Benthic ecology monitoring and analyzing method and system for photovoltaics

Abstract

The invention discloses a photovoltaic benthic ecology monitoring analysis method and system, and relates to the technical field of monitoring systems.The method comprises the steps that spatial distribution data of a target water area is obtained, ecological area information of the target water area is obtained according to a large database, and a layout limitation area is calibrated; generating constraint conditions in combination with the spatial distribution data of the target water area and the layout limitation area, obtaining initial layout variables input by an administrator, generating a plurality of layout variables by using a random generation tool according to the constraint conditions and the initial layout variables, and calculating a target value of each layout variable through a target function; iteration is carried out through a multi-objective evolutionary algorithm decomposition method until convergence conditions are met, an optimal layout variable conforming to a target water area is output, and in practical application, parameters in the optimal layout variable are dynamically optimized. The solution capability of the analysis system to the complex target coupling problem is improved, and a more diversified and selectable optimal solution set is provided for water surface photovoltaic planning.

Description

Technical Field

[0001] The present invention relates to the technical field of monitoring systems, and particularly to a benthic ecological monitoring and analysis method and system for photovoltaic applications. Background Art

[0002] With the rapid development of renewable energy technologies, photovoltaic power generation, as an important part of them, has been widely applied globally. In particular, floating photovoltaic systems (Floating-PV) deployed in water areas have gradually emerged in recent years. It not only effectively utilizes idle water surface resources but also reduces water evaporation through the shading effect. However, the deployment of large-scale water surface photovoltaic projects also has potential impacts on the water ecosystem, especially the benthic ecosystem (such as benthic organisms, microbial communities, underwater plants, etc.).

[0003] The existing technologies have the following defects: 1. Most existing optimization schemes only consider a single objective (such as maximum power generation or minimum construction cost), while ignoring the control of the degree of disturbance to the ecosystem, making the system unable to balance ecological security and energy output under extreme optimization conditions, and lacking a reasonable multi-objective equilibrium model; 2. Since the existing systems do not integrate real-time monitoring feedback and optimization adjustment mechanisms, they cannot respond to and adjust water quality changes, biomass fluctuations, etc. that occur after deployment, making the system lose its ecological regulation ability during long-term operation and reducing the sustainability and system robustness of the deployment.

[0004] Based on this, the present invention proposes a benthic ecological monitoring and analysis method and system for photovoltaic applications, which realizes the Pareto equilibrium between minimizing ecological impact and maximizing power generation efficiency through MOEA / D, improves the solution ability of the analysis system for complex objective coupling problems, and provides a more diversified and optional optimal solution set for water surface photovoltaic planning. Summary of the Invention

[0005] The purpose of the present invention is to provide a benthic ecological monitoring and analysis method and system for photovoltaic applications to solve the deficiencies in the background art.

[0006] To achieve the above purpose, the present invention provides the following technical solution: A benthic ecological monitoring and analysis method for photovoltaic applications, the analysis method comprising the following steps: The analysis system obtains the spatial distribution data of the target water area, obtains the ecological area information of the target water area based on the large database, and demarcates the restricted layout area; Combining the spatial distribution data of the target water area with the restricted layout area to generate constraint conditions, and obtaining the initial layout variables input by the administrator. Based on the constraint conditions and the initial layout variables, a random generation tool is used to generate a number of layout variables; After calculating the objective value of each layout variable through the objective function, iterative calculations are performed using the multi-objective evolutionary algorithm decomposition method until the convergence condition is met. Then, the optimal layout variables that meet the target water area are output, and in practical applications, the parameters in the optimal layout variables are dynamically optimized.

[0007] In a preferred embodiment, calculating the objective value of each layout variable through the objective function includes the following steps: Obtain the ecological impact factor and energy efficiency output factor of each layout variable. After normalizing the ecological impact factor and energy efficiency output factor, calculate the objective value of each layout variable by maximizing the energy efficiency output factor and minimizing the ecological impact factor through weighted calculation.

[0008] In a preferred embodiment, iterative calculations are performed using the multi-objective evolutionary algorithm decomposition method until the convergence condition is met, and then the optimal layout variables that meet the target water area are output, including the following steps: Sort the several layout variables generated in the previous stage according to the objective value to generate a variable list; Select the first K layout variables in the variable list for randomization operations to obtain K + W layout variables, where K represents the number of selected layout variables and W represents the number of layout variables obtained after the randomization operation; Establish a layout variable set for the K + W layout variables, recalculate the objective value, and then perform iterative calculations; When the number of iterations is equal to the iteration threshold, output all layout variable sets; Select the layout variable with the largest objective value among all layout variable sets as the optimal layout variable that meets the target water area for application.

[0009] In a preferred embodiment, sort the several layout variables generated in the previous stage in descending order according to the objective value, select the first K layout variables in the variable list for randomization operations, and for each layout variable in the set perform random perturbation or Gaussian noise mutation to generate new layout variables.

[0010] In a preferred embodiment, the sorted variable list is: , where , N represents the number of layout variables, spir represents the objective value of the layout variable, and select the first layout variables from the variable priority list to form a set: .

[0011] In a preferred embodiment, in practical applications, dynamically optimizing the parameters in the optimal layout variables includes the following steps: Dynamically optimize the layout density in the layout variables, and the optimization algorithm expression is: , where, represents the current layout density, represents the updated layout density, represents the measured energy efficiency output value within the current period, represents the predicted energy efficiency output value based on the optimal layout parameters, represents the measured value of the current ecological disturbance, represents the maximum tolerable threshold for ecological impact.

[0012] In a preferred embodiment, generate constraint conditions by combining the spatial distribution data of the target water area with the layout restricted area, including the following steps: Perform overlay analysis on the obtained multi-category spatial distribution data and the layout restricted area layer, and construct a multi-dimensional ecological space of the water area through the geographic coordinate system and spatial resolution; Based on obtaining the spatial layout candidate units, generate multi-dimensional layout constraint conditions, and the constraint conditions include ecological constraints, hydrological constraints, water quality constraints, terrain and navigation constraints, structural safety constraints, and operation and maintenance accessibility constraints.

[0013] In a preferred embodiment, obtain the initial layout variables input by the administrator, and the initial layout variables include the position coordinates, panel array spacing, inclination angle, height, and density of the photovoltaic layout unit.

[0014] In a preferred embodiment, the analysis system obtains the spatial distribution data of the target water area, obtains the ecological area information of the target water area based on the large database, and demarcates the layout restricted area, including the following steps: Obtain spatial distribution data, including water depth, water flow, water quality, sediment type, and benthic organism habitat density; Access the regional ecological protection large database and extract the ecological red line and protection area information within the target water area; Based on the ecological red line and protection area information, construct a layout restriction factor matrix and generate an ecological sensitivity distribution layer; Perform spatial closed boundary recognition and graphical expression on the layout restricted area, and construct a standardized layout restricted vector map.

[0015] A benthic ecological monitoring and analysis system for photovoltaic includes a regional analysis module, a data acquisition module, a variable generation module, an optimization module, and an optimization module; Regional analysis module: Obtain the spatial distribution data of the target water area, obtain the ecological area information of the target water area based on the large database, and demarcate it as the layout restricted area; Data acquisition module: Generate constraint conditions by combining the spatial distribution data of the target water area and the layout restricted area, and obtain the initial layout variables input by the administrator; Variable generation module: Generate a number of layout variables using a random generation tool according to the constraint conditions and the initial layout variables; Optimization module: After calculating the objective value of each layout variable through the objective function, perform iteration through the multi-objective evolutionary algorithm decomposition method until the convergence condition is met, and then output the optimal layout variables that meet the target water area; Optimization module: In practical applications, dynamically optimize the parameters in the optimal layout variables.

[0016] In the above technical solution, the technical effects and advantages provided by the present invention are as follows: 1. The present invention obtains the spatial distribution data of the target water area, obtains the ecological area information of the target water area based on the large database, calibrates the layout restricted area, generates constraint conditions by combining the spatial distribution data of the target water area and the layout restricted area, and obtains the initial layout variables input by the administrator. According to the constraint conditions and the initial layout variables, a number of layout variables are generated using a random generation tool. After calculating the objective value of each layout variable through the objective function, perform iteration through the multi-objective evolutionary algorithm decomposition method until the convergence condition is met, and then output the optimal layout variables that meet the target water area. In practical applications, dynamically optimize the parameters in the optimal layout variables. Achieve the Pareto equilibrium between minimizing ecological impact and maximizing power generation efficiency through MOEA / D, improve the solution ability of the analysis system for complex target coupling problems, and provide a more diversified and optional optimal solution set for the water surface photovoltaic planning; 2. The present invention realizes the life cycle management and ecological continuous adaptation of the layout scheme by introducing the feedback of dynamic monitoring data (such as real-time benthic biological changes, water quality fluctuations, etc.) and rolling correction of the optimal layout parameters, and provides dynamic adjustment ability for the long-term operation of the water surface photovoltaic system. Description of the drawings

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to these drawings.

[0018] Figure 1 It is the method flow chart of the analysis method of the present invention.

[0019] Figure 2 It is the timing diagram of the analysis method of the present invention.

[0020] Figure 3 It is the system architecture diagram of the analysis system of the present invention. Detailed implementation mode

[0021] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts shall fall within the protection scope of the present invention.

[0022] Embodiment 1: Please refer to Figure 1 - Figure 2 As shown, a benthic ecological monitoring and analysis method for photovoltaics in this embodiment includes the following steps: The analysis system obtains the spatial distribution data of the target water area, including ecological factors such as water depth, water flow, water quality, sediment type, and benthic organism inhabitation density, obtains the ecological region information of the target water area based on the large database, including rare species habitats, sediment disturbance sensitive areas, and aquatic plant growth areas, and calibrates them as restricted layout areas. Combining the spatial distribution data of the target water area with the restricted layout areas to generate constraint conditions, and obtaining the initial layout variables input by the administrator (position coordinates, panel array spacing, inclination angle, height, density, etc. of the photovoltaic layout unit). According to the constraint conditions and the initial layout variables, a random generation tool is used to generate several layout variables. After calculating the objective value of each layout variable through the objective function, iterative calculation is performed through the multi-objective evolutionary algorithm decomposition method until the convergence condition is met, and then the optimal layout variables that meet the target water area are output. In actual applications, the parameters in the optimal layout variables are dynamically optimized.

[0023] This application obtains the spatial distribution data of the target water area, obtains the ecological region information of the target water area based on the large database, calibrates the restricted layout area, combines the spatial distribution data of the target water area with the restricted layout area to generate constraint conditions, and obtains the initial layout variables input by the administrator. According to the constraint conditions and the initial layout variables, a random generation tool is used to generate several layout variables. After calculating the objective value of each layout variable through the objective function, iterative calculation is performed through the multi-objective evolutionary algorithm decomposition method until the convergence condition is met, and then the optimal layout variables that meet the target water area are output. In actual applications, the parameters in the optimal layout variables are dynamically optimized. Through MOEA / D, the Pareto equilibrium between minimizing ecological impact and maximizing power generation efficiency is realized, improving the solving ability of the analysis system for complex target coupling problems, and providing a more diversified and optional optimal solution set for the planning of floating photovoltaics.

[0024] This application realizes the life-cycle management and ecological continuous adaptation of the layout scheme by introducing the feedback of dynamic monitoring data (such as real-time benthic organism changes, water quality fluctuations, etc.) and rolling correcting the optimal layout parameters, providing dynamic adjustment capabilities for the long-term operation of the floating PV system.

[0025] Example 2: The analysis system obtains the spatial distribution data of the target water area, including ecological factors such as water depth, water flow, water quality, sediment type, and benthic organism inhabitation density, and obtains the ecological region information of the target water area, including rare species habitats, sediment disturbance sensitive areas, and aquatic plant growth areas, based on the large database, and calibrates them as layout restricted areas, including the following steps: Before implementing PV layout, the analysis system first needs to comprehensively obtain the ecological spatial distribution data of the target water area. This process relies on the fusion and analysis of multi-source heterogeneous data, covering key ecological factors such as water depth, water flow, water quality, sediment type, and benthic organism inhabitation density. The water depth data is mainly obtained through high-precision sonar measurement, underwater lidar scanning, or an unmanned ship automatic mapping system to construct a three-dimensional terrain model of the water bottom. The water flow information usually comes from hydrological monitoring stations or numerical hydrodynamic models. The system extracts the flow velocity, flow direction, and flow field structure of different regions through a combination of continuous monitoring and model prediction, providing a basis for the stability assessment of subsequent ecological sensitive areas. Water quality factors such as dissolved oxygen, pH, turbidity, total phosphorus, and total nitrogen parameters are obtained by means of buoy-type on-line monitoring equipment or an automatic water quality sampling system, and spatial interpolation analysis is carried out in combination with historical statistical data. The sediment type and distribution rely on grab sampling of bottom sediment, sediment particle size analysis, and pollutant enrichment assessment to judge the degree of its impact on benthic organisms. The benthic organism inhabitation density is quantified based on fixed-point surveys of aquatic organisms, statistics of benthic species diversity indices, microscopic examination and counting, etc., and distribution modeling is carried out to form an important index reflecting ecological carrying capacity.

[0026] After the preliminary acquisition of ecological factor data is completed, the system will access the regional ecological protection large database to further extract the ecological red line and important protection area information within the target water area. This database may include natural reserve boundaries, rare and endangered species distribution databases, key aquatic plant population maps, sediment disturbance sensitive area identification maps, wetland protection zoning, etc. The system conducts semantic analysis of the ecological regions through spatial vector data fusion and raster coding analysis to identify regions with special protection values. For example, ecological function zones such as specific fish breeding sites, bird foraging zones, aquatic plant aggregation zones, and water quality purification zones will be calibrated as restricted areas for layout activities. For areas with pollutants such as heavy metals and organic matter enriched in the bottom sediment, the system also needs to refer to the bottom sediment disturbance sensitive level assessment report to judge whether the layout may cause secondary pollution or ecological disturbance.

[0027] Based on the above ecological elements and regional divisions, the system will construct a matrix of layout restriction factors. This matrix uses spatial grids as basic units (such as 5-meter or 10-meter grids), and assigns an ecological sensitivity level and a layout feasibility label to each unit. The construction of the sensitivity level is calculated by weighted combination of multiple-dimensional indicators. For example, the habitat of rare species is given the highest sensitivity weight (such as 1.0), the sediment disturbance area is given a medium weight (such as 0.7), and the general aquatic plant area is given a secondary weight (such as 0.5), and an ecological sensitivity distribution layer is generated. For areas where the sensitivity score exceeds a specific threshold, the system will automatically demarcate them as "forbidden layout areas", while moderately sensitive areas are marked as "restricted layout areas", and design constraints such as low disturbance, low shielding or appropriate spacing layout need to be followed in the layout design.

[0028] Next, the system performs spatial closed boundary recognition and graphical expression on the layout restricted areas, and constructs a standardized layout restricted vector layer. Through geographic information system (GIS) tools, the spatial topological relationship is further improved, pseudo-boundaries and fragmented patches are eliminated, and the closed modeling of the layout restricted areas is realized. At the same time, the system attaches attribute information to each restricted area, such as area, ecological type, layout restriction level, hydrodynamic conditions, water quality indicators, etc., for subsequent layout algorithms to call parameters and model constraints.

[0029] Finally, to adapt to the dynamic change characteristics of the ecological environment, the analysis system supports real-time data access and dynamic update of the layout restricted areas. By accessing remote sensing satellite images, real-time water quality sensor data and ecological early warning information platforms, the system can identify change trends such as the expansion of aquatic vegetation, sudden water quality changes, and the migration of benthic species. When the monitoring data exceeds the preset threshold, the system will automatically trigger a re-evaluation mechanism for the layout restricted areas, dynamically update the layout feasibility layer, and ensure that the layout design always conforms to the latest ecological reality. This mechanism significantly improves the ecological response ability and sustainability of the floating PV planning.

[0030] Combining the spatial distribution data of the target water area and the layout restricted areas to generate constraint conditions, and obtaining the initial layout variables input by the administrator (the position coordinates, panel array spacing, inclination angle, height, density, etc. of the PV layout unit), including the following steps: The system first performs overlay analysis on various types of spatial distribution data obtained in the early stage (such as water depth, water flow, water quality, benthic density, etc.) and the layout restricted area layer. Through a unified geographic coordinate system and spatial resolution, a multi-dimensional ecological space of the water area is constructed. The system performs grid processing on the entire water surface area (such as dividing it into 10-meter × 10-meter layout units), and sequentially determines whether each unit is within the restricted area.

[0031] For each grid cell, the system will determine its suitability for installation based on its geographical location and habitat environment attributes. If the cell is located in the "Prohibited Installation Area", it will be marked as an unselectable cell and removed from the installation candidate set; if it is in the "Restricted Installation Area", the system will record the specific restrictions of the cell (such as the maximum allowable shading density, minimum spacing requirement, maximum panel height limit, etc.); while the cells in the "Installable Area" are recognized as basic installation candidate points and participate in the subsequent layout generation.

[0032] Based on the obtained spatial installation candidate units, the system further generates multi-dimensional installation constraint conditions according to ecological protection and engineering practice. The establishment of constraint conditions usually includes the following categories: Ecological constraints: Limit the maximum allowable PV coverage rate on each installation unit (to avoid excessive shading), the minimum panel spacing (to ensure water ventilation and biological migration), and avoid crossing the core density area of benthic organisms; Hydrological constraints: It is required to prohibit installation in areas with excessive water flow velocity to avoid structural instability or ecological disorder; Water quality constraints: Restrict high-density installation in eutrophic water bodies or areas prone to cyanobacteria formation to avoid shading affecting water self-purification; Topography and navigation constraints: Ensure that the installation unit does not block the waterway, does not affect the sight of the bank, and does not interfere with flood regulation; Structural safety constraints: Limit the floating body height, load density, and wind load safety factor to ensure the stability of the platform; Operation and maintenance accessibility constraints: Sufficient maintenance spacing and inspection channels need to be reserved for each installation area. These constraint conditions will be used as hard or soft constraint indicators in the optimization algorithm to ensure that the optimization results not only meet the power generation efficiency but also have ecological and engineering feasibility.

[0033] The initial installation variables include: Installation unit position coordinates: Can be manually selected, bounded by a boundary, or imported from a planning sketch; Panel array spacing (horizontal and vertical): In meters, defining the shading buffer zone between units; PV tilt angle: Generally set between 10 and 20 degrees, affecting the power generation efficiency and shading range; PV panel height: Determines the impact on water surface airflow, water body illumination, and benthic animals and plants; Installation density: Defines the number of panels installed per unit area, affecting the water body coverage rate.

[0034] According to the constraint conditions and initial installation variables, use a random generation tool to generate several installation variables, including the following steps: First, set the value range of each parameter according to the installation constraints and the administrator's initial variables. For example: Layout unit coordinate range: Grid numbers of the deployable area; Array spacing range: Between the minimum spacing and the maximum spacing; Tilt angle range: Generally between 5 and 25 degrees; Panel height range: Such as 0.5 to 2 meters; Layout density: A decimal between 0 and 1.

[0035] It is necessary to convert the spatial restriction area information generated by the preliminary analysis into a layout candidate area mask matrix to facilitate the judgment of whether a layout variable is legal. Generate several groups of layout variables through a random sampling algorithm (such as Latin hypercube sampling, uniform sampling, or Gaussian perturbation), and screen out legal samples through constraint judgment. The (Python) code example is as follows: Import_numpy_as_np Import_random # Assume the deployable area is 100 cells, numbered 0 - 99 available_cells=list(range(100)) # Parameter range definition (from administrator input and constraint model) param_ranges={ "spacing":(2.0,10.0),# Array spacing (meters) "tilt_angle":(5,25),# Tilt angle (degrees) "height":(0.5,2.0),# Panel height (meters) "density":(0.1,1.0),# Unit density (between 0 and 1) } # Restriction area mask, 0 means no layout allowed, 1 means allowed # Here is a simple simulation, actually it should be generated from GIS data constraint_mask=np.ones(100) constraint_mask[20:30]=0# For example, 20 - 29 is a protected area, no layout allowed # Structure of a single layout variable def-generate_single_layout(): While-1: cell=random.choice(available_cells) if_constraint_mask[cell]==0: It should be noted that there are some incorrect variable names and syntax in the original code (such as "def-generate_single_layout()" which should be "def generate_single_layout()", and "While-1" which should be "while True" or other appropriate loop conditions). The above translation is based on the original text as much as possible while maintaining the incorrect parts for the purpose of translation.continue # Located in the restricted area, reselect spacing = round(random.uniform(*param_ranges["spacing"]), 2) tilt = round(random.uniform(*param_ranges["tilt_angle"]), 1) height = round(random.uniform(*param_ranges["height"]), 2) density = round(random.uniform(*param_ranges["density"]), 2) # Expandable complex constraint judgment, such as density * area should not exceed a certain threshold, etc. if_height * density > 1.8: # Example limit continue return { "cell_id": cell, "spacing": spacing, "tilt": tilt, "height": height, "density": density } # Generate multiple initial layout variables def _generate_population(n = 50): population = [] while len(population) < n: layout = generate_single_layout() population.append(layout) return population # Example call initial_population = generate_population(n = 30) for i, layout in enumerate(initial_population[:5]): print(f"Layout plan {i + 1}: {layout}") Based on the preset range of photovoltaic layout parameters and ecological constraint conditions, the above code automatically generates a number of combinatorial layout variable combinations in a random sampling manner, including key parameters such as layout position, array spacing, inclination angle, height, and density. Through screening with a restricted area mask and combined parameter constraints, it effectively ensures the compliance of the generated solutions in terms of ecological feasibility and structural rationality. This process provides an initial population input with controllable quality and diverse structures for multi-objective evolutionary algorithms (such as MOEA / D), and has high scalability and engineering adaptability, which is a key supporting step in the optimized planning of eco-friendly floating photovoltaics.

[0036] After calculating the objective values of each layout variable through the objective function, the following steps are included: Obtain the ecological impact factor and energy efficiency output factor of each layout variable. After normalizing the ecological impact factor and energy efficiency output factor (maximum-minimum normalization), calculate the objective value of each layout variable by maximizing the energy efficiency output factor and minimizing the ecological impact factor, and the expression is: spir = α×G - β×U, where spir is the objective value, G is the energy efficiency output factor, U is the ecological impact factor, α and β are weight coefficients, and α + β = 1.

[0037] The weight coefficients play a core role in balancing the weights between the two objectives in multi-objective optimization. The determination of the weight coefficients has a crucial impact on the orientation of the final optimization solution, and the acquisition method is as follows: · Establish a hierarchical structure (objective layer → criterion layer → index layer), construct a pairwise comparison matrix, evaluate the relative importance of "energy efficiency" to "ecology" (scoring from 1 to 9) based on expert experience, calculate the characteristic root and consistency ratio, and finally obtain the weights.

[0038] In this application, the higher the ecological impact factor, the more the ecological system is disturbed. Usually in the engineering impact analysis, the increase amplitude of this value before and after the layout is regarded as the ecological impact degree, that is: , where represents the benthic diversity before the historical layout of the current layout variable, represents the benthic diversity after the historical layout of the current layout variable, and the calculation expression of benthic diversity is: , where represents the benthic biodiversity index, represents the total number of benthic species in the sample, represents the proportion of the number of individuals of the th species in the total number of individuals The energy efficiency output factor can be directly based on the physical modeling of the photovoltaic output prediction model. Using the energy output estimation method in PVWatts (NREL) or Sandia-Model, the formula for calculating the annual power generation per unit area of the currently deployed variables is as follows: , and the parameter definitions are as follows: represents the annual total irradiance, which refers to the annual equivalent irradiance in the direction of the panel tilt (obtained from a weather station or PVGIS), represents the performance ratio, reflecting the total efficiency ratio of factors such as temperature loss, dust, aging, and shading (generally 0.75 - 0.85), represents the inverter efficiency, the efficiency of converting direct current to alternating current, usually 0.95 - 0.98, represents the line loss efficiency, considering cable transmission loss, usually 0.98 - 0.995.

[0039] It can be approximately converted by the following formula: , where, represents the annual total horizontal irradiance on the water surface, represents the panel incident angle (calculated from the azimuth and tilt angle), represents the zenith angle (determined by the geographical location and time) This calculation method takes into account key factors such as climate, azimuth, reflection, temperature, and electricity, and is derived from NREL-PVWatts and IEC standard EN61853.

[0040] Through the multi-objective evolutionary algorithm decomposition method for iteration, until the convergence condition is met, the optimal deployment variables that meet the target water area are output, including the following steps: Sort the several deployment variables generated in the previous stage according to the target value to generate a variable list. The sorted variable list is denoted as: Where , select the first K deployment variables in the variable list for randomization operation, and select the first from the variable priority list deployment variables to form a set: These deployment variables represent the solutions with better target performance in the current stage and form the basis for the next local search. Obtain K + W deployment variables, where K represents the number of selected deployment variables, and W represents the number of deployment variables obtained after the randomization operation. Establish a deployment variable set for the K + W deployment variables, recalculate the target value and then perform iteration. When the number of iterations is equal to the number of iterations threshold, output all deployment variable sets, and select the deployment variable with the largest target value in all deployment variable sets as the optimal deployment variable that meets the target water area for application.

[0041] Specifically, sort a number of layout variables generated in the previous stage in descending order according to the target value, and select the first K layout variables in the variable list for randomization operations.

[0042] For each layout variable in the set , the system performs minor random perturbations or Gaussian noise mutations in the following dimensions to generate new layout variables: 1. Position coordinates (x, y): Move slightly within the neighborhood range (to prevent entering restricted areas); 2. Height parameter: Introduce perturbations of ±Δh (such as ±0.1 - 0.5 m); 3. Inclination parameter: Add angle fine-tuning within a reasonable range (such as ±2 - 5°); 4. Plate spacing or layout density: Float up and down proportionally (such as ±10%).

[0043] For example: If the inclination angle of a certain layout variable is 25°, the system can generate a new variable with an inclination angle of: , where represents a normally distributed random number, and σ is the standard deviation controlling the degree of perturbation. In this way, a total of W new layout variables are generated, denoted as the set: . Combine the original first layout variables with the variables generated by mutation into a new variable set: , represents the union, that is, combine the original first layout variables with the variables generated by mutation into a new variable set. This extended set introduces diversity while maintaining high-quality solutions, which helps the subsequent algorithm to continue to optimize and avoid falling into local optima.

[0044] In practical applications, dynamically optimize the parameters in the optimal layout variables, including the following steps: In practical application scenarios, to enable the photovoltaic layout scheme to continuously adapt to environmental changes and actual operation feedback during long-term operation, it is necessary to perform dynamic parameter optimization on the layout variables initially optimized. This optimization does not rely on multi-objective evolutionary algorithms, but combines periodic data updates with parameter response models to iteratively correct the existing layout parameters, improving the overall stability of the photovoltaic system in terms of ecological adaptability and energy efficiency output.

[0045] Based on the regularly collected environmental monitoring data and operation status data, re-evaluate the environmental factors in the layout area. For example, the monitoring data includes actual sunlight intensity, component temperature, water temperature, water flow changes, aquatic plant coverage, benthic organism migration range, etc. When there are deviations between these data and the values in the initial modeling stage, the optimization process of the layout variable parameters will be triggered.

[0046] Identify the core layout parameters that need to be optimized, mainly including the following items: Tilt angle: The angle between the photovoltaic module and the horizontal plane; Array spacing: The horizontal spacing between adjacent module arrangements; Installation height: The vertical height of the module from the water surface; Layout density: The coverage ratio of modules per unit water surface area; Coordinate position: The actual position coordinates of the module or array in the layout area.

[0047] In this application, the layout density in the layout variables is mainly dynamically optimized, and the optimization algorithm expression is: , where, represents the current layout density, represents the updated layout density, represents the measured energy efficiency output value within the current period, represents the predicted energy efficiency output value based on the optimal layout parameters, represents the measured value of the current ecological disturbance, represents the maximum tolerable threshold of ecological impact.

[0048] The acquisition method of the maximum tolerable threshold of ecological impact: The maximum tolerable threshold of ecological impact refers to the maximum degree of ecological disturbance that the system can tolerate without causing obvious ecological degradation and without destroying the ecological function of the target water area. Its acquisition methods usually include the following several ways: Ecological red line and protection regulation basis: Refer to the red line control requirements of the local water ecological environment. For example, there are quantitative control standards for sediment disturbance, aquatic plant loss, and decline in benthic organism density.

[0049] Analysis of historical ecological monitoring data: Based on years of water area ecological monitoring data, analyze the typical fluctuation range of each ecological factor during the period without ecological degradation as a reference for the threshold upper limit.

[0050] Expert evaluation model: Through the ecological risk assessment model constructed by ecologists, quantify the ecological response intensity caused by different disturbance sources (such as shading, water flow disturbance, temperature rise), and set a reasonable upper limit of impact.

[0051] Simulation and comparison analysis: Use water ecological simulation models (such as EFDC, MIKE21, etc.) to simulate the ecological impacts under different layout scenarios, and determine the maximum disturbance range where the stability of the ecological system does not significantly decline.

[0052] The acquisition method of the predicted energy efficiency output value based on the optimal layout parameters: The predicted energy efficiency output value generally refers to the power generation output per unit time, per unit area or of the overall system calculated through modeling or simulation under the current layout parameter configuration. The general ways to obtain it include the following steps: Photovoltaic power generation model calculation: Using a standard photovoltaic output calculation model, such as the irradiance-temperature-efficiency model (PVWatts, SAM model or self-built model), input variables such as tilt angle, azimuth angle, component parameters, spacing, etc., and combine with local radiation resource data to calculate the theoretical output value.

[0053] Meteorological and environmental data-driven simulation: Combining local historical meteorological data (such as sunshine duration, irradiance, air temperature, water temperature, etc.), conduct time-series energy efficiency simulations for different layout methods to obtain predicted outputs at multiple time scales such as annual average, monthly average, daily average, etc.

[0054] Combined calculation of component performance parameters: Considering multiple influencing factors such as component conversion efficiency, temperature correction factor, shading loss, inverter efficiency, layout loss, etc., establish a comprehensive energy efficiency deduction model.

[0055] Field experiment data comparison: Set up experimental sample areas in some waters and record their operation and power generation data to calibrate the prediction accuracy of the model and back-calculate the theoretical output values for layouts in other areas.

[0056] This formula reflects the dynamic linkage mechanism between layout density and operation performance. When the measured energy efficiency is lower than the expected value, the system tends to moderately increase the layout density, while when the ecological disturbance exceeds the threshold, it automatically reduces the density to relieve the ecological pressure.

[0057] In practical applications, to ensure that the water surface photovoltaic layout scheme has dynamic adaptability, the system needs to continuously optimize the layout variable parameters obtained from the original optimization by combining periodic environmental data updates and operation performance feedback. This dynamic optimization process can not only improve the stability of energy efficiency output, but also play a more active role in protecting ecological sensitive areas and maintaining water quality.

[0058] After the parameter update is completed, the system needs to conduct a round of operation simulation or on-site observation and evaluation to ensure that the parameter adjustment does not cause unacceptable ecological disturbances or equipment operation problems. If the evaluation passes, the system will update the layout parameters to the current valid values and record them in the parameter adjustment log for reference in subsequent dynamic optimization. Through the above dynamic parameter optimization mechanism, the photovoltaic layout system can continuously maintain better energy efficiency output and ecological compatibility under different seasons, hydrological changes and operation years, without large-scale reconstruction of the layout scheme, greatly improving the adaptability and life cycle management efficiency of the system.

[0059] Example 3: Please refer to Figure 3As shown in the figure, a benthic ecological monitoring and analysis system for photovoltaic applications in this embodiment includes a regional analysis module, a data acquisition module, a variable generation module, an optimization module, and an improvement module; Regional analysis module: Obtain the spatial distribution data of the target water area, including ecological factors such as water depth, water flow, water quality, sediment type, and benthic organism inhabitation density. Based on the large database, obtain the ecological area information of the target water area, including rare species habitats, sediment disturbance sensitive areas, and aquatic plant growth areas, and mark them as restricted layout areas. Send the spatial distribution data of the target water area and the restricted layout areas to the data acquisition module; Data acquisition module: Combine the spatial distribution data of the target water area with the restricted layout areas to generate constraint conditions, and obtain the initial layout variables input by the administrator (the position coordinates, panel array spacing, inclination angle, height, density, etc. of the photovoltaic layout units). Send the initial layout variables and the constraint conditions to the variable generation module; Variable generation module: According to the constraint conditions and the initial layout variables, use a random generation tool to generate several layout variables, and send the several layout variables to the optimization module; Optimization module: After calculating the target value of each layout variable through the objective function, perform iteration through the multi-objective evolutionary algorithm decomposition method until the convergence condition is met, and then output the optimal layout variables that meet the target water area. Send the optimal layout variables to the improvement module; Improvement module: In actual applications, dynamically optimize the parameters in the optimal layout variables.

[0060] In the description of this specification, the descriptions referring to terms such as "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0061] The above-disclosed preferred embodiments of the present invention are only used to help explain the present invention. The preferred embodiments do not elaborate on all details, nor do they limit the present invention to only the specific implementation manners. Obviously, according to the content of this specification, many modifications and variations can be made. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the present invention, so that those skilled in the relevant technical fields can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A benthic ecological monitoring and analysis method for photovoltaics, characterized in that: The analysis method includes the following steps: The analysis system obtains the spatial distribution data of the target water area, obtains the ecological region information of the target water area based on the large database, and demarcates the layout restricted area; Combining the spatial distribution data of the target water area with the layout restricted area to generate constraint conditions, and obtaining the initial layout variables input by the administrator. According to the constraint conditions and the initial layout variables, a number of layout variables are generated using a random generation tool; After calculating the objective value of each layout variable through the objective function, iterative calculation is performed through the multi-objective evolutionary algorithm decomposition method until the convergence condition is met. Then, the optimal layout variables that meet the target water area are output, and in practical applications, the parameters in the optimal layout variables are dynamically optimized.

2. The benthic ecological monitoring and analysis method for photovoltaics according to claim 1, wherein: Calculating the objective value of each layout variable through the objective function includes the following steps: Obtaining the ecological impact factor and energy efficiency output factor of each layout variable, performing normalization processing on the ecological impact factor and energy efficiency output factor, and then calculating the objective value of each layout variable through weighted calculation by maximizing the energy efficiency output factor and minimizing the ecological impact factor.

3. The benthic ecological monitoring and analysis method for photovoltaics according to claim 2, wherein: Performing iterative calculation through the multi-objective evolutionary algorithm decomposition method until the convergence condition is met, and outputting the optimal layout variables that meet the target water area, including the following steps: Sorting the several layout variables generated in the previous stage according to the objective value to generate a variable list; Performing a randomization operation on the first K layout variables selected in the variable list to obtain K + W layout variables, where K represents the number of selected layout variables, and W represents the number of layout variables obtained after the randomization operation; Establishing a layout variable set for the K + W layout variables, recalculating the objective value, and then performing iterative calculation; When the number of iterations is equal to the iteration threshold, outputting all layout variable sets; Selecting the layout variable with the largest objective value in all layout variable sets as the optimal layout variable that meets the target water area for application.

4. The benthic ecological monitoring and analysis method for photovoltaics according to claim 3, characterized in that: Sort several layout variables generated in the previous stage in descending order according to the target value, and select the first K layout variables in the variable list for randomization operations. For each layout variable in the set , perform random perturbation or Gaussian noise mutation to generate new layout variables.

5. The benthic ecological monitoring and analysis method for photovoltaics according to claim 4, characterized in that: The sorted list of variables is as follows: , where , N represents the number of layout variables, spir represents the target value of the layout variables, and the first are selected from the variable priority list layout variables to form a set: .

6. The benthic ecological monitoring and analysis method for photovoltaics according to claim 5 is characterized in that: In practical applications, dynamically optimizing the parameters in the optimal layout variables includes the following steps: Dynamically optimizing the layout density in the layout variables, and the optimization algorithm expression is: , where represents the current deployment density, represents the updated deployment density, represents the measured energy efficiency output value within the current period, represents the predicted energy efficiency output value based on the optimal deployment parameters, represents the measured value of the current ecological disturbance, represents the maximum tolerable threshold of ecological impact.

7. A benthic ecological monitoring and analysis method for photovoltaics according to claim 6, characterized in that: Combining the spatial distribution data of the target water area with the layout restricted area to generate constraint conditions includes the following steps: Performing overlay analysis on the obtained multi-category spatial distribution data and the layout restricted area layer, and constructing a multi-dimensional ecological space of the water area through the geographic coordinate system and spatial resolution; On the basis of obtaining the spatial layout candidate units, generating multi-dimensional layout constraint conditions, and the constraint conditions include ecological constraints, hydrological constraints, water quality constraints, terrain and navigation constraints, structural safety constraints, and operation and maintenance accessibility constraints.

8. A benthic ecological monitoring and analysis method for photovoltaics according to claim 7, characterized in that: Obtaining the initial layout variables input by the administrator, and the initial layout variables include the position coordinates, panel array spacing, inclination angle, height, and density of the photovoltaic layout unit.

9. The benthic ecological monitoring and analysis method for photovoltaics according to claim 8, characterized in that: The analysis system obtains the spatial distribution data of the target water area, obtains the ecological region information of the target water area based on the large database, and demarcates the layout restricted area, including the following steps: Obtaining the spatial distribution data, including covering water depth, water flow, water quality, sediment type, and benthic biological habitat density; Connecting to the regional ecological protection large database to extract the ecological red line and protection area information within the target water area; Construct a layout restriction factor matrix based on the ecological red line and protected area information, and generate an ecological sensitivity distribution layer; Perform spatial closed boundary recognition and graphical expression on the layout restricted area, and construct a standardized layout restricted vector map.

10. A benthic ecological monitoring and analysis system for photovoltaics, which is used to implement the analysis method described in any one of claims 1-9, and is characterized in that: It includes a regional analysis module, a data acquisition module, a variable generation module, an optimization module, and an improvement module; Regional analysis module: Obtain the spatial distribution data of the target water area, and obtain the ecological area information of the target water area based on the large database and mark it as the layout restricted area; Data acquisition module: Combine the spatial distribution data of the target water area with the layout restricted area to generate constraint conditions, and obtain the initial layout variables input by the administrator; Variable generation module: Generate a number of layout variables using a random generation tool according to the constraint conditions and the initial layout variables; Optimization module: After calculating the objective value of each layout variable through the objective function, perform iteration through the multi-objective evolutionary algorithm decomposition method until the convergence condition is met, and then output the optimal layout variables that meet the target water area; Improvement module: In practical applications, dynamically optimize the parameters in the optimal layout variables.

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