Method for constructing fish-light composite ecosystem oriented to carbon neutralization and related device

Abstract

The embodiment of the application relates to the technical field of data processing, and provides a fish-light complementary ecological system construction method and related device for carbon neutralization, which comprises the following steps: acquiring environment parameters, equipment core parameters and fishery breeding constraint parameters of a fish-light complementary field; constructing a photovoltaic coverage multi-objective optimization model according to the environment parameters, the equipment core parameters and the fishery breeding constraint parameters; solving the photovoltaic coverage multi-objective optimization model by using an improved non-dominated sorting genetic algorithm to obtain optimal photovoltaic panel coverage of the fish-light complementary field; and constructing a fish-light complementary ecological system by using the optimal photovoltaic panel coverage, which can avoid the problems of insufficient light and ventilation caused by excessively high photovoltaic panel coverage, and solve the problem of waste of power generation potential caused by excessively low coverage, thereby meeting the current demand for power generation carbon neutralization target.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a fish-light complementary ecological system construction method for carbon neutralization and related devices. BACKGROUND

[0002] Fish-light complementation is a comprehensive utilization mode that combines photovoltaic power generation with aquaculture. The core of fish-light complementation is to erect photovoltaic components on the surface of water area to achieve dual benefits of power generation on the upper layer and aquaculture on the lower layer through spatial layering utilization. The construction of existing fish-light complementary fields usually refers to the basic conditions of the site, combines with industry conventional standards or past project experience, determines the layout mode and coverage rate parameters of photovoltaic panels, and then matches corresponding aquaculture facilities to form a composite system with power generation capacity and aquaculture function. This mode has been applied in various water area scenarios and has become one of the important paths to promote energy transformation and sustainable development of agriculture.

[0003] However, in the construction process of the existing fish-light complementary field, the determination of the photovoltaic panel coverage rate often relies on fixed parameter standards or single target orientation, and lacks comprehensive consideration of the actual environmental conditions of the site, the core performance of the equipment, and the ecological needs of the cultured organisms. This fixed parameter setting method cannot dynamically adapt to the environmental differences of different water areas and the demand differences of different cultured species, which may lead to mismatch between the photovoltaic panel coverage rate and the actual scenario. For example, if the coverage rate is too high, it may affect the lighting and ventilation conditions of the aquaculture area, thereby restricting the aquaculture benefits. If the coverage rate is too low, it may not fully utilize the photovoltaic power generation potential, resulting in poor overall comprehensive benefits of the fish-light composite system and failing to achieve the coordinated optimization of power generation and aquaculture under the carbon neutralization target. SUMMARY

[0004] The embodiments of the present application provide a fish-light composite ecological system construction method for carbon neutralization and related devices, which can avoid the problem of insufficient lighting and ventilation caused by high photovoltaic panel coverage rate, and solve the problem of waste of power generation potential caused by low coverage rate, thereby meeting the current demand for carbon neutralization of power generation.

[0005] The first aspect of the embodiments of the present application provides a fish-light composite ecological system construction method for carbon neutralization, which comprises:

[0006] obtaining environmental parameters, equipment core parameters, and fishery aquaculture constraint parameters of a fish-light complementary field;

[0007] constructing a photovoltaic coverage rate multi-objective optimization model according to the environmental parameters, the equipment core parameters, and the fishery aquaculture constraint parameters;

[0008] solving the photovoltaic coverage rate multi-objective optimization model by using an improved non-dominated sorting genetic algorithm to obtain an optimal photovoltaic panel coverage rate of the fish-light complementary field;

[0009] An optimal photovoltaic panel coverage is used to construct the fish-light combined ecological system.

[0010] In a possible implementation, the constructing a photovoltaic coverage multi-objective optimization model according to the environmental parameters, the device core parameters and the fishery breeding constraint parameters comprises:

[0011] The single-area photovoltaic power generation amount objective function, the panel-under-ventilation wind speed objective function and the non-coverage area effective illumination time length objective function are constructed according to the device core parameters and the environmental parameters;

[0012] The single-area photovoltaic power generation amount objective function, the panel-under-ventilation wind speed objective function and the non-coverage area effective illumination time length objective function are adaptively normalized to obtain normalized single-area photovoltaic power generation amount objective function, panel-under-ventilation wind speed objective function and non-coverage area effective illumination time length objective function;

[0013] The fish-light complementary comprehensive fitness function is constructed according to the normalized single-area photovoltaic power generation amount objective function, the panel-under-ventilation wind speed objective function and the non-coverage area effective illumination time length objective function;

[0014] The constraint condition of the multi-objective optimization model is constructed;

[0015] The photovoltaic coverage multi-objective optimization model is constructed according to the constraint condition of the multi-objective optimization model and the fish-light complementary comprehensive fitness function.

[0016] In a possible implementation, the solving the photovoltaic coverage multi-objective optimization model by using the improved non-dominated sorting genetic algorithm to obtain the optimal photovoltaic panel coverage of the fish-light complementary site comprises:

[0017] The single-area photovoltaic power generation amount normalized value is defined as a core objective, and the panel-under-ventilation wind speed normalized value and the non-coverage area effective illumination time length value are defined as ecological constraint objectives;

[0018] The core objective optimal threshold value is constructed according to the core objective;

[0019] The ecological standard threshold value is constructed according to the ecological constraint objective;

[0020] The population individual in the photovoltaic coverage multi-objective optimization model is front layer filtered according to the ecological standard threshold value and the core objective optimal threshold value, and n front layers are obtained;

[0021] The reference line is initialized and density fusion adjustment is performed according to the objective function values of the individuals in the n front layers, and a reference line set adapted to the current population distribution is obtained;

[0022] According to the reference line set and the priority of each front layer, a high-quality individual constituent part sub-population is screened, and a new individual that meets the constraint condition is generated by a chaotic mapping to constitute a chaotic regenerated population;

[0023] A dynamic crossover probability of the population individuals is calculated, the high-quality individuals are paired and crossed according to the crossover probability to generate a crossover sub-population, and the individuals are mutated according to a fixed mutation probability to generate a mutation sub-population;

[0024] The parent population, the part sub-population, the chaotic regenerated population, the crossover sub-population and the mutation sub-population are combined, and repeated individuals and invalid individuals that do not meet the constraint condition are removed, and a fixed-size individual is screened to constitute a new generation of parent population;

[0025] The front layer screening, the reference line adjustment, the selection of crossover and mutation and the new generation population construction steps are repeatedly executed until a preset maximum iteration number is reached, and an individual with an optimal comprehensive fitness value is selected from a first front layer of the final population, and the photovoltaic coverage rate corresponding to the individual is the optimal photovoltaic panel coverage rate of the fish-light complementary site.

[0026] In a possible implementation, the front layer screening of the population individuals in the photovoltaic coverage multi-objective optimization model according to the ecological threshold and the core target optimal threshold includes:

[0027] The domination count and the dominated set of each population individual in the photovoltaic coverage multi-objective optimization model are initialized;

[0028] The attributes of each population individual are marked according to the core target, the ecological constraint target, the core target optimal threshold and the ecological threshold;

[0029] A scenario-based domination model is constructed, all individual pairs composed of the population individuals are traversed, the domination relationship of the population individuals is determined according to the constructed scenario-based domination model, the first front layer is screened, and the subsequent front layers are recursively generated.

[0030] In a possible implementation, the marking of the attributes of each population individual according to the core target, the ecological constraint target, the core target optimal threshold and the ecological threshold includes:

[0031] If the core target value of the individual is greater than or equal to the core target optimal threshold and the values of the two ecological constraint targets are both greater than or equal to the corresponding ecological threshold, the individual is marked as a core protection individual;

[0032] If the values of the ecological constraint targets of the individual are both greater than or equal to the corresponding ecological threshold but the core target value is less than the core target optimal threshold, the individual is marked as an ecological threshold individual;

[0033] If the value of any ecological constraint target of the individual is less than the corresponding ecological threshold, the individual is marked as a non-threshold individual.

[0034] In a possible implementation, the constructing the core target optimal threshold according to the core target comprises:

[0035] extracting single-area photovoltaic power generation capacity normalized values of all individuals in the current population from the photovoltaic coverage multi-target optimization model to construct a core target data set;

[0036] calculating a global maximum value of the core target data set according to the core target data set;

[0037] setting a core target proportion coefficient;

[0038] determining the core target optimal threshold according to the global maximum value and the proportion coefficient.

[0039] In a possible implementation, the constructing the ecological compliance threshold according to the ecological constraint target comprises:

[0040] extracting an ecological basic threshold from the fishery farming constraint parameter;

[0041] determining a normalized initial threshold of a board-under ventilation wind speed and a normalized initial threshold of an effective light duration in a non-coverage area according to the ecological basic threshold by using adaptive normalization processing;

[0042] obtaining a water area type correction coefficient and a farming density correction coefficient;

[0043] correcting the normalized initial threshold of the board-under ventilation wind speed according to the water area type correction coefficient and the farming density correction coefficient to obtain a final board-under ventilation wind speed threshold;

[0044] correcting the normalized initial threshold of the effective light duration in the non-coverage area according to the water area type correction coefficient to obtain a final effective light duration threshold in the non-coverage area;

[0045] obtaining the ecological compliance threshold according to the final board-under ventilation wind speed threshold and the final effective light duration threshold in the non-coverage area.

[0046] In this example, first, the environmental parameters, equipment core parameters and fishery breeding constraint parameters of the fish-light complementary field are obtained, and then a multi-objective optimization model of photovoltaic coverage rate is constructed based on these parameters, which includes a single-area photovoltaic power generation amount, a board under the ventilation wind speed and a non-covered area effective light duration target function. The improved non-dominated sorting genetic algorithm is used to solve the multi-objective optimization model of photovoltaic coverage rate to obtain the optimal photovoltaic panel coverage rate. Finally, the fish-light composite ecosystem is constructed according to the coverage rate. The multi-objective optimization system can be constructed by integrating the actual environment, equipment performance and breeding demand of the site. The improved non-dominated sorting genetic algorithm is used to find the balance point between power generation benefit and ecological demand to avoid the problem of insufficient light and ventilation caused by high photovoltaic panel coverage rate, and to solve the problem of waste of power generation potential caused by low coverage rate, thereby meeting the current demand for carbon neutral power generation.

[0047] The second aspect of the embodiment of the present application provides a fish-light composite ecosystem construction device for carbon neutralization, which comprises:

[0048] A first acquisition unit is configured to acquire environmental parameters, equipment core parameters and fishery breeding constraint parameters of a fish-light complementary field.

[0049] A first processing unit is configured to construct a multi-objective optimization model of photovoltaic coverage rate based on the environmental parameters, equipment core parameters and fishery breeding constraint parameters.

[0050] A second processing unit is configured to solve the multi-objective optimization model of photovoltaic coverage rate by using an improved non-dominated sorting genetic algorithm to obtain the optimal photovoltaic panel coverage rate of the fish-light complementary field.

[0051] A third processing unit is configured to construct a fish-light composite ecosystem by using the optimal photovoltaic panel coverage rate.

[0052] The third aspect of the embodiment of the present application provides a terminal, which comprises a processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are connected to each other, the memory is configured to store a computer program, the computer program comprises program instructions, and the processor is configured to invoke the program instructions to execute the steps of the fish-light composite ecosystem construction method for carbon neutralization as described in the first aspect of the embodiment of the present application.

[0053] The fourth aspect of the embodiment of the present application provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program for electronic data exchange, and the computer program causes a computer to execute part or all of the steps of the fish-light composite ecosystem construction method for carbon neutralization as described in the first aspect of the embodiment of the present application.

[0054] A fifth aspect of the embodiments of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform part or all of the steps described in the carbon neutral-oriented fish-light combined ecological system construction method of the first aspect of the embodiments of the present application. The computer program product can be a software installation package. BRIEF DESCRIPTION OF DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0056] Figure 1 A total flowchart of a carbon neutral-oriented fish-light combined ecological system construction method is provided for the embodiments of the present application;

[0057] Figure 2 A total structure diagram of a carbon neutral-oriented fish-light combined ecological system construction device is provided for the embodiments of the present application;

[0058] Figure 3 A structure diagram of a terminal is provided for the embodiments of the present application;

[0059] Reference signs:

[0060] First acquisition unit-1, first processing unit-2, second processing unit-3, third processing unit-4. DETAILED DESCRIPTION

[0061] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without any creative effort fall within the scope of protection of the present application.

[0062] The terms "first", "second", and the like in the description and in the claims of the present application and above-described drawings are used to distinguish different objects, rather than to describe a particular sequential order. In addition, the terms "comprise" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but can optionally include steps or units not listed, or can optionally include other steps or units inherent to such processes, methods, products, or devices.

[0063] In the present application, the phrase "embodiment" means that the specific features, structures, or characteristics described in conjunction with the embodiment can be included in at least one embodiment of the present application. The appearance of this phrase at various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment to other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described in the present application can be combined with other embodiments.

[0064] In order to better understand the carbon neutral oriented fish and light combined ecological system construction method provided by the embodiments of the present application, the scene of applying the carbon neutral oriented fish and light combined ecological system construction method will be briefly introduced first. At present, when the fish farms on the market lay photovoltaic panels to construct fish and light complementary ecological systems, they often take general industry standards or past project experience as the core basis, set the photovoltaic panel coverage rate as a fixed parameter, and then complete the layout of photovoltaic modules and the matching of breeding facilities according to the parameter. The essence is to directly migrate the parameter experience in a specific scene as a universal construction standard, without establishing a parameter and specific scene mapping mechanism. Firstly, the scene stripping of the parameter setting, the existing method does not include the core influencing factors of the fish and light complementary field in the parameter decision system, which leads to the disconnection between the fixed parameter and the actual conditions of the site. For example, the high coverage rate parameter for open water is directly used in closed water, which will inevitably cause insufficient ventilation under the panel due to the difference in ventilation conditions. Secondly, the single limitation of the target orientation, the parameter determination often focuses on a single target, ignoring the collaborative demand of power generation and breeding, so that the parameter is either biased towards power generation at the expense of breeding ecological conditions, or it is adjusted to the basic breeding demand at the expense of power generation potential. Finally, the lack of dynamic adaptation mechanism, the existing mode lacks parameter optimization logic for different scenes, neither the judgment standard for adjusting parameters according to the environment and breeding demand, nor the quantitative method for balancing multiple targets, which leads to the fact that the fixed parameter cannot respond to the scene difference, ultimately causing the core defect that the photovoltaic panel coverage rate does not match the actual demand, resulting in the double loss of power generation and breeding efficiency, and the collaborative optimization under the carbon neutral target cannot be achieved.

[0065] The carbon neutral oriented fish and light combined ecological system construction method is applied to a carbon neutral oriented fish and light combined ecological system construction device, Figure 1A total flowchart of a carbon neutral-oriented fish-light combined ecosystem construction method is shown. As shown in Figure 1 , comprising:

[0066] S1, obtaining the environmental parameters, equipment core parameters and fishery breeding constraint parameters of the fish-light complementary field.

[0067] Among them, the environmental parameters specifically include the daily average actual solar radiation intensity, the annual average environmental temperature, the basic wind speed without photovoltaic shading, the daily total light duration, the actual minimum value of the under-plate ventilation wind speed, the actual maximum value of the under-plate ventilation wind speed, the actual minimum value of the effective light duration in the non-covered area, and the actual maximum value of the effective light duration in the non-covered area. Among them, the daily average actual solar radiation intensity and the annual average environmental temperature can be obtained from the meteorological monitoring station, and the basic wind speed without photovoltaic shading is measured by erecting a wind speed instrument at a height of 1.5 meters around the fish pond; the daily total light duration is corrected by referring to the statistical data of the local meteorological department in the past three years and combining the field monitoring results; the actual extreme value of the under-plate ventilation wind speed and the actual extreme value of the effective light duration in the non-covered area are determined based on the above measured data and historical statistical data.

[0068] Among them, the equipment core parameters include the rated power under the standard test conditions of the photovoltaic panel, the power temperature coefficient of the photovoltaic panel, the area of a single photovoltaic panel, the wind resistance coefficient of the photovoltaic array, and the projection shading coefficient of the photovoltaic panel. These parameters are directly obtained from the technical specification of the photovoltaic module factory. The wind resistance coefficient of the photovoltaic array and the projection shading coefficient of the photovoltaic panel can be determined by the equipment installation design specification.

[0069] Among them, the fishery breeding constraint parameters specifically include the minimum under-plate ventilation wind speed suitable for breeding fish, the minimum effective light duration in the non-covered area suitable for breeding fish, the water type, and the breeding density. These parameters can be determined by the breeder or by referring to the industry technical standards of the corresponding breeding category. The minimum under-plate ventilation wind speed suitable for breeding fish and the minimum effective light duration in the non-covered area can be obtained by querying the breeding specification according to the breeding variety. The water type is determined by field investigation, and the breeding density is clear from the fry release plan provided by the breeder. All the obtained parameters need to be verified for effectiveness, and after removing the outliers, they are arranged into a standardized data set for subsequent model construction.

[0070] S2, constructing a photovoltaic coverage rate multi-objective optimization model according to the environmental parameters, equipment core parameters and fishery breeding constraint parameters.

[0071] Among them, step S2 includes the following steps:

[0072] S201, constructing a single-area photovoltaic power generation amount objective function, an under-plate ventilation wind speed objective function and an effective light duration in the non-covered area objective function according to the equipment core parameters and the environmental parameters.

[0073] The expression formula of the single-area photovoltaic power generation amount objective function is as follows:

[0074] (1)

[0075] In the formula, is the original target value of the single-area photovoltaic power generation amount, is the rated power under the standard test condition of the photovoltaic panel, is the sunshine intensity correction coefficient, is the temperature correction coefficient, is the annual effective power generation hours of the site, is the area of a single photovoltaic panel.

[0076] (2)

[0077] In the formula, is the original target value of the panel-under-ventilation wind speed, is the base wind speed when the fish-light complementary field is not covered by the photovoltaic panel, is the coverage rate of the photovoltaic panel, is the wind resistance coefficient of the photovoltaic panel array.

[0078] (3)

[0079] In the formula, is the original target value of the effective light illumination time in the non-covered area, is the daily total light illumination time of the site, is the projection shading coefficient of the photovoltaic panel.

[0080] S202, adaptively normalizing the single-area photovoltaic power generation amount objective function, the panel-under-ventilation wind speed objective function, and the effective light illumination time in the non-covered area objective function to obtain the normalized single-area photovoltaic power generation amount objective function, the panel-under-ventilation wind speed objective function, and the effective light illumination time in the non-covered area objective function.

[0081] In the example, the adaptive normalization formula is used to eliminate the dimensional differences of the three objective functions, and the original target values are uniformly mapped to the [0, 1] interval. The core normalization formula is as follows:

[0082] (4)

[0083] In the formula, is the normalized value of the kth objective, is the original calculation value of the kth objective, is the minimum value of the kth objective, is the maximum value of the kth objective, k=1 corresponds to the single-area photovoltaic power generation amount objective, k=2 corresponds to the panel-under-ventilation wind speed objective, and k=3 corresponds to the effective light illumination time in the non-covered area objective.

[0084] According to the normalization formula, the normalized single-area photovoltaic power generation target function, the under-plate ventilation wind speed target function and the effective light duration target function of the non-covered area are obtained 、 and .

[0085] S203, according to the normalized single-area photovoltaic power generation target function, the under-plate ventilation wind speed target function and the effective light duration target function of the non-covered area, a fish-light complementary comprehensive fitness function is constructed.

[0086] Among them, the formula of the fish-light complementary comprehensive fitness function is as follows:

[0087] (5)

[0088] In the formula, is the fish-light complementary comprehensive fitness value of the population individual, 、 、 The weights of the normalized single-area photovoltaic power generation target, the under-plate ventilation wind speed target and the effective light duration target of the non-covered area are respectively, the sum of which is 1, and > 、 > .

[0089] S204, the constraint condition of the multi-objective optimization model is constructed.

[0090] Among them, the photovoltaic panel coverage constraint is:

[0091] (6)

[0092] In the formula, is the photovoltaic panel coverage.

[0093] Among them, the under-plate ventilation wind speed constraint is:

[0094] (7)

[0095] In the formula, is the minimum under-plate ventilation wind speed suitable for the cultured fish.

[0096] Among them, the effective light duration constraint of the non-covered area is:

[0097] (8)

[0098] In the formula, is the minimum effective light duration of the non-covered area suitable for the cultured fish.

[0099] Wherein, the single-area photovoltaic power generation capacity constraint is:

[0100] (9)

[0101] In the formula, is the minimum value of the single-area photovoltaic power generation capacity corresponding to the basic income of the photovoltaic project.

[0102] S205, according to the constraint condition of the multi-objective optimization model and the fish-light complementary comprehensive fitness function, a photovoltaic coverage multi-objective optimization model is constructed.

[0103] Wherein, this step takes the fish-light complementary comprehensive fitness function constructed in S203 as the optimization target, and takes the constraint condition determined in S204 as the boundary limit, to construct a complete photovoltaic coverage multi-objective optimization model. The core optimization direction of the model is to maximize the comprehensive fitness value F while meeting all the constraint conditions, and the expression formula is:

[0104] (10)

[0105] In the formula, is the maximum comprehensive fitness value.

[0106] S3, an improved non-dominated sorting genetic algorithm is used to solve the photovoltaic coverage multi-objective optimization model, and the optimal photovoltaic panel coverage rate of the fish-light complementary site is obtained.

[0107] Wherein, step S3 includes the following substeps:

[0108] S301, define the normalized value of single-area photovoltaic power generation capacity as the core target, and the normalized value of the wind speed under the panel and the effective illumination time value of the non-coverage area as the ecological constraint target.

[0109] Wherein, first, based on the core demand of the fish-light complementary scene, the normalized single-area photovoltaic power generation capacity target function value obtained in step S202 is defined as the core target, which directly reflects the energy output efficiency of the photovoltaic module and is the key indicator to realize carbon neutralization. Therefore, the optimality is prioritized in the optimization, and at the same time, the normalized wind speed under the panel target function value and the normalized non-coverage area effective illumination time target function value are defined as the ecological constraint target. The two correspond to the ventilation condition and the illumination condition of the breeding area respectively, which are the basis for ensuring the survival and growth of breeding organisms, and need to be ensured not to be lower than the ecological standard threshold in optimization.

[0110] S302, a core target optimal threshold is constructed according to the core target.

[0111] Specifically, it includes:

[0112] S3021, extract the single-area photovoltaic power generation normalized value of all individuals in the current population from the photovoltaic coverage multi-objective optimization model, and construct a core target data set.

[0113] Firstly, it is clear that the current population of the photovoltaic coverage multi-objective optimization model is composed of several photovoltaic coverage candidate schemes, each individual corresponds to a single-area photovoltaic power generation normalized value processed by S202, then all individuals in the current population are traversed, the single-area photovoltaic power generation normalized value corresponding to each individual is extracted, and these values are sorted into a core target data set.

[0114] S3022, according to the core target data set, calculate the global maximum value of the core target data set.

[0115] The global maximum value of the core target data set can be obtained by solving the extreme value of the core target data set.

[0116] S3023, set the core target proportion coefficient.

[0117] The core target proportion coefficient is used to balance the optimality of the core target and the adaptability of the ecological constraint target, to avoid too high threshold setting resulting in too few qualified individuals, or too low setting failing to reflect the priority of the core target; the value can be determined in combination with the site type, specifically, in the open water scene, the light and ventilation conditions are better, the core target optimization space is larger, and k is taken as 0.9 to 0.95; in the closed water scene, more ecological adaptation space needs to be reserved, and k is taken as 0.85 to 0.9. The core target proportion coefficient is used to ensure the optimal orientation of the core target, and reasonable space is reserved for the satisfaction of the subsequent ecological constraint target, ensuring the practicality and adaptability of the threshold.

[0118] S3024, according to the global maximum value and the proportion coefficient, determine the core target optimal threshold.

[0119] The core target optimal threshold can be calculated by the product of the global maximum value and the proportion coefficient, and the core formula is:

[0120] (11)

[0121] Wherein, is the final core target optimal threshold, which is used for subsequent population individual attribute marking, is the core target proportion coefficient, is the global maximum value of the core target data set.

[0122] S303, according to the ecological constraint target, construct an ecological standard threshold.

[0123] Specifically, including:

[0124] S3031. Extract the ecological basic threshold from the aquaculture constraint parameters of the fish farm.

[0125] 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.

[0126] 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.

[0127] 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:

[0128] (12)

[0129] 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.

[0130] Furthermore, the expression for the normalized initial threshold of the effective illumination duration in the non-covered area is as follows:

[0131] (13)

[0132] 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.

[0133] S3033, Obtain the water area type correction coefficient and the aquaculture density correction coefficient.

[0134] 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.

[0135] wherein the aquaculture density correction coefficient β is determined according to the fry stocking density, and the aquaculture density ≤ 3 kg / m 3 , the water body ecological pressure is smaller, β = 1.0; when the aquaculture density > 3 kg / m 3 , the oxygen consumption of the water body increases, and the demand for ventilation and illumination is more demanding, β = 1.05.

[0136] S3034, correcting the normalized initial threshold value of the under-plate ventilation wind speed according to the water body type correction coefficient and the aquaculture density correction coefficient to obtain a final under-plate ventilation wind speed threshold value.

[0137] wherein the expression of the final under-plate ventilation wind speed threshold value is as follows:

[0138] (14)

[0139] In the formula, is the final under-plate ventilation wind speed ecological compliance threshold value, is the normalized initial threshold value of the under-plate ventilation wind speed, is the water body type correction coefficient, is the aquaculture density correction coefficient.

[0140] S3035, correcting the normalized initial threshold value of the effective illumination time length of the non-covered area according to the water body type correction coefficient to obtain a final effective illumination time length threshold value of the non-covered area.

[0141] wherein the expression of the final effective illumination time length threshold value of the non-covered area is as follows:

[0142] (15)

[0143] In the formula, is the final effective illumination time length ecological compliance threshold value of the non-covered area, is the normalized initial threshold value of the effective illumination time length of the non-covered area, is the water body type correction coefficient.

[0144] S3036, obtaining an ecological compliance threshold value according to the final under-plate ventilation wind speed threshold value and the final effective illumination time length threshold value of the non-covered area.

[0145] Combining the final under-plate ventilation wind speed threshold value obtained in S3034 and the final effective illumination time length threshold value of the non-covered area obtained in S3035 to construct an ecological compliance threshold value set {T2, T3}.

[0146] S304, according to the ecological compliance threshold value and the core target optimal threshold value, performing front layer screening on the population individuals in the photovoltaic coverage multi-objective optimization model to obtain n front layers.

[0147] Specifically, step S304 includes:

[0148] S3041, initialize the dominance count and dominated set for each population individual in the photovoltaic coverage multi-objective optimization model.

[0149] Wherein, first, it is clear that each individual in the population corresponds to a set of core target normalized values and ecological constraint target normalized values. For each individual, initialize two key parameters: one is the dominance count, which is used to record the number of times the current individual is dominated by other individuals. The initial value is set to 0. Subsequently, if it is determined to be dominated by a certain individual, the count is incremented by 1. The second is the dominated set, which is used to store other individuals that the current individual can dominate. The initial value is set to an empty set. Subsequently, if it is determined to dominate a certain individual, the individual is included in the set. The initialization operation needs to traverse all individuals in the population to ensure that the parameters of each individual are accurately set, providing a clear data record carrier for subsequent individual pair traversal and dominance relationship determination.

[0150] S3042, according to the core target, ecological constraint target, core target optimal threshold and ecological standard threshold, mark the attributes of each population individual.

[0151] If the core target value of the individual ≥ the core target optimal threshold and both ecological constraint target values ≥ the corresponding ecological standard threshold, mark it as a core protection individual;

[0152] If the ecological constraint target values of the individual are all ≥ the corresponding ecological standard threshold but the core target value < the core target optimal threshold, mark it as an ecological standard individual;

[0153] If any ecological constraint target value of the individual < the corresponding ecological standard threshold, mark it as a non-standard individual.

[0154] Specifically, traverse each individual in the population, extract its core target normalized value, under-plate ventilation wind speed normalized value, and non-coverage area effective light duration normalized value, and compare them with the core target optimal threshold T1, ecological standard threshold T2 (under-plate ventilation), and T3 (light duration). If the individual satisfies the condition that the core target value of the individual ≥ the core target optimal threshold and both ecological constraint target values ≥ the corresponding ecological standard threshold, mark it as a core protection individual (mark P=1). This type of individual has optimal power generation benefit and standard ecological conditions. If the ecological constraint target values of the individual are all ≥ the corresponding ecological standard threshold but the core target value < the core target optimal threshold, mark it as an ecological standard individual (mark E=1, P=0). This type of individual has standard ecological conditions but suboptimal power generation benefit. If any ecological constraint target value of the individual < the corresponding ecological standard threshold, mark it as a non-standard individual (mark E=0, P=0). This type of individual does not meet the basic requirements of ecological conditions. All marks are bound to the individual one by one, providing attribute basis for subsequent scenario-based dominance relationship determination.

[0155] S3043, construct a scenario-based dominance model, iterate all individual pairs composed of population individuals, determine the dominance relationship of population individuals according to the constructed scenario-based dominance model, screen the first front layer and recursively generate subsequent front layers.

[0156] The core of this step is to determine the dominance relationship between individuals through the scenario-based dominance model, and then to screen by layers. The specific implementation is as follows: first, construct a scenario-based dominance model. This model explicitly determines the dominance judgment rule of individual A on individual B by a logical calculation formula. The core formula is:

[0157] (16)

[0158] wherein, is the dominance relationship judgment result, 1 indicates that individual A dominates individual B, and 0 indicates that it does not dominate, is the core protection marker of individual A, is the core protection marker of individual B, , is the ecological compliance marker of individual A and B, , , is the normalized value of the core target (single-area photovoltaic power generation capacity) of individual A, the ecological constraint target 1 (under-plate ventilation wind speed), and the ecological constraint target 2 (effective light duration in non-covered area), , , is the corresponding target normalized value of individual B, is a logical AND operator, is a logical NOT operator, is an existential quantifier (k = 1, 2, 3), indicating that at least one target dimension satisfies the "strictly greater than" condition. Specifically, after the dominance relationship is determined, the first front layer is screened. All individuals with a dominance count of 0 (i.e., individuals not dominated by any other individual) are included in the first front layer, which preferentially includes core protection individuals and undominated ecological compliance individuals. Subsequently, the subsequent front layers are recursively generated: iterate through the dominated set of each individual in the first front layer, and reduce the dominance count of each individual in the set by 1; collect individuals with a dominance count of 0 to form the second front layer; take the second front layer as the current layer, and repeat the above "iterate the dominated set, adjust the dominance count, and collect the compliance individuals" operation to generate the third, fourth,..., and nth front layers in turn, until all population individuals are assigned to the corresponding front layer, forming a front layer sequence sorted by merit.

[0159] S305, initialize the reference line according to the target function value of the individual in the n front layers and perform density fusion adjustment to obtain a reference line set adapted to the current population distribution.

[0160] Among them, the present example explores the optimal solution distribution by referring to the line guide algorithm, and optimizes the reference line layout by combining density fusion, which is implemented as follows: first, based on the three target normalized values of all individuals in the n front layers, the value range of each target is determined, and the initial reference line is uniformly set in the [0, 1] interval-the number of reference lines is determined according to the target dimension, and 15-20 reference lines are set in a three-target scenario, each reference line corresponds to a group of target weight combination, which is used to guide the individual to evolve in different target balanced direction. Subsequently, the reference line is adjusted by using the density fusion logic, the core of which is to judge the sparsity of the solution by calculating the individual density, and the density calculation formula is:

[0161] (17)

[0162] wherein, is the individual density corresponding to the coverage rate γ, the larger the value, the sparser the solution around the individual, γ is the photovoltaic coverage scheme to be calculated, is the other coverage rate scheme under the same reference line, is the kth target normalized value corresponding to the coverage rate γ, is the kth target normalized value corresponding to the coverage rate γ', =0.1 is a fixed neighborhood parameter, which adapts to the value range of the normalized target, and G is a set composed of all individuals associated with the current reference line.

[0163] Further, if the individual density corresponding to a reference line is larger (solution sparse), 1-2 reference lines are added near the reference line to strengthen the exploration of the sparse area; if the density is smaller (solution dense), the adjacent reference lines are merged to avoid resource waste. Finally, a reference line set that adapts to the current population distribution is formed to ensure that the algorithm covers the known optimal area and does not miss the potential balanced solution.

[0164] S306, according to the reference line set and the priority of each front layer, filter high-quality individuals to form a part of the sub-population, and generate new individuals that meet the constraint conditions through chaotic mapping for the eliminated individuals to form a chaotic regeneration population.

[0165] Wherein, the present example is divided into two parts: one is the high-quality individual screening, two is the elimination of individual regeneration. High-quality individual screening, according to the priority of the front layer (the first front layer > the second front layer > … > the n front layer), the individual is selected from the high priority front layer, combined with the reference line set - for each reference line, the individual with the highest comprehensive fitness value closest to the reference line is selected, and the population size of 30%~40% is accumulated to form a part of the offspring population, to ensure that the core features of high-quality solutions are retained. For the eliminated individuals (mostly low comprehensive fitness or target imbalance individuals), new individuals are generated by using chaotic mapping to avoid the algorithm falling into local optimum, and the core formula is:

[0166] (18)

[0167] Wherein, is the new photovoltaic coverage generated by chaotic mapping, is the photovoltaic coverage corresponding to the eliminated individual, the coefficient 0.4 is used to limit the value range of the new coverage, to avoid generating invalid solutions <0.4 or >0.8, and to ensure that the new individual meets the physical constraints.

[0168] S307, calculate the dynamic crossover probability of the population individuals, pair and cross the high-quality individuals according to the crossover probability to generate a cross-offspring population, and vary the individuals according to a fixed mutation probability to generate a mutation-offspring population.

[0169] Wherein, this step realizes population evolution through dynamic crossover and fixed mutation, specifically, first calculate the dynamic crossover probability of each high-quality individual, the core formula is:

[0170]

[0171] Wherein, is the crossover probability of the individual, is the comprehensive fitness value of the individual, is the average comprehensive fitness value of all individuals in the current population, is the maximum comprehensive fitness value of the current population, is the fitness entropy, which is calculated by .

[0172] Further, in the crossover operation, the high-quality individuals are paired according to , the crossover probability of high-quality individuals is low (the lowest is 0.2), to avoid the core features being destroyed; The individual crossover probability is high (up to 0.8), which strengthens the exploration of new solutions. The crossover method is arithmetic crossover, the average value of the coverage rate γ of the paired individuals is calculated, a new individual is generated, and a crossover offspring population (30%-40% of the original population) is formed. When the mutation operation is performed, a fixed mutation probability of 0.01-0.05 is set, a small perturbation of ±0.02 is added to the coverage rate γ of the randomly selected individual in the population (to ensure that it is still in the range of 0.4-0.8), and a mutation offspring population (10%-20% of the original population) is generated, to avoid premature convergence of the algorithm.

[0173] S308, the parent population, part of the offspring population, the chaotic regeneration population, the crossover offspring population and the mutation offspring population are combined, and the repeated individuals and invalid individuals that do not meet the constraint conditions are removed, and a new generation of parent population is screened to form a fixed size of individuals.

[0174] Firstly, the parent population (the complete population of the last generation), the part of the offspring population obtained in S306, the chaotic regeneration population, and the crossover offspring population and the mutation offspring population obtained in S307 are fully combined to form a temporary combined population. Then, the temporary combined population is screened in two steps: first, remove the repeated individuals with the same coverage rate γ (keep the individual with higher comprehensive fitness value), to avoid redundant calculation; second, effective screening, remove the individuals that do not meet the constraint condition of S204, finally, sort the selected N individuals (N is the preset population size, usually set to 100-200) in descending order of comprehensive fitness value, and select the first N individuals to form a new generation of parent population, to ensure that the population size is stable and the quality of individuals is gradually improved.

[0175] S309, repeat the steps of front layer screening, reference line adjustment, selection of crossover and mutation, and new generation population construction until the preset maximum iteration number is reached, select the individual with the optimal comprehensive fitness value from the first front layer of the final population, and the corresponding photovoltaic coverage rate is the optimal photovoltaic panel coverage rate of the fishlight complementary site.

[0176] Wherein, the preset maximum iteration number is 50-100 times (adjust according to the scene complexity, take 100 times for complex closed water area scene, and take 50 times for simple open water area scene), take the new generation parent population as input, repeat the complete process of S304 (front layer screening), S305 (reference line adjustment), S306 (high-quality individual screening and chaos regeneration), S307 (cross mutation), S308 (new generation population construction) until the iteration number reaches the preset maximum value. After the iteration is terminated, the first front layer of the final population collects all non-dominated optimal solution individuals, select the individual with the maximum comprehensive fitness value F from the layer, the core target (single-area photovoltaic power generation capacity) and ecological constraint target (ventilation, illumination) of the individual reach optimal balance, and the corresponding photovoltaic coverage rate γ is the optimal photovoltaic panel coverage rate of the fish-light complementary site, which meets the power generation benefit demand under the carbon neutralization target and guarantees the ecological bottom line of aquaculture.

[0177] S4, the optimal photovoltaic panel coverage rate is used to construct a fish-light composite ecological system.

[0178] Wherein, first, according to the optimal photovoltaic panel coverage rate, the water area and terrain distribution of the fish-light complementary site are combined to plan the laying scheme of the photovoltaic module, so as to determine the arrangement area, spacing and installation height of the photovoltaic panel array, ensure that the actual laying coverage rate is consistent with the optimal value, and reserve reasonable channels and ventilation gaps to ensure the air circulation of the panel area and the light intake of the non-covered area. Subsequently, the breeding-related facilities are built, the oxygenation equipment, feeding equipment and water quality monitoring equipment are arranged according to the habits of the breeding varieties under the ecological conditions adapted to the optimal coverage rate, so as to ensure that the breeding environment meets the fish growth requirements. Finally, after the installation and debugging of the photovoltaic module and the breeding facilities are completed, the system trial operation is started, the photovoltaic power generation efficiency, panel ventilation wind speed, non-covered area illumination time and key parameters such as water body dissolved oxygen and temperature are monitored in real time, if there is a small deviation, the photovoltaic panel local layout or breeding facility operation parameter is optimized through fine adjustment, finally the fish-light composite ecological system of power generation and breeding collaborative adaptation is formed, and the double benefits under the carbon neutralization target are realized.

[0179] In this example, first, the environmental parameters, equipment core parameters and fishery breeding constraint parameters of the fish-light complementary field are obtained, and then a multi-objective optimization model of photovoltaic coverage rate is constructed based on these parameters, which includes a single-area photovoltaic power generation amount, a board under the ventilation wind speed and a non-covered area effective light duration target function. The improved non-dominated sorting genetic algorithm is used to solve the multi-objective optimization model of photovoltaic coverage rate and obtain the optimal photovoltaic panel coverage rate. Finally, the fish-light composite ecosystem is constructed according to the coverage rate. The multi-objective optimization system can be constructed by integrating the actual environment, equipment performance and breeding demand of the site. The improved non-dominated sorting genetic algorithm is used to find the balance point between power generation benefit and ecological demand to avoid the problem of insufficient light and ventilation caused by high photovoltaic panel coverage rate. At the same time, the problem of waste of power generation potential caused by low coverage rate is solved, which meets the current demand for carbon neutral power generation.

[0180] Consistent with the above, Figure 2 , Figure 2 A structure diagram of a carbon neutral-oriented fish-light composite ecosystem construction device is provided for the embodiments of the present application. As shown in Figure 2 , the device comprises:

[0181] A first acquisition unit 1 is configured to acquire environmental parameters, equipment core parameters and fishery breeding constraint parameters of a fish-light complementary field.

[0182] A first processing unit 2 is configured to construct a multi-objective optimization model of photovoltaic coverage rate according to the environmental parameters, equipment core parameters and fishery breeding constraint parameters.

[0183] A second processing unit 3 is configured to solve the multi-objective optimization model of photovoltaic coverage rate by using an improved non-dominated sorting genetic algorithm to obtain the optimal photovoltaic panel coverage rate of the fish-light complementary field.

[0184] A third processing unit 4 is configured to construct a fish-light composite ecosystem by using the optimal photovoltaic panel coverage rate.

[0185] Consistent with the above embodiments, please refer to Figure 3 , Figure 3 A structure diagram of a terminal is provided for the embodiments of the present application, as shown in the figure, comprising a processor, an input device, an output device and a memory, the processor, the input device, the output device and the memory are connected with each other, wherein the memory is used to store a computer program, the computer program comprises program instructions, the processor is configured to call the program instructions, the above program comprises instructions for executing the following steps;

[0186] Acquiring environmental parameters, equipment core parameters and fishery breeding constraint parameters of a fish-light complementary field.

[0187] According to the environmental parameters, the equipment core parameters and the fishery cultivation constraint parameters, a photovoltaic coverage multi-objective optimization model is constructed.

[0188] An improved non-dominated sorting genetic algorithm is used to solve the photovoltaic coverage multi-objective optimization model, and the optimal photovoltaic panel coverage of the fish-light complementary site is obtained.

[0189] The optimal photovoltaic panel coverage is used to construct a fish-light composite ecosystem.

[0190] In this example, first, the environmental parameters, equipment core parameters and fishery cultivation constraint parameters of the fish-light complementary site are obtained, and then a photovoltaic coverage multi-objective optimization model containing single-area photovoltaic power generation, panel under-ventilation wind speed and non-coverage area effective illumination time target functions is constructed based on these parameters. The improved non-dominated sorting genetic algorithm with front layer filtering, reference line optimization and cross mutation is used to solve the photovoltaic coverage multi-objective optimization model to obtain the optimal photovoltaic panel coverage. Finally, the fish-light composite ecosystem is constructed according to the coverage. The actual environment of the site, the performance of the equipment and the cultivation demand are integrated to construct a multi-objective optimization system. The improved non-dominated sorting genetic algorithm is used to find the balance point between power generation benefit and ecological demand to avoid the problem of insufficient illumination and ventilation caused by excessive photovoltaic panel coverage. At the same time, the problem of waste of power generation potential caused by low coverage is solved, which meets the current demand for carbon neutral power generation.

[0191] The above mainly introduces the scheme of the embodiments of the present application from the perspective of the execution process of the method. It can be understood that the terminal includes hardware structure and / or software modules corresponding to the execution of each function in order to realize the above functions. Those skilled in the art should easily realize that, in combination with the unit and algorithm steps of each example described in the embodiments provided herein, the present application can be realized in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed in hardware or computer software driven hardware depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0192] The embodiments of the present application can divide the functional units of the terminal according to the above method examples. For example, each functional unit can be divided according to each function, or two or more functions can be integrated in one processing unit. The integrated unit can be realized in the form of hardware or software functional unit. It should be noted that the division of units in the embodiments of the present application is illustrative, and is only a logical function division. There can be another division method when actually implemented.

[0193] The embodiment of the present application further provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program causes a computer to execute part or all of steps of any one of the carbon neutral oriented fish-light combined ecological system construction methods described in the above method embodiments.

[0194] The embodiment of the present application further provides a computer program product, which comprises a non-transitory computer-readable storage medium storing a computer program, and the computer program causes a computer to execute part or all of steps of any one of the carbon neutral oriented fish-light combined ecological system construction methods described in the above method embodiments.

[0195] It should be noted that, for the above-mentioned method embodiments, in order to simply describe, they are all expressed as a combination of a series of actions, but those skilled in the art should know that the present application is not limited to the action sequence described, because according to the present application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions and modules involved are not necessarily necessary for the present application.

[0196] In the above embodiments, the description of each embodiment is focused on, and the part not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0197] In several embodiments provided by the present application, it should be understood that the disclosed device can be implemented by other means. For example, the device embodiments described above are only illustrative, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical or other forms.

[0198] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. According to actual needs, part or all of the units can be selected to achieve the purpose of the embodiment scheme.

[0199] In addition, each functional unit in the embodiments of the application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software program module.

[0200] When the integrated unit is realized in the form of a software program module and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on such an understanding, the technical solutions of the present application essentially or partially contribute to the prior art, or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and various other media that can store program codes.

[0201] Those skilled in the art can understand that all or part of the steps of the various methods in the above embodiments can be completed by a program instructing relevant hardware, and the program can be stored in a computer readable storage medium, which can include a flash disk, a read-only memory, a random access memory, a magnetic disk or an optical disk, etc.

[0202] The embodiments of the present application are described in detail above, and the principles and implementation manners of the present application are described by applying specific examples. The above description of the embodiments is only used to help understand the method of the present application and its core idea; meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation manner and application range can be changed, and the above description of the embodiments should not be understood as a limitation of the present application.

Claims

1. A method for constructing a fish-light combined ecosystem oriented to carbon neutralization, characterized in that, The method comprises the following steps: acquiring environmental parameters, equipment core parameters and fishery breeding constraint parameters of a fish-light complementary field; constructing a photovoltaic coverage multi-objective optimization model according to the environmental parameters, the equipment core parameters and the fishery breeding constraint parameters; solving the photovoltaic coverage multi-objective optimization model by using an improved non-dominated sorting genetic algorithm to obtain an optimal photovoltaic panel coverage of the fish-light complementary field; constructing a fish-light composite ecosystem by using the optimal photovoltaic panel coverage; the solving of the photovoltaic coverage multi-objective optimization model by using the improved non-dominated sorting genetic algorithm to obtain the optimal photovoltaic panel coverage of the fish-light complementary field comprises the following steps: defining a single-area photovoltaic power generation quantity normalized value as a core target, and a panel-under-ventilation wind speed normalized value and a non-coverage area effective light duration value as ecological constraint targets; constructing a core target optimal threshold according to the core target; constructing an ecological standard threshold according to the ecological constraint target; performing front layer screening on population individuals in the photovoltaic coverage multi-objective optimization model according to the ecological standard threshold and the core target optimal threshold to obtain n front layers; initializing a reference line and performing density fusion adjustment according to target function values of individuals in the n front layers to obtain a reference line set suitable for current population distribution; screening high-quality individuals to form a partial offspring population according to the reference line set and priorities of the front layers, and generating new individuals meeting constraint conditions by chaotic mapping to form a chaotic regenerated population; calculating dynamic crossover probabilities of the population individuals, pairing and crossing the high-quality individuals according to the crossover probabilities to generate a crossover offspring population, and generating a variation offspring population by variation of the individuals according to a fixed variation probability; merging the parent population, the partial offspring population, the chaotic regenerated population, the crossover offspring population and the variation offspring population, removing repeated individuals and invalid individuals not meeting constraint conditions, and screening individuals of a fixed size to form a new generation of parent population; repeating the front layer screening, the reference line adjustment, the selection of crossover and variation and the new generation of population construction until a preset maximum iteration number is reached, and selecting an individual with an optimal comprehensive fitness value from a first front layer of the final population, wherein the photovoltaic coverage corresponding to the individual is the optimal photovoltaic panel coverage of the fish-light complementary field.

2. The method according to claim 1, wherein, the construction of the photovoltaic coverage multi-objective optimization model according to the environmental parameters, the equipment core parameters and the fishery breeding constraint parameters comprises the following steps: constructing a single-area photovoltaic power generation quantity target function, a panel-under-ventilation wind speed target function and a non-coverage area effective light duration target function according to the equipment core parameters and the environmental parameters; performing adaptive normalization processing on the single-area photovoltaic power generation quantity target function, the panel-under-ventilation wind speed target function and the non-coverage area effective light duration target function to obtain normalized single-area photovoltaic power generation quantity target function, panel-under-ventilation wind speed target function and non-coverage area effective light duration target function; constructing a fish-light complementary comprehensive fitness function according to the normalized single-area photovoltaic power generation quantity target function, the panel-under-ventilation wind speed target function and the non-coverage area effective light duration target function; constructing constraint conditions of the multi-objective optimization model; According to the constraint condition of the multi-objective optimization model and the fish-light complementary comprehensive fitness function, a photovoltaic coverage multi-objective optimization model is constructed. 3.The method according to claim 1, wherein, According to the ecological threshold and the core target optimal threshold, the population individuals in the photovoltaic coverage multi-objective optimization model are screened in the front layer to obtain n front layers, including: For each population individual in the photovoltaic coverage multi-objective optimization model, the dominance count and the dominated set are initialized; According to the core target, the ecological constraint target, the core target optimal threshold and the ecological threshold, the attributes of each population individual are marked; A scenario-based dominance model is constructed, and all individual pairs composed of population individuals are traversed, the dominance relationship of population individuals is determined according to the constructed scenario-based dominance model, the first front layer is screened and the subsequent front layers are recursively generated.

4. The method according to claim 3, wherein, According to the core target, the ecological constraint target, the core target optimal threshold and the ecological threshold, the attributes of each population individual are marked, including: If the core target value of the individual is greater than or equal to the core target optimal threshold and the values of the two ecological constraint targets are both greater than or equal to the corresponding ecological threshold, the individual is marked as a core protection individual; If the values of the ecological constraint targets of the individual are both greater than or equal to the corresponding ecological threshold but the core target value is less than the core target optimal threshold, the individual is marked as an ecological threshold individual; If any ecological constraint target value of the individual is less than the corresponding ecological threshold, the individual is marked as a non-compliance individual. 5.The method according to claim 1, wherein, The core target optimal threshold is constructed according to the core target, including: The single-area photovoltaic power generation capacity normalized value of all individuals in the current population is extracted from the photovoltaic coverage multi-objective optimization model to construct a core target data set; According to the core target data set, the global maximum value of the core target data set is calculated; The core target proportion coefficient is set; According to the global maximum value and the proportion coefficient, the core target optimal threshold is determined. 6.The method according to claim 1, wherein, The ecological threshold is constructed according to the ecological constraint target, including: The ecological basic threshold is extracted from the fishery breeding constraint parameter; Adaptive normalization processing is adopted to determine the normalized initial threshold of the under-plate ventilation wind speed and the normalized initial threshold of the effective light duration in the non-coverage area according to the ecological basic threshold; The water type correction coefficient and the breeding density correction coefficient are obtained; The under-plate ventilation wind speed normalized initial threshold is corrected according to the water type correction coefficient and the breeding density correction coefficient to obtain the final under-plate ventilation wind speed threshold; The water type correction coefficient is used to correct the normalized initial threshold of the effective light duration in the non-coverage area to obtain the final non-coverage area effective light duration threshold; According to the final under-plate ventilation wind speed threshold and the final non-coverage area effective light duration threshold, the ecological threshold is obtained.

7. A carbon neutral-oriented fish-light combined ecosystem construction device, characterized by comprising: a fish-light combined ecosystem construction device according to any one of claims 1 to 6. It includes: A first acquisition unit is configured to acquire environmental parameters, equipment core parameters and fishery breeding constraint parameters of a fish-light complementary field; A first processing unit is configured to construct a photovoltaic coverage multi-objective optimization model according to the environmental parameters, equipment core parameters and fishery breeding constraint parameters; A second processing unit is configured to solve the photovoltaic coverage multi-objective optimization model by using an improved non-dominated sorting genetic algorithm to obtain an optimal photovoltaic panel coverage rate of the fish-light complementary field. A third processing unit is configured to construct a fish-light combined ecosystem by using the optimal photovoltaic panel coverage; The improved non-dominated sorting genetic algorithm is used to solve the photovoltaic coverage multi-objective optimization model to obtain the optimal photovoltaic panel coverage of the fish-light complementary site, including: A single-area photovoltaic power generation quantity normalized value is defined as a core target, and a panel-under-ventilation wind speed normalized value and a non-coverage area effective light duration value are defined as ecological constraint targets; A core target optimal threshold is constructed according to the core target; An ecological standard threshold is constructed according to the ecological constraint target; According to the ecological standard threshold and the core target optimal threshold, the population individuals in the photovoltaic coverage multi-objective optimization model are screened by a front layer to obtain n front layers; According to the target function values of the individuals in the n front layers, a reference line is initialized and density fusion adjustment is performed to obtain a reference line set suitable for the current population distribution; According to the reference line set and the priority of each front layer, high-quality individuals are screened to form a partial offspring population, and new individuals that meet the constraint conditions are generated by chaotic mapping for the eliminated individuals to form a chaotic regenerated population; The dynamic crossover probability of the population individuals is calculated, the high-quality individuals are paired and crossed according to the crossover probability to generate a crossover offspring population, and the individuals are mutated according to a fixed mutation probability to generate a mutation offspring population; The parent population, partial offspring population, chaotic regenerated population, crossover offspring population and mutation offspring population are merged, and repeated individuals and invalid individuals that do not meet the constraint conditions are removed, and a fixed-size individual is screened to form a new generation of parent population; The front layer screening, reference line adjustment, selection, crossover and mutation, and new generation population construction steps are repeatedly performed until a preset maximum iteration number is reached, and the individual with the optimal comprehensive fitness value is selected from the first front layer of the final population, and the photovoltaic coverage corresponding to the individual is the optimal photovoltaic panel coverage of the fish-light complementary site.

8. A terminal, characterized by comprising: A processor, an input device, an output device and a memory are connected to each other, wherein the memory is used to store a computer program, the computer program includes program instructions, the processor is configured to invoke the program instructions, and the fish-light combined ecosystem construction method facing carbon neutralization according to any one of claims 1-6 is executed.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, the computer program includes program instructions, and the program instructions make the processor execute the fish-light combined ecosystem construction method facing carbon neutralization according to any one of claims 1-6 when the processor executes the program instructions.

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