Vegetation planting priority recommendation method in photovoltaic desertification control environment

Abstract

The invention belongs to the technical field of vegetation planting in a photovoltaic desertification control environment, and discloses a vegetation planting priority recommendation method in the photovoltaic desertification control environment. According to the method, the photovoltaic shielding influence and ecological restoration urgency are comprehensively considered, and the area which does not influence the generating capacity and is urgently needed to be restored can be accurately screened out during secondary reseeding. The problem that the power generation efficiency is reduced or ecological restoration is not timely due to improper region selection during secondary reseeding is solved, and the dual goals of photovoltaic and desertification control are effectively balanced. And on the basis of micro-environment feature clustering, region types are accurately divided, and an accurate environment adaptation basis is provided for plant selection during secondary replanting. The plant survival rate is greatly improved, and the ecological effect of secondary reseeding is ensured. By combining the growth performance, the economic cost and the sand stabilization benefits, the optimal plant species are matched for different region types during secondary replanting. Considering the change of the regional environment during secondary replanting, the recommended plant species are more adaptive, and the desertification control efficiency is significantly improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of photovoltaic sand control environment vegetation planting, and relates to a vegetation planting priority recommendation method in a photovoltaic sand control environment. BACKGROUND

[0002] In the field of photovoltaic sand control, the synergistic optimization of photovoltaic power generation and vegetation planting is a core issue. Vegetation not only can control and improve ecology, but also its growth state will affect the photovoltaic efficiency. Too high and too dense may block the components, and insufficient coverage will exacerbate the sandification. While the secondary planting as a key link to maintain vegetation coverage and consolidate sand control results, often due to lack of scientific guidance, leading to repeated planting, resource waste and other problems. At present, the secondary planting mostly relies on artificial experience or simple indicators, which is difficult to adapt to the complex environment of the site, cannot balance the demand of photovoltaic and sand control, and affects the overall benefit.

[0003] At present, the vegetation planting in the photovoltaic sand control project mostly relies on artificial experience or simple coverage index for decision-making, lacks precise analysis of the complex microenvironment of the photovoltaic site, and is also difficult to balance the dual demands of photovoltaic power generation and ecological sand control, resulting in unreasonable selection of the planting area, low matching degree of plant species and other problems, which affects the overall benefit of photovoltaic sand control.

[0004] The prior art scheme does not consider the dynamic influence of the area on photovoltaic power generation and the urgency of ecological restoration when the secondary planting is carried out. This may make the area which is preferentially planted block the photovoltaic components after the secondary planting, reduce the power generation, or miss the area which is in urgent need of secondary restoration due to the exacerbation of sandification, resulting in repeated sand control effect.

[0005] The prior art scheme does not precisely divide different microenvironments in the photovoltaic site, such as under the photovoltaic panel, gap, edge and the like, and ignores the influence of the differences of environmental factors such as light and soil on the plant growth when the secondary planting is carried out. This makes it still follow the unified planting standard when the secondary planting is carried out, and the plants are difficult to adapt to the specific regional environment, resulting in low survival rate.

[0006] The prior art scheme lacks the adaptability analysis of the plants and the regional environment when the secondary planting is carried out, and only selects the plants according to the past experience. Since the regional environment may have changed when the secondary planting is carried out, the original plant species may no longer be suitable, resulting in poor plant growth state after the planting, and difficult to play a sustained ecological sand control role. SUMMARY

[0007] In view of this, in order to solve the problems raised in the background art, a vegetation planting priority recommendation method in a photovoltaic sand control environment is provided.

[0008] The object of the present application can be achieved by the following technical solution: A vegetation planting priority recommendation method in a photovoltaic sand control environment, comprising: supplementing area identification, obtaining an initial area set to be supplemented in a photovoltaic field area, performing comprehensive scoring on each area to be supplemented in the initial area set, and outputting a priority sequence of the area to be supplemented based on the comprehensive score.

[0009] Region type division, collecting environmental parameters of each region in the photovoltaic field area, dividing the photovoltaic field area into several region types with different environmental characteristics, and constructing a region type-species suitability mapping relationship.

[0010] Supplementing plant screening, identifying the region type of each area to be supplemented in the priority sequence of the area to be supplemented, and obtaining a candidate plant species list matched with the region type of the area to be supplemented based on the region-species suitability mapping relationship.

[0011] Plant species analysis, screening other planted areas belonging to the same region type as the area to be supplemented, obtaining historical growth state data of each candidate plant species, calculating the growth performance score of each candidate plant species, and outputting the recommended supplement plant species priority sequence of each area to be supplemented according to the growth performance score.

[0012] Compared with the prior art, the present application has the following advantages: (1) The present application considers the photovoltaic shading effect and the urgency of ecological restoration, and can accurately select the area that does not affect the power generation capacity and is in urgent need of restoration during secondary planting. Avoids the problem of reduced power generation efficiency or delayed ecological restoration caused by improper selection of the area during secondary planting, effectively balances the dual goals of photovoltaic and sand control, and improves the pertinence of secondary planting.

[0013] (2) The present application is based on microenvironment feature clustering, accurately divides region types, and provides accurate environmental adaptation basis for plant selection during secondary planting. The plants of secondary planting can better adapt to the conditions of light, soil, etc. in specific regions, greatly improve the plant survival rate, and ensure the ecological effect of secondary planting.

[0014] (3) The present application matches the most suitable plant species for different region types during secondary planting by combining growth performance, economic cost and sand fixation benefit. Considering the change of region environment during secondary planting, the recommended plant species is more adaptive, which not only adapts to the environment but also has practical value, and significantly improves the sand control efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the description of the embodiments, obviously, the drawings in the following description are only some of the embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0016] Figure 1 The schematic diagram for implementing the method steps of the present application is shown. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present application will be clearly and completely described in the following with reference to the drawings in the embodiments of the present application, obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0018] Please refer to Figure 1 As shown in the drawings, the present application provides a vegetation planting priority recommendation method in a photovoltaic sand control environment, comprising: supplementing area identification, obtaining an initial area set to be supplemented in a photovoltaic field area, performing comprehensive scoring on each area to be supplemented in the initial area set, and outputting a priority sequence of the area to be supplemented based on the comprehensive scoring.

[0019] In a preferred embodiment of the present application, the specific way of obtaining the initial area set to be supplemented in the photovoltaic field area is as follows: using remote sensing image data and ground sensor data to identify the vegetation coverage in the photovoltaic field area.

[0020] The photovoltaic field area is subjected to grid processing to obtain a plurality of grid units.

[0021] The average vegetation coverage in each grid unit is calculated, and the grid unit with an average vegetation coverage lower than a preset lower coverage threshold is determined as an initial area to be supplemented, forming the initial area set.

[0022] It should be noted that after the photovoltaic field area is subjected to grid processing, for each grid unit, the average vegetation coverage in the unit is calculated in combination with the obtained remote sensing image data and ground sensor data. For the remote sensing image data, the vegetation coverage distribution in the grid unit is inversed through the vegetation index, and then the average value in the unit is calculated through a spatial statistical method. For the ground sensor data, if a sensor is arranged in the grid unit, the vegetation coverage data collected by the sensor is directly used for average calculation to improve the local precision. Finally, the average vegetation coverage of each grid unit is obtained by comprehensively using the two types of data, which quantitatively reflects the overall vegetation coverage condition of the unit.

[0023] It should be noted that the remote sensing image data includes high-resolution visible light images and multispectral images, and the ground sensor data includes data collected by soil moisture sensors and temperature sensors. The calculation of the average vegetation coverage is specifically to count the number of pixel points representing vegetation in the grid cell and calculate the ratio of the number of pixel points representing vegetation to the total number of pixel points in the grid cell.

[0024] It should be noted that the specific value of the coverage lower threshold needs to be determined in combination with the actual situation of the photovoltaic field area, and the following factors are usually referred to: 1. The degree of desertification in the area where the photovoltaic power station is located, such as a severely desertified area, the threshold value can be appropriately increased to strengthen the sand-fixing effect of vegetation. 2. Ecological sand control target, such as whether the target is to curb the expansion of desertification or to restore ecological diversity, the threshold value requirement is different. 3. The potential impact of vegetation on photovoltaic power generation needs to balance the vegetation coverage and the light demand of photovoltaic modules to avoid excessive coverage from blocking photovoltaic panels.

[0025] In a preferred embodiment of the present application, the specific way of comprehensively scoring each to-be-reseeded region in the initial region set is as follows: for any to-be-reseeded region, based on its spatial position and the relative relationship with the photovoltaic array, by analyzing the length, range and other characteristics of the region blocked by the photovoltaic module, the potential impact of the region on photovoltaic power generation after reseeding is evaluated, and then the photovoltaic shading impact factor is calculated.

[0026] The core of the process of analyzing the shading degree to evaluate the potential impact on photovoltaic power generation is to quantify the shading risk of the vegetation growth in the to-be-reseeded region to the light of the photovoltaic module, the lower the shading risk, that is, the smaller the photovoltaic shading impact factor, the more suitable the region for priority reseeding, so as to balance ecological restoration and photovoltaic power generation efficiency.

[0027] For the same to-be-reseeded region, the ecological restoration urgency factor is calculated based on its soil type, slope data and wind erosion modulus.

[0028] In one embodiment, the specific calculation method of the ecological restoration urgency factor is as follows: collect the soil type information, slope data and wind erosion modulus of the to-be-reseeded region. For soil type, according to its wind erosion resistance, water and fertilizer retention and other characteristics, a corresponding quantitative score is given, such as sandy soil with poor wind erosion resistance, higher score, indicating more urgent need for restoration. For slope data, grades are divided according to slope size and quantified, such as the larger the slope, the higher the corresponding score, because steep slopes are more prone to soil erosion. For wind erosion modulus, it is directly normalized based on its value, the larger the wind erosion modulus, the more serious the desertification, and the higher the quantitative score.

[0029] The quantified scores of the soil type, the slope, and the wind erosion modulus are weighted and summed according to preset weights to obtain an ecological restoration urgency factor of the to-be-reseeded region. The higher the score is, the stronger the ecological vulnerability of the region is, and the more urgent the reseeding is needed to restore the region to curb the expansion of desertification and improve the soil stability.

[0030] It should be noted that the setting of the weight factors corresponding to the soil type, the slope, and the wind erosion modulus is based on the operation strategy of the photovoltaic power station. The core sand control needs of the field area, such as increasing the weight of the wind erosion modulus if the wind erosion is dominant, the regional environmental characteristic differences, such as increasing the weight of the slope if the terrain is complex, the phased target of ecological restoration, and the historical data of similar projects, are combined to accurately reflect the ecological vulnerability of the region and support the decision of priority restoration.

[0031] The photovoltaic shading influence factor and the ecological restoration urgency factor are weighted and summed to obtain a comprehensive score of the to-be-reseeded region.

[0032] It should be noted that the setting of the above-mentioned weights is as follows: the weight coefficient of the photovoltaic influence and the weight coefficient of the ecological contribution are included, and can be dynamically adjusted according to the operation strategy of the photovoltaic power station. For example, if the operation of the power station focuses more on guaranteeing the photovoltaic power generation efficiency, the weight coefficient of the photovoltaic influence can be increased. If more emphasis is placed on promoting ecological sand control and restoring the ecology of the region, the weight coefficient of the ecological contribution can be increased. Through this setting, the dual needs of photovoltaic power generation and ecological sand control are balanced, and the comprehensive score is more in line with the actual operation target.

[0033] The sum of the weight coefficient of the photovoltaic influence and the weight coefficient of the ecological contribution is 1, and can be dynamically adjusted according to the operation strategy of the photovoltaic power station.

[0034] In a preferred embodiment of the present application, the specific calculation method of the photovoltaic shading influence factor is as follows: the geographic coordinates and elevation information of the to-be-reseeded region are obtained.

[0035] The accurate spatial position, size, and inclination angle parameters of each photovoltaic component in the photovoltaic field area are obtained, and a three-dimensional model of the photovoltaic array is constructed.

[0036] Based on the sun trajectory model, the altitude angle and azimuth angle of the sun at different times in a preset time period are simulated.

[0037] For each time, it is determined whether the to-be-reseeded region is located in the shadow area formed on the ground by any photovoltaic component.

[0038] The total length of time that the to-be-reseeded region is in the shadow state in the preset time period is counted.

[0039] The total length of time is compared with the total length of the preset time period to obtain a basic shading rate.

[0040] The base shading rate is normalized with a preset reference shading rate to obtain the photovoltaic shading influence factor.

[0041] It should be noted that the setting of the reference shading rate needs to be combined with the actual situation of the photovoltaic field area, and is usually based on the following basis: 1. The design standard of the photovoltaic power station, such as the maximum shadow shading ratio allowed by the photovoltaic module layout.

[0042] 2. The shading rate threshold in the historical monitoring data of the field area which has a significant negative impact on power generation.

[0043] 3. The maximum shading risk value verified in similar photovoltaic projects.

[0044] It should be noted that the longer the total length of the area to be reseeded is shaded by the photovoltaic module, the lower the potential shading risk of the vegetation growth to the photovoltaic power generation, and therefore the smaller the photovoltaic shading influence factor and the higher the reseeding priority.

[0045] It should be noted that the present application comprehensively considers the photovoltaic shading influence and the urgency of ecological restoration, and can accurately select the area which does not affect the power generation and urgently needs to be restored during the secondary reseeding. Avoid the problem of power generation efficiency reduction or ecological restoration not in time caused by improper selection of the area during the secondary reseeding, effectively balance the dual goals of photovoltaic and sand control, and improve the pertinence of the secondary reseeding.

[0046] The area type is divided, the environmental parameters of each area in the photovoltaic field area are collected, the photovoltaic field area is divided into a plurality of area types with different environmental characteristics, and a region type-species suitability mapping relationship is constructed.

[0047] In a preferred embodiment of the present application, the specific way of dividing the photovoltaic field area into a plurality of area types with different environmental characteristics is as follows: obtaining the high-precision digital elevation model and digital surface model of the photovoltaic field area, identifying the spatial position of the photovoltaic panel by comparing the elevation data of the two types of models, identifying the area below the photovoltaic panel, the gap area between the photovoltaic panels, the edge area of the photovoltaic array and the area beside the field road.

[0048] It should be noted that the area below the photovoltaic panel, the gap area between the photovoltaic panels, the edge area of the photovoltaic array and the area beside the field road are identified because the microenvironment of these areas has significant differences, which directly affects the vegetation growth state and the synergistic benefit of photovoltaic sand control. The specific reasons are as follows: the area is affected by factors such as photovoltaic module shading, human activity interference, and has obvious differences in environmental parameters such as light intensity, soil temperature, soil humidity, and soil nutrients. For example, the area below the photovoltaic panel has weak light and relatively low soil temperature; the gap area between the photovoltaic panels has more sufficient light; the edge area of the photovoltaic array may be more affected by external wind and sand; and the area beside the field road may have soil compaction and nutrient loss due to vehicle traffic.

[0049] By accurately identifying these areas, the spatial range for subsequent collection of environmental parameters of each area can be provided, and then through cluster analysis, area types with different environmental characteristics are divided, laying a foundation for constructing the area type-species suitability mapping relationship, ensuring that the subsequent screened replacement plants can adapt to the microenvironment of a specific area, improving the vegetation survival rate and sand control effect, and at the same time avoiding the adverse effects of vegetation growth on photovoltaic power generation.

[0050] Collecting historical period data of light intensity, soil temperature, soil humidity and soil nutrient content of each area.

[0051] For light intensity, the cumulative light intensity and light duration of each area in a typical sunshine period are calculated.

[0052] For soil temperature and humidity, the daily average temperature and humidity, temperature and humidity peak and daily temperature difference of each area are calculated.

[0053] For soil nutrients, the contents of key elements such as nitrogen, phosphorus and potassium are measured.

[0054] The cumulative light intensity, daily average temperature and humidity, key element content, etc. are taken as the area characteristic vector.

[0055] A predetermined clustering algorithm is used to perform cluster analysis on the characteristic vectors of all areas in the photovoltaic field area, and areas with similar characteristic vectors are divided into the same category, and each category is a region type, which reflects a specific microenvironment combination.

[0056] In a preferred embodiment of the present application, the specific method for constructing the area type-species suitability mapping relationship is as follows: a plant attribute database containing candidate sand control plants is established, which records the shade tolerance grade, drought tolerance grade, root sand fixation ability grade and nutrient demand characteristics of the plants.

[0057] It should be noted that the shade tolerance grade quantifies the adaptability of plants to light intensity, reflecting the growth potential of plants in the area blocked by photovoltaic components; the drought tolerance grade measures the survival ability of plants in a dry environment, adapting to areas with different soil humidity conditions; the root sand fixation ability grade evaluates the fixation of plant roots to soil, which is directly related to the ecological sand control effect; and the nutrient demand characteristics record the demand intensity of plants for key nutrients such as nitrogen, phosphorus and potassium in the soil, which are used to match areas with different soil nutrient contents.

[0058] An area type environmental characteristic database is established to record the environmental characteristic parameters of each area type, including light conditions, water conditions and soil nutrient conditions.

[0059] The light matching score is calculated by matching the shade tolerance level of each plant in the plant attribute database with the light condition in each region type database.

[0060] In one embodiment, the specific analysis method of the light matching score is as follows: 1. Quantifying the shade tolerance level of the plant: the shade tolerance level recorded in the plant attribute database includes high shade tolerance, medium shade tolerance, and low shade tolerance, wherein high shade tolerance corresponds to a value of 3, medium shade tolerance corresponds to a value of 2, and low shade tolerance corresponds to a value of 1.

[0061] 2. Quantifying the light condition of the region type: the light condition recorded in the region type database is also quantified into a value, wherein a low-light region corresponds to a value of 3, a medium-light region corresponds to a value of 2, and a high-light region corresponds to a value of 1.

[0062] 3. Calculating the matching degree by matching the shade tolerance level of each plant in the plant attribute database with the light condition in each region type database, specifically, if the corresponding values of the plant shade tolerance level and the region light condition are equal, the matching score is 10 points; if the corresponding values of the plant shade tolerance level and the region light condition differ by 1, the matching score is 7 points; if the corresponding values of the plant shade tolerance level and the region light condition differ by 2, the matching score is 3 points.

[0063] The water matching score is calculated by matching the drought tolerance level of each plant with the water condition in each region type database.

[0064] The nutrient matching score is calculated by matching the nutrient demand characteristics of each plant with the soil nutrient condition in each region type database.

[0065] The light matching score, the water matching score, and the nutrient matching score are weighted and summed to obtain the comprehensive suitability score of each plant and the region type.

[0066] If the comprehensive suitability score of a certain plant and a certain region type is greater than a preset suitability threshold, the plant is determined to be a suitable supplemental plant species for the region type, and the corresponding relationship is stored in the region-species suitability mapping relationship.

[0067] It should be noted that the suitability threshold is set according to multiple factors: first, it is necessary to ensure that the plant can survive in the target region and play a basic ecological function of sand fixation, and reference is made to the growth data of plants in similar regions to ensure that the score of the plant can complete the life cycle; second, it is necessary to combine the core environmental pressure of the region, such as the requirement for water adaptation in arid regions; it is also necessary to consider the coordination of sand control and photovoltaic operation, and to exclude plants that may affect the photovoltaic system; at the same time, the feasibility and cost of supplemental planting are considered, and species with high cost performance are preferred. The threshold needs to be dynamically adjusted according to the preliminary planting data and actual needs, balancing adaptability, functionality, and operability.

[0068] It should be noted that the present application is based on microenvironment feature clustering, accurate division of regional type, and accurate environmental adaptation basis for plant selection in secondary reseeding. The plants reseeded can better adapt to the light, soil and other conditions of a specific area, greatly improve the survival rate of plants, and ensure the ecological effect of secondary reseeding.

[0069] Replanting plant screening, identifying the regional type of each to-be-reseeded region in the priority sequence of the to-be-reseeded region, and obtaining a candidate plant species list matched with the regional type of the to-be-reseeded region based on the regional-species suitability mapping relationship.

[0070] Plant species analysis, screening other planted regions belonging to the same regional type as the to-be-reseeded region, obtaining historical growth state data corresponding to each candidate plant species, calculating the growth performance score of each candidate plant species, and outputting the recommended reseeding plant species priority sequence of each to-be-reseeded region according to the growth performance score.

[0071] In a preferred embodiment of the present application, the specific way of screening other planted regions belonging to the same regional type as the to-be-reseeded region and obtaining historical growth state data corresponding to each candidate plant species is as follows: locating all planted sample plots belonging to the same regional type as the to-be-reseeded region within the photovoltaic field area.

[0072] Identifying plants growing in the planted sample plots and existing in the candidate plant species list.

[0073] Periodically collecting image data of the planted sample plots using a multispectral camera carried by a drone.

[0074] From the multispectral image data, calculating the normalized difference vegetation index, enhanced vegetation index, and vegetation water content index for each identified candidate plant.

[0075] It should be noted that the normalized difference vegetation index is calculated based on the spectral difference of vegetation in the near-infrared band and the red band, and the formula is the ratio of the difference between the near-infrared reflectance and the red reflectance to the sum. The larger the normalized difference vegetation index, the higher the vegetation coverage and the more vigorous the growth, which can directly reflect the overall vitality of the plant.

[0076] The enhanced vegetation index is optimized based on the normalized difference vegetation index, and introduces the blue band to suppress atmospheric scattering and soil background interference. It is more suitable for high-vegetation-cover areas and can more accurately distinguish subtle differences in vegetation growth, especially in scenes where vegetation and non-vegetation areas are mixed in the photovoltaic field area, which can improve the accuracy of growth evaluation.

[0077] The vegetation water content index is calculated by using the reflectivity of vegetation in the short-wave infrared band which is sensitive to water, and the numerical value directly reflects the water content of plant leaves and the whole, which can be used to evaluate the water stress state of plants, and provides a basis for judging the drought resistance and growth health degree of plants.

[0078] The three indexes convert the spectral information in the multispectral image into quantitative indexes from three dimensions of vegetation vigor, growth details and water status, thereby providing data support for subsequent analysis of plant growth performance.

[0079] In a preferred embodiment of the present application, the specific way of calculating the growth performance score of each candidate plant species is as follows: for any candidate plant species, all sub-period historical vegetation index data in all related planted sample plots within a preset observation period are summarized, including normalized vegetation index, enhanced vegetation index and vegetation water content index.

[0080] The average value of each vegetation index of the candidate plant species in the observation period is calculated as the long-term average growth index.

[0081] The growth rate of each vegetation index of the candidate plant species in the observation period is calculated, the slope is obtained by linear regression analysis on the time series data, and the growth rate index is obtained.

[0082] The variance of each vegetation index of the candidate plant species in the observation period is calculated as the growth stability index.

[0083] The long-term average growth index, growth rate index and growth stability index are normalized and then weighted and summed to obtain the final growth performance score of the candidate plant species.

[0084] It should be noted that the setting of the weight should be combined with the evaluation target and the demand of photovoltaic sand control scene: if the long-term ecological benefits such as sand fixation of plants are emphasized, the long-term average growth weight is increased; if it is necessary to quickly cover the bare area to curb desertification, the growth rate weight is increased; if the regional environment fluctuates greatly, the growth stability weight is increased. The sum of the weights is 1, which can be dynamically adjusted according to the actual demand of the field area, so that the score can reflect the comprehensive growth performance of the plants in a specific scene, and provide a quantitative basis for screening suitable high-quality candidate plants.

[0085] It should be noted that the normalized vegetation index is used to represent the growth and health status of plants, the enhanced vegetation index is used to correct the influence of atmospheric and soil background noise in the high vegetation coverage area, and the vegetation water content index is used to represent the water content of plant leaves.

[0086] In a preferred embodiment of the present application, the method further comprises the step of dynamically adjusting the priority sequence of the recommended plant species for each reseeding area, which is specifically analyzed as follows: after the end of a reseeding period, the growth state of the newly reseeded plants is continuously monitored, and the historical growth state data is updated.

[0087] The sand-fixing benefit correction factor of each candidate plant species is calculated based on the root system sand-fixing ability level of each candidate plant species recorded in the plant attribute database.

[0088] The sand-fixing benefit correction factor of each candidate plant species is calculated based on the root system sand-fixing ability level of each candidate plant species recorded in the plant attribute database.

[0089] It should be noted that the growth performance score, as a basic item, reflects the growth adaptability and health status of the plant through the weighted calculation of long-term growth, growth rate, stability and other indicators. The economic cost correction factor reflects the cost of plant seedling, reseeding, management and protection, and the lower the cost, the closer the factor to 1, and vice versa, which is used to balance the ecological value and economic feasibility. The sand-fixing benefit correction factor quantifies the sand-fixing ability of the plant, and the better the sand-fixing effect, the closer the factor to 1, highlighting the priority of ecological function.

[0090] The result of multiplying the three factors not only retains the core evaluation of the growth state of the plant, but also dynamically adjusts the weight of economic cost and ecological benefit through the correction factor. The higher the final score, the better the comprehensive performance of the plant in the three dimensions of growth adaptation, economic control and ecological effectiveness, providing an intuitive and quantitative basis for recommending the optimal species.

[0091] According to the final comprehensive recommendation score, the candidate plant species list is arranged in descending order to form a priority sequence of the recommended plant species for each reseeding area.

[0092] In a preferred embodiment of the present application, the method further comprises the step of dynamically adjusting the priority sequence of the recommended plant species for each reseeding area, which is specifically analyzed as follows: after the end of a reseeding period, the growth state of the newly reseeded plants is continuously monitored, and the historical growth state data is updated.

[0093] When the ecological sand control effect of a certain area or the power generation capacity of a photovoltaic power station changes significantly, beyond the preset normal fluctuation range, the reseeding area recognition step is triggered again, the photovoltaic influence weight coefficient and the ecological contribution weight coefficient are dynamically adjusted, and a new sequence of reseeding areas is generated.

[0094] When a new plant species is introduced or more accurate attribute data about the existing plant species is obtained, the plant attribute database is updated, and the step of constructing the area-species suitability mapping relationship is re-executed.

[0095] It should be noted that the present application matches the most suitable plant species for different regional types at the secondary reseeding time by combining growth performance, economic cost and sand fixation benefit. Considering the change of regional environment at the secondary reseeding time, the recommended plant species is more adaptive, which is adaptive to the environment and has practical value, and significantly improves the sand control efficiency.

[0096] It should be noted that through regular updating and adjustment mechanism, the scheme can be updated in time according to the plant growth state, environmental change and the like after the secondary reseeding. The secondary reseeding scheme is always matched with the actual situation of the field area, so that the effect of the secondary reseeding can be continued, the long-term applicability of the scheme is enhanced, and the sand control results are effectively consolidated.

[0097] The above is only an example and description of the concept of the present application, and those skilled in the art can make various modifications or supplements to the described specific embodiments or use similar ways to replace, as long as the concept of the present application is not deviated or the scope defined by the present application is not exceeded, which shall belong to the protection scope of the present application.

Claims

1. A method for recommending vegetation planting priorities in a photovoltaic sand control environment, characterized in that: include: Replanting area identification: obtaining an initial set of areas to be replanted in the photovoltaic field, comprehensively scoring each area to be replanted in the initial set of areas, and outputting a priority sequence of the areas to be replanted based on the comprehensive scores; Regional type division: collecting environmental parameters of each area within the photovoltaic field, dividing the photovoltaic field into several regional types with different environmental characteristics, and constructing a regional type-species suitability mapping relationship; Replanting plant screening, identifying the area type of each area to be replanted in the priority sequence of the areas to be replanted, and obtaining a list of candidate plant species that match the area type of the areas to be replanted based on the area-species suitability mapping relationship; Plant species analysis: screening other planted areas of the same area type as the area to be replanted, obtaining historical growth status data corresponding to each candidate plant species, calculating the growth performance score of each candidate plant species, and outputting the recommended replanting plant species priority sequence for each area to be replanted based on the growth performance score.

2. The method for recommending vegetation planting priorities in a photovoltaic sand control environment according to claim 1, wherein: The specific method of obtaining the initial set of areas to be replanted in the photovoltaic field is as follows: Identifying vegetation coverage within the photovoltaic field using remote sensing image data and ground sensor data; Gridding the photovoltaic field area to obtain a plurality of grid units; The average vegetation coverage rate in each of the grid cells is calculated, and the grid cells whose average vegetation coverage rate is lower than a preset coverage rate lower threshold are determined as initial areas to be replanted, to form the initial area set.

3. The method for recommending vegetation planting priorities in a photovoltaic sand control environment according to claim 2, characterized in that: The specific method of comprehensively scoring each area to be replanted in the initial area set is as follows: For any area to be replanted, based on its spatial position relative to the photovoltaic array, the degree of shading by the photovoltaic modules in the area is analyzed to assess its potential impact on photovoltaic power generation and calculate its photovoltaic shading impact factor; For the same area to be replanted, the ecological restoration urgency factor is calculated based on its soil type, slope data and wind erosion modulus; The photovoltaic shading impact factor and the ecological restoration urgency factor are summed up according to the weights to obtain a comprehensive score for the area to be replanted; The weight setting method is: including the photovoltaic impact weight coefficient and the ecological contribution weight coefficient, wherein the sum of the photovoltaic impact weight coefficient and the ecological contribution weight coefficient is 1, and can be dynamically adjusted according to the operation strategy of the photovoltaic power station.

4. The method for recommending vegetation planting priorities in a photovoltaic sand control environment according to claim 3, characterized in that: The specific calculation method of the photovoltaic shading impact factor is as follows: Obtaining geographic coordinates and elevation information of the area to be replanted; Obtaining the precise spatial position, size, and inclination parameters of each photovoltaic module in the photovoltaic field area, and constructing a three-dimensional model of the photovoltaic array; Based on the sun trajectory model, simulate the altitude and azimuth of the sun at different times within a preset time period; At each moment, determining whether the area to be replanted is located within a shadow area formed by any photovoltaic module on the ground; Counting the total length of time that the area to be reseeded is in a shadow state within the preset time period; Calculate the ratio of the total duration to the total length of the preset time period to obtain a basic occlusion rate; The basic shading rate is normalized with a preset reference shading rate to obtain the photovoltaic shading impact factor.

5. The method for recommending vegetation planting priorities in a photovoltaic sand control environment according to claim 1, characterized in that: The specific method of dividing the photovoltaic field into several regional types with different environmental characteristics is as follows: Obtaining a high-precision digital elevation model and digital surface model of the photovoltaic field, and identifying the spatial location of the photovoltaic panels by comparing the elevation data of the two models, and identifying the area below the photovoltaic panels, the area between the photovoltaic panels, the edge of the photovoltaic array, and the area next to the field road; Collect historical periodic data on light intensity, soil temperature, soil moisture and soil nutrient content in each area; For light intensity, calculate the cumulative amount of light and duration of light in each area during a typical sunlight cycle; For soil temperature and humidity, calculate the daily average temperature and humidity, temperature and humidity peaks, and daily temperature difference for each area; For soil nutrients, measure the content of key elements; The cumulative sunlight, daily average temperature and humidity, and key element content are used as regional feature vectors; A preset clustering algorithm is used to perform cluster analysis on the characteristic vectors of all regions in the photovoltaic field, and regions with similar characteristic vectors are divided into the same category. Each category is a region type, and the region type reflects a specific microenvironment combination.

6. The method for recommending vegetation planting priorities in a photovoltaic sand control environment according to claim 5, characterized in that: The specific method of constructing the regional type-species suitability mapping relationship is as follows: Establishing a plant attribute database containing candidate sand-control plants, wherein the plant attribute database records the plant's shade tolerance level, drought tolerance level, root sand-fixing ability level, and nutrient requirement characteristics; Establish a regional environmental characteristics database to record the environmental characteristic parameters of each regional type, including light conditions, water conditions and soil nutrient conditions; Calculating the matching degree between the shade tolerance level of each plant in the plant attribute database and the light conditions in each regional type database to obtain a light matching score; The drought tolerance level of each plant was matched with the moisture conditions in the database of each regional type to obtain the moisture matching score; The matching degree between the nutrient requirement characteristics of each plant and the soil nutrient conditions in the regional type database is calculated to obtain the nutrient matching score; The light matching score, water matching score and nutrient matching score are weighted and summed to obtain a comprehensive suitability score for each plant and the region type; If the comprehensive suitability score of a plant and a region type is greater than a preset suitability threshold, the plant is determined to be a suitable replanting plant species for the region type, and this correspondence is stored in the region-species suitability mapping relationship.

7. The method for recommending vegetation planting priorities in a photovoltaic sand control environment according to claim 1, characterized in that: The specific method of screening other planted areas of the same area type as the area to be replanted and obtaining historical growth status data corresponding to each candidate plant species is as follows: Locating, within the photovoltaic field, all planted sample plots that belong to the same area type as the area to be replanted; identifying plants growing in the planted sample plot that are present in the list of candidate plant species; Using a multispectral camera carried by a drone, periodically collecting image data of the planted sample plots; A normalized vegetation index, an enhanced vegetation index, and a vegetation moisture index are calculated for each identified candidate plant from the multispectral image data.

8. The method for recommending vegetation planting priorities in a photovoltaic sand control environment according to claim 7, characterized in that: The specific method of calculating the growth performance score of each candidate plant species is as follows: For any candidate plant species, summarize all historical vegetation index data of all relevant planted sample plots within the preset observation period, including the Normalized Difference Vegetation Index, Enhanced Vegetation Index and Vegetation Water Content Index; Calculating the average value of each vegetation index of the candidate plant species during the observation period as its long-term average growth index; Calculating the growth rate of each vegetation index of the candidate plant species during the observation period, and obtaining the slope by performing linear regression analysis on the time series data as its growth rate indicator; Calculating the variance of each vegetation index of the candidate plant species during the observation period as an indicator of its growth stability; The long-term average growth weight, growth rate weight and growth stability weight are set, and the long-term average growth index, growth rate index and growth stability index are normalized and then weighted and summed to obtain the final growth performance score of the candidate plant species.

9. The method for recommending vegetation planting priorities in a photovoltaic sand control environment according to claim 8, characterized in that: The method of outputting the priority sequence of recommended replanting plant species for each area to be replanted also includes: The economic cost correction factor of each candidate plant species is calculated based on the seedling cost and planting and maintenance cost of each candidate plant species; Calculating a sand fixation benefit correction factor for each candidate plant species based on the root sand fixation ability level of each candidate plant species recorded in the plant attribute database; Multiplying the growth performance score, the economic cost correction factor, and the sand fixation benefit correction factor to obtain a final comprehensive recommendation score; According to the final comprehensive recommendation score, the candidate plant species list is arranged in descending order to form a priority sequence of recommended replanting plant species for each area to be replanted.

10. The method for recommending vegetation planting priorities in a photovoltaic sand control environment according to claim 1, characterized in that: The method further includes a dynamic adjustment step, the specific analysis of which is as follows: After a replanting cycle ends, continuously monitoring the growth status of the newly replanted plants and updating the historical growth status data; When the ecological desertification control effect or the power generation of a photovoltaic power station in a certain area is monitored to have a significant change, exceeding the preset normal fluctuation range, the replanting area identification step is triggered again, the photovoltaic impact weight coefficient and the ecological contribution weight coefficient are dynamically adjusted to generate a new sequence of areas to be replanted; When new plant species are introduced or more accurate attribute data about existing plant species are obtained, the plant attribute database is updated and the step of constructing the region-species suitability mapping relationship is re-executed.

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