Method and device for determining fishery breeding density in wind power plant area and storage medium

Abstract

The invention provides a method and device for determining the fishery breeding density of a wind power plant area and a storage medium. The method comprises the steps that planning parameters of a wind turbine generator, planning parameters of fishery breeding, historical tide data and the fishery breeding variety of a sea area where a wind power plant construction area is located are obtained; according to the planning parameters of the wind turbine generator, the planning parameters of fishery breeding and historical tidal current data, a three-dimensional fluid dynamic model is constructed, and the seawater flow velocity and direction of the fishery breeding area are determined; the method comprises the following steps: constructing a seawater dissolved oxygen diffusion model and a fish metabolic waste concentration model according to a fishery breeding variety and seawater flow velocity and direction, and determining the distribution of dissolved oxygen in seawater and the concentration of fish metabolic waste; and according to the distribution of dissolved oxygen in seawater and the concentration of fish metabolic wastes, determining the breeding density of the fishery breeding varieties in the fishery breeding area.

Description

Technical Field

[0001] This disclosure relates to the field of offshore wind power technology, and in particular to a method, apparatus and storage medium for determining the density of aquaculture in a wind farm area. Background Technology

[0002] "Wind-fishery complementarity," as a cutting-edge model for the integrated development of marine resources, breaks through the limitations of traditional single-development of marine industries through the deep integration of space and function. This model relies on the engineering facilities of offshore wind farms, scientifically planning and deploying various aquaculture facilities around and below the supporting structures such as the wind turbine foundations, constructing a three-dimensional production system of "power generation above water and fish farming below water." This innovative model not only ensures the stable output of clean energy but also makes full use of the ecological space below the wind farm, achieving synergistic development between wind energy resources and the aquaculture industry. By integrating wind power facilities with aquaculture cages, it not only effectively improves the utilization efficiency of marine space but also reduces the overall construction cost of offshore engineering, providing a new path for promoting the green, efficient, and sustainable development of the marine economy. Reasonable aquaculture density helps maintain good water quality and sufficient dissolved oxygen, thereby promoting rapid biological growth. Excessive density may lead to water quality deterioration and insufficient dissolved oxygen, thus affecting growth rate. However, in the traditional approach, the stocking density is set based on experience without taking into account the impact of the wind turbine foundation structure on the water flow field and dissolved oxygen distribution, resulting in low stocking efficiency or excessive ecological burden. Summary of the Invention

[0003] This disclosure provides a method, apparatus, and storage medium for determining the aquaculture density in a wind farm area, in order to solve the problem of low aquaculture efficiency or excessive ecological burden caused by the lack of quantitative aquaculture density in existing offshore wind power and fishery symbiosis scenarios.

[0004] Based on the above problems, in a first aspect, the present disclosure provides a method for determining the aquaculture density in a wind farm area, comprising:

[0005] Obtain planning parameters for wind turbines, planning parameters for aquaculture, historical tidal data, and aquaculture species in the sea area where the wind farm is located;

[0006] Based on the planning parameters of the wind turbine, the planning parameters of the aquaculture, and the historical tidal data, a three-dimensional fluid dynamics model is constructed to determine the seawater flow velocity and direction in the aquaculture area.

[0007] Based on the aquaculture species, seawater flow velocity and direction, a seawater dissolved oxygen diffusion model and a fish metabolic waste concentration model are constructed to determine the distribution of dissolved oxygen in seawater and the concentration of fish metabolic waste.

[0008] The stocking density of the aquaculture species in the aquaculture area is determined based on the dissolved oxygen distribution in the seawater and the concentration of fish metabolic waste.

[0009] In conjunction with the first aspect, in one possible implementation, the planning parameters of the wind turbine include: the location coordinates of the wind turbine, the diameter of the support structure, the submersion depth of the support structure, and the drag coefficient of the support structure to seawater.

[0010] The planning parameters for aquaculture include: the location coordinates of the cages, the submersion depth of the cages, the size of the cages, the mesh size, and the resistance coefficient of the netting to seawater.

[0011] In conjunction with the first aspect, in one possible implementation, the step of constructing a three-dimensional fluid dynamics model based on the planning parameters of the wind turbine, the planning parameters of the aquaculture, and the historical tidal data to determine the seawater flow velocity and direction in the aquaculture area includes:

[0012] The power flow distribution is determined using spatiotemporal interpolation based on the historical power flow data.

[0013] Based on the planning parameters of the wind turbine, the first local flow obstruction zone of the wind turbine for water flow is determined;

[0014] Based on the planning parameters of the aquaculture, the second local obstruction zone of water flow for aquaculture is determined;

[0015] Using the tidal current distribution as the open boundary, the surface of the wind turbine support structure as the solid wall boundary, and the aquaculture area as the porous medium boundary, a three-dimensional fluid dynamics model is constructed with a preset first resolution based on the first local flow obstruction zone and the second local flow obstruction zone to determine the seawater flow velocity and direction in the aquaculture area.

[0016] In conjunction with the first aspect, in one possible implementation, the step of constructing a seawater dissolved oxygen diffusion model and a fish metabolic waste concentration model based on the aquaculture species, the seawater flow velocity and direction, and determining the dissolved oxygen distribution and fish metabolic waste concentration in seawater, includes:

[0017] Based on the aforementioned aquaculture species, determine the biological oxygen demand rate and metabolic waste generation rate for each aquaculture species;

[0018] Based on the biological oxygen consumption rate, the seawater flow velocity and direction, a seawater dissolved oxygen diffusion model is constructed to determine the first relationship between the distribution of dissolved oxygen in seawater and the culture density of the aquaculture species.

[0019] Based on the metabolic waste generation rate, the seawater flow velocity and direction, a fish metabolic waste concentration model is constructed to determine a second relationship between the fish metabolic waste concentration and the culture density of the aquaculture species.

[0020] The step of determining the stocking density of the aquaculture species within the aquaculture area based on the dissolved oxygen distribution in the seawater and the concentration of fish metabolic waste includes:

[0021] The stocking density of the aquaculture species in the aquaculture area is determined based on the first relationship between the dissolved oxygen distribution in the seawater and the stocking density of the aquaculture species, and the second relationship between the concentration of fish metabolic waste and the stocking density of the aquaculture species.

[0022] In conjunction with the first aspect, in one possible implementation, it further includes:

[0023] Real-time monitoring of dissolved oxygen distribution and fish metabolic waste concentration in seawater;

[0024] The stocking density of the aquaculture species is adjusted based on real-time monitoring of dissolved oxygen distribution in seawater and fish metabolic waste concentration.

[0025] Secondly, a device for determining the density of aquaculture in a wind farm area is provided, comprising:

[0026] The parameter acquisition module is used to acquire planning parameters for wind turbines, planning parameters for aquaculture, historical tidal data, and aquaculture species in the sea area where the wind farm is located.

[0027] The seawater flow velocity and direction determination module is used to construct a three-dimensional fluid dynamics model based on the planning parameters of the wind turbine, the planning parameters of the aquaculture, and the historical tidal data, and to determine the seawater flow velocity and direction in the aquaculture area.

[0028] The fishery growth environment monitoring module is used to construct a seawater dissolved oxygen diffusion model and a fish metabolic waste concentration model based on the fishery species, the seawater flow velocity and direction, and to determine the distribution of dissolved oxygen in seawater and the concentration of fish metabolic waste.

[0029] The stocking density determination module is used to determine the stocking density of the aquaculture species in the aquaculture area based on the dissolved oxygen distribution in the seawater and the concentration of fish metabolic waste.

[0030] In conjunction with the second aspect, in one possible implementation, the planning parameters of the wind turbine include: the location coordinates of the wind turbine, the diameter of the support structure, the submersion depth of the support structure, and the drag coefficient of the support structure to seawater.

[0031] The planning parameters for aquaculture include: the location coordinates of the cages, the submersion depth of the cages, the size of the cages, the mesh size, and the resistance coefficient of the netting to seawater.

[0032] In conjunction with the second aspect, in one possible implementation, the seawater flow velocity and direction determination module is used to determine the tidal current distribution based on the historical tidal current data using a spatiotemporal interpolation method;

[0033] Based on the planning parameters of the wind turbine, the first local flow obstruction zone of the wind turbine for water flow is determined;

[0034] Based on the planning parameters of the aquaculture, the second local obstruction zone of water flow for aquaculture is determined;

[0035] Using the tidal current distribution as the open boundary, the surface of the wind turbine support structure as the solid wall boundary, and the aquaculture area as the porous medium boundary, a three-dimensional fluid dynamics model is constructed with a preset first resolution based on the first local flow obstruction zone and the second local flow obstruction zone to determine the seawater flow velocity and direction in the aquaculture area.

[0036] In conjunction with the second aspect, in one possible implementation, the fishery growth environment monitoring module is used to determine the biological oxygen demand rate and metabolic waste generation rate of each aquaculture species based on the aquaculture species.

[0037] Based on the biological oxygen consumption rate, the seawater flow velocity and direction, a seawater dissolved oxygen diffusion model is constructed to determine the first relationship between the distribution of dissolved oxygen in seawater and the culture density of the aquaculture species.

[0038] Based on the metabolic waste generation rate, the seawater flow velocity and direction, a fish metabolic waste concentration model is constructed to determine a second relationship between the fish metabolic waste concentration and the culture density of the aquaculture species.

[0039] The culture density determination module is used to determine the culture density of the aquaculture species within the aquaculture area based on a first relationship between the dissolved oxygen distribution in the seawater and the culture density of the aquaculture species, and a second relationship between the concentration of fish metabolic waste and the culture density of the aquaculture species.

[0040] Thirdly, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, performs the steps of the method for determining the aquaculture density in a wind farm area as described in the first aspect or any possible embodiment in conjunction with the first aspect.

[0041] The beneficial effects of the embodiments disclosed herein include:

[0042] The method, apparatus, and storage medium for determining aquaculture density in wind farm areas provided in this disclosure include: acquiring planning parameters of wind turbines, planning parameters of aquaculture, historical tidal data, and aquaculture species in the sea area where the wind farm is located; constructing a three-dimensional fluid dynamics model based on the planning parameters of wind turbines, aquaculture, and historical tidal data to determine the seawater flow velocity and direction in the aquaculture area; constructing a dissolved oxygen diffusion model and a fish metabolic waste concentration model based on the aquaculture species, seawater flow velocity, and direction to determine the dissolved oxygen distribution and fish metabolic waste concentration in the seawater; and determining the aquaculture density of aquaculture species within the aquaculture area based on the dissolved oxygen distribution and fish metabolic waste concentration. The method for determining aquaculture density in wind farm areas provided in this disclosure considers the planning parameters of wind turbines, aquaculture, historical tidal data, and the characteristics of aquaculture species. By constructing a three-dimensional fluid dynamics model, a dissolved oxygen diffusion model, and a metabolic waste concentration model, precise control of aquaculture density is achieved. This not only helps improve aquaculture efficiency but also effectively reduces environmental pollution, ensures the healthy growth of aquaculture organisms, and achieves coordinated development of aquaculture and offshore wind power. Attached Figure Description

[0043] Figure 1 A flowchart illustrating a method for determining aquaculture density in a wind farm area, as provided in this embodiment of the disclosure;

[0044] Figure 2 A structural diagram of the apparatus for determining the density of aquaculture in a wind farm area provided in an embodiment of this disclosure. Detailed Implementation

[0045] This disclosure provides a method, apparatus, and storage medium for determining the density of aquaculture in a wind farm area. Preferred embodiments of this disclosure are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustrative and explanatory purposes only and are not intended to limit the scope of this disclosure. Furthermore, the embodiments and features described herein can be combined with each other unless otherwise specified.

[0046] This disclosure provides a method for determining the density of aquaculture in a wind farm area, such as... Figure 1 As shown, it includes the following steps:

[0047] S101. Obtain the planning parameters of wind turbine units, planning parameters of aquaculture, historical tidal data, and aquaculture species in the sea area where the wind farm construction area is located.

[0048] S102. Based on the planning parameters of the wind turbine, the planning parameters of aquaculture, and historical tidal data, construct a three-dimensional fluid dynamics model to determine the seawater flow velocity and direction in the aquaculture area.

[0049] S103. Based on the aquaculture species, seawater flow velocity and direction, construct a seawater dissolved oxygen diffusion model and a fish metabolic waste concentration model to determine the distribution of dissolved oxygen in seawater and the concentration of fish metabolic waste.

[0050] S104. Determine the stocking density of aquaculture species within the aquaculture area based on the distribution of dissolved oxygen in seawater and the concentration of fish metabolic waste.

[0051] This disclosure applies to the field of offshore wind power technology. Driven by the strategic need for high-quality development of the marine economy, the "wind-fishery complementarity" model has emerged. This model breaks down traditional industry boundaries, deeply integrating offshore wind power clean energy development with modern aquaculture, achieving the dual value of "green power generation above water and ecological fish farming below water" through three-dimensional space utilization. During the construction of offshore wind farms, the supporting structures such as wind turbine foundations are fully utilized and transformed into carriers for aquaculture. Facilities such as net cage aquaculture are scientifically laid out below the wind farm, constructing a new production system of synergistic "energy-fishery" coexistence. This innovative model not only effectively improves the utilization rate of marine space but also reduces ecological disturbance caused by single-use development through industrial synergy, effectively reducing conflicts over marine use, and achieving a win-win situation for clean energy production and fishery economic development, providing a solution with both economic benefits and ecological value for sustainable marine development. With the rapid development of the marine economy, aquaculture has become an important industry for ensuring the supply of aquatic products and promoting coastal economic growth. Reasonable aquaculture density is a key factor in maintaining a good aquaculture environment and achieving sustainable fishery development. Scientific stocking density ensures stable water quality and sufficient dissolved oxygen in aquaculture areas, providing suitable living and growth conditions for farmed organisms, thereby promoting rapid growth and increasing aquaculture yield and economic benefits. Conversely, excessively high stocking densities lead to the accumulation of metabolic waste by farmed organisms, exceeding the water body's self-purification capacity and causing water quality deterioration. Simultaneously, the respiration of a large number of organisms rapidly consumes dissolved oxygen, leading to insufficient dissolved oxygen, slow growth, frequent diseases, and even large-scale mortality, severely impacting aquaculture efficiency and the ecological environment. In traditional aquaculture, stocking density settings rely heavily on the experience of farmers. Farmers often determine stocking density based on past experience and the surrounding area's aquaculture conditions, lacking a systematic and scientific analysis of the aquaculture environment. With the rise of emerging marine development models such as "wind-fishery complementarity," traditional methods fail to comprehensively consider the impact of wind turbine foundation structures on water flow fields and dissolved oxygen distribution. The supporting structures of wind turbines (such as monopiles) alter local water flow patterns, creating a flow obstruction effect, resulting in reduced water flow velocity and weakened water exchange capacity, thus affecting the transport and distribution of dissolved oxygen. If these factors are not considered when setting aquaculture density, low-oxygen zones and areas of deteriorated water quality may form near wind turbines, threatening the survival of farmed organisms and resulting in low aquaculture efficiency. The marine environment is complex and variable, significantly affected by factors such as seasons, weather, and tides. Traditional experience-based methods cannot adjust aquaculture density in a timely manner according to dynamic environmental changes, leading to a mismatch between the density and the actual carrying capacity of the environment. This can easily cause excessive ecological burden and damage to the marine ecosystem.

[0052] In this embodiment, the planning parameters for the wind turbine include: the location coordinates of the wind turbine, the diameter of the supporting structure, the submersion depth of the supporting structure, and the drag coefficient of the supporting structure to seawater. These parameters not only affect the power generation capacity of the wind farm but also influence the current field of the surrounding sea area, thereby affecting the aquaculture environment. The planning parameters for aquaculture include: the location coordinates of the net cages, the submersion depth of the net cages, the size of the net cages, the mesh size, and the drag coefficient of the net lines to seawater. These parameters define the basic framework and requirements for aquaculture. Historical tidal data can record the dynamic information such as the speed and direction of seawater flow in the sea area where the wind farm is located over a long period. By analyzing historical tidal data, the seasonal changes, periodic patterns, and tidal characteristics under extreme conditions of the currents in the sea area can be understood. Furthermore, accurately identifying the aquaculture species in the sea area where the wind farm is located is also crucial. Different aquaculture species have significantly different requirements for their living environment. For example, some fish are sensitive to environmental factors such as water temperature, salinity, and current velocity, while shellfish and other aquaculture species have specific requirements for water quality and bottom sediment conditions. Various methods are typically used to obtain this data. Planning parameters for wind turbines and aquaculture can be obtained by reviewing project planning documents and communicating with design and construction units. Historical tidal current data mainly comes from long-term accumulated monitoring data from marine monitoring departments, and can also be supplemented and verified using advanced monitoring equipment such as satellite remote sensing, marine buoys, and acoustic Doppler current profilers (ADCP). Information on aquaculture species can be obtained through field surveys, interviews with local fishermen and fishery enterprises, and reference to fishery resource survey reports. After obtaining the planning parameters for wind turbines, aquaculture, and historical tidal current data, a three-dimensional fluid dynamics model is constructed to determine the seawater velocity and direction in the aquaculture area. The three-dimensional fluid dynamics model is a mathematical model based on fluid mechanics principles that can simulate the flow state of seawater in three-dimensional space, taking into account the influence of obstacles such as wind turbines and aquaculture facilities on seawater flow. In the process of constructing the three-dimensional fluid dynamics model, the sea area where the wind farm construction area is located first needs to be spatially discretized, dividing the continuous sea area into multiple regular or irregular grid cells. The size and shape of each grid cell need to be reasonably set according to the complexity of the study area and the required computational accuracy. For wind turbines and aquaculture facilities, precise geometric modeling is required to accurately reflect their obstruction and disturbance effects on seawater flow. Then, the acquired wind turbine planning parameters, aquaculture planning parameters, and historical tidal data are used as input conditions for the model. The presence of wind turbines alters the direction and velocity of seawater flow; similarly, aquaculture facilities create resistance to seawater flow, leading to localized reductions in flow velocity and changes in direction. By solving fluid dynamic equations (such as the Navier-Stokes equations), the seawater velocity and direction for each grid cell at different times can be calculated.During the calculation process, the influence of seawater physical properties (such as density and viscosity), marine meteorological conditions (such as wind speed and direction), and seabed topography on seawater flow can also be considered. To ensure the accuracy and reliability of the model, it needs to be validated and calibrated. Typically, the model calculation results are compared and analyzed with actual observation data. By adjusting parameters in the model (such as roughness coefficient and boundary conditions), the model calculation results are made as close as possible to the actual observation values. After validation and calibration, the three-dimensional fluid dynamics model can accurately simulate the seawater flow velocity and direction in aquaculture areas, providing reliable data support for subsequent analysis.

[0053] Furthermore, constructing a dissolved oxygen diffusion model and a fish metabolic waste concentration model allows us to determine the distribution of dissolved oxygen in seawater and the concentration of fish metabolic waste. Different aquaculture species have different requirements for dissolved oxygen in seawater, and the dissolved oxygen content directly affects the survival, growth, and health of farmed organisms. The seawater dissolved oxygen diffusion model is a mathematical model based on the principle of matter diffusion, considering factors such as seawater flow, physical diffusion of dissolved oxygen, biological respiration, and oxygen exchange between the atmosphere and seawater. During model construction, it is necessary to determine the initial dissolved oxygen concentration and the biological oxygen consumption rate (BOC) of farmed organisms. The BOC of farmed organisms is closely related to factors such as the farmed species, individual size, and water temperature. For example, under higher water temperatures, fish metabolism accelerates, and the BOC increases accordingly. By solving the dissolved oxygen diffusion equation, the distribution of dissolved oxygen concentration in seawater at different locations and times can be calculated. Fish produce metabolic waste during their growth, such as ammonia nitrogen and nitrite. The accumulation of these metabolic wastes can harm the living environment of farmed organisms. The fish metabolic waste concentration model is used to simulate the generation, diffusion, and transformation of metabolic waste in seawater. This model needs to consider factors such as the rate of metabolic waste generation by cultured organisms, the transport effect of seawater, and the decomposition and transformation of metabolic waste by microorganisms. The rate of metabolic waste generation varies among different cultured species, and the velocity and direction of seawater affect the diffusion range and dilution degree of metabolic waste. By solving the metabolic waste concentration equation, the concentration distribution of fish metabolic waste in seawater can be obtained. Both models require validation and calibration during construction. This can be achieved by setting up monitoring points in the culture area, regularly collecting water samples, analyzing the dissolved oxygen content and metabolic waste concentration in the samples, and comparing the results with the model calculations. The model parameters can be adjusted based on the comparison results to improve the model's accuracy. Determining the culture density requires comprehensive consideration of multiple factors, among which the dissolved oxygen content in seawater and the concentration of fish metabolic waste are key limiting conditions. For each aquaculture species, there exists a suitable range of dissolved oxygen concentration and a safe threshold for fish metabolic waste concentration. When the dissolved oxygen concentration in seawater is lower than the dissolved oxygen concentration requirement of the cultured species, the cultured organisms will experience hypoxia and suffocation, resulting in slowed growth and even death; when the concentration of fish metabolic waste exceeds the safe threshold, it will have a toxic effect on the cultured organisms, affecting their health and survival. Therefore, when determining the stocking density, it is necessary to ensure that the dissolved oxygen concentration in the seawater within the stocking area remains within a suitable range, while the concentration of fish metabolic waste does not exceed a safe threshold. A specific calculation method can employ a mathematical optimization model, using maximizing stocking efficiency as the objective function and dissolved oxygen concentration and fish metabolic waste concentration as constraints. The optimal stocking density can be obtained by solving the optimization model. The impact of factors such as stocking costs, market demand, and the level of stocking technology on stocking density can also be considered.For example, if there is a large market demand for a certain aquaculture species and the price is high, the stocking density can be appropriately increased to increase production, provided that the breeding environment is safe. However, if the breeding technology is limited and it is difficult to effectively control the breeding environment, the stocking density needs to be reduced to ensure the healthy growth of the aquaculture organisms.

[0054] This application's embodiments, considering wind turbine planning parameters, aquaculture planning parameters, historical tidal data, and the characteristics of aquaculture species, achieve precise control of aquaculture density by constructing a three-dimensional fluid dynamics model, a dissolved oxygen diffusion model, and a metabolic waste concentration model. This not only helps improve aquaculture efficiency but also effectively reduces environmental pollution, ensures the healthy growth of aquaculture organisms, and achieves coordinated development between aquaculture and offshore wind power.

[0055] In another embodiment of this disclosure, the planning parameters of the wind turbine include: the location coordinates of the wind turbine, the diameter of the support structure, the submersion depth of the support structure, and the drag coefficient of the support structure to seawater.

[0056] Planning parameters for aquaculture include: the location coordinates of the cages, the depth of the cages, the size of the cages, the mesh size, and the resistance coefficient of the netting to seawater.

[0057] In this embodiment, the planning parameters for wind turbines and aquaculture are clearly defined. The planning parameters for wind turbines include: the location coordinates of the wind turbine, the diameter of the supporting structure, the submersion depth of the supporting structure, and the drag coefficient of the supporting structure to seawater. The location coordinates of the wind turbine can refer to its specific location in geographical space, typically expressed using longitude, latitude, and altitude. In offshore wind farms, they can also be expressed using planar coordinates relative to the center of the wind farm or a reference point. Based on the location coordinates of the wind turbines, the distribution of each wind turbine within the wind farm can be determined, such as the row spacing and column spacing. The supporting structure of the wind turbine refers to the structural system used to support the wind turbine (including tower, nacelle, blades, etc.). It must not only bear the weight of the wind turbine itself but also withstand various loads such as wind force, gravity, waves, and ocean currents to ensure the stability of the wind turbine during operation. The supporting structure of the wind turbine typically includes a tower and a foundation. The tower is a vertical structure connecting the wind turbine nacelle and the foundation, used to lift the wind turbine to a suitable height to obtain more stable wind energy resources. The wind turbine tower is typically made of steel or concrete, possessing high strength and rigidity. Its height is generally determined based on the wind turbine's power and the wind conditions at the installation site. The foundation is the connection between the supporting structure and the seabed, used to transfer the loads of the tower and wind turbine to the ground. Foundation design needs to consider various factors such as geological conditions, load magnitude, and construction conditions. Common foundation types include pile foundations, jacket foundations, gravity foundations, and suction cylindrical foundations. The diameter of the supporting structure can refer to the cross-sectional diameter of the wind turbine's supporting structure (such as the tower or pile foundation). It directly affects the strength, stability, and load-bearing capacity of the supporting structure. The submersion depth of the supporting structure refers to the vertical length of the submerged portion of the supporting structure. It depends on the design and installation location of the supporting structure, as well as local water depth conditions. Submersion depth affects the interaction between the supporting structure and seawater, including hydrodynamic loads and corrosion. The drag coefficient is a dimensionless parameter describing the resistance experienced by an object moving in a fluid. The drag coefficient of the supporting structure to seawater can be a dimensionless parameter describing the resistance generated by the supporting structure in seawater flow, reflecting the complex characteristics of the interaction between the supporting structure and the fluid. The drag coefficient of the supporting structure against seawater can be obtained through experimental data. Planning parameters for aquaculture include: the location coordinates of the net cage, the submersion depth of the net cage, the size of the net cage, the mesh size, and the drag coefficient of the netting against seawater. Mesh size includes the mesh size and the diameter of the netting. The location coordinates of the net cage refer to the geographical coordinates determining its specific placement within the aquaculture area, usually expressed as longitude, latitude, and altitude. The submersion depth of the net cage refers to the depth of the net cage underwater, typically the vertical distance between the top or bottom of the net cage and the water surface. These parameters directly affect the hydrodynamic characteristics of the net cage, the living environment of the fish, and the safety of the net cage.For rectangular net cages, the dimensions refer to the length, width, and height. For cylindrical net cages, the dimensions refer to the diameter and height. The dimensions of the net cage affect the surface area facing the current and determine its volume, thus influencing the number of farmed fish and their growth space. Mesh size is a core parameter for measuring the permeability of aquaculture nets, including two closely related dimensions: mesh size and wire diameter. These two dimensions together determine the resistance effect and permeability of the net to seawater flow. Mesh size refers to the geometric dimensions of a single mesh (grid) on the net, usually measured as the straight-line distance between two adjacent knots (net weaving nodes) when the mesh is closed. Wire diameter refers to the outer diameter of a single wire constituting the net, reflecting the thickness of the wire. The drag coefficient of the net against seawater is the proportionality between the resistance experienced by the net in seawater and the hydrodynamic force acting on the net; it reflects the magnitude of the resistance experienced by the net in seawater. The drag coefficient of the mesh to seawater is related to the type of material and the structure of the mesh. For example, different types of mesh have different drag coefficients (such as nylon mesh, which typically has a higher drag coefficient than steel mesh and polyethylene mesh). The structure of the mesh (such as three-strand or single-strand) also affects its drag coefficient. The drag coefficient of the mesh to seawater can be obtained through experimental data. By clarifying the key planning parameters for wind turbines and aquaculture, a quantitative basis is provided for accurately simulating the flow obstruction effect of wind turbines and aquaculture on the ocean flow field, ensuring that the three-dimensional fluid dynamics model can realistically reflect the impact of actual engineering layout on seawater velocity and direction.

[0058] In another embodiment of this disclosure, step S102 above, which involves constructing a three-dimensional fluid dynamics model based on the planning parameters of the wind turbine, the planning parameters of aquaculture, and historical tidal data, to determine the seawater flow velocity and direction in the aquaculture area, includes the following steps:

[0059] Step 1: Determine the power flow distribution using spatiotemporal interpolation based on historical power flow data;

[0060] Step 2: Based on the planning parameters of the wind turbine, determine the first local flow obstruction zone of the wind turbine for the water flow;

[0061] Step 3: Based on the planning parameters for aquaculture, determine the second local obstruction zone of water flow caused by aquaculture.

[0062] Step 4: Using the tidal current distribution as the open boundary, the surface of the wind turbine support structure as the solid wall boundary, and the aquaculture area as the porous medium boundary, construct a three-dimensional fluid dynamics model with a preset first resolution based on the first local flow obstruction zone and the second local flow obstruction zone to determine the seawater flow velocity and direction in the aquaculture area.

[0063] In this embodiment, a three-dimensional fluid dynamics model is constructed by integrating tidal current distribution, multi-source local obstruction zones, and multiple boundary conditions to simulate the seawater flow velocity and direction under the combined effects of wind turbines and aquaculture. For step 1 above, historical tidal current data is collected, including flow velocity and direction at different seasons, tidal levels (high tide, low tide), and water depths (surface, mid-water, and bottom). Outliers (such as extreme data caused by instrument malfunction) are removed, and missing data are filled using Kriging interpolation or inverse distance weighting (IDW). Spatiotemporal interpolation methods are employed; for example, in the time dimension, a time series curve of tidal current velocity is fitted based on the tidal cycle (such as semi-diurnal tide, diurnal tide) to determine the tidal current state at any given time. In the spatial dimension, GIS spatial analysis is used to spatially interpolate the historical tidal current data, generating a tidal current velocity raster map and a flow direction vector map covering the entire study area. This yields the tidal current distribution, which can include: a two-dimensional tidal current distribution map (isotropic velocity lines and main current direction arrows at different tidal times) and three-dimensional tidal current field data (a three-dimensional matrix of water depth, velocity, and time), used as the open boundary input for constructing a three-dimensional fluid dynamics model.

[0064] Regarding step 2 above, a local obstruction zone refers to a specific area around which the water flow velocity decreases and the flow pattern changes due to the construction of infrastructure such as wind turbines and aquaculture facilities, which obstruct the natural flow of seawater. The water flow conditions in this area differ significantly from the surrounding unobstructed seawater flow. For example, the construction of wind turbine support structures creates a first local obstruction zone, and the construction of aquaculture cages creates a second local obstruction zone. Wind turbine planning parameters include the wind turbine's location coordinates, the diameter of the support structure, the submersion depth of the support structure, and the resistance coefficient of the support structure to seawater. Based on the wind turbine's location coordinates, the diameter of the support structure, and the submersion depth of the support structure, the first local obstruction zone can be determined using GIS spatial analysis. The resistance of the wind turbine to the water flow within the first local obstruction zone is determined through theoretical calculations or numerical simulations. For example, using theoretical calculations, the water-blocking area A of the support structure is calculated based on the diameter D and the submersion depth h, expressed as: A = D × h; the resistance F of the wind turbine to the water flow within the first local obstruction zone is determined. d The formula is expressed as: F d =0.5×ρC d Av 2 Among them, C d ρ represents the drag coefficient of the supporting structure against seawater; v represents the density of seawater; and v represents the background seawater velocity. The background seawater velocity can be determined based on the location coordinates of the wind turbine and the tidal current distribution.

[0065] Regarding step 3 above, the planning parameters for aquaculture include: the location coordinates of the net cages, the submersion depth of the net cages, the size of the net cages, the mesh size, and the resistance coefficient of the netting to seawater. The mesh size includes the mesh size and the diameter of the netting. Based on the location coordinates of the net cages, the submersion depth, and the size of the net cages, the second local obstruction zone can be determined using GIS spatial analysis. The resistance of aquaculture to water flow within the second local obstruction zone can be determined using theoretical calculations. For example, the mesh permeability is expressed by the formula:

[0066] β=[a / (a+d)] 2

[0067] Where 'a' represents the mesh size and 'd' represents the wire diameter. The drag coefficient of the net cage against seawater can be approximated as:

[0068] C net =C d (1-β)+β 2

[0069] Among them, C d β represents the drag coefficient of the netting against seawater; β represents the mesh permeability. The upstream area A can be determined based on the size of the net cage. The resistance F of aquaculture to water flow within the second local obstruction zone... d Its formula is expressed as:

[0070] F d =0.5×ρC net Av 2

[0071] Where ρ represents seawater density; v represents background seawater velocity. The background seawater velocity can be determined based on the location coordinates of the wind turbine and the tidal current distribution.

[0072] Further, in step 4 above, the wind farm area is meshed with a preset first resolution, for example, 1 meter for the first and second local obstruction zones. The tidal current distribution is used as the open boundary, such as the offshore tidal current input outside the wind farm area. The surface of the wind turbine support structure is used as the solid wall boundary, and the aquaculture area as the porous medium boundary. Combining the first and second local obstruction zones, a three-dimensional fluid dynamics model is constructed, which can solve the Reynolds-averaged Navier-Stokes equations. The turbulence model can be either the Realizable k-ε model or the SST k-ω model. This allows the seawater velocity and direction in the aquaculture area to be obtained. By constructing the three-dimensional fluid dynamics model, the coupled simulation of wind turbines, aquaculture, and the marine tidal environment is achieved, accurately quantifying the impact of local obstruction zones on the seawater flow field.

[0073] In another embodiment of this disclosure, in step S103 above, a dissolved oxygen diffusion model and a fish metabolic waste concentration model are constructed based on the aquaculture species, seawater flow velocity, and direction to determine the distribution of dissolved oxygen and the concentration of fish metabolic waste in seawater, including:

[0074] Step 1: Determine the biological oxygen demand rate and metabolic waste generation rate for each aquaculture species.

[0075] Step 2: Based on biological oxygen consumption rate, seawater flow velocity and direction, construct a seawater dissolved oxygen diffusion model to determine the primary relationship between dissolved oxygen distribution in seawater and the stocking density of aquaculture species.

[0076] Step 3: Based on the rate of metabolic waste generation, seawater flow velocity and direction, construct a fish metabolic waste concentration model to determine the second relationship between fish metabolic waste concentration and the stocking density of aquaculture species;

[0077] In step S104 above, the stocking density of aquaculture species in the aquaculture area is determined based on the distribution of dissolved oxygen in seawater and the concentration of fish metabolic waste, including:

[0078] Step 4: Based on the first relationship between dissolved oxygen distribution in seawater and the stocking density of aquaculture species, and the second relationship between fish metabolic waste concentration and the stocking density of aquaculture species, determine the stocking density of aquaculture species within the aquaculture area.

[0079] In this embodiment, factors such as biological oxygen consumption rate, metabolic waste generation rate, seawater flow velocity and direction are comprehensively considered. By constructing dissolved oxygen diffusion models and metabolic waste concentration models, precise control of aquaculture density is achieved. This not only helps improve aquaculture efficiency but also effectively reduces environmental pollution, ensures the healthy growth of farmed organisms, and achieves sustainable development of aquaculture. Regarding step 1 above, for different aquaculture species (such as fish, shrimp, shellfish, etc.), biological oxygen consumption rate and metabolic waste generation rate under specific growth stages, water temperature, salinity, and other environmental conditions are obtained through experimental measurements or literature review. Biological oxygen consumption rate refers to the amount of dissolved oxygen consumed by fish in the water through respiration within a certain period of time, mainly related to the physiological activities of fish (such as respiration, digestion, and movement). Metabolic waste generation rate refers to the amount of metabolic waste produced by fish per unit time. It reflects the intensity of fish metabolism and the accumulation rate of metabolic waste. For example, the amount of metabolic waste such as ammonia nitrogen, carbon dioxide, and urea produced by a fish per unit time is its metabolic waste generation rate. Excessive metabolic waste production rates can lead to high concentrations of harmful substances such as ammonia nitrogen and nitrite in the water, thus affecting fish growth and health. For example, in high-density aquaculture areas, if the rate of fish metabolic waste production is too high, equipment such as biological filters is needed to reduce the concentration of harmful substances in the water.

[0080] Regarding step 2 above, constructing a seawater dissolved oxygen diffusion model can describe the dissolved oxygen transport process. Based on biological oxygen consumption rate, seawater flow velocity, and direction, a convection-diffusion equation can be used to construct the seawater dissolved oxygen diffusion model, the formula of which is expressed as:

[0081]

[0082] Where C represents the dissolved oxygen concentration in seawater; u represents the seawater flow velocity and direction; S DO R represents the rate of natural reoxygenation or oxygen production through photosynthesis; ρ represents the stocking density of the aquaculture species. O This represents the biological oxygen demand rate. By incorporating the stocking density of aquaculture species, the primary relationship between dissolved oxygen distribution in seawater and the stocking density of aquaculture species can be established. A finely detailed grid can be created for the aquaculture area, for example, with a preset resolution of 1 meter, to ensure spatial resolution of the velocity gradient and dissolved oxygen distribution in seawater. Explicit or implicit time integration methods (such as the finite volume method) are used to solve for the change in dissolved oxygen concentration over time until a dynamic equilibrium is reached. This yields the dissolved oxygen distribution in seawater, allowing the generation of a dissolved oxygen concentration distribution cloud maps within the aquaculture area, and the identification of low-oxygen risk areas (such as areas where the dissolved oxygen concentration is below the critical value for aquaculture species).

[0083] For step 3 above, similar to the dissolved oxygen diffusion model in seawater, a fish metabolic waste concentration model is constructed. Constructing a fish metabolic waste concentration model can describe the transport process of metabolic waste. Based on the metabolic waste generation rate, seawater flow velocity, and direction, the convection-diffusion equation can be used to construct the fish metabolic waste concentration model, and its formula is expressed as:

[0084]

[0085] Where W represents the concentration of fish metabolic waste; S decay This represents the natural degradation rate of fish metabolic waste (e.g., microbial decomposition). By introducing the stocking density of aquaculture species, a second relationship between the concentration of fish metabolic waste and the stocking density of aquaculture species can be determined.

[0086] Regarding step 4 above, based on the first relationship between dissolved oxygen distribution in seawater and the stocking density of the aquaculture species, and the second relationship between the concentration of fish metabolic waste and the stocking density of the aquaculture species, and with the constraints of dissolved oxygen concentration not falling below the critical value required by the aquaculture species and metabolic waste concentration not exceeding a safe threshold, the maximum sustainable stocking density of the aquaculture species within the aquaculture area is determined through mathematical optimization (e.g., finding the intersection of stocking densities corresponding to the two thresholds). This ensures that aquaculture organisms grow in a healthy and suitable environment, improving aquaculture efficiency and economic benefits. It also reduces the negative impact of wind turbine construction on aquaculture, achieving sustainable development of aquaculture.

[0087] In another embodiment of this disclosure, it further includes:

[0088] Step 1: Monitor the distribution of dissolved oxygen and the concentration of fish metabolic waste in seawater in real time;

[0089] Step 2: Adjust the stocking density of aquaculture species based on real-time monitoring of dissolved oxygen distribution and fish metabolic waste concentration in seawater.

[0090] In this embodiment, sensors and other devices are used to monitor the distribution of dissolved oxygen in seawater and the concentration of fish metabolic waste in real time, acquiring dynamic data. Based on the monitored dissolved oxygen distribution and metabolic waste concentration data, the stocking density of aquaculture species is adjusted accordingly to maintain a favorable aquaculture environment. Regarding step 1 above, the distribution of dissolved oxygen in seawater and the concentration of fish metabolic waste are monitored in real time during aquaculture. Water quality sensors can be used to monitor the distribution of dissolved oxygen in seawater and the concentration of fish metabolic waste in real time to verify the accuracy of the three-dimensional fluid dynamics model, the seawater dissolved oxygen diffusion model, and the fish metabolic waste concentration model, and to optimize the model parameters. Regarding step 2 above, the stocking density of aquaculture species is adjusted based on the distribution of dissolved oxygen in seawater and the concentration of fish metabolic waste. For example, if the dissolved oxygen concentration in seawater is lower than the critical value required by the aquaculture species or the metabolic waste concentration exceeds the safety threshold, the stocking density of the aquaculture species is appropriately reduced, and the growth of the aquaculture species is continuously monitored. Real-time monitoring of the distribution of dissolved oxygen in seawater and the concentration of fish metabolic waste prevents fish mortality and disease outbreaks due to hypoxia or waste accumulation, thereby improving the survival rate of aquaculture.

[0091] Based on the same disclosed concept, this disclosure also provides an apparatus for determining the density of aquaculture in a wind farm area. Since the principle of these apparatuses in solving the problem is similar to the aforementioned method for determining the density of aquaculture in a wind farm area, the implementation of this apparatus can refer to the implementation of the aforementioned method, and the repeated parts will not be described again.

[0092] This disclosure provides a device for determining the density of aquaculture in a wind farm area, such as... Figure 2 As shown, it includes:

[0093] The parameter acquisition module 201 is used to acquire the planning parameters of the wind turbine, the planning parameters of aquaculture, historical tidal data, and the aquaculture species in the sea area where the wind farm construction area is located.

[0094] The seawater flow velocity and direction determination module 202 is used to construct a three-dimensional fluid dynamics model based on the planning parameters of the wind turbine, the planning parameters of the aquaculture, and the historical tidal data, and to determine the seawater flow velocity and direction in the aquaculture area.

[0095] The fishery growth environment monitoring module 203 is used to construct a seawater dissolved oxygen diffusion model and a fish metabolic waste concentration model based on the fishery species, the seawater flow velocity and direction, and to determine the distribution of dissolved oxygen in seawater and the concentration of fish metabolic waste.

[0096] The stocking density determination module 204 is used to determine the stocking density of the aquaculture species in the aquaculture area based on the dissolved oxygen distribution in the seawater and the concentration of fish metabolic waste.

[0097] In another embodiment of this disclosure, the planning parameters of the wind turbine include: the location coordinates of the wind turbine, the diameter of the support structure, the submersion depth of the support structure, and the drag coefficient of the support structure to seawater.

[0098] The planning parameters for aquaculture include: the location coordinates of the cages, the submersion depth of the cages, the size of the cages, the mesh size, and the resistance coefficient of the netting to seawater.

[0099] In another embodiment of this disclosure, the seawater flow velocity and direction determination module 202 is used to determine the tidal current distribution based on the historical tidal current data using a spatiotemporal interpolation method;

[0100] Based on the planning parameters of the wind turbine, the first local flow obstruction zone of the wind turbine for water flow is determined;

[0101] Based on the planning parameters of the aquaculture, the second local obstruction zone of water flow for aquaculture is determined;

[0102] Using the tidal current distribution as the open boundary, the surface of the wind turbine support structure as the solid wall boundary, and the aquaculture area as the porous medium boundary, a three-dimensional fluid dynamics model is constructed with a preset first resolution based on the first local flow obstruction zone and the second local flow obstruction zone to determine the seawater flow velocity and direction in the aquaculture area.

[0103] In another embodiment of this disclosure, the fishery growth environment monitoring module 203 is used to determine the biological oxygen consumption rate and metabolic waste generation rate of each aquaculture species based on the aquaculture species.

[0104] Based on the biological oxygen consumption rate, the seawater flow velocity and direction, a seawater dissolved oxygen diffusion model is constructed to determine the first relationship between the distribution of dissolved oxygen in seawater and the culture density of the aquaculture species.

[0105] Based on the metabolic waste generation rate, the seawater flow velocity and direction, a fish metabolic waste concentration model is constructed to determine a second relationship between the fish metabolic waste concentration and the culture density of the aquaculture species.

[0106] The culture density determination module 204 is used to determine the culture density of the aquaculture species in the aquaculture area based on the first relationship between the dissolved oxygen distribution in the seawater and the culture density of the aquaculture species, and the second relationship between the concentration of fish metabolic waste and the culture density of the aquaculture species.

[0107] In another embodiment of this disclosure, the fishery growth environment monitoring module 203 is also used to monitor the dissolved oxygen distribution in the seawater and the concentration of fish metabolic waste in real time;

[0108] The aquaculture density determination module 204 is also used to adjust the aquaculture density of the aquaculture species based on the real-time monitoring of dissolved oxygen distribution and fish metabolic waste concentration in seawater.

[0109] Based on the same disclosed concept, embodiments of this disclosure provide a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of the method for determining the aquaculture density in a wind farm area as described in any of the above embodiments.

[0110] Through the above description of the embodiments, those skilled in the art can clearly understand that the embodiments of this disclosure can be implemented in hardware or by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solutions of the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) and includes several instructions to cause a computer device (such as a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments of this disclosure.

[0111] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the modules or processes in the drawings are not necessarily essential for implementing this disclosure.

[0112] Those skilled in the art will understand that the modules in the apparatus of the embodiments can be distributed in the apparatus of the embodiments as described in the embodiments, or they can be located in one or more devices different from this embodiment with corresponding changes. The modules of the above embodiments can be combined into one module, or they can be further divided into multiple sub-modules.

[0113] The sequence numbers of the embodiments disclosed above are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0114] Obviously, those skilled in the art can make various modifications and variations to this disclosure without departing from its spirit and scope. Therefore, if such modifications and variations fall within the scope of the claims of this disclosure and their equivalents, this disclosure is also intended to include such modifications and variations.

Claims

1. A method for determining the density of aquaculture in a wind farm area, characterized in that, include: Obtain planning parameters for wind turbines, planning parameters for aquaculture, historical tidal data, and aquaculture species in the sea area where the wind farm is located; Based on the planning parameters of the wind turbine, the planning parameters of the aquaculture, and the historical tidal data, a three-dimensional fluid dynamics model is constructed to determine the seawater flow velocity and direction in the aquaculture area. Based on the aquaculture species, seawater flow velocity and direction, a seawater dissolved oxygen diffusion model and a fish metabolic waste concentration model are constructed to determine the distribution of dissolved oxygen in seawater and the concentration of fish metabolic waste. The stocking density of the aquaculture species in the aquaculture area is determined based on the dissolved oxygen distribution in the seawater and the concentration of fish metabolic waste.

2. The method as described in claim 1, characterized in that, The planning parameters for the wind turbine include: the location coordinates of the wind turbine, the diameter of the support structure, the submersion depth of the support structure, and the drag coefficient of the support structure to seawater. The planning parameters for aquaculture include: the location coordinates of the cages, the submersion depth of the cages, the size of the cages, the mesh size, and the resistance coefficient of the netting to seawater.

3. The method as described in claim 1, characterized in that, The step of constructing a three-dimensional fluid dynamics model based on the planning parameters of the wind turbine, the planning parameters of the aquaculture, and the historical tidal data to determine the seawater flow velocity and direction in the aquaculture area includes: The power flow distribution is determined using spatiotemporal interpolation based on the historical power flow data. Based on the planning parameters of the wind turbine, the first local flow obstruction zone of the wind turbine for water flow is determined; Based on the planning parameters of the aquaculture, the second local obstruction zone of water flow for aquaculture is determined; Using the tidal current distribution as the open boundary, the surface of the wind turbine support structure as the solid wall boundary, and the aquaculture area as the porous medium boundary, a three-dimensional fluid dynamics model is constructed with a preset first resolution based on the first local flow obstruction zone and the second local flow obstruction zone to determine the seawater flow velocity and direction in the aquaculture area.

4. The method as described in claim 1, characterized in that, The process of constructing a dissolved oxygen diffusion model and a fish metabolic waste concentration model based on the aquaculture species, seawater flow velocity, and direction, and determining the distribution of dissolved oxygen and the concentration of fish metabolic waste in seawater, includes: Based on the aforementioned aquaculture species, determine the biological oxygen demand rate and metabolic waste generation rate for each aquaculture species; Based on the biological oxygen consumption rate, the seawater flow velocity and direction, a seawater dissolved oxygen diffusion model is constructed to determine the first relationship between the distribution of dissolved oxygen in seawater and the culture density of the aquaculture species. Based on the metabolic waste generation rate, the seawater flow velocity and direction, a fish metabolic waste concentration model is constructed to determine a second relationship between the fish metabolic waste concentration and the culture density of the aquaculture species. The step of determining the stocking density of the aquaculture species within the aquaculture area based on the dissolved oxygen distribution in the seawater and the concentration of fish metabolic waste includes: The stocking density of the aquaculture species in the aquaculture area is determined based on the first relationship between the dissolved oxygen distribution in the seawater and the stocking density of the aquaculture species, and the second relationship between the concentration of fish metabolic waste and the stocking density of the aquaculture species.

5. The method as described in claim 1, characterized in that, Also includes: Real-time monitoring of dissolved oxygen distribution and fish metabolic waste concentration in seawater; The stocking density of the aquaculture species is adjusted based on real-time monitoring of dissolved oxygen distribution in seawater and fish metabolic waste concentration.

6. A device for determining the density of aquaculture in a wind farm area, characterized in that, include: The parameter acquisition module is used to acquire planning parameters for wind turbines, planning parameters for aquaculture, historical tidal data, and aquaculture species in the sea area where the wind farm is located. The seawater flow velocity and direction determination module is used to construct a three-dimensional fluid dynamics model based on the planning parameters of the wind turbine, the planning parameters of the aquaculture, and the historical tidal data, and to determine the seawater flow velocity and direction in the aquaculture area. The fishery growth environment monitoring module is used to construct a seawater dissolved oxygen diffusion model and a fish metabolic waste concentration model based on the fishery species, the seawater flow velocity and direction, and to determine the distribution of dissolved oxygen in seawater and the concentration of fish metabolic waste. The stocking density determination module is used to determine the stocking density of the aquaculture species in the aquaculture area based on the dissolved oxygen distribution in the seawater and the concentration of fish metabolic waste.

7. The apparatus as claimed in claim 6, characterized in that, The planning parameters for the wind turbine include: the location coordinates of the wind turbine, the diameter of the support structure, the submersion depth of the support structure, and the drag coefficient of the support structure to seawater. The planning parameters for aquaculture include: the location coordinates of the cages, the submersion depth of the cages, the size of the cages, the mesh size, and the resistance coefficient of the netting to seawater.

8. The apparatus as claimed in claim 6, characterized in that, The seawater flow velocity and direction determination module is used to determine the tidal current distribution based on the historical tidal current data using a spatiotemporal interpolation method. Based on the planning parameters of the wind turbine, the first local flow obstruction zone of the wind turbine for water flow is determined; Based on the planning parameters of the aquaculture, the second local obstruction zone of water flow for aquaculture is determined; Using the tidal current distribution as the open boundary, the surface of the wind turbine support structure as the solid wall boundary, and the aquaculture area as the porous medium boundary, a three-dimensional fluid dynamics model is constructed with a preset first resolution based on the first local flow obstruction zone and the second local flow obstruction zone to determine the seawater flow velocity and direction in the aquaculture area.

9. The apparatus as claimed in claim 6, characterized in that, The fishery growth environment monitoring module is used to determine the biological oxygen consumption rate and metabolic waste generation rate of each aquaculture species based on the aquaculture species. Based on the biological oxygen consumption rate, the seawater flow velocity and direction, a seawater dissolved oxygen diffusion model is constructed to determine the first relationship between the distribution of dissolved oxygen in seawater and the culture density of the aquaculture species. Based on the metabolic waste generation rate, the seawater flow velocity and direction, a fish metabolic waste concentration model is constructed to determine a second relationship between the fish metabolic waste concentration and the culture density of the aquaculture species. The culture density determination module is used to determine the culture density of the aquaculture species within the aquaculture area based on a first relationship between the dissolved oxygen distribution in the seawater and the culture density of the aquaculture species, and a second relationship between the concentration of fish metabolic waste and the culture density of the aquaculture species.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the method for determining the aquaculture density in a wind farm area as described in any one of claims 1 to 5.

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