[0055] (1) Grid division

[0056] The coastal flexible protection spatial configuration model first requires dividing the area into grids and marking the locations. The invention divides the study area into L×L rectangular grid cells, where L is the size of the grid cell. Figure 3 As shown, each grid unit is used as a carrier to digitally assign regional attributes (such as elevation, vegetation species, substrate type, etc.) and construct the parameter matrix X(m,n) of the study area to facilitate model calculation and solution.

[0057] (2) Decision variables

[0058] Decision variables are the variables that need to be solved in the model. They represent the quantifiable solutions and measures in the design, reflect the function values ​​of various solutions and measures, and fulfill the constraints expressed by the constraint variables. The invention uses grid cells to form decision variables X. k (i,j) Xk (i,j) It includes the spatial location (i, j) of the grid cell and the protection type and protection attribute (k) in the cell. The protection type includes vegetation in front of the dike and ecological submerged dike. The protection attribute includes the density and type of vegetation, and the width, height, porosity and other characteristic parameters of the ecological submerged dike, as shown in Table 1.

[0059] Table 1. Establishment of Decision Variables

[0060]

[0061] (3) Constraints

[0062] Due to the high salinity of the ocean and the fluctuations of tides, vegetation survival is constrained by the environment. Vegetation survival is a prerequisite for flexible protection. Therefore, the model uses vegetation survival as a constraint. Based on the key parameters of vegetation habitat conditions summarized in the authoritative technical guidance document "Mangrove Ecological Restoration Manual," this invention determines the key constraints to include: inundation time, substrate type, and the range of environmental parameter variations must be within the suitable range for vegetation.

[0063] H x(i,j) < H 适宜 ;T x(i,j) < T 适宜 ; D x(i,j) < D 适宜

[0064] In the formula H x(i,j) T x(i,j) and D x(i,j) For the wave height, inundation time, and substrate type of the grid cells; H 适宜 T 适宜 and D 适宜 The thresholds for vegetation survival wave height, submersion time, and substrate type.

[0065] (4) Objective function

[0066] This invention aims to maximize the nearshore wave height attenuation rate and takes the nearshore wave height meeting the design standards for protective engineering as a feasibility constraint. Under the premise of ensuring vegetation survival, it achieves the optimization of wave dissipation and protection effectiveness.

[0067] The optimization objective is:

[0068]

[0069] Feasibility constraints are:

[0070] H x(m,j) <H 标准 ,

[0071] In the formula, The nearshore average wave height attenuation rate; H represents the incident wave height over the open sea. x(m,j) H represents the wave height after wave dissipation in the j-th grid cell of the m-th row (nearshore boundary); M represents the total number of grid cells in the nearshore boundary; H represents the wave height after wave dissipation in the j-th grid cell of the m-th row (nearshore boundary); 标准 The design wave height for the protective engineering of the demonstration area.

[0072] 2. Model Principles

[0073] The key components of the model in this embodiment mainly include the wave-dissipation module, the vegetation habitat condition discrimination module, and the model iterative solution method.

[0074] (1) Submerged bank wave elimination formula

[0075]

[0076] In the formula K t Let n be the wave height transmission coefficient of the ecological submerged dike. k R is the porosity of the ecological submerged dike, H is the distance from the top of the dike to the water surface, L is the incident wave height, B is the incident wavelength, and B is the width of the dike top.

[0077] (2) Vegetation wave dissipation formula

[0078]

[0079]

[0080] In the formula H x Let H0 be the wave height at the vegetation front, x be the distance from the vegetation front, H0 be the wave height at the vegetation front, k be the wave height attenuation coefficient, and C be the wave height attenuation coefficient. d denoted as the vegetation drag coefficient, D as the average diameter of the vegetation, N as the vegetation density (the amount of vegetation per square meter), and h as the water depth.

[0081] (3) Vegetation habitat conditions

[0082] Submersion time and substrate type are key parameters of vegetation habitat conditions, and these parameters differ among different mangrove types. Taking Kandelia candel and Avicennia marina as examples, the submersion time threshold for Kandelia candel is 7 hours per day, with a silty substrate; while the submersion time threshold for Avicennia marina is 9 hours per day, with a silty or sandy substrate.

[0083] (4) Model framework

[0084] The coastal flexible protection spatial configuration model provided in this embodiment integrates coastal flexible protection assessment technology and vegetation establishment and survival technology, and establishes a technical platform for solving the coastal flexible protection spatial layout. Each module is configured independently to meet the parameter adjustment needs of different regions, improving the model's adaptability. A schematic diagram of the model framework is shown below. Figure 4 As shown. The spatial layout of vegetation for flexible protection measures and the wave dissipation method of "ecological submerged dike + vegetation" is a multi-objective spatial problem, which is solved by an algorithm based on the objective function and constraints.

[0085] 3. Core Optimization Algorithm

[0086] 3.1 Encoding Rules for Gene String Transformation Based on Spatial Layout Scheme

[0087] In this embodiment, the core of the algorithm is to convert the protection type and protection attribute parameters of each grid cell (i,j) into a numerical array (gene string) that can be recognized by the genetic algorithm. The length of the gene string is strongly bound to the number of grid cells and the dimension of the decision variables. A hierarchical real number encoding method is adopted to take into account both the spatial characteristics and numerical continuity of the parameters. The specific design is as follows:

[0088] 1. Overall structure of the gene string: Suppose the study area is divided into M×N grid units, each grid unit corresponds to one sub-gene string, and the overall gene string is the ordered concatenation of all grid unit sub-gene strings, that is: gene string G = [G 11 G 12 ,..., G ij , ..., G MN ], where G ij is the sub-gene string of grid unit (i,j).

[0089] 2. Grid unit sub-gene string G ij Design: The sub-gene string is a hierarchical real number encoding, which is divided into 3 layers: protection type layer, vegetation attribute layer, and ecological embankment attribute layer. The attribute layer value corresponding to the unselected protection type is set to 0. The specific layer and encoding definition are shown in Table 1.

[0090] The protection type layer is a 1-digit real number, defining the protection type of the grid cell: 0 = no protection, 1 = vegetation, 2 = ecological embankment;

[0091] The vegetation attribute layer consists of two real numbers, corresponding to the vegetation type and planting density in the decision variables mentioned above. The values ​​are the actual engineering values ​​of the parameters.

[0092] The ecological submerged embankment attribute layer consists of 3 real numbers, corresponding to the submerged embankment height, embankment crest width, and porosity in the decision variables mentioned above. The values ​​are the actual engineering values ​​of the parameters.

[0093] 3. Gene string numerical range constraints: During encoding, set feasible numerical ranges for each parameter (e.g., vegetation density 0~5 plants / m²). 2 The porosity of the submerged embankment is 0.2~0.8. Values ​​outside this range are directly corrected to the range boundary values ​​during the coding stage to ensure the engineering rationality of the gene string. The above design is summarized in Table 2.

[0094] Table 2 Subgene Strings G of Grid Unit (i,j) ij Hierarchical Real Number Encoding

[0095]

[0096] 3.2 Quantitative Evaluation and Fitness Function Design Based on Layout Scheme and Constraint Penalty

[0097] The fitness function is the core for quantitatively evaluating the merits of spatial layout schemes corresponding to gene strings. It needs to simultaneously quantify the wave-damping effect and penalize the degree of violation of vegetation habitat constraints. This invention adopts a single-objective fitness function F(G) with constraint penalties, as shown in the following formula:

[0098]

[0099] in: For the wave dissipation performance adaptability component, To constrain the penalty component for violations, λ is the penalty coefficient.

[0100] As a preferred implementation example, the penalty coefficient λ can be set to 0.3 based on the severity of the regional habitat conditions, configured such that when the constraint is violated... When >0, penalty item The value is greater than the maximum possible gain of the wave dissipation efficiency component.

[0101] Based on the wave-dissipating formulas for submerged breakwaters and vegetation, the nearshore wave height attenuation rate after wave dissipation in the entire study area is calculated. A higher attenuation rate indicates a higher wave height. The higher the value, the better the wave-dissipating effect. The specific calculation steps are as follows:

[0102] (1) Based on the parameters of gene string G, extract the protection type and submerged dike / vegetation parameters of each grid cell to generate a spatial distribution matrix of regional protection parameters;

[0103] (2) Calculate the wave height transmission coefficient K of the ecological submerged dike in sequence according to the wave propagation direction. t The wave height attenuation coefficient k of the vegetation is used to obtain the outflow wave height of each grid cell;

[0104] (3) Calculate the average nearshore wave height attenuation rate in the study area: =(H0-H x) / H0×100%, where H0 is the incident wave height over the open sea, H x The nearshore average wave height after wave dissipation;

[0105] (4) Wave dissipation performance adaptability component: = .

[0106] To quantify the degree of violation of vegetation habitat constraints, for the vegetation survival constraints defined above (suitable inundation time, suitable substrate type, suitable wave height), the constraint violation of each grid cell is quantitatively summed. The higher the degree of violation, the larger the P(G) value and the smaller the fitness function F(G) value. The specific design is as follows:

[0107] (1) For each grid cell (i,j) with planted vegetation, calculate the single constraint violation degree PT. (i,j) PD (i,j) pH (i,j) Grid cells without vegetation have a violation degree of 0;

[0108] A. Submersion Time Violation: PT (i,j) =1 (inappropriate flooding time) / 0 (appropriate flooding time);

[0109] B. Substrate Type Violation Degree: PD (i,j) =1 (unsuitable substrate) / 0 (suitable substrate);

[0110] C. Wave height violation: PH (i,j) =1 (wave height is not suitable) / 0 (wave height is suitable);

[0111] (2) Comprehensive violation degree of grid cell: P ij (G)=PT (i,j) +PD (i,j) +PH (i,j) ;

[0112] Among them, the comprehensive violation degree P of a single grid cell ij The value of P(G) ranges from 0 to 3. When all vegetation grid cells satisfy the habitat constraints, P(G) = 0, and there is no penalty.

[0113] 3.3 Genetic Operator Design

[0114] Genetic operators include selection, crossover, and mutation. The operator design in this invention is based on the hierarchical real-number encoded gene string structure and the regional continuity of spatial layout, avoiding the problems of spatial layout fragmentation and engineering infeasibility during the iteration process. The specific design is as follows:

[0115] (1) Selection Operator

[0116] The tournament selection method is adopted to balance the convergence speed of the algorithm and the diversity of the population, and to avoid the premature elimination of excellent individuals. The specific steps include:

[0117] Randomly select k individuals from the current population (containing N gene strings);

[0118] Calculate the fitness function F(G) value of k individuals, and select the individual with the largest F(G) value as the parent individual;

[0119] Repeat the above steps until a parent population with the same number of offspring as the original population is selected for subsequent crossover operations.

[0120] (2) Crossover operator

[0121] Considering the spatial characteristics of gene strings, this embodiment abandons the traditional random single-point / multi-point crossover and designs a hierarchical spatial block crossover method to ensure that the offspring gene strings generated after crossover have regional continuity, which meets the spatial layout requirements of coastal protection engineering. The specific steps include:

[0122] Randomly pair up the parent populations to obtain several parent pairs (G1, G2).

[0123] For each pair of parent gene strings, a continuous spatial block region (such as grid cell (i1~i2,j1~j2)) is randomly selected as the intersection region;

[0124] Based on the hierarchical structure of the sub-gene strings, perform hierarchical crossover on the gene strings within the crossover region:

[0125] Protection type layer: Directly swap the values ​​of the intersection region of the parent pair;

[0126] Vegetation / submerged embankment attribute layer: Real number linear crossover is adopted, and the offspring value = α × parent 1 value + (1-α) × parent 2 value (α is a random number from 0 to 1) to ensure the continuity of parameters;

[0127] The crossover probability is determined using conventional techniques in the field, and is preferably set to P. c =0.8 (engineering experience value), the gene string values ​​in non-crossover regions remain unchanged, and 2 offspring gene strings are generated.

[0128] (3) Mutation operator

[0129] A hierarchical adaptive mutation method is adopted, using different mutation methods for the protection type layer and the attribute parameter layer. Simultaneously, the mutation probability is adaptively adjusted with the number of algorithm iterations (high mutation probability in the early stages of iteration ensures diversity, while low mutation probability in the later stages ensures convergence). Specific design includes:

[0130] The mutation probability is adaptively adjusted, gradually decreasing with increasing algorithm iterations to balance global search capability and convergence speed. As a further optimized implementation, the following adaptive adjustment setting can be used: P m =P m0 ×e-β×t / T, where P m0 =0.1 (initial mutation probability), β=0.5 (decay coefficient), t is the current iteration number, and T is the total number of iterations;

[0131] Stratified mutation method:

[0132] Protection type layer (discrete): Randomly replaces the protection type value of the mutated grid with one of 0 / 1 / 2 to achieve local adjustment of the protection type;

[0133] Vegetation / submerged embankment attribute layer (continuous type): Gaussian mutation is adopted, and the offspring value = parent value + σ×N(0,1), where σ is the standard deviation of the parameter (taken as 1 / 10 of the feasible range of the parameter in the project), to ensure that the parameter is still within the reasonable range of the project after mutation.

[0134] Local spatial variation: Variation only occurs in a single randomly selected grid cell, avoiding large-area variation that could lead to layout chaos and meeting the needs of local optimization in engineering.

[0135] 4. Work Process

[0136] Based on the above model design, the workflow of the coastal flexible protection space configuration model provided in this embodiment is as follows: Figure 5 As shown, the specific implementation steps are as follows:

[0137] Step 1: Collect extreme wave characteristics of the region and determine the dominant wave propagation direction. Based on this, divide the target protection area into regular grids. The grid direction should be consistent with the dominant wave propagation direction to ensure the accuracy of subsequent wave propagation calculations.

[0138] Step 2: Obtain basic hydrogeological data of the target area, such as underwater elevation, seabed sediment type, and tidal level; use Kriging interpolation to interpolate the above discrete data onto each grid cell divided in Step 1 to form a basic data matrix consistent with the grid spatial resolution, providing data support for subsequent decision variable assignment and constraint condition judgment.

[0139] Step 3: Select native mangrove species suitable for the target area through field surveys and literature review. Based on the submersion duration tolerance threshold, substrate adaptability threshold, and wave resistance survival threshold of each species, and combined with the design wave height standard of nearshore protection projects, construct a dual constraint system for the model, including hard constraints on vegetation habitat and constraints on nearshore protection effect.

[0140] Step 4: Based on the wave conditions at the outer sea boundary and the wave resistance survival threshold of vegetation, determine on a grid-by-grid basis whether ecological submerged dikes need to be deployed for pre-emptive wave dissipation. For strong wave areas where the wave height exceeds the vegetation survival threshold, ecological submerged dikes are deployed first to reduce wave energy, creating a suitable habitat for subsequent vegetation planting. The wave boundary conditions of the area are then updated based on the wave dissipation effect of the submerged dikes.

[0141] Step 5: Taking the maximization of nearshore wave height attenuation rate as the optimization objective, and combining the wave boundary conditions updated in Step 4 with the constraint system constructed in Step 3, the height, width, porosity of the ecological submerged dike, as well as the type and planting density of vegetation, are used as decision variables to construct an optimization configuration model for flexible protection measures.

[0142] Step six: Use a genetic algorithm to solve the above-mentioned flexible measures optimization configuration model. Iteratively optimize through operations such as population initialization, hierarchical real number encoding, hierarchical spatial block crossover, and hierarchical adaptive mutation. Finally, output the optimal coastal flexible protection spatial distribution configuration scenario that meets the requirements of wave dissipation and protection as well as vegetation habitat requirements, including the protection type, ecological submerged dike parameters, and vegetation planting parameters of each grid unit.

[0143] To verify the effectiveness of the above scheme in this embodiment, the following provides a specific application example for the spatial layout of mangrove coastal protection in a certain province.

[0144] (1) Regional grid division

[0145] An application example demonstrates how planting mangroves can be used for marine ecological protection and restoration, as well as coastal protection. This invention optimizes the vegetation distribution design. A schematic diagram of the regional grid division is shown below. Figure 6 As shown, the grid area covers the research area, the grid direction is consistent with the wave propagation direction, and the grid size is 1m×1m.

[0146] (2) Assigning values ​​to the decision variable matrix

[0147] The grid is assigned values ​​based on the elevation and substrate type of the study area, such as... Figure 7 As shown, the current flooding time in the area is 9-11 hours per day.

[0148] Based on the tidal data of the region, obtain the relationship curve between elevation and inundation time, and calculate the inundation time of the region based on the curve. Figure 8 As shown.

[0149] (3) Vegetation survival assessment

[0150] Kandelia candel and Avicennia marina were selected as the main mangrove species in the region. Based on the habitat requirements of Kandelia candel and Avicennia marina, the survival range of the vegetation was determined, such as... Figure 9As shown, under the existing environmental conditions, most areas cannot meet the requirements for vegetation survival, therefore, it is necessary to modify the elevation and substrate of the area.

[0151] (4) Spatial distribution set of vegetation

[0152] The modified area meets the survival requirements of vegetation. From a wave dissipation perspective, the minimum density required to meet the wave dissipation and protection requirements of vegetation is calculated, such as... Figure 10 As shown, the minimum vegetation density varies in different areas. Overall, a vegetation density of 1m × 1m meets the requirements for wave dissipation and protection. This allows us to obtain the spatial distribution solution set of the vegetation, as shown below. Figure 11 As shown. After obtaining the solution set, a suitable scheme can be selected by further considering parameters such as cost and ecological benefits.

[0153] Those skilled in the art will understand that embodiments of this application can be provided as systems, apparatus, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code.

[0154] This application is described with reference to flowchart illustrations and / or block diagrams of systems, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0155] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0156] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0157] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

[0158] This invention is not limited to the above-described preferred embodiments. Anyone inspired by this invention can derive other various methods for optimizing the spatial layout of coastal flexible protection with synergistic ecological dikes and vegetation. All equivalent variations and modifications made within the scope of the claims of this invention shall fall within the scope of this invention.

Claims

1. A method for optimizing the spatial layout of flexible coastal protection by coordinating ecological submerged dikes and vegetation, characterized in that, Includes the following steps: S1. Divide the target coastal area into grid cells; S2. Using a single grid cell as the decision-making carrier, the protection type and corresponding attribute parameters of each grid cell are set as spatial decision variables; S3. Determine the habitat conditions for vegetation survival as a hard constraint; S4. Set maximizing the nearshore wave height attenuation rate as the single optimization objective; S5. Use a genetic algorithm to iteratively solve the problem and output a spatial configuration scheme that satisfies the hard constraints and makes the single optimization objective optimal. The genetic algorithm performs the following operations during the solution process: a) A hierarchical real number encoding method is adopted to encode the spatial decision variables of each grid cell into a unified gene string, wherein the gene string contains a protection type encoding layer and a corresponding attribute parameter encoding layer; b) Each gene string is evaluated using a single-objective fitness function consisting of a wave-dissipation efficiency component and a habitat constraint violation penalty component; The formula for calculating the single-objective fitness function is: in, To improve wave dissipation efficiency, The severity of punishment for violating habitat constraints This is the penalty coefficient; c) A hierarchical spatial block crossover operator is used to perform crossover operations. The operator exchanges parent gene information in units of continuous spatial grid blocks and performs discrete value exchange on the protection type coding layer and continuous value crossover on the attribute parameter coding layer. d) A hierarchical adaptive mutation operator is used to perform mutation operations; the hierarchical adaptive mutation operator makes the mutation probability adaptively decrease with the number of iterations, and uses discrete random mutation for the protection type coding layer and Gaussian continuous mutation for the attribute parameter coding layer.

2. The method for optimizing the spatial layout of coastal flexible protection based on synergistic ecological submerged dikes and vegetation according to claim 1, characterized in that: The protection types include no protection, vegetation, and ecological submerged dikes; the attribute parameters of the vegetation include vegetation type and planting density; the attribute parameters of the ecological submerged dikes include dike height, dike crest width, and porosity.

3. The method for optimizing the spatial layout of coastal flexible protection based on synergistic ecological submerged dikes and vegetation according to claim 1, characterized in that: The vegetation survival habitat conditions include wave height constraints, inundation time constraints, and substrate type constraints.

4. The method for optimizing the spatial layout of coastal flexible protection based on synergistic ecological submerged dikes and vegetation according to claim 1, characterized in that: The calculation of the wave dissipation efficiency component includes: calculating the wave height after wave dissipation for each grid unit in sequence according to the wave propagation direction, wherein the wave height after wave dissipation for ecological submerged dikes is calculated using the wave height transmission formula for porous submerged dikes, and the wave height after wave dissipation for vegetation is calculated using the wave height attenuation formula for vegetation.

5. The method for optimizing the spatial layout of coastal flexible protection based on synergistic ecological submerged dikes and vegetation according to claim 1, characterized in that: It also includes the following steps: During the solution process, wave intensity is determined grid by grid. For grid cells with wave height exceeding the vegetation survival threshold, ecological submerged dikes are deployed first to mitigate the waves, and the wave boundary conditions of the grid cell are updated.

6. The method for optimizing the spatial layout of coastal flexible protection based on synergistic ecological submerged dikes and vegetation according to claim 1, characterized in that: In step a): The value of the attribute parameter encoding layer corresponding to the unselected protection type is set to zero.

7. The method for optimizing the spatial layout of coastal flexible protection based on synergistic ecological submerged dikes and vegetation according to claim 1, characterized in that: In step c): the continuous spatial grid block is a rectangular region, and the continuous value crossing of the attribute parameter encoding layer adopts a real number linear crossing method.

8. A coastal flexible protection spatial layout optimization system, used to implement the method as described in claim 1, characterized in that, include: The grid generation module is used to divide the target coastal area into grid cells, with each individual grid cell serving as the decision-making unit. The parameter setting module is used to set the protection type and corresponding attribute parameters of each grid cell as spatial decision variables, determine the vegetation survival habitat conditions as hard constraints, and set the maximization of nearshore wave height attenuation rate as the single optimization objective. The algorithm solution module is used to iteratively solve the problem using a genetic algorithm and output a spatial configuration scheme that satisfies the hard constraints and makes the single optimization objective optimal. The algorithm solving module performs steps a) to d) during the solving process.

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