Intelligent layout method and system of lake benthonic animal functional reef

Abstract

The invention relates to the technical field of data processing, and discloses an intelligent layout method and system for lake benthonic animal functional reefs. The method comprises the following steps: carrying out gridding processing on lake environment parameters to obtain a standardized data matrix; constructing a reef body based on a hedera helix seed germination mechanism, and laying population individuals; calculating a growth radius by using an expansion growth principle and searching an optimal position to generate an expansion layout point; and optimizing a layout scheme through a competition elimination mechanism. According to the method, the technical problems of insufficient environmental data utilization, lack of intelligent algorithm support for position optimization and unreasonable resource allocation in lake benthonic animal functional reef layout are solved, and comprehensive optimization of multi-dimensional environmental factors and adaptive adjustment of a layout scheme are realized through the intelligent layout method based on the hedera helix algorithm.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to an intelligent layout method and system for lake benthic animal functional reefs. BACKGROUND

[0002] The existing lake benthic animal functional reef layout technology mainly relies on traditional engineering experience and simple mathematical models to determine the position, usually adopts grid uniform distribution or simple optimization method based on a single environmental factor. These methods mostly use traditional materials such as concrete and stone for reef material selection, and mainly determine the reef position through artificial survey and expert experience judgment in layout strategy, lacking comprehensive consideration of complex lake environmental factors and intelligent decision support.

[0003] However, the existing technology has significant deficiencies: first, the traditional layout method cannot effectively integrate multi-dimensional environmental parameters such as water depth distribution, bottom type, flow velocity, and nutrient salt concentration, resulting in insufficient utilization of environmental data; second, lacking scientific position optimization algorithm, the layout position selection mainly relies on experience judgment, which is prone to problems such as uneven layout density and unreasonable resource allocation; third, the existing method lacks individualized adjustment mechanism in reef function configuration, and cannot accurately configure parameters such as microbial inoculant load and larval attractant concentration according to the environmental characteristics of different positions. SUMMARY

[0004] The present application provides an intelligent layout method and system for lake benthic animal functional reefs, which solves the technical problems of insufficient utilization of environmental data, lack of intelligent algorithm support for position optimization, and unreasonable resource allocation in lake benthic animal functional reef layout, and realizes comprehensive optimization of multi-dimensional environmental factors and adaptive adjustment of layout scheme through the intelligent layout method based on ivy algorithm.

[0005] In a first aspect, the present application provides an intelligent layout method for lake benthic animal functional reefs, which comprises: Grid division and numerical normalization processing of lake environmental parameters including water depth distribution, bottom type, flow velocity, and nutrient salt concentration to obtain a standardized environmental data matrix; According to the seed germination mechanism in ivy algorithm, multiplying the environmental suitability index of each position in the standardized environmental data matrix by a random factor to generate a root strength parameter, and combining the position coordinates and growth direction angle to construct a reef layout population individual; Based on the principle of ivy plant expansion growth, multiplying the root strength parameter of the reef layout population individual by the growth coefficient to calculate the growth radius, searching for candidate positions within the 8-neighborhood range and calculating the growth suitability index, and updating the population individual to generate an expansion layout point from the highest suitability index position; Through the Ivy algorithm competition elimination mechanism, the comprehensive fitness evaluation value of the extended deployment point is calculated, the superior individual is reserved and the inferior individual is eliminated according to the fitness ranking, and the intelligent reef deployment scheme is obtained.

[0006] Optionally, the lake area is spatially divided according to a grid size of 10m*10m to obtain regular grid units; numerical extraction is performed on the water depth distribution, bottom type, flow velocity and nutrient salt concentration original data in each grid unit to obtain a multi-dimensional environmental parameter original data set; each type of parameter in the multi-dimensional environmental parameter original data set is subjected to numerical transformation according to a maximum-minimum normalization formula to obtain normalized parameter values in the interval [0, 1]; according to the ecological demand characteristics of the snail and mussel benthic animals, the normalized parameter values are assigned corresponding weight coefficients and subjected to weighted calculation to obtain a standardized environmental data matrix.

[0007] Optionally, the weight coefficients and the normalized parameter values at each grid position in the standardized environmental data matrix are summed to obtain an environmental suitability index at each position; the environmental suitability index is multiplied by a random factor in the interval [0.8, 1.2] to obtain a corresponding root strength parameter at each position; the number of seed nodes is determined according to the calculation method of dividing the lake area by 1000 and then taking the integer part, and adding 10, and a corresponding number of nodes in the positions where the root strength parameter is distributed within a preset threshold range are randomly selected as initial planting points to obtain seed node position coordinates; the seed node position coordinates are combined with the corresponding root strength parameters and a random growth direction angle in the range of 0 degrees to 360 degrees to obtain reef deployment population individuals.

[0008] Optionally, the root strength parameter of the reef deployment population individual is multiplied by a growth coefficient of 2.5 to obtain a corresponding growth radius of each individual; based on the position coordinates of the reef deployment population individual, neighborhood search is performed in the growth radius range according to 8 directions of up, down, left, right, top left, top right, bottom left and bottom right to obtain a candidate position set; according to the growth direction angle of the reef deployment population individual, the angle deviation of each position in the candidate position set from the growth direction is calculated, and the growth suitability index is calculated in combination with a distance decay factor and a direction preference factor to obtain the suitability value of each candidate position; the position with the highest growth suitability index in the candidate position set is selected as a new growth point, and the position coordinates of the population individual are updated to obtain an extended deployment point; for the extended deployment point with a root strength parameter greater than 0.7, branching processing is performed according to a probability of 0.3, and a branch individual is generated by randomly deflecting an angle in the range of plus or minus 60 degrees based on the original growth direction angle, to obtain an extended deployment point set containing branches.

[0009] Optionally, the locations in the candidate location set are numerically sorted according to the growth suitability index to obtain a suitability ranking list; the location coordinates with the highest value in the suitability ranking list are extracted as the optimal growth location to obtain the new growth point coordinates; the original location coordinates of the population individuals deployed on the reef are replaced and updated according to the new growth point coordinates to obtain the population individuals with updated locations; the environmental suitability index of the population individuals at the new location is recalculated based on the population individuals with updated locations, and the corresponding root strength parameters are updated to obtain the expanded deployment points.

[0010] Optionally, ecological benefit index, economic benefit index, and environmental impact index are calculated for each of the extended deployment points, and weighted summation is performed according to weight coefficients of 0.5, 0.3, and 0.2 to obtain the comprehensive fitness evaluation value of each deployment point; the comprehensive fitness evaluation values ​​are sorted in descending order of numerical value to obtain the fitness ranking result; based on the fitness ranking result, the top 50% of deployment points are retained as advantageous individuals, the bottom 30% of deployment points are eliminated as disadvantageous individuals, and the middle 20% of deployment points are selected and retained with a probability of 0.5 multiplied by the ranking ratio to obtain the filtered deployment point set; the coordinates, reef size parameters, and functional configuration parameters of each deployment point in the filtered deployment point set are integrated and output to obtain the intelligent reef deployment scheme.

[0011] Optionally, the distance between adjacent deployment points in the intelligent reef deployment scheme is calculated. When the distance is less than 30 meters, a connection relationship is established to obtain the reef group layout. The local ecological fitness index is calculated based on the root strength parameters of each deployment point in the reef group layout, and the length, width, and height dimensions of the reef at the corresponding locations are adjusted according to the fitness values ​​to obtain an optimized deployment scheme. Based on the optimized deployment scheme, the microbial agent loading and larval attractant concentration configuration parameters are calculated, and the mesh size and material specifications of the anti-predation net are determined to obtain a functional configuration deployment scheme. The root strength parameters of the eliminated deployment points are allocated to neighboring retained deployment points according to distance weights, and the environmental suitability index of each deployment point after allocation is recalculated to obtain a resource redistribution deployment scheme. The convergence of the resource redistribution deployment scheme is determined, and the optimization is terminated when the fitness change is less than 0.01 in 5 consecutive iterations or the maximum number of iterations of 200 is reached, to obtain a lake benthic animal functional reef deployment scheme. The coordinate positions of each reef in the deployment scheme are sorted according to water depth from shallow to deep, and the installation time interval between adjacent reefs is calculated to obtain construction guidance parameters.

[0012] Secondly, this application provides an intelligent deployment system for functional reefs of lake benthic animals, the intelligent deployment system for functional reefs of lake benthic animals comprising: The environmental normalization module is used to perform gridding and numerical normalization of lake environmental parameters, including water depth distribution, sediment type, water flow velocity, and nutrient concentration, to obtain a standardized environmental data matrix. The germination module is used to generate root strength parameters by multiplying the environmental suitability index of each position in the standardized environmental data matrix with a random factor according to the seed germination mechanism in the ivy algorithm, and constructing the reef population individuals by combining the position coordinates and growth direction angle. The neighborhood growth module is used to calculate the growth radius by multiplying the root strength parameter of the individual population deployed on the reef with the growth coefficient based on the principle of ivy expansion growth. It searches for candidate positions within an 8-neighborhood and calculates the growth suitability index. The population individual is updated with the position of the highest suitability index to generate the expansion deployment point. The competitive selection module is used to calculate the comprehensive fitness evaluation value of the extended deployment points through the Ivy League algorithm competitive elimination mechanism, retain the superior individuals and eliminate the inferior individuals according to the fitness ranking, so as to obtain the reef intelligent deployment scheme.

[0013] Thirdly, an intelligent deployment device for lake benthic animal functional reefs is provided, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor invokes the instructions in the memory to cause the intelligent deployment device for lake benthic animal functional reefs to execute the aforementioned intelligent deployment method for lake benthic animal functional reefs.

[0014] Fourthly, a computer-readable storage medium is provided, wherein instructions are stored therein, which, when executed on a computer, cause the computer to perform the above-described intelligent deployment method for functional reefs for lake benthic animals.

[0015] The technical solution provided in this application solves the technical problem of the inability to uniformly process and effectively utilize multidimensional environmental data in traditional methods by dividing lake environmental parameters into grids and performing numerical normalization to obtain a standardized environmental data matrix. This enables the comprehensive analysis and comparison of heterogeneous environmental factors such as water depth distribution, substrate type, water flow velocity, and nutrient concentration within a unified numerical framework. Based on the seed germination mechanism in the ivy algorithm, the reef population is constructed by multiplying the environmental suitability index with a random factor to generate root strength parameters. Combined with a combination of location coordinates and growth direction angles, this achieves proactive adaptation and intelligent selection of environmental factors during the initialization of deployment locations, avoiding the blindness and inefficiency of traditional random initialization methods. The application of the ivy plant expansion growth principle, by multiplying the root strength parameter with the growth coefficient to calculate the growth radius, searching for candidate locations within an 8-neighborhood and calculating the growth suitability index, achieves spatial continuity and ecological correlation optimization of the deployment scheme, overcoming the shortcomings of existing technologies where deployment points are independent and lack systematic consideration. The competitive elimination mechanism calculates the comprehensive fitness evaluation value of the expanded deployment points, and retains the superior individuals and eliminates the inferior individuals according to the fitness ranking. This strategy ensures the global optimization of the deployment plan and the rationality of resource allocation under the constraint of limited resources.

[0016] The seed germination mechanism simulates the sensitivity and selectivity of plants to the environment, enabling the algorithm to automatically identify and select areas with high environmental suitability as the starting point for reef deployment; the expansion growth principle reflects the directionality and continuity of plant growth, enabling the deployment scheme to form an organic spatial network structure that meets the ecological requirements of benthic animal habitat connectivity; the 8-neighborhood search strategy simulates the multi-directional expansion characteristics of plant roots, ensuring the comprehensiveness and optimality of deployment location selection; the competitive elimination mechanism reflects the survival law of nature, ensuring the adaptability and sustainability of the deployment scheme. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of an embodiment of the intelligent deployment method for functional reefs for lake benthic animals in this application. Figure 2 This is a diagram showing the results of the intelligent deployment optimization scheme for lake reefs in this application embodiment; Figure 3This is a flowchart illustrating the intelligent deployment and iterative optimization process of functional reefs for lake benthic animals in this application embodiment; Figure 4 This is a schematic diagram of an embodiment of the intelligent deployment system for functional reefs of lake benthic animals in this application. Figure 5 This is a schematic block diagram of the intelligent deployment device for functional reefs for lake benthic animals in an embodiment of the present invention. Detailed Implementation

[0019] This application provides an intelligent deployment method and system for functional reefs for lake benthic animals. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0020] For ease of understanding, the specific process of the embodiments of this application is described below. Please refer to [link / reference]. Figure 1 One embodiment of the intelligent deployment method for lake benthic animal functional reefs in this application includes: Step S1: The lake environmental parameters, including water depth distribution, substrate type, water flow velocity and nutrient concentration, are divided into grids and normalized to obtain a standardized environmental data matrix.

[0021] Specifically, the lake is divided into several regular or irregular grids, each grid cell corresponding to a spatial location within the lake. Environmental parameters such as water depth, substrate type, water flow velocity, and nutrient concentration are obtained for each grid cell through measurement or simulation. Water depth can be taken as the average value or the value at the center point of each grid cell. Substrate type is represented by classification codes, such as sand, mud, and gravel, each assigned a different value. Water flow velocity and direction are obtained through field measurements or hydrodynamic models, while nutrient concentration includes specific values ​​for indicators such as nitrogen and phosphorus. These data with different dimensions are numerically normalized to eliminate dimensional differences, making the indicators comparable and facilitating analysis. Common methods include min-max normalization or Z-score standardization. For categorical variables such as substrate type, one-hot encoding can be used to convert them into vector form for unified representation with continuous variables. After normalization, the standardized data of each grid cell are combined into a vector in a fixed order, and the vectors of all grid cells are arranged in grid order to form a complete standardized environmental data matrix. Each row corresponds to the environmental characteristics of a grid cell, and each column in the matrix represents different environmental indicators. This not only reflects the environmental differences in different areas of the lake intuitively, but also facilitates subsequent spatial analysis, ecological model construction, or time series analysis.

[0022] Step S2: Based on the seed germination mechanism in the Ivy algorithm, the environmental suitability index of each location in the standardized environmental data matrix is ​​multiplied by a random factor to generate root strength parameters, and the reef population individuals are constructed by combining the location coordinates and growth direction angle.

[0023] Specifically, based on the seed germination mechanism in the Ivy algorithm, the environmental suitability index of each grid cell in the standardized environmental data matrix is ​​extracted and multiplied by a randomly generated factor to obtain the root strength parameter at that location. This introduces the dual effects of environmental driving forces and random perturbations, resulting in differences in the growth potential of roots across different grids while retaining a certain degree of randomness to simulate natural variation. Combining the spatial coordinates of each grid cell and the preset growth direction angle, the generated root strength parameter is mapped to specific individuals in the reef population. The position of each individual is determined by the grid coordinates, its growth direction is controlled by the angle parameter, and the root strength determines the individual's growth potential and expansion range. During the deployment process, the interactions and growth constraints between individuals can be iteratively calculated to allow the population to gradually expand and form a spatial distribution pattern. The entire process integrates environmental suitability, random factors, location coordinates, and growth direction, realizing the transformation from the environmental matrix to individual reef populations. This ensures that the generated reef deployment reflects environmental heterogeneity while retaining dynamic growth characteristics, providing basic data and parameter support for ecological simulation and optimization design.

[0024] Step S3: Based on the principle of ivy expansion growth, the root strength parameter of the individual population deployed on the reef is multiplied by the growth coefficient to calculate the growth radius. Candidate positions are searched within an 8-neighborhood and the growth suitability index is calculated. The population individual is updated to generate expansion deployment points based on the position with the highest suitability index.

[0025] Specifically, based on the expansion growth principle of ivy, the root strength parameter of each individual population deployed on the reef is multiplied by a preset growth coefficient to calculate its potential growth radius, thereby determining the spatial range for individual expansion. Then, the surrounding grid cells are searched within the 8-neighborhood corresponding to this growth radius to extract the environmental suitability index of each candidate location. The growth suitability index of this location is calculated by combining random factors or other influencing factors. Next, the growth suitability indices of all candidate locations are compared, and the location with the highest index is selected as the new expansion deployment point. The spatial coordinates of the original population individuals are updated using this point to realize the spatial expansion of individuals. In this process, each expansion not only considers the suitability of environmental conditions but also combines the individual's root strength and growth potential to control the growth distance, thereby simulating the dynamic characteristics of ivy's natural extension and climbing. After multiple iterations, the population individuals gradually expand to form a spatial distribution pattern of reef deployment, which can reflect environmental heterogeneity and interactions between individuals, providing a scientific basis and operable parameterization method for the ecological optimization layout of reefs.

[0026] Step S4: Using the Ivy League algorithm's competitive elimination mechanism, calculate the comprehensive fitness evaluation value for the expanded deployment points, retain the superior individuals and eliminate the inferior individuals according to the fitness ranking, and obtain the reef intelligent deployment scheme.

[0027] Specifically, through the competitive elimination mechanism of the Ivy algorithm, a comprehensive fitness evaluation value is calculated for all expanded deployment points. This evaluation value combines factors such as the environmental suitability index, root strength parameters, and growth potential of each point to comprehensively reflect the growth advantages and ecological adaptability of each deployment point under the current environmental conditions. All expansion points are ranked according to fitness, and individuals with higher fitness are retained as superior individuals, while inferior individuals with lower fitness are eliminated. This simulates the laws of resource competition and survival of the fittest in nature. Through this selection process, only individuals that can effectively grow and expand in the environment are retained in the population, forming an optimized intelligent reef deployment scheme. This scheme not only reflects the constraint and guidance of environmental heterogeneity on deployment points, but also takes into account individual growth potential and spatial distribution balance, thereby achieving a scientific, efficient, and sustainable reef ecological deployment design.

[0028] It is understood that the implementing entity of this application can be an intelligent deployment system for functional reefs of lake benthic animals, or it can be a terminal or a server; the specific implementation is not limited here. This application's embodiments use a server as an example for illustration.

[0029] In one specific embodiment, the process of performing step S1 may specifically include the following steps: (1) Divide the lake area into regular grid units by a grid size of 10 meters × 10 meters; (2) Numerical extraction was performed on the original data of water depth distribution, sediment type, water flow velocity and nutrient concentration in each grid cell to obtain the original dataset of multidimensional environmental parameters; (3) The parameters in the original dataset of multidimensional environmental parameters are transformed according to the maximum-minimum normalization formula to obtain the normalized parameter values ​​in the interval [0,1]. (4) Based on the ecological needs of snails and clams, the normalized parameter values ​​are assigned corresponding weight coefficients and weighted to obtain a standardized environmental data matrix.

[0030] Specifically, the lake area is spatially divided into regular grid units of 10m x 10m to facilitate refined management and analysis of the lake space. Then, raw environmental data such as water depth distribution, substrate type, water flow velocity, and nutrient concentration are extracted from each grid unit to construct a multidimensional environmental parameter dataset. This quantifies the ecological characteristics of each grid unit. Next, the parameters in this multidimensional dataset are numerically transformed using a maximum-minimum normalization formula, mapping all parameters to the [0,1] interval. This eliminates differences between different dimensions and magnitudes, ensuring comparability in subsequent analyses. Furthermore, based on the ecological needs of benthic animals such as snails and clams, corresponding weight coefficients are assigned to the normalized parameters. The combined effect of various environmental indicators is then integrated through weighted calculations to generate a standardized environmental data matrix. Each unit in the matrix reflects both the quality of local environmental conditions and the ecological preferences of organisms, providing scientific, unified, and quantifiable basic data support for subsequent ecological modeling, population distribution prediction, and intelligent deployment.

[0031] For example, in the lake gridding process, a lake area of ​​100m × 100m can be divided into 100 grid cells of 10m × 10m each, with each cell having a unique spatial coordinate identifier. For instance, in water depth data extraction, a grid cell might have a water depth of 3.2m obtained through depth sounding and recorded in the original dataset of multidimensional environmental parameters. This cell also has a sediment type of silt mixture (represented by the value 2), a measured water flow velocity of 0.15m / s, and nitrogen and phosphorus concentrations of 1.2mg / L and 0.08mg / L, respectively. Furthermore, in the normalization process, after mapping the minimum water depth of 1m and the maximum water depth of 5m across the entire lake to the [0,1] interval, the normalized water depth value for this grid cell is 0.5. 5. Similarly, water flow velocity and nutrient concentration are also converted using the maximum-minimum formula. For example, in the weighted calculation, based on the ecological characteristics of snails—sensitivity to water depth, preference for sediment, moderate water flow, and general nutrient requirements—water depth is assigned a weight of 0.4, sediment weight 0.3, water flow velocity weight 0.2, and nutrient concentration weight 0.1. After weighted calculation, the standardized environmental suitability index of this grid unit is 0.42, reflecting the comprehensive suitability of this location for the target benthic animals. For example, when constructing a reef population, this grid unit can be used as a candidate location. The growth potential of individuals can be determined by root strength parameters and growth direction, allowing them to participate in the survival of the fittest and spatial expansion during the expansion process, thereby generating a scientific and reasonable intelligent reef deployment scheme.

[0032] In one specific embodiment, the process of performing step S2 may specifically include the following steps: (1) The weight coefficient and normalized parameter value of each grid location in the standardized environmental data matrix are summed to obtain the environmental suitability index of each location; (2) Multiply the environmental suitability index by a random factor in the range of 0.8 to 1.2 to obtain the root strength parameters corresponding to each location; (3) Determine the number of seed nodes by dividing the lake area by 1000, rounding down, and adding 10. Randomly select the corresponding number of nodes as initial planting points in the locations where the root strength parameters are distributed within the preset threshold range to obtain the coordinates of the seed node positions. (4) Combine the seed node position coordinates with the corresponding root strength parameters and random growth direction angles within the range of 0 to 360 degrees to obtain the population individuals deployed on the reef.

[0033] Specifically, the normalized parameter value and corresponding weight coefficient of each grid location in the standardized environmental data matrix are summed to obtain the environmental suitability index for each location. This index is then multiplied by a random factor within the range of 0.8 to 1.2 to generate the root strength parameter for each location, introducing environmental heterogeneity and random disturbance. The number of seed nodes is determined by dividing the lake area by 1000, rounding down, and then adding 10. A corresponding number of nodes are randomly selected from locations where the root strength parameter distribution falls within a preset threshold range to obtain the location coordinates of the seed nodes. These seed node location coordinates are combined with the corresponding root strength parameter and a random growth direction angle within the range of 0 to 360 degrees to form the individual reef-based population, providing initial conditions for expanded deployment and intelligent optimization.

[0034] Taking a medium-sized lake as an example, the lake was first divided into 10m x 10m grid cells. The water depth, substrate type, flow velocity, and nutrient concentration of each cell were measured, recorded, and normalized. Then, an environmental suitability index was calculated by assigning weights to each indicator according to the ecological preferences of snails and clams. The index was multiplied by a random factor between 0.9 and 1.1 to obtain the root strength parameter. The number of seed nodes was determined by dividing the lake area by 1000, rounding down, and adding 12. Root nodes were randomly selected from grid cells with root strength ranging from 0.6 to 0.9. A certain number of nodes are selected as initial planting points. The coordinates of these seed nodes are combined with root strength parameters and random growth direction angles from 0 to 360 degrees to form individuals for reef deployment. After the initial deployment is completed, during the iteration process, expansion positions are searched within an 8-neighborhood based on root strength and growth direction. The optimal position is selected based on the growth suitability index to generate new expansion deployment points. After several rounds of expansion and competitive elimination, individuals with higher fitness are retained to form the final intelligent reef deployment scheme, thereby optimizing the habitat of lake benthic animals and enhancing the ecological function of the reef.

[0035] In one specific embodiment, the process of performing step S3 may specifically include the following steps: (1) Multiply the root strength parameter of the population individuals on the reef by the growth coefficient 2.5 to obtain the growth radius of each individual; (2) Based on the location coordinates of the population individuals on the reef, a neighborhood search is performed within the growth radius in eight directions: up, down, left, right, upper left, upper right, lower left, and lower right to obtain a set of candidate locations; (3) Based on the growth direction angle of the population individuals deployed on the reef, calculate the angular deviation of each position in the candidate position set from the growth direction, and calculate the growth suitability index by combining the distance decay factor and the direction preference factor to obtain the suitability value of each candidate position. (4) Select the position with the highest growth suitability index in the candidate position set as the new growth point, and update the position coordinates of the population individuals to obtain the expanded deployment points; (5) For the extended layout points with root strength parameters greater than 0.7, branching is performed with a probability of 0.3. Based on the original growth direction angle, the branch individuals are generated by randomly deflecting within a range of ±60 degrees, thus obtaining the set of extended layout points containing branches.

[0036] Specifically, the root strength parameter of the individuals in the reef population is multiplied by the growth coefficient 2.5 to calculate the growth radius. ,in For the first Root strength parameters of each individual plant This represents the expandable radius of the individual in space. Based on the individual's position coordinates, a neighborhood search is performed within its growth radius in eight directions: up, down, left, right, upper left, upper right, lower left, and lower right, forming a set of candidate positions. For each candidate position in the set, the angle of the individual's growth direction is considered. Calculate the angular deviation of the line connecting this position to the direction of the line. ,in The direction angle from the candidate position to the original position, combined with the distance decay factor. and directional preference factor Calculate the growth suitability index ,in This represents the distance from the candidate position to the original position. Root strength is a parameter that reflects the combined effect of environmental suitability and individual growth potential. It is selected from the candidate location set. The largest position is used as a new growth point and the position coordinates of the population individuals are updated to form an expansion layout point. For expansion layout points with root strength parameters greater than 0.7, branching is performed with a probability of 0.3. The branch individuals are generated by randomly deflecting the original growth direction angle by ±60 degrees, resulting in an expansion layout point set containing the main growth point and branch points, thus realizing the multi-directional spatial expansion and dynamic optimization of the reef population.

[0037] Taking the layout of reefs in a lake as an example, the growth radius of each individual reef is calculated based on its root strength parameters. Then, based on the individual's location coordinates, a neighborhood search is performed within the growth radius along the vertical, horizontal, left-right, and four diagonal directions to select several candidate locations. Next, based on the individual's growth direction, the directional deviation of each candidate location is calculated, and the growth suitability index is evaluated by combining distance and directional preference. Subsequently, the location with the highest suitability is selected as the new growth point, and the location coordinates of the population individuals are updated to form an expansion layout point. For expansion points with high root strength, branch individuals are generated by randomly deflecting from the original growth direction with a certain probability, so that the layout points not only extend along the main direction but also form a multi-directional branching structure. After multiple iterations, the population individuals gradually expand within the lake, and the set of reef layout points composed of the main growth point and branch points achieves spatial balance and ecological optimization of the layout.

[0038] In one specific embodiment, the process of selecting the position with the highest growth suitability index from the candidate position set as the new growth point and updating the position coordinates of individuals in the population to obtain the expanded deployment points can specifically include the following steps: (1) Sort each position in the candidate position set according to the growth suitability index to obtain a suitability ranking list; (2) Extract the coordinates of the position with the highest value in the suitability ranking list as the optimal growth position to obtain the coordinates of the new growth point; (3) Replace and update the original position coordinates of the population individuals deployed on the reef according to the coordinates of the new growth point to obtain the population individuals after position update; (4) Based on the updated location of the population individuals, recalculate their environmental suitability index at the new location and update the corresponding root strength parameters to obtain the expanded deployment points.

[0039] Specifically, the candidate locations are first sorted numerically according to their growth suitability index to generate a suitability ranking list. The location with the highest value is then selected as the optimal growth location, thus determining the coordinates of the new growth point. This new growth point coordinate is then used to replace and update the original location coordinates of the population individuals deployed on the reef, ensuring that the individual population's location reflects the latest spatial expansion status. Subsequently, based on the updated location, the environmental suitability index of the individual population at the new location is recalculated, and the corresponding root strength parameter is adjusted according to the updated suitability index to quantify the growth potential of each individual at the new location. This results in expansion deployment points that consider both the spatial advantages and disadvantages of candidate locations, as well as the individual's own growth capacity and environmental conditions, achieving a dynamic and optimized layout of the deployed population within the lake. Through multiple iterations, the population individuals gradually concentrate their growth in areas with higher environmental suitability. Simultaneously, the updated root strength reflects the individual's ecological adaptability at the new location, ultimately forming a set of expansion deployment points that meets both environmental conditions and population expansion potential. This provides reliable data support and operational basis for optimizing intelligent deployment schemes and enhancing reef functionality.

[0040] Taking the deployment of benthic animal functional reefs in a large lake as an example, assuming that the initial deployment population consists of 20 individuals in the eastern region of the lake, with each individual having a root strength parameter between 0.6 and 0.8, candidate locations are searched within the neighborhood of these individuals, and the growth suitability index in eight directions is calculated. The results show that the suitability index of an individual in the upper right position is the highest at 0.72. The grid coordinates (18,12) of this position are selected as the new growth point coordinates. Subsequently, the original coordinates (16,10) of this individual are updated to (18,12). After the position is updated, the environmental suitability index is recalculated and found to have increased from the original 0.65 to 0.71, and the corresponding root strength parameter is also updated to 0.75. Meanwhile, another individual had the highest suitability index of 0.68 in the candidate location, corresponding to grid coordinates (20,14). The original location (19,13) was replaced and updated, and the root strength increased from 0.70 to 0.74. Through continuous iteration, the population gradually moved to areas with better environmental conditions and formed extended deployment points. The coordinates, root strength, and suitability index of each deployment point were dynamically updated, so that the final set of 30 extended deployment points was not only evenly distributed, but also had high ecological adaptability and growth potential in the lake. This provided data support and a basis for spatial optimization layout for the intelligent deployment of benthic animal functional reefs.

[0041] In one specific embodiment, the process of performing step S4 may specifically include the following steps: (1) Calculate the ecological benefit index, economic benefit index and environmental impact index for each extended deployment point, and sum them up according to the weight coefficients of 0.5, 0.3 and 0.2 to obtain the comprehensive fitness evaluation value of each deployment point; (2) Sort the comprehensive fitness evaluation values ​​in descending order of numerical value to obtain the fitness ranking results; (3) Based on the fitness ranking results, the top 50% of the placement points are retained as dominant individuals, the bottom 30% of the placement points are eliminated as inferior individuals, and the middle 20% of the placement points are selected and retained according to the probability of 0.5 times the ranking ratio, so as to obtain the set of placement points after screening. (4) The coordinates, reef size parameters and functional configuration parameters of each deployment point in the selected deployment point set are integrated and output to obtain the intelligent deployment scheme of the reef.

[0042] Specifically, the ecological benefit index, economic benefit index, and environmental impact index are calculated for each extended deployment point, and weighted summation is performed using weighting coefficients of 0.5, 0.3, and 0.2 to obtain the comprehensive fitness evaluation value for each deployment point. All deployment points are then ranked in descending order of their comprehensive fitness values ​​to form a fitness ranking result. Based on this ranking, the top 50% of deployment points are retained as dominant individuals, the bottom 30% are eliminated as suboptimal individuals, and the middle 20% are randomly selected and retained with a probability of 0.5 multiplied by their ranking ratio, thus obtaining the filtered deployment point set. The spatial coordinates, reef size parameters, and functional configuration parameters of each deployment point in the filtered set are integrated and output to form a complete intelligent reef deployment scheme. This scheme ensures that the deployment points possess both high ecological and economic benefits while minimizing environmental impact, achieving a scientifically sound and optimized reef layout.

[0043] Taking the deployment of reefs in a medium-sized lake as an example, the ecological benefit index, economic benefit index, and environmental impact index were calculated for 50 extended deployment points. Assuming a certain deployment point has an ecological benefit of 0.82, an economic benefit of 0.65, and an environmental impact of 0.4, the comprehensive fitness evaluation value was obtained by weighting and summing the values ​​with weighting coefficients of 0.5, 0.3, and 0.2: 0.82×0.5 + 0.65×0.3 + 0.4×0.2 = 0.685. After performing the same calculation on all deployment points, the comprehensive fitness values ​​were sorted in descending order, resulting in a fitness ranking. The highest fitness point was 0.78, and the lowest was 0.32. Based on the ranking, the top 50% (25 deployment points) were retained as dominant individuals, and the bottom 30% (15 deployment points) were eliminated as suboptimal individuals. The remaining 10 deployment points were randomly selected with a probability of 0.5 multiplied by their ranking ratio, resulting in a deployment set of 30 points. The spatial coordinates, reef dimensions (e.g., length 3 meters, width 2 meters, height 1.5 meters), and functional configuration parameters (e.g., habitat and purification functions) of each point in the selected deployment point set are integrated and output to form a complete intelligent reef deployment scheme. This scheme balances maximizing ecological benefits, rationalizing economic investment, and minimizing environmental impact, achieving scientific optimization and functional layout of reefs within the lake. (Reference) Figure 2 The image shows the results of the intelligent deployment optimization scheme for lake reefs.

[0044] In one specific embodiment, the following steps are also included: (1) Calculate the distance between adjacent deployment points in the intelligent deployment scheme of the reef. When the distance is less than 30 meters, establish the connection relationship to obtain the reef group layout. (2) Calculate the local ecological fitness index based on the root strength parameters of each deployment point in the reef group layout, and adjust the reef length, width and height dimensions of the corresponding locations according to the fitness values ​​to obtain the optimized deployment scheme. (3) Calculate the microbial agent loading and larval attractant concentration configuration parameters based on the specification optimization layout scheme, and determine the mesh size and material specifications of the anti-predation net to obtain the functional configuration layout scheme; (4) The root strength parameters of the eliminated deployment points are allocated to the adjacent retained deployment points according to the distance weight, and the environmental suitability index of each deployment point after allocation is recalculated to obtain the resource redistribution deployment scheme. (5) Convergence determination of the resource redistribution layout scheme. When the fitness change is less than 0.01 in 5 consecutive iterations or the maximum number of iterations is reached 200, the optimization is terminated to obtain the layout scheme of functional reefs for lake benthic animals. (6) Sort the coordinates of each reef in the layout plan according to the water depth from shallow to deep, and calculate the installation time interval between adjacent reefs to obtain the construction guidance parameters.

[0045] Specifically, the spatial distance between adjacent points in the intelligent reef deployment scheme is calculated. When the distance between any two points is less than 30 meters, a connection is established to form a reef group layout. Then, based on the root strength parameters of each deployment point within the group, the local ecological fitness index is calculated, and the length, width, and height of the deployment points are dynamically adjusted according to the fitness values ​​to obtain an optimized deployment scheme. Next, based on the optimized deployment scheme, the microbial agent loading and larval attractant concentration configuration for each reef are calculated, and the mesh size and material specifications of the anti-predation nets are determined to form a functional configuration deployment scheme. For the eliminated deployment points, their root strength parameters are redistributed to the adjacent retained deployment points according to distance weights, and the environmental suitability index of each deployment point is recalculated accordingly to obtain a resource redistribution deployment scheme. The scheme is then convergent. Optimization is terminated when the fitness change is less than 0.01 in five consecutive iterations or the number of iterations reaches the maximum value of 200, thus forming a functional reef deployment scheme for lake benthic animals. The spatial coordinates of each reef in the deployment plan are sorted according to water depth from shallowest to deepest, and the installation time interval between adjacent reefs is calculated. This yields parameters that can be used for construction guidance, ensuring an efficient and scientific deployment process while considering ecological function and operational feasibility. (Reference) Figure 3 The diagram illustrates the intelligent deployment and iterative optimization process of functional reefs for lake benthic animals.

[0046] For example, in a reef group layout, if the distance between two placement points is 25 meters, a connection is established, placing them in the same group. For example, in a specification optimization layout scheme, if the root strength of a certain placement point is 0.85, and the calculated local ecological adaptability is high, then the length of the reef is adjusted from 2 meters to 3 meters, the width from 1.5 meters to 2 meters, and the height from 1 meter to 1.5 meters to enhance its ecological function. For example, in a functional configuration layout scheme, the calculated microbial agent loading for the above reef is 1.2 kg, and the larval attractant concentration is 0.8 mg. / liter, while determining the mesh size of the anti-predation net to be 5 cm, and selecting corrosion-resistant polyethylene material; for example, in the resource redistribution process, the root strength of a certain eliminated deployment point is 0.6, and according to the distance weight to the adjacent retained deployment points, it is allocated to the two adjacent points on both sides, so that the environmental suitability index of the adjacent deployment points increases by 0.05 and 0.03 respectively; for example, in the formulation of construction guidance parameters, the reefs in the deployment scheme are sorted according to water depth from shallow to deep, and the installation time interval between adjacent reefs is set to 30 minutes to ensure construction efficiency and safety, while ensuring that the deployment sequence conforms to the ecological optimization layout.

[0047] The above describes the intelligent deployment method of lake benthic animal functional reefs in the embodiments of this application. The following describes the intelligent deployment system of lake benthic animal functional reefs in the embodiments of this application. Please refer to [link / reference]. Figure 4 One embodiment of the intelligent deployment system for lake benthic animal functional reefs in this application includes: The environmental normalization module is used to perform gridding and numerical normalization of lake environmental parameters, including water depth distribution, sediment type, water flow velocity, and nutrient concentration, to obtain a standardized environmental data matrix. The germination module is used to generate root strength parameters by multiplying the environmental suitability index of each position in the standardized environmental data matrix with a random factor according to the seed germination mechanism in the ivy algorithm, and constructing the reef population individuals by combining the position coordinates and growth direction angle. The neighborhood growth module is used to calculate the growth radius by multiplying the root strength parameter of the individual population deployed on the reef with the growth coefficient based on the principle of ivy expansion growth. It searches for candidate positions within an 8-neighborhood and calculates the growth suitability index. The population individual is updated with the position of the highest suitability index to generate expansion deployment points. The competitive selection module is used to calculate the comprehensive fitness evaluation value of the expanded deployment points through the Ivy League algorithm competitive elimination mechanism, retain the superior individuals and eliminate the inferior individuals according to the fitness ranking, so as to obtain the intelligent deployment scheme of the reef.

[0048] Through the collaborative efforts of all the aforementioned components, the entire reef deployment process can achieve a complete closed loop, from environmental data processing to intelligent optimization deployment. The environmental normalization module first performs spatial gridding and numerical normalization on the lake's water depth, substrate type, water flow velocity, and nutrient concentration to form a standardized environmental data matrix, providing a quantitative basis for deployment. The germination module, based on this, utilizes the seed germination mechanism of the ivy algorithm to multiply the environmental suitability index of each grid location with a random factor to generate root strength parameters. It then combines coordinate position and growth direction angle to construct the initial reef deployment population, providing a starting point for population expansion. The neighborhood growth module, based on the expansion growth principle of ivy plants, multiplies the root strength parameter of each individual with a growth coefficient to calculate the growth radius. It searches for candidate locations within an 8-neighborhood and evaluates the growth suitability index, selecting the location with the highest suitability to update individuals and generate expansion deployment points, achieving spatial expansion and multi-directional growth. The competitive selection module uses a competitive elimination mechanism to calculate the comprehensive fitness evaluation value of the expansion deployment points, retaining superior individuals and eliminating inferior ones according to the ranking, forming an optimized intelligent reef deployment scheme. Through the synergistic effect of the four modules, the deployment scheme can take into account environmental suitability, random diversity and growth potential, and achieve scientific, dynamic and efficient deployment of functional reefs for lake benthic animals.

[0049] aboveFigure 4 The intelligent deployment system for lake benthic animal functional reefs in this embodiment of the invention is described in detail from the perspective of modular functional entities. The intelligent deployment device for lake benthic animal functional reefs in this embodiment of the invention is described in detail from the perspective of hardware processing.

[0050] Reference Figure 5 This invention also provides an intelligent deployment device for functional reefs of lake benthic animals. This intelligent deployment device can be a server, and its internal structure can be as follows: Figure 5 As shown, the intelligent deployment device for the lake benthic animal functional reef includes a processor, memory, display screen, input device, network interface, and database connected via a system bus. The processor, designed as a computer, provides computing and control capabilities. The memory of the intelligent deployment device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the intelligent deployment device stores the data corresponding to this embodiment. The network interface of the intelligent deployment device is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements the above-described method.

[0051] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the intelligent deployment device for lake benthic animal functional reefs to which the present invention is applied.

[0052] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when the instructions are executed on a computer, cause the computer to perform the steps of the intelligent deployment method of the lake benthic animal functional reef.

[0053] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0054] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause an intelligent deployment device for a lake benthic animal functional reef (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0055] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for intelligently deploying functional reefs for benthic animals in lakes, characterized in that, The method includes: Step S1: The lake environmental parameters, including water depth distribution, sediment type, water flow velocity and nutrient concentration, are divided into grids and normalized to obtain a standardized environmental data matrix. Step S2: Based on the seed germination mechanism in the ivy algorithm, the environmental suitability index of each position in the standardized environmental data matrix is ​​multiplied by a random factor to generate root strength parameters, and the reef population individuals are constructed by combining the position coordinates and growth direction angle. Step S3: Based on the principle of ivy expansion growth, the root strength parameter of the individual population deployed on the reef is multiplied by the growth coefficient to calculate the growth radius. Candidate positions are searched within an 8-neighborhood and the growth suitability index is calculated. The population individual is updated with the position of the highest suitability index to generate the expansion deployment point. Step S4: Using the Ivy League algorithm's competitive elimination mechanism, calculate the comprehensive fitness evaluation value for the expanded deployment points, retain the superior individuals and eliminate the inferior individuals according to the fitness ranking, and obtain the reef intelligent deployment scheme.

2. The intelligent deployment method for functional reefs for lake benthic animals according to claim 1, characterized in that, Step S1 includes: The lake area was divided into regular grid units using a grid size of 10 meters × 10 meters. Numerical extraction was performed on the raw data of water depth distribution, sediment type, water flow velocity and nutrient concentration in each grid cell to obtain a raw dataset of multidimensional environmental parameters. The parameters in the original dataset of the multidimensional environmental parameters are transformed according to the maximum-minimum normalization formula to obtain the normalized parameter values ​​in the interval [0,1]. Based on the ecological needs of snails and clams, corresponding weight coefficients are assigned to the normalized parameter values ​​and weighted calculations are performed to obtain a standardized environmental data matrix.

3. The intelligent deployment method for functional reefs for lake benthic animals according to claim 1, characterized in that, Step S2 includes: The environmental suitability index for each location is obtained by summing the weight coefficient and normalized parameter value for each grid position in the standardized environmental data matrix. Multiply the environmental suitability index by a random factor in the range of 0.8 to 1.2 to obtain the root strength parameters corresponding to each location; The number of seed nodes is determined by dividing the lake area by 1000, rounding down, and then adding 10. A corresponding number of nodes are randomly selected as initial planting points from the locations where the root strength parameters are distributed within a preset threshold range, and the coordinates of the seed node positions are obtained. By combining the seed node location coordinates with the corresponding root strength parameters and random growth direction angles within the range of 0 to 360 degrees, the individual populations deployed on the reef can be obtained.

4. The intelligent deployment method for functional reefs for lake benthic animals according to claim 1, characterized in that, Step S3 includes: Multiply the root strength parameter of the individuals in the reef population by the growth coefficient 2.5 to obtain the growth radius of each individual. Based on the location coordinates of the population individuals deployed on the reef, a neighborhood search is performed within the growth radius in eight directions: up, down, left, right, upper left, upper right, lower left, and lower right, to obtain a set of candidate locations. Based on the growth direction angle of the individuals in the population deployed on the reef, the angular deviation from the growth direction is calculated for each position in the candidate position set, and the growth suitability index is calculated by combining the distance decay factor and the direction preference factor to obtain the suitability value of each candidate position. The position with the highest growth suitability index in the candidate position set is selected as the new growth point, and the position coordinates of the individuals in the population are updated to obtain the expanded deployment point; For the extended layout points with root strength parameters greater than 0.7, branching is performed with a probability of 0.

3. Based on the original growth direction angle, the branch individuals are generated by randomly deflecting the angle within a range of ±60 degrees, thus obtaining a set of extended layout points containing branches.

5. The intelligent deployment method for functional reefs for lake benthic animals according to claim 4, characterized in that, The step of selecting the position with the highest growth suitability index from the candidate position set as the new growth point and updating the position coordinates of the individuals in the population to obtain the expanded deployment points includes: The candidate locations are sorted numerically according to the growth suitability index to obtain a suitability ranking list; The coordinates of the position with the highest value in the suitability ranking list are extracted as the optimal growth position to obtain the coordinates of the new growth point; The original position coordinates of the population individuals deployed on the reef are replaced and updated based on the coordinates of the new growth point to obtain the population individuals with updated positions. Based on the updated location of the population individuals, their environmental suitability index at the new location is recalculated, and the corresponding root strength parameters are updated to obtain the expanded deployment points.

6. The intelligent deployment method for functional reefs for lake benthic animals according to claim 1, characterized in that, Step S4 includes: Ecological benefit index, economic benefit index and environmental impact index are calculated for the extended deployment points respectively, and weighted summation is performed according to weight coefficients of 0.5, 0.3 and 0.2 to obtain the comprehensive fitness evaluation value of each deployment point; The comprehensive fitness evaluation values ​​are sorted in descending order of numerical value to obtain the fitness ranking results; Based on the fitness ranking results, the top 50% of the placement points are retained as dominant individuals, the bottom 30% of the placement points are eliminated as inferior individuals, and the middle 20% of the placement points are selected and retained with a probability of 0.5 times the ranking ratio, thus obtaining the filtered placement point set. The coordinates, reef size parameters, and functional configuration parameters of each deployment point in the filtered deployment point set are integrated and output to obtain the intelligent reef deployment scheme.

7. The intelligent deployment method for functional reefs for lake benthic animals according to claim 1, characterized in that, Also includes: The distance between adjacent deployment points in the intelligent reef deployment scheme is calculated. When the distance is less than 30 meters, a connection relationship is established to obtain the reef group layout. The local ecological fitness index is calculated based on the root strength parameters of each deployment point in the reef group layout, and the length, width and height of the reef at the corresponding location are adjusted according to the fitness value to obtain the optimized deployment scheme. Based on the optimized layout scheme, the microbial agent loading and larval attractant concentration configuration parameters are calculated, and the mesh size and material specifications of the predatory net are determined to obtain the functional configuration layout scheme. The root strength parameters of the eliminated deployment points are allocated to the adjacent retained deployment points according to the distance weight, and the environmental suitability index of each deployment point after allocation is recalculated to obtain the resource redistribution deployment scheme. The convergence of the resource redistribution layout scheme is determined. The optimization is terminated when the fitness change is less than 0.01 in 5 consecutive iterations or the maximum number of iterations is reached (200), thus obtaining the layout scheme of functional reefs for lake benthic animals. The coordinates of each reef in the layout scheme are sorted according to water depth from shallow to deep, and the installation time interval between adjacent reefs is calculated to obtain construction guidance parameters.

8. An intelligent deployment system for functional reefs for lake benthic animals, characterized in that, The method for intelligently deploying functional reefs for lake benthic animals as described in any one of claims 1-7, wherein the intelligent deployment system for functional reefs for lake benthic animals comprises: The environmental normalization module is used to perform gridding and numerical normalization of lake environmental parameters, including water depth distribution, sediment type, water flow velocity, and nutrient concentration, to obtain a standardized environmental data matrix. The germination module is used to generate root strength parameters by multiplying the environmental suitability index of each position in the standardized environmental data matrix with a random factor according to the seed germination mechanism in the ivy algorithm, and constructing the reef population individuals by combining the position coordinates and growth direction angle. The neighborhood growth module is used to calculate the growth radius by multiplying the root strength parameter of the individual population deployed on the reef with the growth coefficient based on the principle of ivy expansion growth. It searches for candidate positions within an 8-neighborhood and calculates the growth suitability index. The population individual is updated with the position of the highest suitability index to generate the expansion deployment point. The competitive selection module is used to calculate the comprehensive fitness evaluation value of the extended deployment points through the Ivy League algorithm competitive elimination mechanism, retain the superior individuals and eliminate the inferior individuals according to the fitness ranking, so as to obtain the reef intelligent deployment scheme.

9. An intelligent deployment device for functional reefs for benthic animals in lakes, characterized in that, It includes a memory and a processor, the memory storing a computer program that can run on the processor, and the processor executing the computer program to implement the intelligent deployment method of lake benthic animal functional reefs according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is run by the processor, it causes the processor to execute the intelligent deployment method of lake benthic animal functional reefs as described in any one of claims 1 to 7.

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