Method for screening desert windproof anti-corrosion plants used in photovoltaic array

Abstract

The invention relates to the technical field of plant screening, in particular to a desert windproof anti-corrosion plant screening method used in a photovoltaic array, which comprises the following steps: screening candidate plant species according to sand and dust resistance, drought resistance and saline-alkaline resistance, and generating a first candidate plant list; a multispectral laser scattering sensor is used for collecting sand and dust characteristics, wherein the sand and dust characteristics comprise kinetic energy intensity; in combination with a computational fluid dynamics simulation technology, a dynamic diffusion path of the wind-sand flow in the target photovoltaic array area is reconstructed; and evaluating the sand reduction effect of each candidate plant variety around the photovoltaic array in the first candidate plant list, and screening to generate a second candidate plant list which is a final selected plant. According to the method, the comprehensive score is comprehensively calculated by quantifying indexes such as the wind speed attenuation rate, the sand and dust settling rate and the planting cost, comprehensive quantification and scientific optimization of the screening process are achieved, and the problems that in traditional screening, subjectivity is high, and quantitative basis is lacked are solved.

Description

Technical Field

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

[0002] With the rapid development of photovoltaic power generation technology, the deployment of large-scale photovoltaic power stations in desert areas has become an important way to meet energy demand. However, strong winds and high-concentration sand and dust in the desert environment pose significant challenges to the operation of photovoltaic arrays, specifically including the following problems: Sand and dust coverage and equipment loss: The flow of wind and sand causes a large amount of sand and dust to deposit on the surface of photovoltaic panels, reducing their photoelectric conversion efficiency. At the same time, the impact of sand and dust particles on the surface of equipment at high wind speeds causes abrasion and shortens the service life of the equipment.

[0003] Limitations of traditional protection means: Although currently widely used physical barriers (such as windbreak walls) and chemical sand-fixing agents can partially reduce wind and sand erosion, they have problems such as complex construction, high cost, and poor environmental friendliness. At the same time, these methods lack pertinence to the specific dynamic diffusion characteristics of sand and dust.

[0004] Insufficiency of ecological protection technology: Constructing an ecological barrier by planting desert windbreak plants is a sustainable solution. However, traditional plant screening methods mostly rely on empirical judgment and lack scientific evaluation criteria, and cannot accurately adapt to the dynamic characteristics of sand and dust in the target area and the ecological needs of plants, resulting in the protection efficiency being difficult to meet actual requirements. Summary of the Invention

[0005] The present invention provides a method for screening desert windbreak and erosion-resistant plants used in a photovoltaic array, aiming to evaluate the sand reduction effect and ecological adaptability of plants in the target photovoltaic array area, and ensure that the selected plant species have the best protection efficiency.

[0006] The method for screening desert windbreak and erosion-resistant plants used in a photovoltaic array includes the following steps: S1, based on the native plant database in the desert area, screen candidate plant species according to sand and dust tolerance, drought tolerance, and salt and alkali tolerance to generate a first candidate plant list; S2, based on the target photovoltaic array area, use a multi-spectral laser scattering sensor to collect sand and dust characteristics, and the sand and dust characteristics include kinetic energy intensity; S3, based on the measured wind field data and sand and dust characteristics, combined with computational fluid dynamics simulation technology, reconstruct the dynamic diffusion path of the wind and sand flow in the target photovoltaic array area; S4. Construct a three-dimensional interactive simulation model, input the dynamic diffusion path and the candidate plants in the first candidate plant list, and simulate the protection effectiveness of the candidate plants under the dynamic diffusion path in the target photovoltaic array area, including the wind speed attenuation rate and the dust settlement 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 used as the final selected plant.

[0007] Further, the specific steps of S1 are as follows: S11. Extract the environmental adaptability data of each plant from the native plant database in the desert area, including dust tolerance, drought tolerance, and salt and alkali tolerance. S12. Quantify the screening indicators: Set quantification indicators for dust tolerance, drought tolerance, and salt and alkali tolerance respectively, including: Dust tolerance: Refer to the wind erosion resistance ability of plant stems and leaves and the self-cleaning ability of dust deposition. Drought tolerance: Refer to the water absorption ability of plant roots. Salt and alkali tolerance: Refer to the survival rate and growth rate of plants in high salt and alkali environments. Calculate the comprehensive score: Assign weight values to the quantification indicators of each plant, and use the weighted scoring algorithm to calculate the comprehensive adaptability score. The formula is as follows: , where is the comprehensive adaptability score, normalized to [0, 1], , , are the quantification scores of dust tolerance, drought tolerance, and salt and alkali tolerance respectively, , , are the corresponding weights; Set the screening rule: Set the screening rule according to the comprehensive adaptability score, and only retain the plant species with a comprehensive adaptability score higher than the preset threshold of 0.6 to generate the first candidate plant list.

[0008] Further, the specific steps of S2 are as follows: S21. Sensor layout and calibration: Layout multi-spectral laser scattering sensors in the target photovoltaic array area. The installation height of the multi-spectral laser scattering sensors is set within the range of 1m - 2m from the ground, and calibrate the sensitivity of the multi-spectral laser scattering sensors in the optical wavelength range of 400nm - 1000nm. S22. Measure the wind and sand kinetic energy: Detect the density change of sand and dust flow through the light scattering intensity of the laser beam and sand and dust particles, and obtain the wind speed data in real time in combination with the wind speed sensor.

[0009] Further, the kinetic energy intensity is calculated as: , where is the dust density, and

[0010] Further, the S3 specifically includes: S31, wind field data collection: The wind speed sensors arranged in the target photovoltaic array area are used to measure the wind speed , wind direction at different spatial positions, and obtain the spatio-temporal distribution of the wind speed , the spatio-temporal distribution of the wind direction , and generate the initial wind field velocity field of the target photovoltaic array area. The initial wind field velocity field vector is expressed as: , where is the velocity field vector, and are the unit vectors of the horizontal components; represents the spatial position, represents the time, represents that the spatial position is , and the time is when the wind speed is ; represents that the spatial position is and the time is when the wind direction is ; S32, input of dust kinetic energy intensity: Convert the kinetic energy intensity of unit volume of dust into the corresponding dust flow load , which is used as the input parameter for fluid dynamics simulation;

[0011] S33, turbulence model modeling: Based on the wind field data and dust kinetic energy intensity of the target photovoltaic array area, use the turbulence model to simulate the diffusion behavior of dust particles in the wind field, set the boundary conditions, and reconstruct the velocity field and concentration field of the dust flow; S34, dynamic diffusion path simulation: Calculate the dynamic diffusion path of dust particles in the wind field through computational fluid dynamics, reconstruct the movement trajectory of dust in the wind field, and generate the dust diffusion path. The dust diffusion path includes the main flow direction, high-concentration area, settlement area and dust return area of the dust. where is the dust flow load, is the unit area, is the action time, and the spatial and temporal distribution of is used as the external input boundary condition for computational fluid dynamics simulation, representing the dynamic force of dust on the target photovoltaic array area.

[0012] Further, the movement trajectory of the reconstructed dust in the wind field in S34 is expressed as: , where is the position vector of the dust particle, is the drag coefficient, is the relative velocity vector of the dust particle, is the gravitational acceleration, is the cross-sectional area of the particle, is the air density; The result output is the dust diffusion path, showing the main flow direction, high-concentration area, sedimentation area, and dust backflow area of the dust flow.

[0013] Further, S4 specifically includes: S41, model environment initialization: Based on the target photovoltaic array area, combined with actual terrain data and the simulation results of the dynamic diffusion path, a three-dimensional space environment is constructed, which includes the surface height, the position and geometric shape of the photovoltaic array, and the dust diffusion path; S42, input of candidate plant attributes: Extract the protection attributes of each plant from the first candidate plant list, including plant height, leaf area index, surface roughness of stems and leaves, ventilation rate, and root stability, and input the plant position and planting density as initial configuration parameters to simulate the interference effect of the plants on the wind speed and dust flow in the target photovoltaic array area; S43, protection efficiency simulation: Based on the dynamic diffusion path, superimpose the velocity field and concentration field of the dust flow on the plant attribute model to simulate the dynamic protection behavior of the candidate plants in the target photovoltaic array area. The simulation results include: Wind speed attenuation rate : By calculating the effect of the plant population on reducing the local wind speed, evaluate the ability of the plants to weaken the kinetic energy of the dust flow; Dust sedimentation rate : By the spatial distribution of dust particles settling on the plant surface and the ground, evaluate the ability of the plants to reduce the concentration of the dust flow; S44, comprehensive effect evaluation: For each plant in the first candidate plant list, quantify its protection efficiency in the target photovoltaic array area, generate a comprehensive effect evaluation including the wind speed attenuation rate, dust sedimentation rate, and vegetation coverage cost, obtain a comprehensive effect evaluation score based on the comprehensive effect evaluation, screen out the candidate plants with a comprehensive effect evaluation score greater than the preset value, and generate a second candidate plant list.

[0014] Further, the wind speed attenuation rate represents the ability of the plant population to weaken the local wind speed and is calculated as: , where is the initial wind speed when the wind enters the plant protection area, is the remaining wind speed after the wind passes through the plant protection area; The dust settlement rate represents the proportion of dust settling from the air flow to the ground or adhering to plants within the plant area, and is calculated as: , where is the dust concentration per unit volume when entering the plant area, is the dust concentration per unit volume after passing through the plant area.

[0015] Furthermore, the comprehensive effect evaluation in S44 also considers the reduction amount of the kinetic energy intensity of dust , which represents the weakening of the kinetic energy intensity by the plants, and the calculation formula is: , where is the kinetic energy intensity of the dust when entering the plant area, is the kinetic energy intensity of the dust after passing through the plant area; The comprehensive effect evaluation score is calculated as:

[0016] where is the reduction amount of the kinetic energy intensity, is the maximum reduction amount of the dust kinetic energy in the current candidate plant group for normalization, is the wind speed attenuation rate, is the dust settlement rate, is the plant planting cost, with the unit being the cost value per unit area, is the weight coefficient of each evaluation element.

[0017] Advantages of the present invention: 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 quantifies the protection effectiveness (wind speed attenuation rate, dust settlement rate, kinetic energy reduction amount) of candidate plants. This screening method based on dynamic environment and fine calculation can accurately evaluate the sand reduction effect and ecological adaptability of plants in the target area, ensure that the selected plant species have the best protection effectiveness, and can significantly reduce the impact of wind and sand erosion on the photovoltaic array compared with the traditional experience-based screening method.

[0018] The technical solution of the present invention introduces the kinetic energy intensity of sand and dust as a core parameter, converts it into the sand and dust flow load, and inputs it into the three-dimensional interactive simulation model. Combined with the protection characteristics of candidate plants (such as height, leaf area index, ventilation rate, etc.), a dynamic simulation environment is established. By quantifying indicators such as wind speed attenuation rate, sand and dust settlement rate, and planting cost, a comprehensive score is calculated, realizing the comprehensive quantification and scientific optimization of the screening process, and avoiding the problems of strong subjectivity and lack of quantitative basis in traditional screening.

[0019] The technical solution of the present invention combines dynamic diffusion path simulation, plant ecological characteristic evaluation, and three-dimensional interactive model visualization technology to form a closed-loop process from environmental data collection, fine calculation to protection effect verification. Especially 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 sand and dust characteristics by adjusting input parameters, significantly improving the applicability and promotion value of the solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only those of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0021] Figure 1 It is a schematic flow chart of the screening method according to the embodiment of the present invention; Figure 2 It is a schematic diagram of the reconstruction of the dynamic diffusion path according to the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] The present invention will be described in detail below with reference to the drawings and specific embodiments. At the same time, it should be noted here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments. For some well-known technologies, those skilled in the art can also adopt other alternative methods for implementation; and the drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0023] As Figure 1 - Figure 2 shown, the method for screening desert windproof and erosion-resistant plants used in the photovoltaic array provided by the present invention includes the following steps: S1. Based on the native plant database in the desert area, screen candidate plant species according to sand and dust tolerance, drought tolerance, and salt and alkali tolerance to generate a first list of candidate plants; S2. Based on the target photovoltaic array area, use a multi-spectral laser scattering sensor to collect sand and dust characteristics, and the sand and dust characteristics include kinetic energy intensity; S3. Based on the measured wind field data and sand and dust characteristics, combined with computational fluid dynamics simulation technology, reconstruct the dynamic diffusion path of the wind-sand flow in the target photovoltaic array area; 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 efficiency of the candidate plants under the dynamic diffusion path in the target photovoltaic array area, including the wind speed attenuation rate and the sand and dust sedimentation 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.

[0024] S1 specifically includes: S11. Extract the environmental adaptability data of each plant from the native plant database in the desert area, including sand and dust tolerance, drought tolerance, and salt and alkali tolerance S12. Quantification of screening indicators: Set quantification indicators for sand and dust tolerance, drought tolerance, and salt and alkali tolerance respectively, including: Sand and dust tolerance: Refer to the wind erosion resistance ability of plant stems and leaves and the self-cleaning ability of sand and dust deposition; Drought tolerance: Refer to the water absorption ability of plant roots; Salt and alkali tolerance: Refer to the survival rate and growth rate of plants in high salt and alkali environments; Comprehensive score calculation: Assign weight values to the quantification indicators of each plant, and use the weighted scoring algorithm to calculate the comprehensive adaptability score. The formula is as follows: , where is the comprehensive adaptability score, normalized to [0, 1], , , are the quantification scores of sand and dust tolerance, drought tolerance, and salt and alkali tolerance respectively, , , are the corresponding weights; Screening rule setting: Set screening rules according to the comprehensive adaptability score, and only retain the plant species with a comprehensive adaptability score higher than the preset threshold of 0.6, that is, the plant species with a comprehensive adaptability score reaching more than 60% are retained, and a first candidate plant list is generated.

[0025] Obtain (the score of sand and dust tolerance) method: (1) Anti-wind and sand erosion ability test: Experimental method: Simulate the sand and dust flow conditions in the wind tunnel test and measure the loss rate of plant stems and leaves; Normalization calculation: ; (2) Self-cleaning ability test: Experimental method: Determine the proportion of sand and dust falling off the plant surface under natural environment or simulated rainfall conditions; Normalization calculation: ; (3)Composite score calculation: Normalize the scores of each index and then take the mean: .

[0026] Obtain (Drought tolerance score) method: (1)Survival ability test: Experimental method: Record the number of days the plant survives in a low-water environment; Normalization calculation: ; (2)Water absorption ability test: Experimental method: Measure the rate at which the plant's roots absorb water from the soil; Normalization calculation: ; (3)Composite score calculation: Also perform a mean calculation: .

[0027] Obtain (Alkaline-saline tolerance score) method: (1)Soil pH adaptation range test: Experimental method: Plant the plant in saline-alkali soil with different pH values and observe its growth status and survival rate; Normalization calculation: ; where the width of the pH range that the plant can adapt to refers to the difference between the lowest pH value and the highest pH value at which the plant can survive and grow normally; (2)Physiological performance test under salt stress: Experimental method: Measure the relative growth rate (RGR) of the plant or the change in relative leaf conductivity (reflecting stress tolerance) under high salt concentration conditions; Normalization calculation: ; Or: ; (3)Composite score calculation: Take the mean of the normalized scores: .

[0028] S2 specifically includes: S21, Sensor layout and calibration: Install multi-spectral laser scattering sensors in the target photovoltaic array area. The installation height of the multi-spectral laser scattering sensors is set within the range of 1m - 2m from the ground to ensure that the energy dynamics of the actual sand flow can be captured; Calibrate the sensitivity of the multi-spectral laser scattering sensors in the optical wavelength range of 400nm - 1000nm to adapt to the strong scattering characteristics of the dust environment; S22, Sand and wind kinetic energy measurement: Detect the density change of sand flow through the light scattering intensity of the laser beam and sand particles, and obtain the wind speed data in real time in combination with the wind speed sensor; 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, a time-series change curve of the kinetic energy intensity distribution of sand and dust in the target area is generated, and the high-intensity area, transition area, and low-intensity area of the kinetic energy intensity are analyzed, providing a direct basis for reconstructing the dynamic diffusion path of sand and dust flow in the photovoltaic array.

[0029] Kinetic energy intensity It is calculated as: , where is the sand and dust density (total mass of particles per unit volume), is the wind speed; Estimated using a multi-spectral laser scattering sensor, which can infer the total number of sand and dust particles by measuring the scattered light intensity of the sand and dust particles ; The sensor measures the scattered signal intensity through an air sample of a certain volume (such as 1 cubic meter) ; The scattered light intensity is converted into the total number of particles using a scattering model (Mie scattering theory) ; Assume the average density of the particles is known, and the sand and dust density is calculated by the following formula: , where is the mass of a single particle, calculated according to its average particle size and the material density : .

[0030] S3 specifically includes: S31, Wind field data collection: The wind speed sensors deployed in the target photovoltaic array area are used to measure the wind speed , wind direction at different spatial positions to obtain the spatial and temporal distribution of the wind speed , the spatial and temporal distribution of the wind direction , and generate the initial wind field velocity field of the target area. The initial wind field velocity field vector is expressed as: , where is the velocity field vector, and are the unit vectors of the horizontal components; represents the spatial position, represents the time; S32, Input of sand and dust kinetic energy intensity: Convert the kinetic energy intensity of sand and dust per unit volume into the corresponding sand and dust flow load , which is used as an input parameter for fluid dynamics simulation; S33, Turbulence model modeling: Based on the wind field data and sand dust kinetic energy intensity in the target photovoltaic array area, use the turbulence model to simulate the diffusion behavior of sand dust particles in the wind field, set boundary conditions (such as the geometric shape of the photovoltaic array device and the ground friction coefficient), and reconstruct the velocity field and concentration field of the sand dust flow; S34, Dynamic diffusion path simulation: Through computational fluid dynamics, simulate the dynamic diffusion path of sand dust particles in the wind field, reconstruct the movement trajectory of sand dust in the wind field, and generate a diffusion path map. The diffusion path includes the main flow direction of sand dust, high-concentration areas, sedimentation areas, and sand dust recirculation areas.

[0031] The computational fluid dynamics modeling in S33 is a macroscopic-level calculation, targeting the movement state of sand dust as a whole fluid; the continuity equation and momentum conservation equation describe the velocity field, pressure field, and density distribution of the air flow and sand dust flow in the entire flow field; the global flow characteristics of the sand dust diffusion path are output, defining the flow law of sand dust in the wind field and providing global background information for particle trajectory calculation.

[0032] The dynamic diffusion path calculation in S34 is a microscopic-level calculation based on the macroscopic flow field, finely modeling the movement of a single or a group of sand dust particles, describing the movement of a single particle in the wind field, and considering the influence of specific forces such as resistance and gravity; the actual movement trajectory of sand dust particles is output, showing how the particles are carried, sedimented, or recirculated in the flow field.

[0033] That is to say, S33 provides a "global map" of sand dust diffusion (flow field and concentration distribution), and the dynamic diffusion path calculation is refined to particle behavior, describing how these particles specifically move along this "map".

[0034] Specifically, the reconstruction logic of this part of the dynamic diffusion path is described as follows: (1) First, through the wind speed sensor, collect the actual wind speed and wind direction data in the target photovoltaic array area. The spatial and temporal distribution of these data constitutes the initial wind field model. The initial model describes the overall dynamic characteristics of the wind field, captures the macroscopic features of the wind field, and provides a basis for the next analysis.

[0035] (2) The kinetic energy intensity is further converted into the impact force of sand dust on the unit area (i.e., sand dust flow load). This process considers how the kinetic energy of sand dust acts on the photovoltaic array or the ground. The sand dust flow load describes the direct physical action of sand dust on the photovoltaic array, and its spatial and temporal distribution is used as the input boundary condition of the fluid dynamics model.

[0036] (3) Based on wind field data and dust flow loads, use a turbulence model to simulate the diffusion behavior of dust flow in the wind field. The model describes the global dynamic characteristics of the aeolian 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, which reflect the global movement law and spatial diffusion characteristics of the dust in the target photovoltaic array area.

[0037] (4) Further analyze the movement trajectory of a single dust particle. The simulation considers various forces such as the drag force and gravity acting on the particle, and calculates the actual path of the particle in the wind field, including how the particle is carried, settled, or refluxed. The dynamic diffusion path refines the microscopic characteristics of the dust flow and can clarify the movement behavior of the particle at different positions. The results are visualized as a diffusion path map and an impact force distribution map.

[0038] Dust flow load is the flow impact force of the dust kinetic energy on a unit area, calculated as: , where is the dust flow load, is the unit area, is the action time, and the spatial and time distributions are used as the external input boundary conditions for the computational fluid dynamics simulation, representing the dynamic force of the dust on the target photovoltaic array area.

[0039] The turbulence model in S33 calculates the diffusion range of the dust in the wind field. The key equations of the turbulence model include: Continuity equation (mass conservation): ; Momentum conservation equation (Navier - Stokes equation): ; where is the velocity field vector, is the fluid density, is the 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 the velocity field and pressure field in space.

[0040] The movement trajectory of the reconstructed dust in the wind field in S34 is expressed as: , where is the dust particle position vector, is the drag coefficient, is the relative velocity vector of the dust particle, obtained by subtracting the initial wind field velocity field from the current velocity of the dust particle, is the acceleration due to gravity, is the cross-sectional area of the particle, is the air density.

[0041] The simulation results are visualized as a sand-dust diffusion path map, showing the main flow direction, high-concentration areas, sedimentation areas, and recirculation areas of the sand-dust flow.

[0042] S4 specifically includes: S41, model environment initialization: Based on the target photovoltaic array area, combined with actual terrain data and the simulation results of the dynamic diffusion path, a three-dimensional spatial environment is constructed. The three-dimensional spatial environment includes the surface height, the position and geometric shape of the photovoltaic array, and the dynamic characteristics of the sand-dust diffusion path (the three-dimensional distribution of the velocity field, sand-dust concentration field, and kinetic energy intensity field).

[0043] S42, candidate plant attribute input: Extract the protection attributes of each plant from the first candidate plant list, including plant height, leaf area index, stem and leaf surface roughness, ventilation rate, and root stability. Input the plant position and planting density as initial configuration parameters to simulate the interference effect of the plants on the wind speed and sand-dust flow in the target photovoltaic array area; S43, protection efficiency simulation: Based on the dynamic diffusion path, superimpose the velocity field and concentration field of the sand-dust flow on the plant attribute model to simulate the dynamic protection behavior of the candidate plants in the target photovoltaic array area. The simulation results include: Wind speed attenuation rate : Evaluate the ability of the plants to weaken the kinetic energy of the sand-dust flow by calculating the effect of the plant population on reducing the local wind speed; Sand-dust sedimentation rate : Evaluate the ability of the plants to reduce the concentration of the sand-dust flow by the spatial distribution of the sand-dust particles settling on the plant surface and the ground; S44, comprehensive effect evaluation: For each plant in the first candidate plant list, quantify its protection efficiency in the target photovoltaic array area, generate a comprehensive effect evaluation including the wind speed attenuation rate, sand-dust sedimentation rate, and vegetation coverage cost. Obtain the comprehensive effect evaluation score based on the comprehensive effect evaluation, screen out the candidate plants with excellent performance, and generate the second candidate plant list.

[0044] Wind speed attenuation rate Represents the ability of the plant population to weaken the local wind speed, calculated as: , where is the initial wind speed when the wind enters the plant protection area, is the remaining wind speed after the wind passes through the plant protection area; The wind speed uses the velocity field in the dynamic diffusion path, and several measurement points are taken within the plant area to calculate and average value; Dust sedimentation rate It represents the proportion of dust sedimenting from the air current to the ground or adhering to plants within the plant area, and is calculated as: , where is the dust concentration per unit volume when entering the plant area, 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, and the average concentration is calculated by selecting the inlet and outlet positions within the plant area respectively; The comprehensive effect evaluation in S44 also considers the reduction amount of the kinetic energy intensity of dust , which represents the weakening of the kinetic energy intensity by plants, and the calculation formula is: , where is the kinetic energy intensity when dust enters the plant area, is the kinetic energy intensity after dust passes through the plant area; Comprehensive effect evaluation score is calculated as:

[0045] where is the reduction amount of kinetic energy intensity, is the maximum reduction amount of dust kinetic energy in the current candidate plant group for normalization, is the wind speed attenuation rate, is the dust sedimentation rate, is the plant planting cost, with the unit being the cost value per unit area, are the weight coefficients of each evaluation element, respectively reflecting the relative importance of wind speed attenuation, dust sedimentation, planting cost, and kinetic energy intensity, , and they can be evenly divided.

[0046] Sort the candidate plants in descending order according to the comprehensive effect evaluation score Sp. According to the required final number of candidate plants Q, select the top Q plants with the highest comprehensive effect evaluation scores to form the second candidate plant list, which is the finally selected list.

[0047] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for screening desert wind and erosion resistant plants used in photovoltaic arrays, characterized in that: The following steps are involved: S1, based on the native plant database in desert areas, candidate plant species are screened according to sand and dust resistance, drought resistance and salt and alkali resistance to generate the first candidate plant list; S2, based on the target photovoltaic array area, uses a multi-spectral 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, reconstruct the dynamic diffusion path of wind and sand flow in the target photovoltaic array area; 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 PV array area, including the wind speed attenuation rate and the dust deposition rate, evaluate the sand reduction effect of each candidate plant species in the first candidate plant list around the target PV array, and generate the second candidate plant list after screening as the final selected plants.

2. The method for screening desert wind and erosion resistant plants used in photovoltaic arrays according to claim 1, characterized in that: The S1 specifically includes: S11, extract the environmental adaptability data of each plant from the native plant database in desert areas, including resistance to sand and dust, drought and salt and alkali; S12, Quantification of screening indicators: Quantitative indicators are set for dust resistance, drought resistance and salinity resistance, including: Dust resistance: Take the wind erosion resistance of plant stems and leaves and the self-cleaning ability of dust deposition as reference; Drought tolerance: based on the water absorption capacity of plant roots; Salt-alkali tolerance: Take the survival rate and growth rate of plants in high salt-alkali environment as a reference; Comprehensive score calculation: Assign a weight value to the quantitative index 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], , , They are the quantitative scores of sand and dust resistance, drought resistance and salt and 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 wind and erosion resistant plants used in photovoltaic arrays according to claim 1, characterized in that: The S2 specifically includes: S21, sensor placement and calibration: a multi-spectral laser scattering sensor is placed in the target photovoltaic array area. The installation height of the multi-spectral laser scattering sensor is set within the range of 1m-2m from the ground. The sensitivity of the multi-spectral laser scattering sensor is calibrated within the optical wavelength range of 400nm-1000nm. S22, measurement of wind and sand kinetic energy: 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 wind and erosion resistant plants used in photovoltaic arrays according to claim 1, characterized in that: The 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 , the 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 the spatial position, Indicates time, Indicates spatial location , time is The wind speed at Represents the spatial position , time is Wind direction at the time; S32, input of kinetic energy intensity of dust: the kinetic energy intensity of dust per unit volume Converted into corresponding dust flow load , as input parameters for fluid dynamics simulation; S33, turbulence model modeling: Based on the wind field data and dust kinetic energy intensity of the target photovoltaic array area, the 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 dust diffusion path is generated. The dust diffusion path includes the main flow direction of dust, high concentration area, sedimentation area and dust reflow area.

5. The method for screening desert wind and erosion resistant plants used in photovoltaic arrays according to claim 4, characterized in that: The dust flow load is the flow impact force of dust kinetic energy on unit area, 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 simulations to represent the dynamic force of dust on the target PV array area.

6. The method for screening desert wind and erosion resistant plants used in photovoltaic arrays according to claim 5, 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 dust particle relative velocity vector, is the acceleration due to gravity, is the particle cross-sectional area, is the air density; The result output is the dust dispersion path, showing the main direction of the dust flow, high concentration areas, sedimentation areas and dust return areas.

7. The method for screening desert wind and erosion resistant plants used in photovoltaic arrays according to claim 6, characterized in that: The S4 specifically includes: S41, model environment initialization: based on the target photovoltaic array area, combined with the actual terrain data and the simulation results of the dynamic diffusion path, a three-dimensional space environment is constructed. The three-dimensional space environment includes the ground height, the location and geometry of the photovoltaic array, and the dust diffusion path; S42, candidate plant attribute input: extract the protection 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 position and planting density as initial configuration parameters, and 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 protection 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 according to the required final number of candidate plants Q to form the second candidate plant list.

8. The method for screening desert wind and erosion resistant plants used in photovoltaic arrays according to claim 7, characterized in that: The wind speed attenuation rate It represents the ability of plant population to weaken 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, calculated as: ,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.

9. The method for screening desert wind and erosion resistant plants used in photovoltaic arrays according to claim 8, characterized in that: The comprehensive effect evaluation in S44 also considers the reduction in kinetic energy intensity of dust , which indicates 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 plant planting cost, expressed as the cost per unit area, is the weight coefficient of each evaluation element.

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