Carbon-neutralization-oriented fishery-light composite ecological system construction method and related device

Abstract

The embodiment of the invention relates to the technical field of data processing, and provides a carbon-neutralization-oriented fishing-light composite ecosystem construction method and a related device, and the method comprises the steps: obtaining the environment parameters, equipment core parameters and fishing ground breeding constraint parameters of a fishing-light complementary field; constructing a photovoltaic coverage rate multi-objective optimization model according to the environmental parameters, the equipment core parameters and the fishery culture constraint parameters; solving the photovoltaic coverage rate multi-objective optimization model by adopting an improved non-dominated sorting genetic algorithm to obtain the optimal photovoltaic panel coverage rate of the fishing-light complementary site; by adopting the optimal photovoltaic panel coverage rate to construct the fishing-light composite ecological system, the problem of insufficient culture illumination and ventilation caused by too high photovoltaic panel coverage rate can be avoided, meanwhile, the problem of power generation potential waste caused by too low coverage rate is solved, and the current demand for a power generation carbon neutralization target is met.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to a method and related apparatus for constructing a carbon-neutral aquaculture-solar integrated ecosystem. Background Technology

[0002] Solar-aquaculture hybridization is a comprehensive utilization model that combines photovoltaic power generation with aquaculture. Its core involves installing photovoltaic modules on the water surface, achieving the dual benefits of power generation above and aquaculture below through spatial stratification. The construction of existing solar-aquaculture hybrid farms typically takes into account the site's basic conditions, combined with industry standards or past project experience, to determine the layout and coverage parameters of the photovoltaic panels, and then adds corresponding aquaculture facilities to form a composite system with both power generation and aquaculture functions. This model has been applied in various aquatic scenarios, becoming one of the important pathways to promote energy transition and sustainable agricultural development.

[0003] However, in the construction of existing aquaculture-solar hybrid farms, the determination of photovoltaic panel coverage often relies on fixed parameter standards or a single objective, lacking comprehensive consideration of the actual environmental conditions of the site, the core performance of the equipment, and the ecological needs of the aquaculture organisms. This fixed parameter setting method makes it difficult to dynamically adapt to the environmental differences of different water areas and the different needs of aquaculture species, easily leading to a mismatch between photovoltaic panel coverage and the actual scenario: either the coverage is too high, affecting the lighting and ventilation conditions of the aquaculture area, thus restricting the aquaculture benefits; or the coverage is too low, failing to fully utilize the potential of photovoltaic power generation, ultimately resulting in poor overall comprehensive benefits of the aquaculture-solar hybrid system and failing to achieve the synergistic optimization of power generation and aquaculture under the goal of carbon neutrality. Summary of the Invention

[0004] This application provides a method and related apparatus for constructing a carbon-neutral aquaculture-solar integrated ecosystem, which can avoid the problems of insufficient light and ventilation for aquaculture caused by excessive photovoltaic panel coverage, while solving the problem of wasted power generation potential caused by excessively low coverage, thus meeting the current demand for carbon neutrality in power generation.

[0005] The first aspect of this application provides a method for constructing a carbon-neutral aquaculture-solar integrated ecosystem, the method comprising: Obtain environmental parameters, core equipment parameters, and aquaculture constraint parameters of the solar-aquaculture hybrid farm; Based on the environmental parameters, core equipment parameters, and fish farm aquaculture constraints, a multi-objective optimization model for photovoltaic coverage is constructed. An improved non-dominated sorting genetic algorithm was used to solve the multi-objective optimization model of photovoltaic coverage rate to obtain the optimal photovoltaic panel coverage rate of the fishery-solar complementary site. The optimal photovoltaic panel coverage rate is used to construct a fishery-solar integrated ecosystem.

[0006] In one possible implementation, the step of constructing a multi-objective optimization model for photovoltaic coverage based on the environmental parameters, core equipment parameters, and aquaculture constraint parameters includes: Based on the core parameters of the equipment and environmental parameters, objective functions for single-area photovoltaic power generation, ventilation wind speed under the panel, and effective sunshine duration in the uncovered area are constructed. Adaptive normalization is performed on the objective functions of single-area photovoltaic power generation, ventilation wind speed under the panel, and effective sunshine duration in the uncovered area to obtain the normalized objective functions of single-area photovoltaic power generation, ventilation wind speed under the panel, and effective sunshine duration in the uncovered area. Based on the normalized single-area photovoltaic power generation objective function, the under-panel ventilation wind speed objective function, and the effective illumination duration objective function of the non-covered area, a comprehensive fitness function for fishery-solar complementary systems is constructed. Constraints for constructing a multi-objective optimization model; Based on the constraints of the multi-objective optimization model and the integrated fitness function of solar-fishery complementarity, a multi-objective optimization model for photovoltaic coverage is constructed.

[0007] In one possible implementation, the step of using an improved non-dominated sorting genetic algorithm to solve the multi-objective optimization model for photovoltaic coverage to obtain the optimal photovoltaic panel coverage for the fishery-solar complementary site includes: The normalized value of photovoltaic power generation per unit area is defined as the core objective, while the normalized value of ventilation wind speed under the panel and the effective sunshine duration in the non-covered area are defined as ecological constraint objectives. Construct the optimal threshold for the core objective based on the core objective; Construct ecological compliance thresholds based on the stated ecological constraint objectives; Based on the ecological compliance threshold and the optimal threshold of the core objective, the frontier layer of the population in the multi-objective optimization model of photovoltaic coverage is screened to obtain n frontier layers; Based on the objective function values ​​of individuals in the n frontier layers, reference lines are initialized and density fusion adjustments are performed to obtain a set of reference lines that fit the current population distribution. Based on the reference line set and the priority of each frontier layer, high-quality individuals are selected to form a partial offspring population, and new individuals that meet the constraints are generated by chaotic mapping of the eliminated individuals to form a chaotic regeneration population. Calculate the dynamic crossover probability of individuals in the population, pair superior individuals according to the crossover probability to generate a crossover offspring population, and mutate individuals according to a fixed mutation probability to generate a mutated offspring population. The parent population, part of the offspring population, chaotic regeneration population, crossover offspring population and mutated offspring population are merged, duplicate individuals and invalid individuals that do not meet the constraints are removed, and individuals of a fixed size are selected to form a new generation of parent population. Repeat the steps of frontier layer screening, reference line adjustment, selection crossover mutation and new generation population construction until the preset maximum number of iterations is reached. Select the individual with the best comprehensive fitness value from the first frontier layer of the final population. The corresponding photovoltaic coverage rate is the optimal photovoltaic panel coverage rate of the fishery-solar complementary site.

[0008] In one possible implementation, the process of screening the population individuals in the photovoltaic coverage multi-objective optimization model based on the ecological compliance threshold and the core objective optimal threshold yields n frontier layers, including: For each individual in the multi-objective optimization model of photovoltaic coverage, initialize the dominance count and the dominated set; The attributes of each individual in the population are marked based on the core objective, ecological constraint objective, optimal threshold for the core objective, and ecological achievement threshold. Construct a scenario-based dominance model, traverse all pairs of individuals in the population, determine the dominance relationships of individuals in the population based on the constructed scenario-based dominance model, select the first frontier layer, and recursively generate subsequent frontier layers.

[0009] In one possible implementation, the step of labeling the attributes of each individual in the population based on the core objective, ecological constraint objective, optimal threshold for the core objective, and ecological achievement threshold includes: If an individual's core target value is greater than or equal to the optimal threshold for the core target and both of its ecological constraint target values ​​are greater than or equal to the corresponding ecological compliance threshold, it is marked as a core protected individual. If an individual's ecological constraint target value is greater than or equal to the corresponding ecological compliance threshold, but the core target value is less than the optimal core target threshold, it is marked as an ecologically compliant individual. If any ecological constraint target value of an individual is less than the corresponding ecological compliance threshold, it is marked as a non-compliant individual.

[0010] In one possible implementation, constructing the optimal threshold for the core objective based on the core objective includes: The normalized values ​​of single-area photovoltaic power generation of all individuals in the current population are extracted from the multi-objective optimization model of photovoltaic coverage to construct the core objective dataset; Based on the core target dataset, calculate the global maximum value of the core target dataset; Set the core target ratio coefficient; The optimal threshold for the core target is determined based on the global maximum value and the scaling factor.

[0011] In one possible implementation, constructing an ecological compliance threshold based on the ecological constraint target includes: Extract ecological baseline thresholds from the aforementioned aquaculture constraint parameters of the fish farm; Adaptive normalization processing is adopted to determine the initial normalization threshold for ventilation wind speed under the board and the initial normalization threshold for effective illumination duration in the non-covered area based on the ecological basic threshold. Obtain the correction coefficients for water type and aquaculture density; The initial threshold for normalized ventilation velocity under the board is corrected based on the water type correction coefficient and the aquaculture density correction coefficient to obtain the final threshold for ventilation velocity under the board. The initial threshold for normalized effective illumination duration in the uncovered area is corrected based on the water type correction coefficient to obtain the final threshold for effective illumination duration in the uncovered area. The ecological compliance threshold is obtained based on the final ventilation wind speed threshold under the slab and the final effective light duration threshold in the non-covered area.

[0012] In this example, the environmental parameters, core equipment parameters, and aquaculture constraints of the aquaculture-solar hybrid farm are first obtained. Then, based on these parameters, a multi-objective optimization model for photovoltaic coverage is constructed, including objective functions for single-area photovoltaic power generation, under-panel ventilation wind speed, and effective sunlight duration in uncovered areas. An improved non-dominated sorting genetic algorithm, employing frontier layer screening, reference line optimization, and crossover mutation, is used to solve the multi-objective optimization model for photovoltaic coverage, thereby obtaining the optimal photovoltaic panel coverage. Finally, based on this coverage, an aquaculture-solar hybrid ecosystem is constructed. This approach integrates the actual site environment, equipment performance, and aquaculture needs to build a multi-objective optimization system. The improved non-dominated sorting genetic algorithm finds a balance between power generation efficiency and ecological needs, avoiding insufficient aquaculture sunlight and ventilation caused by excessively high photovoltaic panel coverage, while simultaneously solving the problem of wasted power generation potential due to excessively low coverage, thus meeting the current demand for carbon neutrality in power generation.

[0013] A second aspect of this application provides a device for constructing a carbon-neutral aquaculture-solar integrated ecosystem, the device comprising: The first acquisition unit is used to acquire environmental parameters, core equipment parameters, and aquaculture constraint parameters of the fishery-solar hybrid farm. The first processing unit is used to construct a multi-objective optimization model for photovoltaic coverage based on the environmental parameters, core equipment parameters, and fish farm aquaculture constraint parameters. The second processing unit is used to solve the photovoltaic coverage multi-objective optimization model by using an improved non-dominated sorting genetic algorithm to obtain the optimal photovoltaic panel coverage of the fishery-solar complementary site. The third processing unit is used to construct a fishery-solar composite ecosystem using the optimal photovoltaic panel coverage rate.

[0014] A third aspect of this application provides a terminal including a processor, an input device, an output device, and a memory, wherein the processor, input device, output device, and memory are interconnected, and the memory is used to store a computer program, the computer program including program instructions, and the processor is configured to invoke the program instructions to execute the step instructions as described in the carbon-neutral aquaculture-solar integrated ecosystem construction method in the first aspect of this application.

[0015] A fourth aspect of this application provides a computer-readable storage medium storing a computer program for electronic data interchange, wherein the computer program causes a computer to perform some or all of the steps described in the carbon-neutral aquaculture-solar integrated ecosystem construction method of the first aspect of this application.

[0016] A fifth aspect of this application provides a computer program product, comprising a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps described in the carbon-neutral aquaculture-solar integrated ecosystem construction method of the first aspect of this application. The computer program product may be a software installation package. Attached Figure Description

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

[0018] Figure 1 This application provides a schematic diagram of the overall process for constructing a carbon-neutral aquaculture-solar integrated ecosystem. Figure 2 This application provides a schematic diagram of the overall structure of a solar-aquaculture integrated ecosystem construction device for carbon neutrality. Figure 3 This application provides a schematic diagram of the structure of a terminal. Figure label: First acquisition unit-1, first processing unit-2, second processing unit-3, third processing unit-4. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0021] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application can be combined with other embodiments.

[0022] To better understand the carbon-neutral aquaculture-solar hybrid ecosystem construction method provided in this application, a brief introduction to the application scenarios of this method is given below. Currently, when constructing aquaculture-solar hybrid ecosystems by laying photovoltaic panels, fish farms often rely on general industry standards or past project experience as the core basis, setting the photovoltaic panel coverage rate as a fixed parameter. The layout of photovoltaic modules and the matching of aquaculture facilities are then completed based on this parameter. Essentially, this involves directly transferring parameter experience from a specific scenario to a universal construction standard, without establishing a correlation mapping mechanism between parameters and specific scenarios. Firstly, the parameter setting lacks scenario separation; existing methods do not incorporate the core influencing factors of aquaculture-solar hybrid farms into the parameter decision-making system, leading to a disconnect between fixed parameters and actual site conditions. For example, directly applying the high coverage rate parameter of open water to closed water will inevitably cause problems due to differences in ventilation conditions. The problems include: insufficient ventilation; secondly, the limitation of a singular goal-oriented approach, where parameter determination often focuses on a single objective, ignoring the synergistic needs of power generation and aquaculture. This results in parameters either favoring power generation at the expense of aquaculture ecological conditions or accommodating basic aquaculture needs while wasting power generation potential. Finally, there is a lack of dynamic adaptation mechanisms. Existing models lack parameter optimization logic for different scenarios, lacking both criteria for adjusting parameters based on environmental and aquaculture needs and quantitative methods for balancing multiple objectives. This leads to fixed parameters failing to respond to scenario differences, ultimately causing the core defect of a mismatch between photovoltaic panel coverage and actual needs, resulting in a double loss of power generation and aquaculture benefits, and failing to achieve synergistic optimization under the goal of carbon neutrality.

[0023] The method for constructing a carbon-neutral aquaculture-solar integrated ecosystem is applied to a carbon-neutral aquaculture-solar integrated ecosystem construction device. Figure 1 A schematic diagram illustrating the overall process of constructing a carbon-neutral aquaculture-solar integrated ecosystem is shown. Figure 1 As shown, it includes: S1. Obtain environmental parameters, core equipment parameters, and aquaculture constraint parameters of the aquaculture-solar hybrid farm.

[0024] The environmental parameters specifically include the site's average daily actual solar radiation intensity, annual average ambient temperature, base wind speed without photovoltaic shading, average daily total sunshine duration, actual minimum and maximum actual ventilation wind speed under the slab, actual minimum and maximum effective sunshine duration in the uncovered area. The average daily actual solar radiation intensity and annual average ambient temperature can be obtained from the meteorological monitoring station. The base wind speed without photovoltaic shading is measured by setting up an anemometer at a height of 1.5 meters around the fishpond. The average daily total sunshine duration is based on statistical data from the local meteorological department for the past three years and corrected by combining on-site monitoring results. The actual extreme values ​​of ventilation wind speed under the slab and effective sunshine duration in the uncovered area are determined based on the above measured data and historical statistical data.

[0025] Among them, the core parameters of the equipment include the rated power of the photovoltaic panel under standard test conditions, the power temperature coefficient of the photovoltaic panel, the area of ​​a single photovoltaic panel, the wind protection coefficient of the photovoltaic array, and the shading coefficient of the photovoltaic panel projection. These parameters can be obtained directly from the technical specifications of the photovoltaic module manufacturer. The wind protection coefficient of the photovoltaic array and the shading coefficient of the photovoltaic panel projection can be determined through the equipment installation design specifications.

[0026] The specific constraints of fish farming include the minimum suitable underfloor ventilation speed, the minimum suitable effective light duration in the uncovered area, the water type, and the stocking density. These parameters can be determined by the farmers or by referring to the industry technical standards for the corresponding aquaculture species. The minimum suitable underfloor ventilation speed and the minimum effective light duration in the uncovered area can be obtained by consulting the aquaculture specifications based on the aquaculture species. The water type is determined by on-site surveys, and the stocking density is specified by the fish fry stocking plan provided by the farmers. All obtained parameters need to be validated, outliers are removed, and the data is compiled into a standardized dataset for subsequent model construction.

[0027] S2. Based on the environmental parameters, core equipment parameters, and fish farm aquaculture constraints, construct a multi-objective optimization model for photovoltaic coverage.

[0028] Step S2 includes the following steps: S201. Based on the core parameters of the equipment and environmental parameters, construct the objective function for single-area photovoltaic power generation, the objective function for ventilation wind speed under the panel, and the objective function for effective illumination duration in the uncovered area.

[0029] The objective function for photovoltaic power generation per unit area is expressed as follows: (1) In the formula, This represents the original target value for photovoltaic power generation per unit area. Rated power under standard test conditions for photovoltaic panels. This is the solar radiation intensity correction factor. This is the temperature correction factor. This refers to the annual effective power generation hours of the site. This represents the area of ​​a single photovoltaic panel.

[0030] (2) In the formula, This represents the original target value for the ventilation velocity under the slab. The base wind speed for a solar-fishery hybrid farm without photovoltaic panels blocking the view. For photovoltaic panel coverage, This refers to the wind protection coefficient of the photovoltaic panel array.

[0031] (3) In the formula, This represents the original target value for effective illumination duration in the non-covered area. This represents the average daily total sunshine duration of the site. This represents the shading coefficient of the photovoltaic panel projection.

[0032] S202. Adaptive normalization processing is performed on the single-area photovoltaic power generation objective function, the under-panel ventilation wind speed objective function, and the non-covered area effective sunshine duration objective function to obtain the normalized single-area photovoltaic power generation objective function, the under-panel ventilation wind speed objective function, and the non-covered area effective sunshine duration objective function.

[0033] In this example, an adaptive normalization formula is used to eliminate the dimensional differences of the three target functions, uniformly mapping the original target values ​​to the [0,1] interval. The core normalization formula is: (4) In the formula, Let k be the normalized value of the target. This is the original calculated value of the k-th item. The minimum value of the k-th item. The maximum value of the k-th target is given by k=1, which corresponds to the photovoltaic power generation target per single area, k=2 which corresponds to the ventilation wind speed target under the panel, and k=3 which corresponds to the effective sunshine duration target in the non-covered area.

[0034] Based on the normalization formula, the normalized objective functions for single-area photovoltaic power generation, under-panel ventilation wind speed, and effective sunshine duration in uncovered areas are obtained. , and .

[0035] S203. Based on the normalized single-area photovoltaic power generation objective function, the under-panel ventilation wind speed objective function, and the non-covered area effective illumination duration objective function, construct the integrated fitness function for fishery-solar complementarity.

[0036] The formula for the integrated fitness function of fishery-solar complementarity is shown below: (5) In the formula, This represents the overall fitness value of solar-fishery complementarity for individual individuals within the population. , , These are the weights of the normalized single-area photovoltaic power generation target, the under-panel ventilation wind speed target, and the effective sunshine duration target in the non-covered area, respectively, which satisfy the condition that their sum is 1, and > , > .

[0037] S204. Constraints for constructing a multi-objective optimization model.

[0038] The photovoltaic panel coverage constraint is as follows: (6) In the formula, This refers to the coverage rate of photovoltaic panels.

[0039] The ventilation velocity constraint under the slab is as follows: (7) In the formula, The minimum suitable under-plate ventilation velocity for aquaculture fish.

[0040] The effective illumination duration constraint for the non-coverage area is as follows: (8) In the formula, The minimum effective light duration in the uncovered area suitable for farmed fish.

[0041] The constraint on photovoltaic power generation per unit area is as follows: (9) In the formula, This represents the minimum photovoltaic power generation per unit area corresponding to the basic revenue of a photovoltaic project.

[0042] S205. Based on the constraints of the multi-objective optimization model and the integrated fitness function of solar-fishery complementarity, construct a multi-objective optimization model for photovoltaic coverage.

[0043] In this step, the integrated fitness function of solar-fishery complementarity constructed in S203 is used as the optimization objective, and the constraints determined in S204 are used as boundary restrictions to construct a complete multi-objective optimization model for photovoltaic coverage. The core optimization direction of this model is to maximize the integrated fitness value F while satisfying all constraints, expressed by the following formula: (10) In the formula, To maximize the overall fitness value.

[0044] S3. An improved non-dominated sorting genetic algorithm is used to solve the multi-objective optimization model of photovoltaic coverage to obtain the optimal photovoltaic panel coverage of the fishery-solar complementary site.

[0045] Step S3 includes the following sub-steps: S301 defines the normalized value of photovoltaic power generation per unit area as the core objective, and the normalized value of ventilation wind speed under the panel and the effective sunshine duration in the non-covered area as ecological constraint objectives.

[0046] Firstly, based on the core requirements of the fishery-solar complementary scenario, the normalized single-area photovoltaic power generation objective function value obtained in step S202 is defined as the core objective, which directly reflects the energy output efficiency of photovoltaic modules and is a key indicator for achieving carbon neutrality. Therefore, optimality is prioritized in the optimization process. At the same time, the normalized ventilation wind speed objective function value under the panel and the normalized effective sunshine duration objective function value in the non-covered area are defined as ecological constraint objectives. These two values ​​correspond to the ventilation and sunshine conditions of the aquaculture area, respectively, and are the basis for ensuring the survival and growth of aquaculture organisms. They must be ensured to be no less than the ecological compliance threshold in the optimization process.

[0047] S302. Construct the optimal threshold for the core objective based on the core objective.

[0048] Specifically, including: S3021. Extract the normalized value of single-area photovoltaic power generation of all individuals in the current population from the photovoltaic coverage multi-objective optimization model to construct the core objective dataset.

[0049] First, it is clarified that the current population of the multi-objective optimization model for photovoltaic coverage consists of several photovoltaic coverage candidate schemes. Each individual corresponds to a normalized value of photovoltaic power generation per unit area after normalization by S202. Then, all individuals in the current population are traversed, and the normalized value of photovoltaic power generation per unit area corresponding to each individual is extracted. These values ​​are then organized into the core objective dataset.

[0050] S3022. Calculate the global maximum value of the core target dataset based on the core target dataset.

[0051] One approach is to determine the optimal level of the core objective in the current population by solving the extremum of the core objective dataset, thereby obtaining the global maximum value of the core objective dataset.

[0052] S3023, Set the core target ratio coefficient.

[0053] The core objective proportion coefficient is used to balance the optimality of the core objective with the adaptability of the ecological constraint objective, avoiding setting the threshold too high, resulting in too few eligible individuals, or setting it too low, failing to reflect the priority of the core objective. The value can be determined based on the site type. Specifically, in open water scenarios, with better lighting and ventilation conditions, there is more room for core objective optimization, and k is taken as 0.9 to 0.95; in enclosed water scenarios, more ecological adaptation space needs to be reserved, and k is taken as 0.85 to 0.9. The core objective proportion coefficient ensures the optimal guidance of the core objective and reserves reasonable space for satisfying subsequent ecological constraint objectives, ensuring the practicality and adaptability of the threshold.

[0054] S3024. Determine the optimal threshold for the core target based on the global maximum value and the scaling factor.

[0055] In this example, the optimal threshold for the core objective can be calculated by multiplying the global maximum value by the scaling factor. The core formula is as follows: (11) in, The optimal threshold is used to determine the core objective and is subsequently used for individual attribute labeling within the population. The core target ratio coefficient, This represents the global maximum value of the core target dataset.

[0056] S303. Construct ecological compliance thresholds based on the ecological constraint objectives.

[0057] Specifically, including: S3031. Extract the ecological basic threshold from the aquaculture constraint parameters of the fish farm.

[0058] Among them, indicators directly related to ecological constraint objectives can be screened from the already obtained fish farm aquaculture constraint parameters, such as the actual value of the minimum suitable ventilation wind speed under the board for farmed fish and the actual value of the minimum effective light duration in the uncovered area.

[0059] S3032. Adaptive normalization processing is adopted to determine the initial normalization threshold for ventilation wind speed under the board and the initial normalization threshold for effective illumination duration in the non-covered area based on the ecological basic threshold.

[0060] The ecological baseline threshold can be converted into a standardized threshold in the [0,1] range through normalization to ensure dimensional consistency with the normalized value of the core target. Specifically, the expression for the initial normalized threshold of the under-plate ventilation wind speed is as follows: (12) In the formula, This is the initial threshold for normalized ventilation velocity under the slab. The actual value of the minimum suitable under-plate ventilation velocity for fish farming. This represents the actual minimum ventilation velocity beneath the floor of the solar-fishery integrated facility. This represents the actual maximum value of the ventilation wind speed under the floor of the solar-fishery complementary farm.

[0061] Furthermore, the expression for the normalized initial threshold of the effective illumination duration in the non-covered area is as follows: (13) In the formula, The initial threshold for normalizing the effective illumination duration in the non-covered area. The actual value of the minimum effective light duration in uncovered areas suitable for farmed fish. This represents the actual minimum ventilation velocity beneath the floor of the solar-fishery integrated facility. This represents the actual maximum effective illumination duration in the non-covered area.

[0062] S3033, Obtain the water area type correction coefficient and the aquaculture density correction coefficient.

[0063] Among them, the correction coefficient α for water type is determined according to the degree of enclosure of the water area. Open water has good natural ventilation and lighting conditions, so there is no need to increase the threshold, α=1.0; enclosed water has weak ventilation and poor light penetration, so the threshold needs to be appropriately increased to ensure ecological needs, α=1.1.

[0064] The stocking density correction factor β is determined based on the stocking density of fish fry, with a stocking density ≤ 3 kg / m³. 3 At that time, the ecological pressure on the water body was relatively low, β=1.0; the stocking density was >3kg / m³. 3 At this time, the oxygen consumption of the water increases, and the requirements for ventilation and lighting become more stringent, β=1.05.

[0065] S3034. The initial threshold for normalized ventilation velocity under the board is corrected according to the water type correction coefficient and the aquaculture density correction coefficient to obtain the final threshold for ventilation velocity under the board.

[0066] The expression for the final under-slab ventilation velocity threshold is as follows: (14) In the formula, This is the final threshold for achieving ecological compliance in terms of under-slab ventilation velocity. The initial threshold for normalized ventilation velocity under the slab. This is a correction factor for water body type. This is the correction factor for stocking density.

[0067] S3035. Correct the initial threshold of the normalized effective illumination duration of the uncovered area according to the water area type correction coefficient to obtain the final threshold of the effective illumination duration of the uncovered area.

[0068] The expression for the final effective illumination duration threshold in the non-covered area is as follows: (15) In the formula, This is the final ecological threshold for effective illumination duration in uncovered areas. The initial threshold for normalizing the effective illumination duration in the non-covered area. This is a correction factor for water area type.

[0069] S3036. Based on the final under-panel ventilation wind speed threshold and the final effective light duration threshold for the non-covered area, the ecological compliance threshold is obtained.

[0070] The final under-panel ventilation wind speed threshold obtained from S3034 and the final effective light duration threshold for non-covered areas obtained from S3035 are combined to construct an ecological compliance threshold set {T2,T3}.

[0071] S304. Based on the ecological compliance threshold and the core objective optimal threshold, the frontier layer of the population in the photovoltaic coverage multi-objective optimization model is screened to obtain n frontier layers.

[0072] Specifically, step S304 includes: S3041. For each individual in the photovoltaic coverage multi-objective optimization model, initialize the dominance count and the dominated set.

[0073] First, it is clarified that each individual in the population corresponds to a set of core objective normalized values ​​and ecological constraint objective normalized values. For each individual, two key parameters are initialized: one is the dominance count, which records the number of times the current individual is dominated by other individuals, initially set to 0, and incremented by 1 if it is determined to be dominated by an individual; the other is the dominated set, which stores other individuals that the current individual can dominate, initially set to an empty set, and added to the set if it is determined to dominate an individual. The initialization operation needs to traverse all individuals in the population to ensure that the parameters of each individual are set accurately, providing a clear data record carrier for subsequent traversal of individual pairs and determination of dominance relationships.

[0074] S3042. Based on the core objective, ecological constraint objective, optimal threshold of core objective, and ecological achievement threshold, mark the attributes of each individual in the population.

[0075] If an individual's core target value is greater than or equal to the optimal threshold for the core target and both of its ecological constraint target values ​​are greater than or equal to the corresponding ecological compliance threshold, it is marked as a core protected individual. If an individual's ecological constraint target value is greater than or equal to the corresponding ecological compliance threshold, but the core target value is less than the optimal core target threshold, it is marked as an ecologically compliant individual. If any ecological constraint target value of an individual is less than the corresponding ecological compliance threshold, it is marked as a non-compliant individual.

[0076] Specifically, for each individual in the population, the normalized values ​​of its core objective, under-floor ventilation wind speed, and effective sunlight duration in the uncovered area are extracted and compared with the optimal threshold T1 for the core objective, the ecological compliance thresholds T2 (under-floor ventilation), and T3 (sunlight duration). If an individual satisfies that its core objective value is ≥ the optimal threshold and both ecological constraint objective values ​​are ≥ the corresponding ecological compliance thresholds, it is marked as a core protected individual (marked P=1). This type of individual possesses both optimal power generation efficiency and compliant ecological conditions. If an individual's ecological constraint objective values ​​are all ≥ the corresponding ecological compliance thresholds but its core objective value is < the optimal threshold, it is marked as an ecologically compliant individual (marked E=1, P=0). This type of individual meets the ecological conditions but its power generation efficiency is not optimal. If an individual satisfies that any ecological constraint objective value is < the corresponding ecological compliance threshold, it is marked as a non-compliant individual (marked E=0, P=0). This type of individual does not meet the minimum ecological requirements. All marks are bound to individuals, providing attribute basis for subsequent scenario-based dominance relationship determination.

[0077] S3043. Construct a scenario-based dominance model, traverse all pairs of individuals in the population, determine the dominance relationship of individuals in the population based on the constructed scenario-based dominance model, select the first frontier layer and recursively generate subsequent frontier layers.

[0078] The core of this step is to determine the dominance relationship between individuals through a scenario-based dominance model, and then to perform tiered screening. The specific implementation is as follows: First, a scenario-based dominance model is constructed. This model uses logical calculation formulas to clarify the rules for determining the dominance of individual A over individual B. The core formula is: (16) in, The result of the dominance relationship determination is 1, which indicates that individual A dominates individual B, and 0 indicates no dominance. For individual A, For individual B, the core protective marker , As ecological compliance markers for individuals A and B, , , The normalized values ​​are the core objective (single-area photovoltaic power generation), ecological constraint objective 1 (ventilation wind speed under the panel), and ecological constraint objective 2 (effective sunshine duration in the non-covered area) for individual A. , , Let B be the target normalized value for individual B. For logical AND operator, For logical NOT operator, The existence quantifier (k=1,2,3) indicates that at least one target dimension satisfies the "strictly greater than" condition. Specifically, after determining the dominance relationship, the first frontier layer is selected, and all individuals with a dominance count of 0 (i.e., individuals not dominated by any other individuals) are included in the first frontier layer, prioritizing core protected individuals and ecologically qualified individuals that are not dominated. Subsequently, subsequent frontier layers are generated recursively: the dominated set of each individual in the first frontier layer is traversed, and the dominance count of each individual in the set is decremented by 1; individuals with a dominance count reduced to 0 are collected to form the second frontier layer; with the second frontier layer as the current layer, the above operations of "traversing the dominated set, adjusting the dominance count, and collecting qualified individuals" are repeated to generate the third, fourth, ... nth frontier layer in sequence, until all individuals in the population are assigned to the corresponding frontier layer, forming a frontier layer sequence sorted by superiority.

[0079] S305. Based on the objective function values ​​of the individuals in the n frontier layers, initialize the reference lines and perform density fusion adjustment to obtain a set of reference lines that are adapted to the current population distribution.

[0080] This example explores the optimal solution distribution using a reference line-guided algorithm, while simultaneously optimizing the reference line layout through density fusion. The specific implementation is as follows: First, based on the normalized values ​​of the three objectives of all individuals in the n frontier layers, the value range of each objective is determined. Initial reference lines are uniformly set within the [0,1] interval—the number of reference lines is determined according to the objective dimension; in a three-objective scenario, 15-20 reference lines are set, each corresponding to a set of objective weight combinations to guide individuals to evolve towards different objective equilibrium directions. Then, density fusion logic is used to adjust the reference lines. The core is to determine the sparsity of the solution by calculating the individual density. The density calculation formula is: (17) in, Let γ be the individual density corresponding to the coverage rate. A larger value indicates that the solutions around that individual are more sparse. γ is the photovoltaic coverage rate scheme with the density to be calculated. For other coverage plans under the same reference line, Let γ be the normalized value of the k-th target corresponding to the coverage rate. Let γ′ be the normalized value of the k-th target corresponding to the coverage rate. =0.1 is a fixed neighborhood parameter, which adapts to the normalized range of values ​​of the target. G is the set of all individuals associated with the current reference line.

[0081] Furthermore, if the individual density corresponding to a certain reference line... If the density is large (solution is sparse), add 1-2 new reference lines near the current reference line to enhance the exploration of sparse regions; if the density is small (solution is dense), merge adjacent reference lines to avoid wasting resources. This ultimately forms a set of reference lines adapted to the current population distribution, ensuring that the algorithm covers known optimal regions without overlooking potential equilibrium solutions.

[0082] S306. Based on the reference line set and the priority of each frontier layer, select high-quality individuals to form a partial offspring population, and generate new individuals that meet the constraints through chaotic mapping for eliminated individuals, thus forming a chaotic regeneration population.

[0083] This example is implemented in two parts: first, the selection of high-quality individuals, and second, the regeneration of discarded individuals. When selecting high-quality individuals, they are prioritized according to the frontier layer priority (first frontier layer > second frontier layer > ... > nth frontier layer), prioritizing the selection from high-priority frontier layers. Combined with the reference line set—for each reference line, individuals closest to that reference line and with a high overall fitness value are selected, accumulating to select 30%~40% of the population size to form part of the offspring population, ensuring that the core characteristics of high-quality solutions are preserved. For discarded individuals (mostly those with low overall fitness or unbalanced goals), chaotic mapping is used to generate new individuals to avoid the algorithm getting trapped in local optima. The core formula is: (18) in, New photovoltaic coverage generated by chaotic mapping To limit the range of photovoltaic coverage corresponding to the eliminated individual, a coefficient of 0.4 is used to restrict the value of the new coverage, avoid generating invalid solutions <0.4 or >0.8, and ensure that the new individual meets the physical constraints.

[0084] S307. Calculate the dynamic crossover probability of individuals in the population, pair high-quality individuals according to the crossover probability to generate a crossover offspring population, and mutate individuals according to a fixed mutation probability to generate a mutated offspring population.

[0085] This step achieves population evolution through dynamic crossover and fixed mutation. Specifically, it first calculates the dynamic crossover probability of each high-quality individual, using the core formula: in, The crossover probability of individuals. This represents the individual's overall fitness score. This represents the average overall fitness value of all individuals in the current population. This represents the maximum overall fitness value of the current population. For fitness entropy, through Calculated.

[0086] Furthermore, during crossover operations, press Pair up high-quality individuals. The crossover probability of high-quality individuals is low (minimum 0.2), thus avoiding the destruction of core features; The crossover probability of individuals is relatively high (up to 0.8), which enhances the exploration of new schemes. Arithmetic crossover is used, where the average coverage γ of paired individuals is calculated to generate new individuals, forming the crossover offspring population (30%~40% of the original population). During mutation, a fixed mutation probability of 0.01~0.05 is set. For randomly selected individuals in the population, a small perturbation of ±0.02 is added to their coverage γ (ensuring it remains within the 0.4~0.8 range), generating a mutated offspring population (10%~20% of the original population), preventing premature convergence of the algorithm.

[0087] S308. Merge the parent population, part of the offspring population, the chaotic regeneration population, the crossover offspring population, and the mutated offspring population, remove duplicate individuals and invalid individuals that do not meet the constraints, and select individuals of a fixed size to form a new generation of parent population.

[0088] First, the parent population (the complete previous generation population), a portion of the offspring population obtained from S306, the chaotic regeneration population, and the crossover and mutated offspring populations obtained from S307 are all merged to form a temporary merged population. Then, the temporary merged population undergoes a two-step screening process: the first step is deduplication, removing duplicate individuals with identical coverage γ (retaining individuals with higher overall fitness values) to avoid redundant calculations; the second step is effectiveness screening, removing individuals that do not meet the constraints of S204. Finally, from the selected effective individuals, they are sorted in descending order of overall fitness value, and the top N individuals (N is the preset population size, usually set to 100-200) are selected to form the new generation parent population, ensuring a stable population size and a gradual improvement in individual quality.

[0089] S309. Repeat the steps of frontier layer screening, reference line adjustment, selection crossover mutation and new generation population construction until the preset maximum number of iterations is reached. Select the individual with the best comprehensive fitness value from the first frontier layer of the final population. The corresponding photovoltaic coverage rate is the optimal photovoltaic panel coverage rate of the fishery-solar complementary site.

[0090] The preset maximum number of iterations is 50-100 (adjusted according to scenario complexity; 100 iterations for complex enclosed water scenarios and 50 iterations for simple open water scenarios). Using the new generation of parent populations as input, the complete process of S304 (frontier layer selection), S305 (reference line adjustment), S306 (high-quality individual selection and chaotic regeneration), S307 (crossover mutation), and S308 (new generation population construction) is repeated until the preset maximum number of iterations is reached. After the iteration terminates, the first frontier layer of the final population gathers all non-dominated optimal solution individuals. From this layer, the individual with the largest comprehensive fitness value F is selected. This individual achieves an optimal balance between its core objective (single-area photovoltaic power generation) and ecological constraint objectives (ventilation and illumination). The corresponding photovoltaic coverage rate γ is the optimal photovoltaic panel coverage rate for the fishery-solar complementary site, satisfying both the power generation efficiency requirements under the carbon neutrality objective and ensuring the ecological bottom line of aquaculture.

[0091] S4. Construct a fishery-solar composite ecosystem using the optimal photovoltaic panel coverage rate.

[0092] The process begins by planning the photovoltaic (PV) module installation scheme based on the optimal PV panel coverage rate, combined with the water area and topography of the aquaculture-solar hybrid farm. This determines the layout area, spacing, and installation height of the PV panel array, ensuring the actual coverage rate matches the optimal value. Reasonable passageways and ventilation gaps are also provided to guarantee air circulation in the aquaculture area and sunlight intake in the uncovered areas. Subsequently, aquaculture-related facilities are constructed. Based on the habits of the aquaculture species and under ecological conditions suitable for the optimal coverage rate, aeration equipment, feeding equipment, and water quality monitoring equipment are deployed to ensure the aquaculture environment meets the growth needs of the fish. Finally, after the installation and commissioning of the PV modules and aquaculture facilities are completed, the system is put into trial operation. Real-time monitoring of key parameters such as PV power generation efficiency, ventilation speed under the panels, sunlight duration in the uncovered areas, and dissolved oxygen and temperature in the water is conducted. If minor deviations occur, optimization is achieved by fine-tuning the local layout of the PV panels or the operating parameters of the aquaculture facilities. Ultimately, this forms a synergistic aquaculture-solar hybrid ecosystem that integrates power generation and aquaculture, achieving dual benefits under the goal of carbon neutrality.

[0093] In this example, the environmental parameters, core equipment parameters, and aquaculture constraints of the aquaculture-solar hybrid farm are first obtained. Then, based on these parameters, a multi-objective optimization model for photovoltaic coverage is constructed, including objective functions for single-area photovoltaic power generation, under-panel ventilation wind speed, and effective sunlight duration in uncovered areas. An improved non-dominated sorting genetic algorithm, employing frontier layer screening, reference line optimization, and crossover mutation, is used to solve the multi-objective optimization model for photovoltaic coverage, thereby obtaining the optimal photovoltaic panel coverage. Finally, based on this coverage, an aquaculture-solar hybrid ecosystem is constructed. This approach integrates the actual site environment, equipment performance, and aquaculture needs to build a multi-objective optimization system. The improved non-dominated sorting genetic algorithm finds a balance between power generation efficiency and ecological needs, avoiding insufficient aquaculture sunlight and ventilation caused by excessively high photovoltaic panel coverage, while simultaneously solving the problem of wasted power generation potential due to excessively low coverage, thus meeting the current demand for carbon neutrality in power generation.

[0094] For those consistent with the above, please refer to Figure 2 , Figure 2 This application provides a schematic diagram of a structure for constructing a carbon-neutral aquaculture-solar integrated ecosystem. For example... Figure 2 As shown, the device includes: The first acquisition unit 1 is used to acquire environmental parameters, core equipment parameters, and aquaculture constraint parameters of the fishery-solar hybrid farm. The first processing unit 2 is used to construct a multi-objective optimization model for photovoltaic coverage based on the environmental parameters, core equipment parameters, and fish farm aquaculture constraint parameters. The second processing unit 3 is used to solve the photovoltaic coverage multi-objective optimization model by using an improved non-dominated sorting genetic algorithm to obtain the optimal photovoltaic panel coverage of the fishery-solar complementary site. The third processing unit 4 is used to construct a fishery-solar composite ecosystem using the optimal photovoltaic panel coverage rate.

[0095] For examples consistent with the above embodiments, please refer to... Figure 3 , Figure 3 A schematic diagram of a terminal structure provided in an embodiment of this application is shown in the figure. It includes a processor, an input device, an output device, and a memory. The processor, input device, output device, and memory are interconnected. The memory is used to store a computer program, which includes program instructions. The processor is configured to call the program instructions. The program includes instructions for performing the following steps. Obtain environmental parameters, core equipment parameters, and aquaculture constraint parameters of the aquaculture-solar hybrid farm.

[0096] Based on the environmental parameters, core equipment parameters, and fish farm aquaculture constraints, a multi-objective optimization model for photovoltaic coverage is constructed.

[0097] An improved non-dominated sorting genetic algorithm was used to solve the multi-objective optimization model of photovoltaic coverage, and the optimal photovoltaic panel coverage of the fishery-solar complementary site was obtained.

[0098] The optimal photovoltaic panel coverage rate is used to construct a fishery-solar integrated ecosystem.

[0099] In this example, the environmental parameters, core equipment parameters, and aquaculture constraints of the aquaculture-solar hybrid farm are first obtained. Then, based on these parameters, a multi-objective optimization model for photovoltaic coverage is constructed, including objective functions for single-area photovoltaic power generation, under-panel ventilation wind speed, and effective sunlight duration in uncovered areas. An improved non-dominated sorting genetic algorithm, employing frontier layer screening, reference line optimization, and crossover mutation, is used to solve the multi-objective optimization model for photovoltaic coverage, thereby obtaining the optimal photovoltaic panel coverage. Finally, based on this coverage, an aquaculture-solar hybrid ecosystem is constructed. This approach integrates the actual site environment, equipment performance, and aquaculture needs to build a multi-objective optimization system. The improved non-dominated sorting genetic algorithm finds a balance between power generation efficiency and ecological needs, avoiding insufficient aquaculture sunlight and ventilation caused by excessively high photovoltaic panel coverage, while simultaneously solving the problem of wasted power generation potential due to excessively low coverage, thus meeting the current demand for carbon neutrality in power generation.

[0100] The above mainly describes the solutions of the embodiments of this application from the perspective of the method execution process. It is understood that, in order to achieve the above functions, the terminal includes the corresponding hardware structure and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments provided herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0101] This application embodiment can divide the terminal into functional units according to the above method example. For example, each function can be divided into a separate functional unit, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this application embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0102] This application also provides a computer storage medium storing a computer program for electronic data interchange, which causes a computer to perform some or all of the steps of any of the carbon-neutral aquaculture-solar integrated ecosystem construction methods described in the above method embodiments.

[0103] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program that causes a computer to perform some or all of the steps of any of the carbon-neutral aquaculture-solar integrated ecosystem construction methods described in the above method embodiments.

[0104] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0105] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0106] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0107] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0108] Furthermore, the functional units in the various embodiments of the application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software program module.

[0109] If the integrated unit is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, 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 memory and includes several instructions to cause a computer device (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 this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0110] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage device, which may include: a flash drive, a read-only memory, a random access memory, a magnetic disk, or an optical disk, etc.

[0111] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for constructing a carbon-neutral aquaculture-solar integrated ecosystem, characterized in that, include: Obtain environmental parameters, core equipment parameters, and aquaculture constraint parameters of the solar-aquaculture hybrid farm; Based on the environmental parameters, core equipment parameters, and fish farm aquaculture constraints, a multi-objective optimization model for photovoltaic coverage is constructed. An improved non-dominated sorting genetic algorithm was used to solve the multi-objective optimization model of photovoltaic coverage rate to obtain the optimal photovoltaic panel coverage rate of the fishery-solar complementary site. The optimal photovoltaic panel coverage rate is used to construct a fishery-solar integrated ecosystem.

2. The method for constructing a carbon-neutral aquaculture-solar integrated ecosystem according to claim 1, characterized in that, The process involves constructing a multi-objective optimization model for photovoltaic coverage based on the environmental parameters, core equipment parameters, and fish farm aquaculture constraints, including: Based on the core parameters of the equipment and environmental parameters, objective functions for single-area photovoltaic power generation, ventilation wind speed under the panel, and effective sunshine duration in the uncovered area are constructed. Adaptive normalization is performed on the objective functions of single-area photovoltaic power generation, ventilation wind speed under the panel, and effective sunshine duration in the uncovered area to obtain the normalized objective functions of single-area photovoltaic power generation, ventilation wind speed under the panel, and effective sunshine duration in the uncovered area. Based on the normalized single-area photovoltaic power generation objective function, the under-panel ventilation wind speed objective function, and the effective illumination duration objective function of the non-covered area, a comprehensive fitness function for fishery-solar complementary systems is constructed. Constraints for constructing a multi-objective optimization model; Based on the constraints of the multi-objective optimization model and the integrated fitness function of solar-fishery complementarity, a multi-objective optimization model for photovoltaic coverage is constructed.

3. The method for constructing a fishery-solar integrated ecosystem for carbon neutrality according to claim 1, characterized in that, The improved non-dominated sorting genetic algorithm is used to solve the multi-objective optimization model for photovoltaic coverage, obtaining the optimal photovoltaic panel coverage for the fishery-solar complementary site, including: The normalized value of photovoltaic power generation per unit area is defined as the core objective, while the normalized value of ventilation wind speed under the panel and the effective sunshine duration in the non-covered area are defined as ecological constraint objectives. Construct the optimal threshold for the core objective based on the core objective; Construct ecological compliance thresholds based on the stated ecological constraint objectives; Based on the ecological compliance threshold and the optimal threshold of the core objective, the frontier layer of the population in the multi-objective optimization model of photovoltaic coverage is screened to obtain n frontier layers; Based on the objective function values ​​of individuals in the n frontier layers, reference lines are initialized and density fusion adjustments are performed to obtain a set of reference lines that fit the current population distribution. Based on the reference line set and the priority of each frontier layer, high-quality individuals are selected to form a partial offspring population, and new individuals that meet the constraints are generated by chaotic mapping of the eliminated individuals to form a chaotic regeneration population. Calculate the dynamic crossover probability of individuals in the population, pair superior individuals according to the crossover probability to generate a crossover offspring population, and mutate individuals according to a fixed mutation probability to generate a mutated offspring population. The parent population, part of the offspring population, chaotic regeneration population, crossover offspring population and mutated offspring population are merged, duplicate individuals and invalid individuals that do not meet the constraints are removed, and individuals of a fixed size are selected to form a new generation of parent population. Repeat the steps of frontier layer screening, reference line adjustment, selection crossover mutation and new generation population construction until the preset maximum number of iterations is reached. Select the individual with the best comprehensive fitness value from the first frontier layer of the final population. The corresponding photovoltaic coverage rate is the optimal photovoltaic panel coverage rate of the fishery-solar complementary site.

4. The method for constructing a carbon-neutral aquaculture-solar integrated ecosystem according to claim 3, characterized in that, Based on the ecological compliance threshold and the optimal threshold for the core objective, the population individuals in the multi-objective optimization model for photovoltaic coverage are screened for the frontier layer, resulting in n frontier layers, including: For each individual in the multi-objective optimization model of photovoltaic coverage, initialize the dominance count and the dominated set; The attributes of each individual in the population are marked based on the core objective, ecological constraint objective, optimal threshold for the core objective, and ecological achievement threshold. Construct a scenario-based dominance model, traverse all pairs of individuals in the population, determine the dominance relationships of individuals in the population based on the constructed scenario-based dominance model, select the first frontier layer, and recursively generate subsequent frontier layers.

5. The method for constructing a carbon-neutral aquaculture-solar integrated ecosystem according to claim 4, characterized in that, The method of labeling the attributes of each individual in the population based on the core objective, ecological constraint objective, optimal threshold for the core objective, and ecological achievement threshold includes: If an individual's core target value is greater than or equal to the optimal threshold for the core target and both of its ecological constraint target values ​​are greater than or equal to the corresponding ecological compliance threshold, it is marked as a core protected individual. If an individual's ecological constraint target value is greater than or equal to the corresponding ecological compliance threshold, but the core target value is less than the optimal core target threshold, it is marked as an ecologically compliant individual. If any ecological constraint target value of an individual is less than the corresponding ecological compliance threshold, it is marked as a non-compliant individual.

6. The method for constructing a carbon-neutral aquaculture-solar integrated ecosystem according to claim 3, characterized in that, The step of constructing the optimal threshold for the core objective based on the core objective includes: The normalized values ​​of single-area photovoltaic power generation of all individuals in the current population are extracted from the multi-objective optimization model of photovoltaic coverage to construct the core objective dataset; Based on the core target dataset, calculate the global maximum value of the core target dataset; Set the core target ratio coefficient; The optimal threshold for the core target is determined based on the global maximum value and the scaling factor.

7. The method for constructing a carbon-neutral aquaculture-solar integrated ecosystem according to claim 3, characterized in that, Based on the stated ecological constraint objectives, ecological compliance thresholds are constructed, including: Extract ecological baseline thresholds from the aforementioned aquaculture constraint parameters of the fish farm; Adaptive normalization processing is adopted to determine the initial normalization threshold for ventilation wind speed under the board and the initial normalization threshold for effective illumination duration in the non-covered area based on the ecological basic threshold. Obtain the correction coefficients for water type and aquaculture density; The initial threshold for normalized ventilation velocity under the board is corrected based on the water type correction coefficient and the aquaculture density correction coefficient to obtain the final threshold for ventilation velocity under the board. The initial threshold for normalized effective illumination duration in the uncovered area is corrected based on the water type correction coefficient to obtain the final threshold for effective illumination duration in the uncovered area. The ecological compliance threshold is obtained based on the final ventilation wind speed threshold under the slab and the final effective light duration threshold in the non-covered area.

8. A device for constructing a carbon-neutral aquaculture-solar integrated ecosystem, characterized in that, include: The first acquisition unit is used to acquire environmental parameters, core equipment parameters, and aquaculture constraint parameters of the fishery-solar hybrid farm. The first processing unit is used to construct a multi-objective optimization model for photovoltaic coverage based on the environmental parameters, core equipment parameters, and fish farm aquaculture constraint parameters. The second processing unit is used to solve the photovoltaic coverage multi-objective optimization model by using an improved non-dominated sorting genetic algorithm to obtain the optimal photovoltaic panel coverage of the fishery-solar complementary site. The third processing unit is used to construct a fishery-solar composite ecosystem using the optimal photovoltaic panel coverage rate.

9. A terminal, characterized in that, The system includes a processor, an input device, an output device, and a memory, which are interconnected. The memory stores a computer program, which includes program instructions. The processor is configured to invoke the program instructions to execute the carbon-neutral aquaculture-solar integrated ecosystem construction method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, the computer program including program instructions that, when executed by a processor, cause the processor to perform the method for constructing a carbon-neutral aquaculture-solar integrated ecosystem as described in any one of claims 1-7.

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