An Evaluation Method and System for the Carbon Sequestration Benefit of Ecological Restoration in a Gobi Photovoltaic Base

Abstract

The present invention discloses a method and system for evaluating the ecological restoration and carbon sequestration benefits of a gobi photovoltaic base, specifically related to the technical field of ecological environment governance, including the following steps: dividing the photovoltaic base area into the area under the photovoltaic panels, the passage area between the photovoltaic panels, and the peripheral area of the photovoltaic field, establishing corresponding ecological simulation models for different areas, obtaining simulation data, and substituting them into the common calculation model one and the common calculation model two respectively. Summarize the calculation results of all areas and substitute them into the machine learning model that has been pre-trained. The output result is the ecological restoration and carbon sequestration benefit value used to represent the overall ecological restoration final score of the photovoltaic base. The present invention can dynamically simulate the vegetation restoration and soil carbon storage processes. Compared with the traditional static monitoring method, it can predict the future ecological restoration trend, provide scientific support for long-term ecological management, and can provide targeted ecological optimization schemes based on regional simulation, improving the ecological value of renewable energy.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological environment governance, and more specifically, to a method and system for evaluating the carbon fixation benefits of ecological restoration of a Gobi photovoltaic base. Background Art

[0002] In the context of the global energy structure transformation, photovoltaic power generation has become a core component of low-carbon renewable energy, especially in areas with sufficient sunlight but fragile ecological environment (such as Gobi, desert, semi-desert), the deployment scale of photovoltaic bases continues to expand. However, the construction of large-scale photovoltaic bases may have a profound impact on the ecosystem, mainly including: Changes in the surface environment: Photovoltaic panels block solar radiation, affecting vegetation growth, soil moisture balance and local climate. Intensified soil erosion: Increased surface damage and wind and sand activities may lead to soil loss and reduced carbon storage. Uncertainty in vegetation restoration: The vegetation restoration between rows of photovoltaic panels is affected by factors such as moisture, light, and wind and sand movement, and requires scientific quantitative evaluation.

[0003] In response to these problems, existing ecological assessment methods mainly rely on remote sensing monitoring and field surveys, which make it difficult to dynamically simulate the ecological restoration process and carbon storage capacity. Therefore, the present invention proposes a method and system for evaluating the carbon fixation benefits of ecological restoration of a Gobi photovoltaic base, in order to solve the above problems. Summary of the invention

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A method for evaluating the carbon sequestration benefits of ecological restoration of a Gobi photovoltaic base comprises the following steps:

[0006] The photovoltaic base area is divided into the area under the photovoltaic panels, the channel area between the photovoltaic panels and the peripheral area of the photovoltaic field, and corresponding ecological simulation models are established for different areas;

[0007] For each divided area, simulation data is obtained from the corresponding ecological simulation model and substituted into the shared calculation model 1 that reflects the progress and quality of regional vegetation restoration, and the shared calculation model 2 that reflects the soil restoration effect and carbon sequestration capacity;

[0008] The calculation results of all regions are summarized and substituted into the pre-trained machine learning model. The output result is the ecological restoration carbon fixation benefit value used to represent the final score of the overall ecological restoration of the photovoltaic base.

[0009] In a preferred embodiment, the ecological simulation model logic of the area under the photovoltaic panel is:

[0010] The photovoltaic array arrangement is used to simulate the light intensity distribution and construct a light distribution model:

[0011] Simulate water infiltration, evaporation and migration based on the Richards equation to construct a soil water dynamics model;

[0012] Adopt the Logistic growth model to construct a vegetation growth model;

[0013] Use the constructed light distribution model, soil water dynamics model and vegetation growth model for simulation.

[0014] In a preferred embodiment, the ecological simulation model logic for the inter-row channel area of the photovoltaic panels is as follows:

[0015] Adopt the Markov chain model to predict the succession of vegetation communities, simulate the competition and succession processes of different species under different restoration strategies, and construct a vegetation restoration model;

[0016] Use the soil carbon cycle model to simulate the processes of organic matter accumulation and mineralization, predict the changes in soil carbon storage, and construct a soil quality evolution model;

[0017] Use the constructed vegetation restoration model and soil quality evolution model for simulation.

[0018] In a preferred embodiment, the ecological simulation model logic for the peripheral area of the photovoltaic field is as follows:

[0019] Simulate the sand movement based on the Saltation-Balance equation to construct a sand transport model;

[0020] Use the RUSLE model to correct the soil loss rate, evaluate the soil protection effect of vegetation, and construct a soil erosion model;

[0021] Use the constructed sand transport model and soil erosion model for simulation.

[0022] In a preferred embodiment, the common calculation model one refers to the vegetation restoration index calculation model.

[0023] In a preferred embodiment, the common calculation model two refers to the soil carbon storage index calculation model.

[0024] In a preferred embodiment, the pre-trained machine learning model is a convolutional neural network model.

[0025] In a preferred embodiment, a gobi photovoltaic base ecological restoration and carbon sequestration benefit evaluation system includes:

[0026] A regional division module that divides the photovoltaic base area into the area under the photovoltaic panels, the inter-row channel area of the photovoltaic panels, and the peripheral area of the photovoltaic field;

[0027] A simulation module for establishing corresponding ecological simulation models for different regions;

[0028] A data acquisition module for obtaining simulation data from the corresponding ecological simulation models for each divided region;

[0029] A regional evaluation module for respectively substituting the obtained simulation data into a common calculation model one that reflects the progress and quality of regional vegetation restoration, and a common calculation model two that reflects the soil remediation effect and carbon sequestration capacity;

[0030] An overall evaluation module for summarizing the calculation results of all regions and substituting them into a pre-trained machine learning model, and the output result is an ecological restoration carbon sequestration benefit value used to represent the final score of the overall ecological restoration of the photovoltaic base.

[0031] The technical effects and advantages of the present invention:

[0032] Traditional ecological assessment methods mainly rely on remote sensing images and field surveys, but these methods cannot dynamically predict the ecological restoration process of photovoltaic base areas, and the assessment results are greatly affected by factors such as data collection frequency. Existing carbon sequestration calculation methods mostly use empirical formulas or statistical models, making it difficult to accurately predict soil carbon storage changes. The present invention is based on ecological simulation modeling to accurately simulate the ecological restoration process. For the areas under photovoltaic panels, the inter-row channels of photovoltaic panels, and the peripheral areas of the photovoltaic field, simulation models of light distribution, soil moisture, vegetation growth, wind-sand transport, and soil carbon cycle are respectively constructed to dynamically simulate the vegetation restoration and soil carbon storage processes. Compared with traditional static monitoring methods, the present invention can predict the future ecological restoration trend and provide more scientific support for long-term ecological management. By constructing a vegetation restoration index (VRI) and a soil carbon storage index (SCI), cross-regional comparability is achieved. Through non-linear transformation and normalization processing, it is ensured that the index calculation results of different regions are comparable, making the assessment more scientific and objective. Compared with the single NDVI index, the VRI index of the present invention combines factors such as light, soil moisture, and species diversity, and can more comprehensively reflect the quality of vegetation restoration.

[0033] Traditional carbon sink assessment methods mostly use soil sampling analysis or empirical formula calculations, but due to the long-term evolution characteristics of soil carbon cycle, a single measurement is difficult to reflect long-term carbon storage changes. Existing carbon sink calculations do not take into account the impact of ecological restoration in the photovoltaic base area on carbon storage, resulting in large deviations in the assessment results. The present invention is based on a soil carbon cycle simulation model, dynamically calculates carbon sink capacity, and uses multiple factors such as organic carbon accumulation, nitrogen storage, soil density, and soil moisture regulation to calculate the SCI index to accurately predict soil carbon sink capacity. Through long-term simulation, the contribution of different ecological restoration measures (such as vegetation restoration and sand control) in the photovoltaic base area to soil carbon storage is evaluated, providing a scientific basis for carbon trading and ecological compensation. By accurately calculating the soil carbon storage capacity, this method can provide quantitative support for photovoltaic companies to apply for carbon credits, and promote the photovoltaic industry to develop in a low-carbon, eco-friendly way.

[0034] The present invention adopts convolutional neural network (CNN) to automatically learn the nonlinear relationship between indexes. The method processes the VRI and SCI indexes of three regions through CNN, which can automatically identify different ecological restoration modes and improve prediction accuracy. CNN can explore the potential interactive relationship between indexes in different regions, such as how vegetation restoration affects soil carbon storage and how wind and sand movement affects vegetation growth, thereby optimizing ecological management strategies.

[0035] Traditional evaluation methods usually conduct a unified analysis of the photovoltaic base area, failing to consider the differences in ecological characteristics under the photovoltaic panels, between-row channels and peripheral areas, resulting in inaccurate restoration strategies. Existing management strategies often use a "one-size-fits-all" approach to carry out ecological restoration, lacking refined management methods. Based on regional simulation, the present invention provides targeted ecological optimization solutions. Through regional modeling, the following are analyzed separately: The area under the photovoltaic panels: optimize the arrangement of photovoltaic panels, improve light transmittance, select shade-tolerant vegetation, and improve the ecological restoration effect. The area between the rows of photovoltaic panels: use the vegetation restoration model to optimize the community succession process and improve the soil carbon storage capacity. The peripheral area of the photovoltaic field: combine the wind and sand transport model, reasonably arrange the sand-fixing vegetation, and improve the windproof and soil-fixing capacity. It also supports dynamic adjustment of ecological restoration strategies to improve the long-term ecological benefits of photovoltaic bases. Through long-term monitoring and simulation calculations, the ecological restoration plan can be dynamically adjusted. For example, in areas with severe wind and sand erosion, biological sand fixation measures (such as shrub belts) are added, and in areas with low carbon storage capacity, soil improvement measures (such as increasing organic matter) are optimized.

[0036] The construction of traditional photovoltaic bases mainly focuses on energy benefits, while ecological impacts and carbon sequestration benefits are not fully incorporated into decision-making, which may lead to long-term ecological degradation problems. The current ecological value of renewable energy cannot be fully quantified, affecting its application in the carbon market and ecological compensation mechanisms. The present invention establishes a collaborative optimization system for ecological restoration and photovoltaic power generation. By optimizing ecological restoration measures, it can not only reduce the negative environmental impacts of photovoltaic bases, but also improve the adaptability of ecosystems to climate change, achieving sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] For the convenience of those skilled in the art to understand, the present invention will be further described below in conjunction with the accompanying drawings;

[0038] Figure 1 It is a schematic diagram of the evaluation method for the carbon sequestration benefit of ecological restoration in a gobi photovoltaic base in the present invention.

[0039] Figure 2 It is a schematic diagram of the evaluation system for the carbon sequestration benefit of ecological restoration in a gobi photovoltaic base in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0040] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0041] Refer to Figure 1 - Figure 2 The following embodiments are obtained:

[0042] Embodiment 1: In the context of the global energy structure transformation, as the main force of renewable energy, photovoltaic power generation has been widely applied in vast areas such as gobi, desert, and semi-desert regions. These regions have abundant light resources and low land use value, which are very suitable for the construction of photovoltaic bases. However, the large-scale deployment of photovoltaic bases still has uncertainties in the impact on the ecological environment, which are mainly manifested in the following aspects:

[0043] Ecological environment challenges of gobi photovoltaic bases:

[0044] Photovoltaic panels affect the surface ecosystem: The installation of photovoltaic panels changes the original surface environment, forming a shaded area under the photovoltaic panels, which affects the photosynthesis and growth of vegetation. The evaporation of soil moisture under the photovoltaic panels decreases, which may retain water to a certain extent, but may also inhibit the adaptability of native vegetation.

[0045] Fragile ecosystem and limited recovery ability: The natural ecosystems in gobi and desert areas are extremely fragile, with slow growth of native vegetation and extremely low soil organic carbon content. Human intervention (such as the construction of photovoltaic bases) may disrupt the original ecological balance, leading to difficulties in ecological restoration.

[0046] Wind and sand erosion and soil loss: The wind and sand movement outside the photovoltaic base is strong. If not properly managed, it may exacerbate desertification. The bare ground in the area between the photovoltaic panels is vulnerable to wind erosion and water erosion, resulting in soil loss and affecting the stability of the photovoltaic base.

[0047] Uncertain carbon sequestration capacity: At present, there is a lack of systematic evaluation of the carbon sequestration benefits of gobi photovoltaic bases, and it is impossible to determine the vegetation restoration and soil carbon storage growth rates in this area.

[0048] Limitations of traditional ecological assessment methods:

[0049] Limitations of remote sensing monitoring: Existing ecological restoration monitoring usually relies on remote sensing data (such as NDVI and vegetation coverage), but lacks refined assessment of soil moisture and carbon storage, making it difficult to accurately quantify the carbon sequestration capacity.

[0050] Lack of regional refined ecological simulation: Traditional ecological assessments are mostly based on historical data, and independent simulation models have not been constructed for the ecological characteristics of different regions, making it impossible to accurately simulate the impact of photovoltaic bases on soil moisture and vegetation restoration.

[0051] Insufficient data-driven intelligent assessment: At present, empirical formulas or statistical methods are mostly used for the ecological impact of photovoltaic bases, and there is a lack of a comprehensive assessment framework based on machine learning, making it impossible to effectively integrate multi-source data for decision support.

[0052] The proposal of the present invention not only fills the gap in the assessment of the ecological restoration benefits of photovoltaic bases, but also has important significance in aspects such as ecological environmental protection, optimized design of photovoltaic bases, and carbon sequestration calculation:

[0053] Ecological environmental protection: Calculate the vegetation restoration and soil carbon storage capacities in the area under the photovoltaic panels, the area of the passage between the photovoltaic panels, and the area outside the photovoltaic field through an ecological simulation model, clarify the ecological restoration trends in each area, and provide a scientific basis for the ecological management of photovoltaic bases. Optimize water resource utilization through dynamic modeling of soil moisture to improve the ecological restoration efficiency in arid areas. Evaluate the impact of the peripheral vegetation on wind and sand erosion through wind and sand transport simulation to reduce the risk of desertification in the photovoltaic base area.

[0054] Optimized design of photovoltaic bases: Evaluate the light environment under the photovoltaic panels through a light distribution model, optimize the arrangement of photovoltaic modules, and improve ecological compatibility. Guide the construction of ecological corridors inside the photovoltaic field through a vegetation restoration model to promote the restoration of biodiversity. Provide a scientific quantification standard for the green certification and sustainable development assessment of photovoltaic bases through the assessment of ecological restoration benefits.

[0055] Carbon Sink Calculation and Carbon Market Application: The Soil Carbon Storage Index (SCI) is used to calculate soil organic carbon, total nitrogen content, and soil moisture changes, accurately assessing the carbon sink capacity of the photovoltaic base. Through a machine learning model (CNN), the accuracy of carbon sink prediction is improved, providing a scientific basis for the carbon trading market, enabling the photovoltaic base to not only contribute clean energy but also benefit from carbon sink value addition.

[0056] The present invention proposes an ecological restoration carbon sequestration benefit assessment method combining regional simulation + index calculation + intelligent evaluation, and the core content includes the following three aspects:

[0057] Ecological Simulation Modeling Based on Regional Division: This method divides the photovoltaic base area into the area under the photovoltaic panels, the inter-row channel area of the photovoltaic panels, and the peripheral area of the photovoltaic field. For the ecological characteristics of different areas, corresponding simulation models are established respectively:

[0058] Area under the photovoltaic panels: Light distribution model: Simulates the influence of the arrangement of photovoltaic panels on light intensity. Soil moisture dynamic model: Calculates water infiltration, evaporation, and migration based on the Richards equation. Vegetation growth model: Uses the Logistic growth model to predict vegetation recovery.

[0059] Inter-row channel area of the photovoltaic panels: Vegetation restoration model: Predicts the succession process of different vegetation species based on the Markov chain model. Soil quality evolution model: Calculates organic matter accumulation and mineralization based on the soil carbon cycle model.

[0060] Peripheral area of the photovoltaic field: Wind-sand transport model: Calculates the influence of wind-sand flow on surface vegetation using the Saltation-Balance equation. Soil erosion model: Calculates the role of vegetation in soil conservation based on the RUSLE formula.

[0061] Comprehensive Evaluation of Machine Learning Model: Input data: The photovoltaic base area is divided into the area under the photovoltaic panels (Area A), the inter-row channel area of the photovoltaic panels (Area B), and the peripheral area of the photovoltaic field (Area C). The vegetation restoration index (VRI) and soil carbon storage index (SCI) of Area A, Area B, and Area C are used as input features. Evaluation model: A convolutional neural network (CNN) is used to extract cross-regional ecological restoration features. Final output: Calculate the overall ecological restoration carbon sequestration benefit value of the photovoltaic base for ecological benefit evaluation and carbon sink trading support.

[0062] The present invention discloses an ecological restoration carbon sequestration benefit assessment method for a gobi photovoltaic base, including the following steps:

[0063] The photovoltaic base area is divided into the area under the photovoltaic panels, the inter-row channel area of the photovoltaic panels, and the peripheral area of the photovoltaic field. Corresponding ecological simulation models are established for different areas. Since there are significant differences in aspects such as light, moisture, vegetation succession, and soil environment in different areas of the photovoltaic base, unified modeling cannot accurately reflect local ecological changes. Through area division, phenomena such as photovoltaic panel shading, soil moisture migration, and sand drift are simulated respectively, improving the applicability of the simulation model. The area under the photovoltaic panels (Area A) is affected by photovoltaic panel shading, resulting in reduced soil evaporation but decreased photosynthetic efficiency. It is necessary to study suitable shade-tolerant plant restoration plans. The inter-row channel of the photovoltaic panels (Area B) has good light and precipitation conditions and is suitable as an ecological restoration zone. It is necessary to simulate vegetation succession and soil carbon sink changes. The periphery of the photovoltaic field (Area C) is significantly affected by sand erosion. It is necessary to simulate the role of sand-fixing vegetation in sand control and soil conservation.

[0064] For each divided area, simulation data are obtained from the corresponding ecological simulation model and substituted into the common calculation model one that reflects the progress and quality of regional vegetation restoration, and the common calculation model two that reflects the soil restoration effect and carbon sink capacity respectively. Traditional ecological assessment methods are mostly based on historical statistics or remote sensing data. This method can dynamically predict the change trends of different ecological factors, including vegetation restoration rate, soil moisture dynamics, sand drift, etc., by establishing a physical ecological model. By calculating VRI and SCI from the simulation results, the problem of data loss is avoided, and the scientificity and operability of the assessment are improved. Two general indices (VRI and SCI) are used to ensure that the ecological restoration and carbon sequestration capabilities can be uniformly quantified under different environmental conditions. Although the simulation data of the area under the photovoltaic panels, the inter-row channel, and the peripheral area are different, they can all be calculated through VRI and SCI, making the model have stronger applicability. Multi-scale data fusion improves the ecological restoration prediction ability.

[0065] The simulation data of different areas can be analyzed on multiple time scales. For example: Short-term effects (1 - 3 years): Focus on the impact of photovoltaic panel shading on vegetation and soil moisture. Medium-term effects (5 - 10 years): Analyze the improvement of carbon sink capacity by ecological restoration measures (such as vegetation planting, irrigation). Long-term effects (more than 10 years): Predict the soil organic carbon accumulation and sand control effect during the operation period of the photovoltaic base. VRI (Vegetation Restoration Index): Measures the restoration speed, health, and diversity of vegetation. SCI (Soil Carbon Storage Index): Measures the accumulation of soil organic carbon. Moreover, cross-regional comparison can optimize ecological restoration strategies. For example, if the VRI of a certain area is lower than expected, it indicates that the vegetation restoration is not ideal, and the vegetation species, density, and irrigation strategy can be adjusted. If the SCI is low, it means that the soil carbon storage capacity is insufficient, and organic matter input (such as biological cover) can be increased and fertilization measures can be optimized. The trend change of the same index helps users observe the long-term effectiveness of ecological restoration measures.

[0066] Summarize the calculation results of all regions and substitute them into a pre-trained machine learning model. The output result is the carbon sequestration benefit value of ecological restoration, which is used to represent the final score of the overall ecological restoration of the photovoltaic base. Summarize the calculation results of all regions and substitute them into a pre-trained Convolutional Neural Network (CNN) model. Finally, output the carbon sequestration benefit value of ecological restoration, which is used to represent the overall ecological restoration effect of the photovoltaic base. Use CNN to automatically extract cross-regional ecological features, avoid the uncertainty of artificially setting weights, and improve the calculation accuracy. Combine the data of multiple regions for comprehensive learning to make the evaluation results more in line with the actual ecological change law. Ecological restoration involves multi-variable non-linear interactions (light, precipitation, soil moisture, wind sand, etc.), and traditional linear models are difficult to accurately predict long-term changes. CNN can extract multi-dimensional features through convolutional kernels, can better predict the trends of vegetation and soil restoration, has strong scalability, can be applied to photovoltaic bases in different regions, including desert type, grassland type, agricultural-photovoltaic complementary type, etc., and can be combined with remote monitoring systems, which is conducive to subsequent automated ecological monitoring and dynamic adjustment of management strategies.

[0067] The logic of the ecological simulation model for the area under the photovoltaic panels is as follows:

[0068] Use the arrangement of the photovoltaic array to simulate the light intensity distribution and construct a light distribution model:

[0069] Based on the Richards equation, simulate water infiltration, evaporation and migration, and construct a soil moisture dynamic model;

[0070] Adopt the Logistic growth model to construct a vegetation growth model;

[0071] Use the constructed light distribution model, soil moisture dynamic model and vegetation growth model for simulation.

[0072] The light distribution model uses the arrangement of the photovoltaic array to simulate the spatial distribution of light intensity, calculates the photosynthetically active radiation (PAR) under and around the photovoltaic panels, and serves as an input variable for vegetation growth and soil evaporation calculations. The formula is: ; is the effective light under the photovoltaic panel (W / m²), is the direct light intensity of the sun (W / m²), is the shading ratio of the photovoltaic panel, defined as: ; is the shadow length, which is calculated from the height of the photovoltaic panel and the solar altitude angle and is calculated as, ; is the row spacing of the photovoltaic panels; is the diffused light part, and the calculation method is: ; is the proportion of scattered light, and is the total incident light. By quantifying the impact of photovoltaic panels on light, the vegetation growth and evaporation potential can be determined, and the ecological restoration strategy can be optimized by adjusting the spacing or height of the photovoltaic panels.

[0073] The soil moisture dynamic model calculates water infiltration, evaporation and migration based on the Richards equation, simulates the soil moisture distribution under the influence of photovoltaic panel shading, and provides necessary moisture information for vegetation growth. The formula is: ; is the soil water content (m³ / m³), is (a time variable, unit: seconds or days), representing the time evolution process of soil moisture, that is, the change of soil water content over time, which can be used to describe the dynamic change of moisture, including the evolution of processes such as precipitation infiltration, soil moisture evaporation, and plant absorption over time. is the depth variable, unit: meter, representing the depth in the soil profile, usually taking positive values from the ground surface downwards, that is, taking 0 as the ground surface, which is used to describe the distribution and migration of moisture in the vertical direction. For example: surface moisture infiltrates downwards (infiltration), capillary water migrates upwards from deep soil (transpiration), and moisture in deep soil may further leak to the ground due to gravity. is the hydraulic conductivity (m / s), representing the infiltration rate of moisture in the soil, depending on the water content: ; is the saturated permeability, is the saturated water content, n is the empirical exponent, and here is the soil matrix potential (m), representing the adsorption capacity of moisture in the soil, is the evaporation rate (m / s), and the calculation method is: ; is the maximum evaporation rate, is the maximum light intensity. By calculating the change of soil moisture, it is determined whether the vegetation can survive and grow, which is beneficial to evaluating the potential water resource benefit of photovoltaic panels in reducing evaporation.

[0074] The vegetation growth model is based on the Logistic growth model, combines the influencing factors of light (PAR) and soil moisture (SWC), and simulates the vegetation restoration situation, including the changes in vegetation coverage, leaf area index (LAI) and biomass. The formula is: ; is the vegetation coverage (% ), is the growth rate (% / day), which determines the vegetation growth rate, is the maximum vegetation coverage (% ), is the time variable, representing the vegetation coverage The growth process over time, this variable is used to describe the growth rate of vegetation, that is, how vegetation grows from an initial state to a stable value (maximum coverage) within a certain period of time. ). is the light adaptation factor: ; is the optimal light level, is the soil moisture adaptation factor: ; is the optimal soil water content. By predicting the vegetation restoration rate under the photovoltaic panels, it can provide scientific guidance for ecological restoration, and can optimize the suitable vegetation species and management plans by combining the effects of light and moisture.

[0075] The ecological simulation model logic for the inter-row channel area of the photovoltaic panels is as follows:

[0076] Use the Markov chain model to predict the succession of the vegetation community, simulate the competition and succession processes of different species under different restoration strategies, and construct a vegetation restoration model;

[0077] Use the soil carbon cycle model to simulate the processes of organic matter accumulation and mineralization, predict the changes in soil carbon storage, and construct a soil quality evolution model;

[0078] Use the constructed vegetation restoration model and soil quality evolution model for simulation.

[0079] The inter-row channel area of the photovoltaic panels (Area B) is the key area for ecological restoration within the photovoltaic base. Due to the good light and moisture conditions, this area is suitable as an ecological restoration zone to promote vegetation restoration and soil carbon storage accumulation. Therefore, the ecological simulation of this area mainly consists of a vegetation restoration model and a soil quality evolution model. The vegetation restoration model uses the Markov chain model to predict the succession of the vegetation community, simulate the competition, adaptation and succession processes of different species under different ecological restoration strategies, and evaluate the vegetation restoration situation in this area. The formula is: ; is time The vegetation distribution state vector at time, representing the coverage ratio of different vegetation types: ; is the th plant at time, the coverage ratio, is time the vegetation distribution state vector at time, is the vegetation succession transition probability matrix: ; represents the vegetation changing from type to type The probability is affected by factors such as climate, soil nutrients, and competition relationships. Simulation steps description: Combine the light distribution model, soil moisture dynamics model, and vegetation growth model to conduct spatio-temporal dynamic simulation and predict the ecological restoration process in the area under the photovoltaic panels. It can predict the dynamic changes of different vegetation types, evaluate the ecological restoration path, facilitate considering the long-term impact of species competition and restoration measures on vegetation restoration, and optimize the ecological restoration strategy.

[0080] The soil quality evolution model uses the soil carbon cycle model to calculate the accumulation and mineralization processes of organic matter, predict the change of soil carbon storage (SOC), and construct the soil quality evolution model. The formula is: ; is the soil organic carbon content, unit: g / m², representing the organic carbon stored in the soil, is the input carbon amount, unit: g / m² / year, ; is the coverage ratio of the th type of vegetation (from the vegetation restoration model), litter input amount (g / m² / year) of the th type of vegetation, representing the contribution of this species to soil organic carbon, and n represents the total number of vegetation types. is the mineralization loss rate, ; is the reference mineralization rate, is the current soil temperature (°C), is the reference temperature (°C), is the preset temperature sensitivity coefficient (determining the impact of temperature on the mineralization rate), is the soil carbon change time step. Since the soil carbon change is slow, it is different from the time steps of the water or vegetation growth models. Simulation steps description: Combine the vegetation restoration model (Markov chain) and the soil quality evolution model (carbon cycle) to conduct multi-time scale simulation and predict the long-term ecological restoration trend in the inter-row channel area of the photovoltaic panels. By optimizing the soil carbon sink capacity in the photovoltaic base area, it provides a scientific basis for carbon trading. It can combine vegetation restoration, evaluate the contribution of different vegetation types to soil carbon storage, and optimize vegetation selection. Input the initial vegetation distribution, calculate the vegetation succession path, calculate the soil carbon input amount according to the vegetation type and coverage ratio, input the current soil temperature, calculate the carbon mineralization rate, iteratively update the soil organic carbon content, and predict the long-term carbon storage change.

[0081] The ecological simulation model logic for the peripheral area of the photovoltaic field is: Based on the Saltation-Balance equation, simulate the sand movement and construct the sand transport model:

[0082] Use the RUSLE model to correct the soil loss rate, evaluate the role of vegetation in soil protection, and construct the soil erosion model;

[0083] Use the established sand and wind transport model and soil erosion model for simulation.

[0084] In the peripheral area of the photovoltaic power station (Zone C), due to exposure to strong wind environments, sand and wind erosion and soil loss are extremely likely to occur, affecting the stability of photovoltaic equipment and exacerbating regional desertification. Therefore, the ecological simulation in this area mainly includes: a sand and wind transport model (based on the Saltation-Balance equation) to simulate the characteristics of sand and wind movement, a soil erosion model (based on the RUSLE formula) to evaluate the role of vegetation in soil conservation and predict the soil loss rate, and a sand and wind transport + soil erosion simulation to predict the long-term ecological evolution trend of the periphery of the photovoltaic base.

[0085] In the sand and wind transport model (based on the Saltation-Balance equation), sand and wind transport refers to the process in which sand grains on the ground surface are blown up, collided, and transported under the action of wind, and this process is affected by factors such as wind speed, sand grain size, and surface roughness. The Saltation-Balance equation is used to describe the dynamic balance of sand and wind transport. The formula is: ; is the sand and wind transport flux per unit width (i.e., the amount of sand passing through a certain cross-section per unit time), is the horizontal coordinate in the wind direction, is the wind erosion supply, representing the amount of sand blown up from the ground surface by the wind, and the calculation formula is: ; is the air density (kg / m³), is the preset surface wind erosion coefficient, dimensionless, is the wind speed (m / s), is the threshold wind speed for sand movement (m / s), which determines whether wind erosion occurs. is the sand grain sedimentation amount, representing the amount of sand sedimented due to gravity or vegetation blockage after sand and wind are transported to a certain place, and the calculation formula is: ; is the preset sedimentation coefficient, which is related to factors such as surface roughness and vegetation density.

[0086] In the soil erosion model (based on the RUSLE formula), soil erosion refers to the process of soil particle loss under the action of water erosion and wind erosion, and the presence of vegetation can effectively reduce the soil loss rate. In this study, the RUSLE is used to correct the soil erosion rate to quantify the role of vegetation in soil conservation.

[0087] ; A (unit: t / ha / year), is the soil erosion rate, representing the amount of soil loss per unit area per year, R (unit: MJ·mm / ha·h·year), is the rainfall erosivity factor, representing the impact of rainfall on soil erosion, and the calculation formula is: ; is the kinetic energy (MJ / ha) of the th rainfall, is the maximum rainfall intensity (mm / h) in 30 minutes, n represents the total number of rainfall events, (unit: t·ha·h / MJ·mm), the preset soil erodibility factor, representing the susceptibility of the soil to erosion, and (dimensionless), the preset slope length factor and slope factor, affecting the erosion of soil by water flow, (dimensionless), the vegetation cover factor, representing the role of vegetation in soil conservation, ; is an empirical parameter, is the leaf area index, derived from the vegetation restoration model, (dimensionless), the soil and water conservation measures factor, reflecting the impact of human governance measures, obtained by assigning values according to the specific type of soil and water conservation measures and regional characteristics. Examples of assignments (based on empirical values): If the periphery of the photovoltaic base is completely bare without soil and water conservation measures, then take P = 1. If shrubs or herbaceous vegetation are planted (such as desert green belts), then take P = 0.1 - 0.5. If combined with windbreak and sand fixation projects (such as straw checkerboards, sand barriers), then take P = 0.05 - 0.2. n represents the number of rainfall events in rainfall calculations, while in the Markov chain model, it represents the number of vegetation species. Description of the simulation steps: Combine the wind-sand transport model and the soil erosion model for multi-factor simulation to predict the ecological evolution trend in the peripheral area of the photovoltaic field. Input the wind speed and surface characteristics, and calculate the wind-sand transport flux , input the rainfall and vegetation conditions, calculate the soil erosion rate A, simulate the wind-sand sedimentation and vegetation blocking effects, and predict the soil conservation effect. By predicting the wind-sand erosion trend in the periphery of the photovoltaic base, optimizing the protection system, the inhibitory effects of vegetation on wind erosion and water erosion can be quantified, and the ecological restoration efficiency can be improved.

[0088] The common calculation model one refers to the vegetation restoration index calculation model. Specifically: ; NDVI is the normalized difference vegetation index, i.e., vegetation coverage, and its calculation method is: ; NIR is the near-infrared reflectance, and REDRED is the red light reflectance.

[0089] The reason for using logarithmic transformation is to prevent NDVI from changing too violently when approaching 0 and improve the sensitivity in the small value range. LAI is the leaf area index, i.e., the leaf area ratio obtained by simulation, which is obtained by dividing the total leaf area by the surface area; using power function transformation ( (where is a preset empirical coefficient) to enhance the impact of LAI under different vegetation density conditions. H is the vegetation height, which reflects the growth of vegetation and is obtained by calculating the average vegetation height in the area. Fractional transformation is used to make the contribution of height tend to be stable and avoid the unreasonable impact of too high single-plant height on the index. S is the species diversity index, which measures the ecological stability of the vegetation community and is calculated using the Shannon diversity index. are all preset weight coefficients, determined through data training, and may have different values in different regions. The tanh function is used for normalization, and the final vegetation restoration index is restricted to the interval [-1, 1] to prevent extreme values from affecting the model stability.

[0090] The shared calculation model two refers to the soil carbon storage index calculation model. The soil carbon storage index is used to measure the soil carbon sequestration capacity of different regions. Its calculation is based on key factors such as organic carbon accumulation, nitrogen storage, soil structure stability, and water retention capacity, combined with nonlinear transformation, normalization processing, and the soil carbon cycle model to ensure that the calculation results can comprehensively and scientifically reflect the soil carbon storage capacity.

[0091] Based on the soil quality evolution model and the ecological simulation model, the following data are obtained: Soil organic carbon content: Calculated by the soil carbon cycle simulation, which reflects the soil carbon sequestration capacity. Total soil nitrogen content: Derived from the nutrient dynamics simulation, which determines the soil fertility and organic matter storage. Soil density: Calculated by physical simulation, which affects the soil carbon sequestration capacity. Soil water content: Calculated by the soil water dynamics model, which determines the soil carbon decomposition rate. For the soil carbon state at time t: ; is the current soil organic carbon stock, is the soil organic carbon stock at time moment, is the mineralization loss of soil carbon per unit time, is the elapsed time interval.

[0092] Soil carbon storage not only depends on organic carbon input but is also affected by the soil physical structure. Soil density and porosity determine the soil carbon sequestration capacity, and the calculation is as follows: ; is the contribution value of the soil structure to the carbon storage capacity (dimensionless), BD is the soil density (g / cm³), the greater the density, the smaller the porosity, and the weaker the carbon sequestration capacity.

[0093] Water affects two key processes of soil carbon storage: An overly dry environment will inhibit microbial activity and reduce carbon decomposition, while an overly wet environment will promote carbon decomposition, resulting in accelerated carbon loss. The calculation is as follows:

[0094] ; is a moisture regulation factor (dimensionless), is the current soil moisture content (%), is the soil moisture content most suitable for carbon sequestration (%), is a preset regulation parameter that determines the influence degree of moisture on carbon storage capacity;

[0095] Integrate the above calculation results and use a non-linear transformation to obtain the final soil carbon storage index:

[0096] ; N represents the total soil nitrogen content (g / m²), indicating the influence of the current nitrogen storage on carbon storage stability, C represents the current soil organic carbon stock, 、 、 and are all preset influence coefficients used to measure the influence degree of different parameters on the soil carbon storage index , and the settings may vary in different regions.

[0097] The pre-trained machine learning model is a convolutional neural network model. Convolutional neural network (CNN) is a deep learning model that can automatically extract features, identify patterns and make non-linear decisions, and is suitable for complex environmental data analysis. In this method, CNN is used to comprehensively process three soil carbon storage indexes and three vegetation restoration indexes to calculate the overall ecological restoration and carbon sequestration benefit value of the photovoltaic base. This model can capture the non-linear relationship between the indexes of each region and provide a more accurate evaluation than traditional weighted calculation.

[0098] Structuring of input data: Each photovoltaic base area (the area under the photovoltaic panels, the inter-row channel area of the photovoltaic panels, the peripheral area of the photovoltaic field) generates two core indexes: vegetation restoration index (measuring vegetation growth and coverage), soil carbon storage index (measuring soil carbon fixation ability), forming a data matrix with six input features (three regions × two indexes), which is input into the CNN for calculation.

[0099] Hierarchical Structure Analysis: A CNN mainly consists of an input layer, a convolutional layer, an activation layer, a pooling layer, and a fully connected layer. The functions of each layer are as follows: Input Layer: This layer receives six input data, representing the soil carbon storage index and vegetation restoration index of three regions respectively. These data will be converted into a multi-dimensional feature matrix for subsequent feature extraction. Convolutional Layer: The convolutional layer is the core part of the CNN. It uses multiple filters (also known as convolutional kernels) to scan the input data to capture the patterns and interactions between different regional indices. The main objectives of this layer are: to identify the relationship between soil carbon storage and vegetation restoration in a specific region; to identify the mutual influence of indices between different regions (such as the potential promotion of vegetation restoration in one region on soil carbon storage in adjacent regions), and to discover hidden ecological restoration trends and features. Activation Layer: This layer processes the output of the convolutional layer through a non-linear activation function (such as ReLU), enabling the model to learn complex non-linear relationships. For example: when the vegetation restoration index in a certain region is high, but the soil carbon storage index is low, the model will automatically adjust the weights to evaluate the long-term carbon sink potential of this region. If the indices of all regions show an increasing trend, the CNN may give a higher ecological restoration score.

[0100] Pooling Layer: The function of this layer is to reduce the data dimension, retain the most crucial information, and at the same time reduce the computational complexity. In this application scenario, the pooling layer helps to: aggregate the index data of each region, eliminate the minor fluctuations between regions; extract the most representative ecological restoration patterns and improve the prediction accuracy. Fully Connected Layer: This layer unfolds the pooled features and passes them to the final decision layer. The neurons here comprehensively calculate the weights of different indices to generate a final ecological restoration carbon sequestration benefit score. Output Layer: This layer is responsible for generating the final score result, which is used to quantify the overall ecological restoration and carbon sequestration benefits of the photovoltaic base. The score is usually between 0 and 1 (can be normalized to 0 - 100), which is used to intuitively represent the ecological restoration effect.

[0101] Example 2: An ecological restoration carbon sequestration benefit evaluation system for a gobi photovoltaic base, including:

[0102] Region Division Module, which divides the photovoltaic base area into the area under the photovoltaic panels, the inter-row channel area of the photovoltaic panels, and the peripheral area of the photovoltaic field;

[0103] Simulation and Modeling Module, which is used to establish corresponding ecological simulation models for different regions;

[0104] Data Acquisition Module, which is used to obtain simulation data from the corresponding ecological simulation models for each divided region respectively;

[0105] Region Evaluation Module, which is used to substitute the obtained simulation data into the common calculation model one that reflects the progress and quality of regional vegetation restoration, and the common calculation model two that reflects the soil restoration effect and carbon sequestration capacity respectively;

[0106] An overall evaluation module is used to summarize the calculation results of all regions and substitute them into a pre-trained machine learning model, and the output result is a carbon sequestration benefit value for representing the final score of the overall ecological restoration of the photovoltaic base.

[0107] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0108] It should be understood that in various embodiments of the present application, the magnitudes of the serial numbers of the above processes do not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0109] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0110] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0111] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present application, and all should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for evaluating the ecological restoration and carbon sequestration benefits of a gobi photovoltaic base, characterized in that, It includes the following steps: Divide the photovoltaic base area into the area under the photovoltaic panels, the inter-row channel area of the photovoltaic panels, and the peripheral area of the photovoltaic field, and establish corresponding ecological simulation models for different areas; For each divided area, obtain simulation data from the corresponding ecological simulation model respectively, and substitute them into the common calculation model one that reflects the progress and quality of regional vegetation restoration, and the common calculation model two that reflects the soil remediation effect and carbon sequestration capacity respectively; Summarize the calculation results of all areas and substitute them into the pre-trained machine learning model, and the output result is the ecological restoration carbon sequestration benefit value used to represent the final score of the overall ecological restoration of the photovoltaic base; The common calculation model 1 refers to the vegetation restoration index calculation model; specifically: ; NDVI is the normalized vegetation index, i.e., vegetation coverage, and is calculated as follows: ; NIR is the near infrared reflectance, REDRED is the red light reflectance; LAI is the leaf area index, i.e. the leaf area ratio obtained by simulation, which is obtained by dividing the total leaf area by the surface area; is the preset empirical coefficient, H is the vegetation height, which reflects the vegetation growth, and is obtained by calculating the average height of vegetation in the area. S is the species diversity index, which measures the ecological stability of the vegetation community and is calculated using the Shannon diversity index. All are preset weight coefficients, determined through data training; The shared calculation model two refers to the soil carbon storage index calculation model; ; N represents the total nitrogen content of the soil, and C represents the current soil organic carbon stock, , , and are all preset influence coefficients used to measure the influence degree of different parameters on the soil carbon storage index ; ; where Cs is the contribution value of soil structure to carbon storage capacity, and BD is soil density; ; is a moisture regulation factor, is the current soil moisture content, is the most suitable soil moisture content for carbon sequestration, is a preset regulation parameter.

2. The ecological restoration and carbon sequestration benefit evaluation method for a gobi photovoltaic base according to claim 1, wherein The logic of the ecological simulation model for the area under the photovoltaic panels is as follows: Use the arrangement of the photovoltaic array to simulate the light intensity distribution, and construct a light distribution model: Based on the Richards equation, simulate water infiltration, evaporation and migration, and construct a soil water dynamic model; Adopt the Logistic growth model to construct a vegetation growth model; Use the constructed light distribution model, soil water dynamic model and vegetation growth model for simulation.

3. The ecological restoration and carbon sequestration benefit assessment method for a gobi photovoltaic base according to claim 2, wherein The logic of the ecological simulation model for the inter-row channel area of the photovoltaic panels is as follows: Adopt the Markov chain model to predict the succession of vegetation communities, simulate the competition and succession processes of different species under different restoration strategies, and construct a vegetation restoration model; Use the soil carbon cycle model to simulate the process of organic matter accumulation and mineralization, predict the change of soil carbon storage, and construct a soil quality evolution model; Use the constructed vegetation restoration model and soil quality evolution model for simulation.

4. A method for evaluating the ecological restoration and carbon sequestration benefits of a gobi photovoltaic base according to claim 3, characterized in that The logic of the ecological simulation model for the peripheral area of the photovoltaic field is as follows: Based on the Saltation-Balance equation, simulate the wind-sand movement and construct a wind-sand transport model; Use the RUSLE model to correct the soil loss rate, evaluate the soil protection effect of vegetation, and construct a soil erosion model; Use the constructed wind-sand transport model and soil erosion model for simulation.

5. A method for evaluating the ecological restoration and carbon sequestration benefits of a gobi photovoltaic base according to claim 4, characterized in that, The pre-trained machine learning model is a convolutional neural network model.

6. A gobi photovoltaic base ecological restoration carbon sequestration benefit evaluation system for implementing a gobi photovoltaic base ecological restoration carbon sequestration benefit evaluation method according to any one of claims 1-5, characterized in that, It includes: An area division module that divides the photovoltaic base area into the area under the photovoltaic panels, the inter-row channel area of the photovoltaic panels, and the peripheral area of the photovoltaic field; A simulation module used to establish corresponding ecological simulation models for different areas; A data acquisition module used to obtain simulation data from the corresponding ecological simulation model for each divided area; An area evaluation module used to substitute the obtained simulation data into the common calculation model one that reflects the progress and quality of regional vegetation restoration and the common calculation model two that reflects the soil remediation effect and carbon sequestration capacity respectively; An overall evaluation module used to summarize the calculation results of all areas and substitute them into the pre-trained machine learning model, and the output result is the ecological restoration carbon sequestration benefit value used to represent the final score of the overall ecological restoration of the photovoltaic base.

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