A fishery culture method, device, equipment and medium based on wind and light power generation

Abstract

The application discloses a fishery breeding method and device based on wind and light power generation, equipment and medium, and relates to the technical field of fishery breeding. The method comprises the following steps: acquiring resource data of a breeding project location and power load demand of an algae and fish breeding system, constructing a capacity optimization configuration model according to the resource data and the power load demand, iteratively solving the capacity optimization configuration model to obtain optimal capacity matching based on power generation cost, and delivering power to the algae and fish breeding system according to the optimal capacity matching. The temperature and light intensity are adjusted based on the power and a preset gradient through the algae and fish breeding system, the microalgae strains are domesticated and cultured according to the adjusted temperature and light intensity, the corresponding domesticated microalgae strains are screened based on fishpond wastewater, and target microalgae strains are obtained. The active cell liquid of the microalgae strains is acquired, and the active cell liquid of the microalgae strains is used for water purification and feed supply, so that fishery breeding is realized. The application improves the microalgae so that the microalgae can purify wastewater.

Description

Technical Field

[0001] This invention relates to the field of aquaculture technology, and in particular to a method, apparatus, equipment and medium for aquaculture based on wind and solar power generation. Background Technology

[0002] Since the beginning of the 21st century, the scale of renewable energy power generation, such as wind, solar, and biomass energy, has gradually expanded, becoming an extremely important source of electricity. my country has abundant wind and solar resources, but their uneven distribution and the spatial imbalance between resource distribution and load demand present a significant contradiction. The transmission of these new energy projects after completion has also become a pressing issue. Integrated energy projects such as wind-solar-hydrogen-ethanol production are important routes to address wind and solar curtailment, but the consumption, transportation, and storage of hydrogen-ethanol also incur substantial costs. To improve the economic viability of these projects, integrated wind-solar-algae-fishery smart ecological projects can be constructed in suitable aquaculture areas. However, aquaculture wastewater contains organic matter, ammonia nitrogen, nitrates, phosphorus, as well as uneaten feed and fish and shrimp excrement, containing a large amount of organic matter. Failure to treat it promptly can lead to eutrophication and water quality deterioration. Scenedesmus has a good effect on treating organic wastewater, while also fixing carbon dioxide. Its microalgae protein content is as high as 40% or more, making it a high-quality protein feed for fish and shrimp. Currently, integrated wind-solar-algae-fishery aquaculture models are mostly in the demonstration industrial park stage, with very few large-scale applications. In most projects, the wind and solar storage capacity and the greenhouse environment for microalgae cultivation are determined based on the engineer's personal experience, which fails to allow for the modification of microalgae to tolerate fishpond wastewater and achieve wastewater purification. Therefore, how to select wind and solar storage capacity to meet the needs of algae-fish farming and how to modify microalgae to tolerate fishpond wastewater and grow successfully are key issues that need to be addressed. Summary of the Invention

[0003] In view of this, the purpose of this invention is to provide a method, apparatus, equipment, and medium for aquaculture based on wind and solar power generation, which can achieve the maximum system benefit through capacity configuration, ensuring that wind and solar storage capacity meets the needs of algae-fish farming, while simultaneously improving microalgae to tolerate pond wastewater and grow smoothly, thereby realizing aquaculture. The specific solution is as follows:

[0004] In a first aspect, this application discloses a fishery aquaculture method based on wind and solar power generation, comprising:

[0005] Obtain resource data of the location of the aquaculture project and the power load demand of the corresponding algae-fish farming system. Construct a capacity optimization configuration model based on the resource data and power load demand. The resource data includes wind speed, irradiance, and temperature.

[0006] The capacity optimization configuration model is iteratively solved using the ant colony optimization algorithm to obtain the optimal capacity ratio based on the cost per kilowatt-hour, and the electricity generated by wind power and photovoltaic power is transmitted to the algae and fish farming system according to the optimal capacity ratio.

[0007] The algae-fish farming system adjusts the temperature and light intensity of the microalgae cultivation greenhouse based on the electricity and preset gradient. The microalgae strains are domesticated and cultivated according to the adjusted temperature and light intensity. The domesticated microalgae strains are screened based on fishpond wastewater of different concentrations to obtain the target microalgae strains.

[0008] Based on the target microalgae strain, obtain the active cell liquid of the microalgae strain, and use the active cell liquid of the microalgae strain to purify the water quality and supply feed to fish ponds, so as to realize aquaculture.

[0009] Optionally, the step of constructing a capacity optimization configuration model based on the resource data and electricity load demand includes:

[0010] A capacity optimization configuration model is constructed based on the resource data and electricity load demand; the capacity optimization configuration model includes a wind power generation model, a photovoltaic power generation model, and an energy storage system model.

[0011] The wind power generation model is as follows:

[0012] ;

[0013] in, Let v be the wind power output at time t. t The wind speed at the current time step, v ci and v co These are the cut-in and cut-out wind speeds for the wind turbine, v r P is the rated wind speed of the wind turbine. r This refers to the rated power of the wind turbine generator set;

[0014] The photovoltaic power generation model is as follows:

[0015] ;

[0016] Among them, P pv I contributes to photovoltaic power generation t I represents the irradiance at the current time step. stc P represents the irradiance under standard test conditions. r This represents the rated power of a photovoltaic system under standard test conditions. T is the power temperature coefficient of photovoltaics. t Let T be the photovoltaic temperature at the current time step. stc Photovoltaic temperature under standard test conditions;

[0017] The energy storage system model is as follows:

[0018] ;

[0019] ;

[0020] ;

[0021] ;

[0022] Where SOC(t+1) and SOC(t) are the states of charge of the energy storage system at times t+1 and t, respectively, and P c and P d These are the charging and discharging powers, and E represents the charge and discharge efficiency of the energy storage system. r The rated capacity of the energy storage system is [missing information]. and These are the maximum charging power and the maximum discharging power, respectively. and These are the minimum and maximum charge capacities of the energy storage system, respectively.

[0023] Optionally, after constructing the capacity optimization configuration model based on the resource data and electricity load demand, the method further includes:

[0024] A power shortage rate constraint is set so that the capacity optimization configuration model can determine whether to terminate the iteration based on the power shortage rate constraint; wherein, the formula for determining the power shortage rate based on the power shortage during the production process and the total power load is:

[0025] ;

[0026] ;

[0027] in, The power shortage rate, For the maximum power shortage rate, This refers to the power shortage during the production process. The total electrical load is denoted as .

[0028] Optionally, the step of iteratively solving the capacity optimization configuration model using the ant colony optimization algorithm to obtain the optimal capacity allocation based on the cost per kilowatt-hour includes:

[0029] Calculate the output, energy storage, and load status of the wind and solar power generation system, and determine whether the output of the wind and solar power generation system meets the electricity load demand based on the corresponding calculation results.

[0030] If the output of the wind and solar power generation system does not meet the electricity load demand, it is determined whether the sum of the output of the wind and solar power generation system and the capacity of the energy storage system meets the electricity load demand, and the power shortage or the capacity of the energy storage system at the next moment is calculated according to the corresponding judgment result to obtain the corresponding first calculation result.

[0031] If the output of the wind and solar power generation system meets the electricity load demand, then determine whether the output of the wind and solar power generation system meets the electricity load demand and whether the capacity of the energy storage system is met.

[0032] If the output of the wind and solar power generation system meets the electricity load demand but does not meet the capacity of the energy storage system, then the excess wind and solar power output beyond meeting the electricity load demand is used to charge the energy storage system, and the capacity of the energy storage system at the next moment is calculated.

[0033] If the output of the wind and solar power generation system meets the electricity load demand and then meets the capacity of the energy storage system, then surplus electricity will be fed into the grid or abandoned, and it will be determined whether the solution corresponding to the current capacity optimization configuration model meets the preset constraints.

[0034] If the preset constraints are not met, the capacity initialization parameters of each device in the capacity optimization configuration model are updated, and the process jumps back to the step of calculating the output, energy storage and load status of the wind and solar power generation system.

[0035] If the preset constraint conditions and the iteration termination conditions are met, the optimal capacity allocation ratio is determined based on the cost per kilowatt-hour and all solutions that meet the conditions corresponding to the capacity optimization configuration model.

[0036] Optionally, the step of transmitting the electricity generated by wind power and photovoltaic power to the algae-fish farming system according to the optimal capacity ratio includes:

[0037] According to the optimal capacity ratio, the electricity generated by wind power and photovoltaic power generation is fed into the DC bus through rectifiers and inverters and then transmitted to the algae and fish farming system.

[0038] Optionally, the acclimatization and cultivation of the microalgae strain according to the adjusted temperature and light intensity includes:

[0039] The microalgae strain was cultured three times based on the first adjusted temperature, the first carbon dioxide concentration, and the first adjusted light intensity to obtain the first cultured microalgae strain.

[0040] The first cultured algal strain was cultured three times based on the second adjusted temperature, the second carbon dioxide concentration, and the second adjusted light intensity to obtain the second cultured microalgae strain.

[0041] The second cultured algal strain was cultured three times based on the third adjusted temperature, the third adjusted carbon dioxide concentration, and the third adjusted light intensity to obtain the domesticated microalgae strain.

[0042] Optionally, the screening of corresponding acclimated microalgae strains based on fishpond wastewater of different concentrations to obtain target microalgae strains includes:

[0043] The domesticated microalgae strains were cultured sequentially in fishpond wastewater of different concentrations. The domesticated microalgae strains were screened based on their growth density, pH value, and chlorophyll content during the culture process to obtain the target microalgae strain.

[0044] Secondly, this application discloses a fish farming device based on wind and solar power generation, comprising:

[0045] The model building module is used to acquire resource data of the location of the aquaculture project and the power load demand of the corresponding algae-fish farming system, and to build a capacity optimization configuration model based on the resource data and power load demand; the resource data includes wind speed, irradiance, and temperature.

[0046] The power transmission module is used to iteratively solve the capacity optimization configuration model using an ant colony optimization algorithm in order to obtain the optimal capacity ratio based on the cost per kilowatt-hour, and to transmit the power generated by wind power and photovoltaic power to the algae and fish farming system according to the optimal capacity ratio.

[0047] The screening module is used to adjust the temperature and light intensity of the microalgae cultivation greenhouse based on the power and preset gradient through the algae-fish farming system, to acclimate and cultivate microalgae strains according to the adjusted temperature and light intensity, and to screen the corresponding acclimatized microalgae strains based on fishpond wastewater of different concentrations in order to obtain the target microalgae strains.

[0048] The aquaculture implementation module is used to obtain the active cell liquid of the target microalgae strain, and use the active cell liquid of the microalgae strain to purify the water quality and supply feed to the fishpond, so as to realize aquaculture.

[0049] Thirdly, this application discloses an electronic device, comprising:

[0050] Memory, used to store computer programs;

[0051] A processor for executing the computer program to implement the aforementioned wind and solar power-based aquaculture method.

[0052] Fourthly, this application discloses a computer-readable storage medium for storing a computer program, which, when executed by a processor, implements the aforementioned aquaculture method based on wind and solar power generation.

[0053] In this application, when conducting aquaculture based on wind and solar power, the first step is to acquire resource data of the aquaculture project site and the electricity load demand of the corresponding algae-fish farming system. A capacity optimization configuration model is then constructed based on this resource data and electricity load demand. The resource data includes wind speed, irradiance, and temperature. Next, an ant colony optimization algorithm is used to iteratively solve the capacity optimization configuration model to obtain the optimal capacity ratio based on the cost per kilowatt-hour. Based on this optimal capacity ratio, electricity generated by wind and solar power is transmitted to the algae-fish farming system. Then, the algae-fish farming system adjusts the temperature and light intensity of the microalgae cultivation greenhouse based on the electricity and a preset gradient. Microalgae strains are then domesticated and cultivated according to the adjusted temperature and light intensity. The domesticated microalgae strains are screened based on different concentrations of fishpond wastewater to obtain target microalgae strains. Finally, active cell liquid from the target microalgae strains is obtained and used for water purification and feed supply in the fishpond to achieve aquaculture. It is evident that this application utilizes wind and solar power to provide electricity for microalgae cultivation, and controls the temperature and light environment for microalgae growth. This enhances the microalgae's resilience, enabling it to tolerate the complex environment of fishpond wastewater and continue to grow and reproduce. Furthermore, by constructing a capacity optimization model, a capacity optimization scheme with maximum benefit can be obtained, increasing system profitability. This reduces the cost of purifying fishpond wastewater, while the microalgae cultivated from wastewater can be used to supply fish and shrimp, increasing their yield. Thus, it increases the profitability of fish and shrimp farming while reducing wastewater treatment and feed purchase costs. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of the present invention 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 embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0055] Figure 1 This is a flowchart of a fish farming method based on wind and solar power disclosed in this application;

[0056] Figure 2 This is a schematic diagram of a wind and solar power-based aquaculture system disclosed in this application;

[0057] Figure 3This application discloses a flowchart of a capacity optimization configuration calculation process.

[0058] Figure 4 This is a schematic diagram of a microalgae cultivation and domestication method disclosed in this application;

[0059] Figure 5 This is a schematic diagram of a wind and solar power-based aquaculture device disclosed in this application.

[0060] Figure 6 This is a structural diagram of an electronic device disclosed in this application. Detailed Implementation

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

[0062] Current integrated wind-solar-algae-fish farming models are mostly in the demonstration industrial park stage, with very few large-scale applications. In most projects, the wind and solar capacity and the greenhouse environment for cultivating microalgae are determined based on the individual experience of engineers, failing to improve the microalgae to tolerate fishpond wastewater and achieve wastewater purification. To address these technical problems, this application discloses a wind-solar power-based aquaculture method that achieves the highest system benefit through capacity configuration, ensuring that the wind and solar storage capacity meets the needs of algae-fish farming while simultaneously improving the microalgae to tolerate fishpond wastewater and grow successfully.

[0063] See Figure 1 As shown in the figure, an embodiment of the present invention discloses a fishery aquaculture method based on wind and solar power generation, comprising:

[0064] Step S11: Obtain resource data of the location of the aquaculture project and the power load demand of the algae-fish farming system corresponding to the aquaculture project, and construct a capacity optimization configuration model based on the resource data and power load demand; the resource data includes wind speed, irradiance and temperature.

[0065] In this embodiment, when constructing the capacity optimization configuration model, data such as wind speed, irradiance, temperature, and density at the project site are first obtained to clarify the electricity load demand and upper and lower limits of the algae-fish farming system. Based on wind and solar power generation and load demand, a capacity optimization configuration model is constructed, which mainly includes a wind power generation model, a photovoltaic power generation model, and an energy storage system model. Specifically, the wind power generation model calculates the wind turbine power generation based on the obtained local historical wind speed data. The relationship between wind turbine power generation and wind speed is as follows:

[0066] ;

[0067] in, Let v be the wind power output at time t. t The wind speed at the current time step, v ci and v co These are the cut-in and cut-out wind speeds for the wind turbine, v r P is the rated wind speed of the wind turbine. r This refers to the rated power of the wind turbine.

[0068] Photovoltaic power generation model: The photovoltaic power generation is calculated based on the obtained local historical irradiance data. The relationship between photovoltaic power generation and irradiance is as follows:

[0069] ;

[0070] Among them, P pv I contributes to photovoltaic power generation t I represents the irradiance at the current time step. stc P represents the irradiance under standard test conditions. r This represents the rated power of a photovoltaic system under standard test conditions. T is the power temperature coefficient of photovoltaics. t Let T be the photovoltaic temperature at the current time step. stc The photovoltaic temperature is the value under standard test conditions.

[0071] The energy storage system model is as follows:

[0072] ;

[0073] ;

[0074] ;

[0075] ;

[0076] Where SOC(t+1) and SOC(t) are the states of charge of the energy storage system at times t+1 and t, respectively, and P c and P d These are the charging and discharging powers, and E represents the charge and discharge efficiency of the energy storage system. r The rated capacity of the energy storage system is [missing information]. and These are the maximum charging power and the maximum discharging power, respectively. and These are the minimum and maximum charge capacities of the energy storage system, respectively.

[0077] In this embodiment, when the output of wind, solar, and energy storage can meet the enterprise's electricity load demand, it is determined that the wind and solar output can meet the maximum load demand. If it can, surplus electricity is fed into the grid or abandoned according to policy. When the wind and solar output cannot meet the enterprise's minimum electricity load demand, it is defined as a power shortage, the system's power shortage is calculated, and the power shortage portion is purchased from the grid. Figure 2 As shown, when wind and solar power output is high, in addition to supplying the electricity demand of the algae-fish farming system, surplus electricity will be fed into the grid in proportion according to relevant local policies, or appropriate energy storage will be used to increase the project's revenue, with excess electricity being abandoned. When wind power and energy storage output are insufficient, electricity will be purchased from the grid or the greenhouse load will be reduced to achieve optimal economic efficiency. In the calculation process, the system sets a power shortage rate constraint, defined as follows:

[0078] ;

[0079] ;

[0080] in, The power shortage rate, For the maximum power shortage rate, This refers to the power shortage during the production process. The total electrical load is denoted as .

[0081] Step S12: Iteratively solve the capacity optimization configuration model using the ant colony optimization algorithm to obtain the optimal capacity ratio based on the cost per kilowatt-hour, and transmit the electricity generated by wind power and photovoltaic power to the algae and fish farming system according to the optimal capacity ratio.

[0082] In this embodiment, after the model is constructed, based on the objective of minimizing the levelized cost of electricity in the wind-solar-algae-fishery system, the ant colony optimization algorithm is used to solve the constructed model, and finally the capacity optimization configuration scheme with the maximum system benefit is obtained. The system benefit is calculated as follows:

[0083] ;

[0084] in, For the benefit of the system, The electricity price for the system. The electricity provided by wind, solar, and energy storage to enterprises; , , These represent the costs of the wind turbine, the photovoltaic system, and the energy storage system, respectively. In this way, this application optimizes the capacity of each module in the wind-solar-storage system, obtaining a capacity configuration scheme that maximizes system benefits and achieves optimal economic efficiency for the fishery.

[0085] In the solution process, the ant colony optimization algorithm is used to iteratively solve the capacity optimization configuration model in order to obtain the optimal capacity allocation based on the cost per kilowatt-hour. Figure 3 As shown, the process first calculates the output, energy storage, and load status of the wind and solar power generation system. Based on the calculation results, it is determined whether the output of the wind and solar power generation system meets the electricity load demand. If the output of the wind and solar power generation system does not meet the electricity load demand, it is determined whether the sum of the output of the wind and solar power generation system and the capacity of the energy storage system meets the electricity load demand. Based on the corresponding determination results, the capacity of the energy storage system at the next moment or the power shortage is calculated to obtain the corresponding first calculation result. If the output of the wind and solar power generation system meets the electricity load demand, it is determined whether the energy storage system capacity is met after the output of the wind and solar power generation system meets the electricity load demand. If the energy storage system capacity is not met after the output of the wind and solar power generation system meets the electricity load demand, then the wind and solar power generation system is used to meet the electricity load demand. Excess wind and solar power output beyond meeting the electricity load demand is used to charge the energy storage system, and the capacity of the energy storage system at the next time step is calculated. If the wind and solar power output meets the electricity load demand and then meets the energy storage system's capacity, the surplus electricity is either fed into the grid or curtailed, and it is determined whether the solution corresponding to the current capacity optimization configuration model meets the preset constraints. If the preset constraints are not met, the capacity initialization parameters of each device in the capacity optimization configuration model are updated, and the process jumps back to the steps of calculating the wind and solar power output, energy storage, and load status. If the preset constraints and the iteration termination condition are met, the optimal capacity allocation is determined based on the levelized cost of electricity (LCOE) and all solutions that meet the conditions in the capacity optimization configuration model. That is, the LCOE is calculated, and all solutions that meet the conditions are sorted in ascending order of LCOE to obtain the optimal capacity allocation.

[0086] Finally, based on the optimal capacity ratio, the power generated by wind power and photovoltaic power generation is fed into the DC bus through rectifiers and inverters and transmitted to the algae and fish farming system, thereby providing power to the algae and fish farming system.

[0087] Step S13: The temperature and light intensity of the microalgae cultivation greenhouse are adjusted by the algae-fish farming system based on the power and preset gradient. The microalgae strains are domesticated and cultivated according to the adjusted temperature and light intensity. The domesticated microalgae strains are screened based on fishpond wastewater of different concentrations to obtain the target microalgae strains.

[0088] In this embodiment, as Figure 2As shown, wind and solar power are fed into the DC bus and transmitted to the algae-fish farming system via rectifiers and inverters. The integrated coordination and control system is responsible for coordinating the operation of each system. The greenhouse system for cultivating microalgae is divided into zones, each equipped with LED (light-emitting diode) lights with wavelengths in the 480-700nm range. The temperature control system uses temperature sensors for feedback, incorporating a fresh air system and a heat pump for heating. This allows the greenhouse to maintain a constant temperature, adjustable within the range of 16-40℃. Different temperature and light adjustments are made for different microalgae; for example, for Scenedesmus, 35℃ and 120μmol•m -2 •s -1 Under these conditions, compared with normal outdoor aquaculture systems, the microalgae yield increased by more than 5 times under the same land area, and the efficiency of CO2 fixation was also greatly improved.

[0089] Next, the algae-fish farming system adjusts the temperature and light intensity of the microalgae cultivation greenhouse based on the electricity and a preset gradient. The microalgae strains are then cultured three times according to the first adjusted temperature, first carbon dioxide concentration, and first adjusted light intensity to obtain a first cultured microalgae strain. This process is repeated three times with the second adjusted temperature, second carbon dioxide concentration, and second adjusted light intensity to obtain a second cultured microalgae strain. Finally, the second cultured microalgae strains are cultured three times with the third adjusted temperature, third carbon dioxide concentration, and third adjusted light intensity to obtain the acclimatized microalgae strain. In other words, the microalgae are acclimatized under different conditions. Then, the acclimatized microalgae strains are cultured sequentially in fishpond wastewater of different concentrations. The acclimatized microalgae strains are screened based on their growth density, pH (Pondus Hydrogenii) value, and chlorophyll content during the cultivation process to obtain the target microalgae strain. Finally, algae species that can tolerate 100% wastewater and grow rapidly are selected.

[0090] Step S14: Obtain the active cell liquid of the target microalgae strain, and use the active cell liquid of the microalgae strain to purify the water quality and supply feed to the fishpond, so as to realize aquaculture.

[0091] In this embodiment, wastewater is pretreated, and domesticated and expanded algal strains are inoculated into 100L of wastewater. The microalgae are then grown in a raceway pond photoreactor for three days, resulting in rapid growth and reproduction of the microalgae. This produces a culture medium (active cell slurry) containing the microalgae, which is then introduced into a fishpond. The active cell slurry from the microalgae is used for water purification and feed supply. The levels of ammonia nitrogen, total nitrogen, total phosphorus, and COD (Chemical Oxygen Demand) in the pond wastewater are monitored daily. Thus, carbon-fixing microalgae can produce nutritional foods, functional feeds, and organic fertilizers, achieving significant environmental, social, and economic benefits. The active cell slurry formed by the iterative reproduction of microalgae is rich in protein and can be used in fish and shrimp farming, greatly increasing fish and shrimp yields and reducing feed purchase costs through self-production and self-consumption. Furthermore, the microalgae fix CO2 (carbon dioxide) through photosynthesis, with a carbon fixation amount 1.83 times the microalgae production, creating a negative carbon effect that can offset the company's carbon emissions.

[0092] In summary, this application, when conducting aquaculture based on wind and solar power, firstly acquires resource data of the aquaculture project site and the electricity load demand of the corresponding algae-fish farming system. Based on this resource data and electricity load demand, a capacity optimization configuration model is constructed. The resource data includes wind speed, irradiance, and temperature. Then, an ant colony optimization algorithm is used to iteratively solve the capacity optimization configuration model to obtain the optimal capacity ratio based on the cost per kilowatt-hour. Based on this optimal capacity ratio, electricity generated by wind and solar power is transmitted to the algae-fish farming system. Next, the algae-fish farming system adjusts the temperature and light intensity of the microalgae cultivation greenhouse based on the electricity and a preset gradient. Microalgae strains are then domesticated and cultivated according to the adjusted temperature and light intensity. The domesticated microalgae strains are screened based on different concentrations of fishpond wastewater to obtain target microalgae strains. Finally, active cell liquid of the target microalgae strains is obtained and used for water purification and feed supply in the fishpond, thereby achieving aquaculture. It is evident that this application utilizes wind and solar power to provide electricity for microalgae cultivation, and controls the temperature and light environment for microalgae growth. This enhances the microalgae's resilience, enabling it to tolerate the complex environment of fishpond wastewater and continue to grow and reproduce. Furthermore, by constructing a capacity optimization model, a capacity optimization scheme with maximum benefit can be obtained, increasing system profitability. This reduces the cost of purifying fishpond wastewater, while the microalgae cultivated from wastewater can be used to supply fish and shrimp, increasing their yield. Thus, it increases the profitability of fish and shrimp farming while reducing wastewater treatment and feed purchase costs.

[0093] As can be seen from the previous embodiment, before using the active cell liquid of microalgae strains to purify the water quality and supply feed to fish ponds, this application will first acclimate and cultivate the microalgae. Next, the specific acclimatization and cultivation process will be described.

[0094] like Figure 4 As shown, in the domestication and cultivation of microalgae, this application first divides the greenhouse into three zones, with temperatures of 25-30-35℃ in each zone and corresponding light intensities of 60-80-120 μmol•m². -2 •s -1 Then, the sulfur concentration in the culture medium was set to 4.2 mmol / L, and temperature and light gradient acclimatization culture was carried out. In the specific implementation process, taking *Scenedesmus* as an example, the *Scenedesmus* was initially cultured at 25℃ for three iterations, each for 2 days. The pH of the algal solution was controlled at 7.2±0.3, the CO2 concentration at 3%, and the light intensity at 60 μmol•m-2•s-1 using hepes (N-2-Hydroxyethylpiperazine-N-2-Ethane Sulfonic Acid, 4-hydroxyethylpiperazine ethanesulfonic acid) buffer. Then, the algal strain was taken and the growth conditions were adjusted for three more iterations, each for 2 days, with the pH of the algal solution controlled at 7.2±0.3, the CO2 concentration at 5%, the culture temperature at 30℃, and the light intensity at 80 μmol•m-2•s-1. Finally, the algal strain was used to adjust the growth conditions and continue to be cultured and iterated three times, each time for 2 days. The pH of the algal solution was controlled at 7.2±0.3, the CO2 concentration was controlled at 10%, the culture temperature of the algal solution was controlled at 35℃, and the light intensity was controlled at 120μmol•m-2•s-1, thus completing three temperature gradient and light acclimatization cultures.

[0095] Then, a certain amount of fishpond wastewater was pretreated to degrade large organic molecules into smaller molecules. Next, in a constant-temperature greenhouse, *Scenedesmus* was acclimatized in wastewater ranging from 50% to 100% concentration, starting with low concentrations. The pH of the algal solution was controlled at 7.2 ± 0.3, the CO2 concentration at 10%, the temperature at 35℃, and the light intensity at 120 μmol•m⁻²•s⁻¹. Finally, algal strains that could tolerate 100% wastewater and grow rapidly were selected. A growth control system monitored the microalgae's growth density, pH, chlorophyll content, and other indicators daily. When abnormal growth occurred, the greenhouse's light and temperature were adjusted to maintain normal microalgae growth.

[0096] Finally, the domesticated and expanded algal strains were inoculated into 100L of wastewater and grown in a raceway pond photoreactor for three days. The microalgae rapidly grew and multiplied, yielding a culture medium (active cell broth) containing microalgae. This medium was then introduced into a fishpond, and the ammonia nitrogen, total nitrogen, total phosphorus, and COD levels in the pond wastewater were measured daily to calculate the removal rate and determine the degree of wastewater purification. Therefore, this application employs a high-temperature and high-light-intensity coupled sulfur-based model to stress microalgae into functional changes. Under this stress, the microalgae, through multiple reactions such as photosynthesis, respiration, and metabolism, form more sulfur-containing metabolites, such as glutathione. The sulfur metabolism pathway of the microalgae is enhanced, synthesizing more antioxidant and detoxification proteins, improving the survival ability of microalgal cells under adverse stress, and enabling them to maintain survival activity and successfully iterate and reproduce under unfavorable conditions.

[0097] In this way, this application improves the stress resistance of microalgae through temperature and light conditioning. The improved microalgae are then added to fishponds, where they absorb carbon, nitrogen, and phosphorus, continuously multiplying to achieve a high biomass yield and thus utilizing waste. Simultaneously, this reduces the ammonia nitrogen, total nitrogen, total phosphorus, and COD content in the fishpond wastewater, purifying it. Furthermore, the active cell sap formed by the iterative reproduction of microalgae is rich in protein and can be used in fish and shrimp farming, significantly increasing fish and shrimp yields and reducing feed purchase costs through self-production and self-consumption. On the other hand, microalgae fix CO2 through photosynthesis, creating a negative carbon effect that can offset the company's carbon emissions.

[0098] See Figure 5 As shown, an embodiment of the present invention discloses a fish farming device based on wind and solar power generation, comprising:

[0099] The model building module 11 is used to acquire resource data of the location of the aquaculture project and the power load demand of the algae-fish farming system corresponding to the aquaculture project, and to build a capacity optimization configuration model based on the resource data and power load demand; the resource data includes wind speed, irradiance and temperature.

[0100] Power transmission module 12 is used to iteratively solve the capacity optimization configuration model using ant colony optimization algorithm in order to obtain the optimal capacity ratio based on the cost per kilowatt-hour, and to transmit the power generated by wind power generation and photovoltaic power generation to the algae and fish farming system according to the optimal capacity ratio.

[0101] The screening module 13 is used to adjust the temperature and light intensity of the microalgae cultivation greenhouse based on the power and preset gradient through the algae-fish farming system, to acclimate and cultivate the microalgae strains according to the adjusted temperature and light intensity, and to screen the corresponding acclimatized microalgae strains based on fishpond wastewater of different concentrations in order to obtain the target microalgae strains.

[0102] The aquaculture implementation module 14 is used to obtain the active cell liquid of the target microalgae strain, and use the active cell liquid of the microalgae strain to purify the water quality and supply feed to the fishpond, so as to realize aquaculture.

[0103] It is evident that this application utilizes wind and solar power to provide electricity for microalgae cultivation, and controls the temperature and light environment for microalgae growth. This enhances the microalgae's resilience, enabling it to tolerate the complex environment of fishpond wastewater and continue to grow and reproduce. Furthermore, by constructing a capacity optimization model, a capacity optimization scheme with maximum benefit can be obtained, increasing system profitability. This reduces the cost of purifying fishpond wastewater, while the microalgae cultivated from wastewater can be used to supply fish and shrimp, increasing their yield. Thus, it increases the profitability of fish and shrimp farming while reducing wastewater treatment and feed purchase costs.

[0104] In some specific embodiments, the model building module 11 can be used to build a capacity optimization configuration model based on the resource data and electricity load demand; the capacity optimization configuration model includes a wind power generation model, a photovoltaic power generation model, and an energy storage system model;

[0105] The wind power generation model is as follows:

[0106] ;

[0107] in, Let v be the wind power output at time t. t The wind speed at the current time step, v ci and v co These are the cut-in and cut-out wind speeds for the wind turbine, v r P is the rated wind speed of the wind turbine. r This refers to the rated power of the wind turbine generator set;

[0108] The photovoltaic power generation model is as follows:

[0109] ;

[0110] Among them, P pv I contributes to photovoltaic power generation t I represents the irradiance at the current time step. stc P represents the irradiance under standard test conditions. r This represents the rated power of a photovoltaic system under standard test conditions. T is the power temperature coefficient of photovoltaics. t Let T be the photovoltaic temperature at the current time step. stc Photovoltaic temperature under standard test conditions;

[0111] The energy storage system model is as follows:

[0112] ;

[0113] ;

[0114] ;

[0115] ;

[0116] Where SOC(t+1) and SOC(t) are the states of charge of the energy storage system at times t+1 and t, respectively, and P c and P d These are the charging and discharging powers, and E represents the charge and discharge efficiency of the energy storage system. r The rated capacity of the energy storage system is [missing information]. and These are the maximum charging power and the maximum discharging power, respectively. and These are the minimum and maximum charge capacities of the energy storage system, respectively.

[0117] In some specific embodiments, the device can also be used to set a power shortage rate constraint, so that the capacity optimization configuration model determines whether to terminate the iteration based on the power shortage rate constraint; wherein, the formula for determining the power shortage rate based on the power shortage during the production process and the total power load is:

[0118] ;

[0119] ;

[0120] in, The power shortage rate, For the maximum power shortage rate, This refers to the power shortage during the production process. The total electrical load is denoted as .

[0121] In some specific embodiments, the power transmission module 12 can be used to calculate the output, energy storage, and load status of the wind and solar power generation system, and determine whether the output of the wind and solar power generation system meets the power load demand based on the corresponding calculation results; if the output of the wind and solar power generation system does not meet the power load demand, it is determined whether the sum of the output of the wind and solar power generation system and the capacity of the energy storage system meets the power load demand, and the capacity of the energy storage system at the next moment is calculated based on the corresponding judgment result to obtain the corresponding first calculation result; if the output of the wind and solar power generation system meets the power load demand, it is determined whether the capacity of the energy storage system is met after the output of the wind and solar power generation system meets the power load demand; if the capacity of the energy storage system is not met after the output of the wind and solar power generation system meets the power load demand, it is determined whether the capacity of the energy storage system is met after the output of the wind and solar power generation system meets the power load demand; If the wind and solar power generation system's output exceeds the electricity load demand, the excess wind and solar power output is used to charge the energy storage system, and the capacity of the energy storage system at the next moment is calculated. If the wind and solar power generation system's output meets the electricity load demand and then meets the energy storage system's capacity, then surplus electricity is fed into the grid or abandoned, and it is determined whether the solution corresponding to the current capacity optimization configuration model meets the preset constraints. If the preset constraints are not met, the capacity initialization parameters of each device corresponding to the capacity optimization configuration model are updated, and the process jumps back to the steps of calculating the wind and solar power generation system output, energy storage, and load status. If the preset constraints are met and the iteration termination condition is met, then the optimal capacity ratio is determined based on the cost per kilowatt-hour and all solutions corresponding to the capacity optimization configuration model that meet the conditions.

[0122] In some specific embodiments, the power transmission module 12 can be used to feed the power generated by wind power generation and photovoltaic power generation into the DC bus and transmit it to the algae and fish farming system through the rectifier and inverter according to the optimal capacity ratio.

[0123] In some specific embodiments, the screening module 13 can be used to perform three culture iterations on the microalgae strain according to a first adjusted temperature, a first carbon dioxide concentration, and a first adjusted light intensity to obtain a first cultured microalgae strain; perform three culture iterations on the first cultured microalgae strain according to a second adjusted temperature, a second carbon dioxide concentration, and a second adjusted light intensity to obtain a second cultured microalgae strain; and perform three culture iterations on the second cultured microalgae strain according to a third adjusted temperature, a third carbon dioxide concentration, and a third adjusted light intensity to obtain the domesticated microalgae strain.

[0124] In some specific embodiments, the screening module 13 can be used to sequentially cultivate the domesticated microalgae strains in fishpond wastewater of different concentrations, and screen the domesticated microalgae strains based on their growth density, pH value, and chlorophyll content during the cultivation process to obtain the target microalgae strain.

[0125] Furthermore, embodiments of this application also disclose an electronic device, Figure 6 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application.

[0126] Figure 6 This is a schematic diagram of the structure of an electronic device 20 provided in an embodiment of this application. Specifically, the electronic device 20 may include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the wind and solar power-based aquaculture method disclosed in any of the foregoing embodiments. Alternatively, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0127] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0128] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk, or optical disk, etc. The resources stored thereon can include an operating system 221, computer programs 222, etc., and the storage method can be temporary storage or permanent storage.

[0129] The operating system 221 is used to manage and control the various hardware devices on the electronic device 20 and the computer program 222, which may be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program capable of performing the wind and solar power-based aquaculture method executed by the electronic device 20 as disclosed in any of the foregoing embodiments, the computer program 222 may further include computer programs capable of performing other specific tasks.

[0130] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned disclosed aquaculture method based on wind and solar power generation. Specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0131] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0132] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software 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.

[0133] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0134] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0135] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods 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 fishery aquaculture method based on wind and solar power generation, characterized in that, include: Obtain resource data of the location of the aquaculture project and the power load demand of the corresponding algae-fish farming system. Construct a capacity optimization configuration model based on the resource data and power load demand. The resource data includes wind speed, irradiance, and temperature. The capacity optimization configuration model is iteratively solved using the ant colony optimization algorithm to obtain the optimal capacity ratio based on the cost per kilowatt-hour, and the electricity generated by wind power and photovoltaic power is transmitted to the algae and fish farming system according to the optimal capacity ratio. The algae-fish farming system adjusts the temperature and light intensity of the microalgae cultivation greenhouse based on the electricity and preset gradient. The microalgae strains are domesticated and cultivated according to the adjusted temperature and light intensity. The domesticated microalgae strains are screened based on fishpond wastewater of different concentrations to obtain the target microalgae strains. Based on the target microalgae strain, obtain the active cell liquid of the microalgae strain, and use the active cell liquid of the microalgae strain to purify the water quality and supply feed to fish ponds, so as to realize aquaculture. The process of acclimatizing and cultivating microalgae strains according to adjusted temperature and light intensity includes: The microalgae strain was cultured three times based on the first adjusted temperature, the first carbon dioxide concentration, and the first adjusted light intensity to obtain the first cultured microalgae strain. The first cultured microalgae strain was cultured three times based on the second adjusted temperature, the second carbon dioxide concentration, and the second adjusted light intensity to obtain the second cultured microalgae strain. The second cultured microalgae strain was cultured and iterated three times based on the third adjusted temperature, the third adjusted carbon dioxide concentration, and the third adjusted light intensity to obtain the domesticated microalgae strain. The first adjusted temperature, the second adjusted temperature, and the third adjusted temperature increase sequentially; the first adjusted light intensity, the second adjusted light intensity, and the third adjusted light intensity increase sequentially; and the first carbon dioxide concentration, the second carbon dioxide concentration, and the third carbon dioxide concentration increase sequentially.

2. The aquaculture method based on wind and solar power generation according to claim 1, characterized in that, The step of constructing a capacity optimization configuration model based on the resource data and electricity load demand includes: A capacity optimization configuration model is constructed based on the resource data and electricity load demand; the capacity optimization configuration model includes a wind power generation model, a photovoltaic power generation model, and an energy storage system model. The wind power generation model is as follows: ； in, Let t be the wind power output at time t. The wind speed at the current time step. and These are the cut-in and cut-out wind speeds for the wind turbine, respectively. The rated wind speed of the wind turbine unit. This refers to the rated power of the wind turbine generator set; The photovoltaic power generation model is as follows: ； in, Contribute to photovoltaic power generation, The irradiance at the current time step. The irradiation intensity under standard test conditions. This refers to the rated power of photovoltaic power under standard test conditions. The power temperature coefficient of photovoltaics. The photovoltaic temperature at the current time step. Photovoltaic temperature under standard test conditions; The energy storage system model is as follows: ； ； ； ； Where SOC(t+1) and SOC(t) are the states of charge of the energy storage system at times t+1 and t, respectively. and These are the charging and discharging powers, and These are the charging and discharging efficiencies of the energy storage system, respectively. The rated capacity of the energy storage system is [missing information]. and These are the maximum charging power and the maximum discharging power, respectively. and These are the minimum and maximum charge capacities of the energy storage system, respectively.

3. The aquaculture method based on wind and solar power generation according to claim 1, characterized in that, After constructing the capacity optimization configuration model based on the resource data and electricity load demand, the method further includes: A power shortage rate constraint is set so that the capacity optimization configuration model can determine whether to terminate the iteration based on the power shortage rate constraint; wherein, the formula for determining the power shortage rate based on the power shortage during the production process and the total power load is: ； ； in, The power shortage rate, For the maximum power shortage rate, This refers to the power shortage during the production process. The total electrical load is denoted as .

4. The aquaculture method based on wind and solar power generation according to claim 1, characterized in that, The step of iteratively solving the capacity optimization configuration model using the ant colony optimization algorithm to obtain the optimal capacity allocation based on the cost per kilowatt-hour includes: Calculate the output, energy storage, and load status of the wind and solar power generation system, and determine whether the output of the wind and solar power generation system meets the electricity load demand based on the corresponding calculation results. If the output of the wind and solar power generation system does not meet the electricity load demand, it is determined whether the sum of the output of the wind and solar power generation system and the capacity of the energy storage system meets the electricity load demand, and the power shortage or the capacity of the energy storage system at the next moment is calculated according to the corresponding judgment result to obtain the corresponding first calculation result. If the output of the wind and solar power generation system meets the electricity load demand, then determine whether the output of the wind and solar power generation system meets the electricity load demand and whether the capacity of the energy storage system is met. If the output of the wind and solar power generation system meets the electricity load demand but does not meet the capacity of the energy storage system, then the excess wind and solar power output beyond meeting the electricity load demand is used to charge the energy storage system, and the capacity of the energy storage system at the next moment is calculated. If the output of the wind and solar power generation system meets the electricity load demand and then meets the capacity of the energy storage system, then surplus electricity will be fed into the grid or abandoned, and it will be determined whether the solution corresponding to the current capacity optimization configuration model meets the preset constraints. If the preset constraints are not met, the capacity initialization parameters of each device in the capacity optimization configuration model are updated, and the process jumps back to the step of calculating the output, energy storage and load status of the wind and solar power generation system. If the preset constraints and the iteration termination condition are met, the optimal capacity allocation is determined based on the cost per kilowatt-hour and all solutions that meet the conditions corresponding to the capacity optimization configuration model.

5. The aquaculture method based on wind and solar power generation according to claim 1, characterized in that, The step of transmitting electricity generated by wind power and photovoltaic power to the algae and fish farming system according to the optimal capacity ratio includes: According to the optimal capacity ratio, the electricity generated by wind power and photovoltaic power generation is fed into the DC bus through rectifiers and inverters and then transmitted to the algae and fish farming system.

6. The aquaculture method based on wind and solar power generation according to any one of claims 1 to 5, characterized in that, The screening of corresponding domesticated microalgae strains based on fishpond wastewater of different concentrations to obtain target microalgae strains includes: The domesticated microalgae strains were cultured sequentially in fishpond wastewater of different concentrations. The domesticated microalgae strains were screened based on their growth density, pH value, and chlorophyll content during the culture process to obtain the target microalgae strain.

7. A fish farming device based on wind and solar power generation, characterized in that, include: The model building module is used to acquire resource data of the location of the aquaculture project and the power load demand of the corresponding algae-fish farming system, and to build a capacity optimization configuration model based on the resource data and power load demand; the resource data includes wind speed, irradiance, and temperature. The power transmission module is used to iteratively solve the capacity optimization configuration model using an ant colony optimization algorithm in order to obtain the optimal capacity ratio based on the cost per kilowatt-hour, and to transmit the power generated by wind power and photovoltaic power to the algae and fish farming system according to the optimal capacity ratio. The screening module is used to adjust the temperature and light intensity of the microalgae cultivation greenhouse based on the power and preset gradient through the algae-fish farming system, to acclimate and cultivate microalgae strains according to the adjusted temperature and light intensity, and to screen the corresponding acclimatized microalgae strains based on fishpond wastewater of different concentrations in order to obtain the target microalgae strains. The aquaculture implementation module is used to obtain the active cell liquid of the target microalgae strain, and use the active cell liquid of the microalgae strain to purify the water quality and supply feed to the fishpond, so as to realize aquaculture. The screening module is configured to perform three culture iterations on the microalgae strain based on a first adjusted temperature, a first carbon dioxide concentration, and a first adjusted light intensity to obtain a first cultured microalgae strain; perform three culture iterations on the first cultured microalgae strain based on a second adjusted temperature, a second carbon dioxide concentration, and a second adjusted light intensity to obtain a second cultured microalgae strain; and perform three culture iterations on the second cultured microalgae strain based on a third adjusted temperature, a third carbon dioxide concentration, and a third adjusted light intensity to obtain the domesticated microalgae strain; wherein the first, second, and third adjusted temperatures, the first, second, and third adjusted light intensities, and the first, second, and third adjusted light intensities, as well as the first, second, and third carbon dioxide concentrations, are sequentially increased.

8. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the wind and solar power-based aquaculture method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, Used to store a computer program, which, when executed by a processor, implements the aquaculture method based on wind and solar power generation as described in any one of claims 1 to 6.

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