Screening method for desert windbreak and erosion-resistant plants used in photovoltaic arrays

Abstract

The present invention relates to the field of plant screening technology, and specifically to a method for screening desert windbreak and erosion-resistant plants for use in photovoltaic arrays, comprising the following steps: screening candidate plant species based on their resistance to sand and dust, drought, and salinity, generating a first candidate plant list; utilizing a multispectral laser scattering sensor to collect sand and dust characteristics, including kinetic energy intensity; combining computational fluid dynamics simulation technology to reconstruct the dynamic diffusion path of wind and sand flow in the target photovoltaic array area; evaluating the sand reduction effect of each candidate plant species in the first candidate plant list around the photovoltaic array, and generating a second candidate plant list after screening, which is the final selected plant. The present invention achieves comprehensive quantification and scientific optimization of the screening process by comprehensively calculating a comprehensive score through quantitative indicators such as wind speed attenuation rate, sand and dust deposition rate, and planting cost, thereby avoiding the problems of strong subjectivity and lack of quantitative basis in traditional screening.

Description

Technical Field

[0001] The invention relates to the technical field of plant screening, in particular to a method for screening desert windbreak and erosion-resistant plants used in photovoltaic arrays. Background Art

[0002] With the rapid development of photovoltaic power generation technology, the deployment of large-scale photovoltaic power plants in desert areas has become an important way to meet energy needs. However, strong winds and high concentrations of sand and dust in desert environments pose significant challenges to the operation of photovoltaic arrays, including the following issues:

[0003] Dust coverage and equipment loss: The movement of wind and sand will cause a large amount of dust to settle on the surface of photovoltaic panels, reducing their photoelectric conversion efficiency. At the same time, dust particles hitting the equipment surface at high wind speeds will cause wear and tear, shortening the service life of the equipment.

[0004] Limitations of traditional protection measures: While currently widely used physical barriers (such as windbreaks) and chemical sand fixation agents can partially reduce wind-blown sand erosion, they are complex to construct, costly, and environmentally unfriendly. Furthermore, these methods lack specificity to the dynamic dispersion characteristics of specific dust sources.

[0005] Deficiencies in ecological protection technology: Building an ecological barrier by planting desert windbreak plants is a sustainable development solution. However, traditional plant screening methods rely heavily on empirical judgment and lack scientific evaluation standards. They are unable to accurately adapt to the dynamic characteristics of sand and dust and the ecological needs of plants in the target area, resulting in protection effectiveness that is difficult to meet actual needs. Summary of the Invention

[0006] The present invention provides a method for screening desert windbreak and erosion-resistant plants for use in photovoltaic arrays, aiming to evaluate the sand reduction effect and ecological adaptability of plants in the target photovoltaic array area, and ensure that the screened plant species have the best protective efficiency.

[0007] The method for screening desert windbreak and erosion-resistant plants used in photovoltaic arrays comprises the following steps:

[0008] S1, based on the native plant database of desert areas, candidate plant species are screened according to their resistance to sand and dust, drought and salinity, and a first candidate plant list is generated;

[0009] S2, based on the target photovoltaic array area, uses a multispectral laser scattering sensor to collect dust characteristics, including kinetic energy intensity;

[0010] S3, based on measured wind field data and dust characteristics, combined with computational fluid dynamics simulation technology, reconstructs the dynamic diffusion path of wind and sand flow in the target photovoltaic array area;

[0011] S4, construct a three-dimensional interactive simulation model, input the dynamic diffusion path and the candidate plants in the first candidate plant list, simulate the protection effectiveness of the candidate plants under the dynamic diffusion path in the target photovoltaic array area, including wind speed attenuation rate and sand and dust deposition rate, evaluate the sand reduction effect of each candidate plant species in the first candidate plant list around the target photovoltaic array, and generate a second candidate plant list after screening as the final selected plants.

[0012] Furthermore, the S1 specifically includes:

[0013] S11, extract the environmental adaptability data of each plant from the native plant database in desert areas, including sand and dust tolerance, drought tolerance, and saline-alkali tolerance;

[0014] S12, Quantification of screening indicators: Quantitative indicators are set for dust resistance, drought resistance, and saline-alkali resistance, including:

[0015] Dust resistance: The resistance of plant stems and leaves to wind erosion and the self-cleaning ability of dust deposits are used as references;

[0016] Drought tolerance: Take the water absorption capacity of plant roots as a reference;

[0017] Salt-alkali tolerance: Take the survival rate and growth rate of plants in high salt-alkali environments as a reference;

[0018] Comprehensive score calculation: Assign weight values to the quantitative indicators of each plant, and use the weighted scoring algorithm to calculate the comprehensive adaptability score. The formula is as follows:

[0019] ,in is the comprehensive adaptability score, normalized to [0,1], 、 、 are the quantitative scores of sand and dust resistance, drought resistance and saline-alkali resistance, 、 、 is the corresponding weight;

[0020] Screening rule setting: Screening rules are set according to the comprehensive adaptability score, and only plant species with a comprehensive adaptability score higher than the preset threshold of 0.6 are retained to generate the first candidate plant list.

[0021] Furthermore, the S2 specifically includes:

[0022] S21, sensor placement and calibration: Deploy multispectral laser scattering sensors in the target photovoltaic array area. The installation height of the multispectral laser scattering sensors is set to be within the range of 1m-2m from the ground. The sensitivity of the multispectral laser scattering sensors is calibrated within the optical wavelength range of 400nm-1000nm.

[0023] S22, wind and sand kinetic energy measurement: The density change of sand and dust flow is detected by the light scattering intensity of the laser beam and sand particles, and the wind speed data is obtained in real time in combination with the wind speed sensor.

[0024] Furthermore, the kinetic energy intensity Calculated as: ,in, is the dust density, is the wind speed.

[0025] Furthermore, the S3 specifically includes:

[0026] S31, wind field data collection: wind speed sensors are deployed in the target photovoltaic array area to measure wind speeds at different spatial locations ,wind direction , obtain the spatiotemporal distribution of wind speed , temporal and spatial distribution of wind direction , generate the initial wind field velocity field of the target photovoltaic array area, and the initial wind field velocity field vector is expressed as: ,in, is the velocity field vector, and is the unit vector of the horizontal component; Indicates spatial location, Indicates time, Indicates the spatial position , time is The wind speed at Indicates the spatial position , time is Wind direction at that time;

[0027] S32, input of dust kinetic energy intensity: input the kinetic energy intensity of dust per unit volume Converted into corresponding dust flow load , as input parameters for fluid dynamics simulation;

[0028] S33, Turbulence Modeling: Based on the wind field data and dust kinetic energy intensity of the target PV array area, a turbulence model is used to simulate the diffusion behavior of dust particles in the wind field, set boundary conditions, and reconstruct the velocity field and concentration field of the dust flow;

[0029] S34, dynamic diffusion path simulation: Through computational fluid dynamics simulation of the dynamic diffusion path of dust particles in the wind field, the movement trajectory of dust in the wind field is reconstructed to generate the dust diffusion path. The dust diffusion path includes the main direction of dust, high concentration area, sedimentation area and dust backflow area.

[0030] Furthermore, the dust flow load is the impact force of dust kinetic energy on unit area, which is calculated as:

[0031] ,in, is the dust flow load, is the unit area, is the action time, The spatial and temporal distribution of dust is used as the external input boundary conditions for computational fluid dynamics simulation to represent the dynamic force of dust on the target photovoltaic array area.

[0032] Furthermore, the movement trajectory of the reconstructed dust in the wind field in S34 is expressed as:

[0033] ,in, is the dust particle position vector, is the drag coefficient, is the relative velocity vector of dust particles, is the acceleration due to gravity, is the cross-sectional area of the particle, is the air density;

[0034] The output result is a dust dispersion path, showing the main direction of the dust flow, high concentration areas, sedimentation areas and dust return areas.

[0035] Furthermore, the S4 specifically includes:

[0036] S41, model environment initialization: Based on the target PV array area, combined with actual terrain data and simulation results of dynamic diffusion paths, a three-dimensional spatial environment is constructed. The three-dimensional spatial environment includes the ground surface height, PV array location and geometry, and dust diffusion paths;

[0037] S42, candidate plant attribute input: Extract the protective attributes of each plant from the first candidate plant list, including plant height, leaf area index, stem and leaf surface roughness, air permeability, and root stability. Input plant location and planting density as initial configuration parameters to simulate the interference effect of plants on wind speed and dust flow in the target photovoltaic array area.

[0038] S43, Protection Effectiveness Simulation: Based on the dynamic diffusion path, the velocity field and concentration field of the dust flow are superimposed on the plant attribute model to simulate the dynamic protection behavior of candidate plants in the target photovoltaic array area. The simulation results include:

[0039] Wind speed attenuation rate : By calculating the effect of plant groups on reducing local wind speed, the ability of plants to weaken the kinetic energy of dust flow is evaluated;

[0040] Dust deposition rate : Evaluate the ability of plants to reduce dust flow concentration through the spatial distribution of dust particles on plant surfaces and ground deposition;

[0041] S44, comprehensive effect evaluation: for each plant in the first candidate plant list, quantify its protective effectiveness in the target photovoltaic array area, generate a comprehensive effect evaluation including wind speed attenuation rate, dust deposition rate and vegetation coverage cost, obtain a comprehensive effect evaluation score based on the comprehensive effect evaluation, screen out candidate plants with a comprehensive effect evaluation score greater than a preset value, and generate a second candidate plant list.

[0042] Furthermore, the wind speed attenuation rate It represents the ability of plant groups to reduce local wind speed and is calculated as:

[0043] ,in, is the initial wind speed when the wind enters the plant protection zone, is the remaining wind speed after the wind passes through the plant protection area;

[0044] The dust deposition rate It represents the proportion of dust in the plant area that settles from the air flow to the ground or adheres to the plants, and is calculated as:

[0045] ,in, is the dust concentration per unit volume when entering the plant area, It is the concentration of dust per unit volume after passing through the plant area.

[0046] Furthermore, the comprehensive effect evaluation in S44 also considers the reduction in kinetic energy intensity of dust. , which represents the weakening of kinetic energy intensity by plants, and the calculation formula is: ,in, is the kinetic energy intensity of dust when it enters the plant area, is the kinetic energy intensity of dust after passing through the plant area;

[0047] The comprehensive effect evaluation score Calculated as:

[0048] in, is the reduction in kinetic energy intensity, is the maximum dust kinetic energy reduction in the current candidate plant group, used for normalization. is the wind speed decay rate, is the dust deposition rate, is the cost of plant cultivation, expressed as the cost per unit area, is the weight coefficient of each evaluation element.

[0049] Beneficial effects of the present invention:

[0050] The technical solution of the present invention dynamically combines the kinetic energy intensity of dust and computational fluid dynamics technology to simulate the diffusion path of dust in the target photovoltaic array area and quantify the protective effectiveness of candidate plants (wind speed attenuation rate, dust deposition rate, kinetic energy reduction). This screening method based on dynamic environment and precise calculation can accurately evaluate the sand reduction effect and ecological adaptability of plants in the target area, ensuring that the selected plant species have the best protective effectiveness. Compared with traditional experience-based screening methods, it can significantly reduce the impact of wind and sand erosion on photovoltaic arrays.

[0051] The technical solution of the present invention introduces dust kinetic energy intensity as a core parameter, converts it into dust flow load and inputs it into a three-dimensional interactive simulation model. It is combined with the protective characteristics of the candidate plants (such as height, leaf area index, air permeability, etc.) to establish a dynamic simulation environment. Through quantitative indicators such as wind speed attenuation rate, dust deposition rate and planting cost, a comprehensive score is calculated comprehensively, realizing comprehensive quantification and scientific optimization of the screening process, avoiding the problems of strong subjectivity and lack of quantitative basis in traditional screening.

[0052] The technical solution of the present invention combines dynamic diffusion path simulation, plant ecological characteristics assessment and three-dimensional interactive model visualization technology to form a closed-loop process from environmental data collection, precise calculation to protection effect verification. In particular, the linkage between the dynamic simulation model and the comprehensive score makes the screening results not only applicable to the desert conditions of the current target photovoltaic array area, but also can be quickly adapted to different terrains, wind fields and dust characteristics by adjusting the input parameters, significantly improving the applicability and promotion value of the solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0054] Figure 1 Schematic diagram of the screening method according to an embodiment of the present invention;

[0055] Figure 2 Schematic diagram of dynamic diffusion path reconstruction according to an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. It is also noted that, to provide a more detailed description, the following embodiments are best and preferred embodiments, and those skilled in the art may employ alternative methods for implementing certain known technologies. Furthermore, the accompanying drawings are intended only to provide a more detailed description of the embodiments and are not intended to limit the present invention.

[0057] like Figure 1-Figure 2 As shown, the method for screening desert windbreak and erosion-resistant plants used in photovoltaic arrays provided by the present invention comprises the following steps:

[0058] S1, based on the native plant database of desert areas, candidate plant species are screened according to their resistance to sand and dust, drought and salinity, and a first candidate plant list is generated;

[0059] S2, based on the target photovoltaic array area, uses a multispectral laser scattering sensor to collect dust characteristics, including kinetic energy intensity;

[0060] S3, based on measured wind field data and dust characteristics, combined with computational fluid dynamics simulation technology, reconstructs the dynamic diffusion path of wind and sand flow in the target photovoltaic array area;

[0061] S4, construct a three-dimensional interactive simulation model, input the dynamic diffusion path and the candidate plants in the first candidate plant list, simulate the protection effectiveness of the candidate plants under the dynamic diffusion path in the target photovoltaic array area, including wind speed attenuation rate and sand and dust deposition rate, evaluate the sand reduction effect of each candidate plant species in the first candidate plant list around the target photovoltaic array, and generate a second candidate plant list after screening, which is the final selected plant.

[0062] S1 specifically includes:

[0063] S11, extract the environmental adaptability data of each plant from the native plant database in the desert area, including sand and dust resistance, drought resistance and salt and alkali resistance

[0064] S12, Quantification of screening indicators: Quantitative indicators are set for dust resistance, drought resistance, and saline-alkali resistance, including:

[0065] Dust resistance: The resistance of plant stems and leaves to wind erosion and the self-cleaning ability of dust deposits are used as references;

[0066] Drought tolerance: Take the water absorption capacity of plant roots as a reference;

[0067] Salt-alkali tolerance: Take the survival rate and growth rate of plants in high salt-alkali environments as a reference;

[0068] Comprehensive score calculation: Assign weight values to the quantitative indicators of each plant, and use the weighted scoring algorithm to calculate the comprehensive adaptability score. The formula is as follows:

[0069] ,in, is the comprehensive adaptability score, normalized to [0,1], 、 、 are the quantitative scores of sand and dust resistance, drought resistance and saline-alkali resistance, 、 、 is the corresponding weight;

[0070] Screening rule setting: Screening rules are set according to the comprehensive adaptability score, and only plant species with a comprehensive adaptability score higher than the preset threshold of 0.6 are retained, that is, plant species with a comprehensive adaptability score of more than 60% are retained to generate the first candidate plant list.

[0071] Get (Dust resistance score) method:

[0072] (1) Test of resistance to wind and sand damage: Experimental method: simulate dust flow conditions in a wind tunnel test and measure the loss rate of plant stems and leaves;

[0073] Normalized calculation: ;

[0074] (2) Self-cleaning ability test: Experimental method: Determine the proportion of dust falling from the plant surface under natural environment or simulated rainfall conditions;

[0075] Normalized calculation: ;

[0076] (3) Calculation of comprehensive score: Normalize the scores of each indicator and take the average value: .

[0077] Get Method for drought tolerance scoring:

[0078] (1) Survival test: Experimental method: Record the number of days the plants survive in a low-water environment;

[0079] Normalized calculation: ;

[0080] (2) Water absorption capacity test: Experimental method: measuring the rate at which plant roots absorb water from the soil;

[0081] Normalized calculation: ;

[0082] (3) Calculation of comprehensive score: The mean is calculated in the same way: .

[0083] Get (Salt-alkali resistance score) method:

[0084] (1) Soil pH adaptation range test: Experimental method: Plants were planted in saline-alkali soils with different pH values and their growth status and survival rate were observed;

[0085] Normalized calculation: The width of the pH range to which plants can adapt refers to the difference between the lowest pH value and the highest pH value at which plants can survive and grow normally.

[0086] (2) Salt stress tolerance physiological performance test: Experimental method: measuring the relative growth rate (RGR) of plants or changes in leaf relative electrical conductivity (reflecting stress tolerance) under high salt concentration conditions;

[0087] Normalized calculation: ;

[0088] or: ;

[0089] (3) Calculation of comprehensive score: Take the average of the normalized scores: .

[0090] S2 specifically includes:

[0091] S21, Sensor Deployment and Calibration: Deploy multispectral laser scattering sensors in the target photovoltaic array area. The sensors are installed at a height of 1-2 meters above the ground to ensure they can capture the energy dynamics of actual wind-blown sand. The sensors are calibrated for sensitivity across the optical wavelength range (400-1000 nm) to accommodate the strong scattering characteristics of the dusty environment.

[0092] S22, wind and sand kinetic energy measurement: The density change of dust flow is detected by the light scattering intensity of the laser beam and dust particles, and the wind speed data is obtained in real time in combination with the wind speed sensor;

[0093] The sensor collects wind speed and kinetic energy intensity data at a set sampling frequency of 10 times per second. Based on the monitoring data, it generates a time-series change curve of the kinetic energy intensity distribution of dust in the target area, analyzes the high-intensity areas, transition areas and low-intensity areas of kinetic energy intensity, and provides a direct basis for reconstructing the dynamic diffusion path of dust flow in the photovoltaic array.

[0094] Kinetic energy intensity Calculated as: ,in, is the dust density (total mass of particles per unit volume), is the wind speed;

[0095] The multispectral laser scattering sensor is used to estimate the total number of dust particles by measuring the scattered light intensity of dust particles. ;

[0096] The sensor passes a certain volume (such as 1 cubic meter) of air sample and measures the intensity of the scattered signal ;

[0097] Use the scattering model (Mie scattering theory) to convert the scattered light intensity into the total number of particles ;

[0098] Assume that the average density of particles It is known that the dust density can be calculated by the following formula: ,in is the mass of a single particle, based on its average particle size and material density calculate: .

[0099] S3 specifically includes:

[0100] S31, wind field data collection: wind speed sensors are deployed in the target photovoltaic array area to measure wind speeds at different spatial locations ,wind direction , obtain the spatiotemporal distribution of wind speed , temporal and spatial distribution of wind direction , generate the initial wind field velocity field of the target area, and the initial wind field velocity field vector is expressed as: ,in, is the velocity field vector, and is the unit vector of the horizontal component; Indicates spatial location, Indicates time;

[0101] S32, input of dust kinetic energy intensity: input the kinetic energy intensity of dust per unit volume Converted into corresponding dust flow load , as input parameters for fluid dynamics simulation;

[0102] S33, Turbulence Modeling: Based on the wind field data and dust kinetic energy intensity of the target PV array area, a turbulence model is used to simulate the diffusion behavior of dust particles in the wind field. Boundary conditions (such as the geometry of the PV array equipment and the ground friction coefficient) are set to reconstruct the velocity and concentration fields of the dust flow.

[0103] S34, dynamic diffusion path simulation: Through computational fluid dynamics simulation of the dynamic diffusion path of dust particles in the wind field, the movement trajectory of dust in the wind field is reconstructed to generate a diffusion path map. The diffusion path includes the main direction of dust, high concentration area, sedimentation area and dust return flow area.

[0104] The computational fluid dynamics modeling in S33 is a macro-level calculation, focusing on the motion state of dust as a whole fluid; the continuity equation and momentum conservation equation describe the velocity field, pressure field, and density distribution of the airflow and dust flow in the entire flow field; the global flow characteristics of the dust diffusion path are output, defining the flow law of dust in the wind field and providing global background information for particle trajectory calculation.

[0105] The dynamic diffusion path calculation in S34 is a micro-level calculation based on the macroscopic flow field. It performs detailed modeling on the movement of a single or a group of dust particles, describes the movement of a single particle in the wind field, and takes into account the influence of specific forces such as resistance and gravity; it outputs the actual movement trajectory of the dust particles, showing how the particles are carried, settled, or refluxed in the flow field.

[0106] In other words, S33 provides a "global map" of dust diffusion (flow field and concentration distribution), and the dynamic diffusion path calculation is refined to particle behavior, describing how these particles move specifically along this "map".

[0107] Specifically, the logic of this part of dynamic diffusion path reconstruction is described as follows:

[0108] (1) First, the actual wind speed and direction data are collected in the target photovoltaic array area through wind speed sensors. The spatial and temporal distribution of these data constitute the initial wind field model. The initial model describes the overall dynamic characteristics of the wind field, captures the macroscopic characteristics of the wind field, and provides a basis for the next step of analysis.

[0109] (2) The kinetic energy intensity is further converted into the impact force of dust on unit area (i.e., dust flow load). This process takes into account how the kinetic energy of dust acts on the photovoltaic array or the ground. The dust flow load describes the direct physical effect of dust on the photovoltaic array, and its spatial and temporal distribution is used as the input boundary condition of the fluid dynamics model.

[0110] (3) Based on wind field data and dust flow loads, a turbulence model is used to simulate the diffusion behavior of dust flow in the wind field. The model describes the global dynamic characteristics of the wind and sand flow through the continuity equation and the momentum conservation equation. The output results include the velocity field, pressure field and concentration distribution of the dust. These results reflect the global movement law and spatial diffusion characteristics of the dust in the target photovoltaic array area.

[0111] (4) Further analyze the motion trajectory of individual dust particles, simulate and consider various forces such as resistance and gravity on the particles, and calculate the actual path of the particles in the wind field, including how the particles are carried, settled or refluxed. The dynamic diffusion path refines the microscopic characteristics of the dust flow and can clarify the movement behavior of particles at different positions. The results are visualized as a diffusion path map and an impact force distribution map.

[0112] Dust flow load is the impact force of dust kinetic energy on unit area, which is calculated as:

[0113] ,in, is the dust flow load, is the unit area, is the action time, The spatial and temporal distribution of dust is used as the external input boundary conditions for computational fluid dynamics simulation to represent the dynamic force of dust on the target photovoltaic array area.

[0114] The turbulence model in S33 calculates the diffusion range of dust in the wind field. The key equations of the turbulence model include:

[0115] Continuity equation (conservation of mass): ;

[0116] Momentum conservation equation (Navier-Stokes equation):

[0117] ;

[0118] in, is the velocity field vector, is the fluid density, It's a pressure field. is the fluid dynamic viscosity coefficient, is the dust flow load, Represents the gradient operator, which is used to describe the changes of velocity field and pressure field in space.

[0119] The motion trajectory of the reconstructed dust in the wind field in S34 is expressed as:

[0120] ,in, is the dust particle position vector, is the drag coefficient, is the relative velocity vector of the dust particles, which is the current velocity of the dust particles minus the initial wind velocity field get, is the acceleration due to gravity, is the cross-sectional area of the particle, is the air density.

[0121] The simulation results are output and visualized as a dust dispersion path map, showing the main flow direction, high concentration areas, sedimentation areas and backflow areas of the dust flow.

[0122] S4 specifically includes:

[0123] S41, model environment initialization: Based on the target photovoltaic array area, combined with actual terrain data and simulation results of dynamic diffusion paths, a three-dimensional spatial environment is constructed. The three-dimensional spatial environment includes surface height, photovoltaic array location and geometry, and the dynamic characteristics of the dust diffusion path (three-dimensional distribution of velocity field, dust concentration field, and kinetic energy intensity field).

[0124] S42, candidate plant attribute input: Extract the protective attributes of each plant from the first candidate plant list, including plant height, leaf area index, stem and leaf surface roughness, air permeability, and root stability. Input plant location and planting density as initial configuration parameters to simulate the interference effect of plants on wind speed and dust flow in the target photovoltaic array area.

[0125] S43, Protection Effectiveness Simulation: Based on the dynamic diffusion path, the velocity field and concentration field of the dust flow are superimposed on the plant attribute model to simulate the dynamic protection behavior of candidate plants in the target photovoltaic array area. The simulation results include:

[0126] Wind speed attenuation rate : By calculating the effect of plant groups on reducing local wind speed, the ability of plants to weaken the kinetic energy of dust flow is evaluated;

[0127] Dust deposition rate : Evaluate the ability of plants to reduce dust flow concentration through the spatial distribution of dust particles on plant surfaces and ground deposition;

[0128] S44, comprehensive effect evaluation: For each plant in the first candidate plant list, quantify its protective effectiveness in the target photovoltaic array area, generate a comprehensive effect evaluation including wind speed attenuation rate, dust deposition rate and vegetation coverage cost, obtain a comprehensive effect evaluation score based on the comprehensive effect evaluation, screen out candidate plants with excellent performance and generate a second candidate plant list.

[0129] Wind speed attenuation rate It represents the ability of plant groups to reduce local wind speed and is calculated as:

[0130] ,in, is the initial wind speed when the wind enters the plant protection zone, It is the remaining wind speed after the wind passes through the plant protection area. The wind speed is calculated by taking several measurement points in the plant area using the velocity field in the dynamic diffusion path. and The average value of

[0131] Dust deposition rate It represents the proportion of dust in the plant area that settles from the air flow to the ground or adheres to the plants, and is calculated as:

[0132] ,in, is the dust concentration per unit volume when entering the plant area, It is the dust concentration per unit volume after passing through the plant area. The dust concentration is derived from the concentration field model of the dynamic diffusion path. The average concentration is calculated at the inlet and outlet locations within the plant area.

[0133] The comprehensive effect assessment in S44 also considers the reduction in kinetic energy intensity of dust , which represents the weakening of kinetic energy intensity by plants, and the calculation formula is: ,in, is the kinetic energy intensity of dust when it enters the plant area, is the kinetic energy intensity of dust after passing through the plant area;

[0134] Comprehensive effect evaluation score Calculated as:

[0135] in, is the reduction in kinetic energy intensity, is the maximum dust kinetic energy reduction in the current candidate plant group, used for normalization. is the wind speed decay rate, is the dust deposition rate, is the cost of plant cultivation, expressed as the cost per unit area, is the weight coefficient of each evaluation element, reflecting the relative importance of wind speed attenuation, dust deposition, planting cost and kinetic energy intensity. , can be divided equally.

[0136] Sort the candidate plants from high to low according to the comprehensive effect evaluation score Sp, and according to the required final number of candidate plants Q, select the first Q plants with the highest comprehensive effect evaluation score to form the second candidate plant list, which is the final selected list.

[0137] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for screening desert windbreak and erosion-resistant plants used in photovoltaic arrays, characterized in that: The following steps are involved: S1, based on the native plant database of desert areas, candidate plant species are screened according to their resistance to sand and dust, drought and salinity, and a first candidate plant list is generated; S2, based on the target photovoltaic array area, uses a multispectral laser scattering sensor to collect dust characteristics, including kinetic energy intensity; S3, based on measured wind field data and dust characteristics, combined with computational fluid dynamics simulation technology, reconstructs the dynamic diffusion path of wind and sand flow in the target photovoltaic array area; S3 specifically includes: S31, wind field data collection: wind speed sensors are deployed in the target photovoltaic array area to measure wind speeds at different spatial locations ,wind direction , obtain the spatiotemporal distribution of wind speed , temporal and spatial distribution of wind direction , generate the initial wind field velocity field of the target photovoltaic array area, and the initial wind field velocity field vector is expressed as: ,in, is the velocity field vector, and is the unit vector of the horizontal component; Indicates spatial location, Indicates time, Indicates the spatial position Time is The wind speed at Indicates the spatial position Time is Wind direction at that time; S32, input of dust kinetic energy intensity: input the kinetic energy intensity of dust per unit volume Converted into corresponding dust flow load , as input parameters for fluid dynamics simulation; S33, Turbulence Modeling: Based on the wind field data and dust kinetic energy intensity of the target PV array area, a turbulence model is used to simulate the diffusion behavior of dust particles in the wind field, set boundary conditions, and reconstruct the velocity field and concentration field of the dust flow; S34, Dynamic Diffusion Path Simulation: The dynamic diffusion path of dust particles in the wind field is simulated by computational fluid dynamics, the movement trajectory of dust in the wind field is reconstructed, and the dynamic diffusion path of dust is generated. The dynamic diffusion path of dust includes the main dust direction, high concentration area, sedimentation area and dust return flow area; S4: Construct a three-dimensional interactive simulation model, input the dynamic diffusion path and the candidate plants from the first candidate plant list, simulate the protective effectiveness of the candidate plants under the dynamic diffusion path in the target photovoltaic array area, including wind speed attenuation rate and dust deposition rate, evaluate the sand reduction effect of each candidate plant species in the first candidate plant list around the target photovoltaic array, and generate a second candidate plant list after screening as the final selected plants; S4 specifically includes: S41, model environment initialization: Based on the target PV array area, combined with actual terrain data and simulation results of dynamic diffusion paths, a three-dimensional spatial environment is constructed. The three-dimensional spatial environment includes the ground surface height, PV array location and geometry, and dust diffusion paths; S42, candidate plant attribute input: Extract the protective attributes of each plant from the first candidate plant list, including plant height, leaf area index, stem and leaf surface roughness, air permeability, and root stability. Input plant location and planting density as initial configuration parameters to simulate the interference effect of plants on wind speed and dust flow in the target photovoltaic array area. S43, Protection Effectiveness Simulation: Based on the dynamic diffusion path, the velocity field and concentration field of the dust flow are superimposed on the plant attribute model to simulate the dynamic protection behavior of candidate plants in the target photovoltaic array area. The simulation results include: Wind speed attenuation rate : By calculating the effect of plant groups on reducing local wind speed, the ability of plants to weaken the kinetic energy of dust flow is evaluated; Dust deposition rate : Evaluate the ability of plants to reduce dust flow concentration through the spatial distribution of dust particles on plant surfaces and ground deposition; S44, comprehensive effect evaluation: For each plant in the first candidate plant list, quantify its protective effectiveness in the target photovoltaic array area, generate a comprehensive effect evaluation including wind speed attenuation rate, dust deposition rate and vegetation coverage cost, obtain a comprehensive effect evaluation score based on the comprehensive effect evaluation, sort the candidate plants from high to low according to the comprehensive effect evaluation score, and select the top Q plants with the highest comprehensive effect evaluation score based on the required final number of candidate plants Q to form the second candidate plant list.

2. The method for screening desert windbreak and erosion-resistant plants used in photovoltaic arrays according to claim 1, characterized in that: Said S1 specifically includes: S11, extract the environmental adaptability data of each plant from the native plant database in desert areas, including sand and dust tolerance, drought tolerance, and saline-alkali tolerance; S12, Quantification of screening indicators: Quantitative indicators are set for dust resistance, drought resistance, and saline-alkali resistance, including: Dust resistance: The resistance of plant stems and leaves to wind erosion and the self-cleaning ability of dust deposits are used as references; Drought tolerance: Take the water absorption capacity of plant roots as a reference; Salt-alkali tolerance: Take the survival rate and growth rate of plants in high salt-alkali environments as a reference; Comprehensive score calculation: Assign weight values to the quantitative indicators of each plant, and use the weighted scoring algorithm to calculate the comprehensive adaptability score. The formula is as follows: ,in, is the comprehensive adaptability score, normalized to [0,1], 、 、 are the quantitative scores of sand and dust resistance, drought resistance and saline-alkali resistance, 、 、 is the corresponding weight; Screening rule setting: Screening rules are set according to the comprehensive adaptability score, and only plant species with a comprehensive adaptability score higher than the preset threshold of 0.6 are retained to generate the first candidate plant list.

3. The method for screening desert windbreak and erosion-resistant plants for use in photovoltaic arrays according to claim 1, characterized in that: The S2 specifically includes: S21, sensor placement and calibration: Deploy multispectral laser scattering sensors in the target photovoltaic array area. The installation height of the multispectral laser scattering sensors is set to be within the range of 1m-2m from the ground. The sensitivity of the multispectral laser scattering sensors is calibrated within the optical wavelength range of 400nm-1000nm. S22, wind and sand kinetic energy measurement: The density change of dust flow is detected by the light scattering intensity of the laser beam and dust particles, and the wind speed data is obtained in real time in combination with the wind speed sensor; The kinetic energy intensity Calculated as: ,in, is the dust density, is the wind speed.

4. The method for screening desert windbreak and erosion-resistant plants for use in photovoltaic arrays according to claim 3, characterized in that: The dust flow load is the impact force of dust kinetic energy on unit area, which is calculated as: ,in, is the dust flow load, is the unit area, is the action time, The spatial and temporal distribution of dust is used as the external input boundary conditions for computational fluid dynamics simulation to represent the dynamic force of dust on the target photovoltaic array area.

5. The method for screening desert windbreak and erosion-resistant plants for use in photovoltaic arrays according to claim 4, characterized in that: The movement trajectory of the reconstructed dust in the wind field in S34 is expressed as: ,in, is the dust particle position vector, is the drag coefficient, is the relative velocity vector of dust particles, is the acceleration due to gravity, is the cross-sectional area of the particle, is the air density; The output result is a dust dispersion path, showing the main direction of the dust flow, high concentration areas, sedimentation areas and dust return areas.

6. The method for screening desert windbreak and erosion-resistant plants for use in photovoltaic arrays according to claim 5, characterized in that: The wind speed attenuation rate It represents the ability of plant groups to reduce local wind speed and is calculated as: ,in, is the initial wind speed when the wind enters the plant protection zone, is the remaining wind speed after the wind passes through the plant protection area; The dust deposition rate It represents the proportion of dust in the plant area that settles from the air flow to the ground or adheres to the plants, and is calculated as: ,in, is the dust concentration per unit volume when entering the plant area, It is the concentration of dust per unit volume after passing through the plant area.

7. The method for screening desert windbreak and erosion-resistant plants for use in photovoltaic arrays according to claim 6, characterized in that: The comprehensive effect evaluation in S44 also considers the reduction in kinetic energy intensity of dust , which represents the weakening of kinetic energy intensity by plants, and the calculation formula is: ,in, is the kinetic energy intensity of dust when it enters the plant area, is the kinetic energy intensity of dust after passing through the plant area; The comprehensive effect evaluation score Calculated as: ,in, is the reduction in kinetic energy intensity, is the maximum dust kinetic energy reduction in the current candidate plant group, used for normalization. is the wind speed decay rate, is the dust deposition rate, is the cost of plant cultivation, expressed as the cost per unit area, is the weight coefficient of each evaluation element.

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