A method and system for assessing ecological influence of a photovoltaic power station based on multi-source information

Abstract

The present application relates to the technical field of ecological impact assessment of photovoltaic power stations, and particularly relates to a photovoltaic power station ecological impact assessment method and system based on multi-source information. The method first matches bird observation data, data of power stations already put into operation and environmental data of the evaluation range of the site to be built, and a similar area close to the site to be built; from the database of power stations already built, historical power station data matching the site to be built in the similar area is found out, so that the functional group damage risk index of each bird functional group is obtained according to the bird observation historical data and the historical data of power stations already put into operation before and after the historical power stations are put into operation, and the relative abundance proportion of each bird functional group in the evaluation range; the functional group damage risk indexes of all bird functional groups are traversed and summed to obtain a total risk index, and then the total risk index is corrected based on the data of power stations already put into operation to obtain a biodiversity damage risk index, so as to evaluate the ecological risk in advance in the site selection stage.

Description

Technical Field

[0001] This invention relates to the field of ecological impact assessment technology for photovoltaic power plants, and in particular to a method and system for ecological impact assessment of photovoltaic power plants based on multi-source information. Background Technology

[0002] With the energy structure shifting towards cleaner and lower-carbon energy, photovoltaic (PV) power generation continues its rapid growth. By the end of 2024, the global cumulative installed PV capacity had exceeded 2,000 gigawatts (GW). my country's cumulative installed capacity exceeded 887 GW, accounting for approximately 40% of the global total. This scale is expected to continue to grow significantly in the foreseeable future. PV deployment is shifting from distributed rooftop power stations to centralized ground-mounted power stations. Because the power generation per unit area of ​​PV is relatively low, centralized ground-mounted power stations require large areas of land. A single project can occupy tens or even hundreds of square kilometers. These power stations are increasingly expanding into areas with diverse ecosystems, such as deserts, Gobi, grasslands, and wetlands. Studies have found that a significant proportion (over 70% in some areas) of centralized PV facilities are located adjacent to nature reserves. This indicates that some PV sites are spatially highly proximate to, or even overlap with, wildlife habitats and migration routes.

[0003] The expansion of photovoltaic (PV) power generation can help mitigate climate change. However, if site selection or design is inadequate, large-scale ground-mounted PV facilities can also have sustained and significant adverse impacts on surrounding bird populations. Firstly, power plant construction requires clearing and leveling large areas of surface vegetation. This directly encroaches on bird habitats and fragments previously contiguous habitats. Secondly, the smooth, reflective surface of PV panels resembles natural water surfaces. This can induce aquatic birds, which rely on water surface visual cues, to mistake the panels for water and land on them. These birds thus face risks such as collisions and entrapment. Considering that most PV facilities are yet to be built, it is crucial to conduct prior assessments and mitigation of these ecological risks during the site selection and planning phase.

[0004] However, currently, no existing photovoltaic (PV) site selection method incorporates the aforementioned ecological impacts into the site selection decision-making process. The assessment objects are consistently limited to non-animal factors such as vegetation, carbon cycle, soil, and crops, completely neglecting the ecological impact of PV facilities on animal populations such as birds. This makes it impossible to quantify the specific impact of PV construction on surrounding bird habitats, hindering decision-makers from avoiding site selection schemes with severe ecological damage. Furthermore, current technologies related to renewable energy facilities involving birds only address methods for detecting or deterring birds around the facilities. These technologies cannot assess ecological risks in advance during the site selection phase. Of particular note is their failure to quantify two types of cumulative impacts: spatial cumulative impacts, i.e., the cumulative exposure resulting from the clustering of multiple PV facilities; and temporal cumulative impacts, i.e., the dynamic cumulative effects that develop year by year after commissioning. Summary of the Invention

[0005] This invention aims to provide a method and system for assessing the ecological impact of photovoltaic power plants based on multi-source information, in order to quantify the specific impact of photovoltaic power plant construction on the habitat and activities of surrounding birds, thereby avoiding photovoltaic site selection schemes that have a serious impact on the surrounding ecology and reducing the adverse impact of the large-scale development of the photovoltaic industry on the ecological environment.

[0006] To achieve the above objectives, the first aspect of the present invention provides a method for assessing the ecological impact of photovoltaic power plants based on multi-source information, comprising the following steps: Obtain the parameters and coordinates of the proposed power station, and determine the evaluation range based on the coordinates of the proposed power station; Obtain bird observation data, operational power plant data, and environmental data within the assessment scope; based on the bird observation data, obtain bird functional groups within the assessment scope; and obtain the relative abundance percentage of each bird functional group. Based on the bird observation data, the operational power plant data, and the environmental data within the assessment scope, similar areas corresponding to the assessment scope are matched. Based on the parameters of the proposed power station, the coordinates of the proposed power station, and the evaluation range, historical power stations corresponding to the proposed power station are matched in the similar areas; Obtain historical bird observation data and historical data of operational power plants in the similar areas; Based on the relative abundance of each bird functional group, the parameters of the proposed power station, the historical power station, the historical bird observation data, and the historical data of the operational power station, the functional group damage risk index of each bird functional group is obtained. The functional group damage risk index of all the bird functional groups is summed to obtain the total risk index. Then, the total risk index is corrected for the cumulative effect of the power station based on the historical data of the power station already in operation to obtain the biodiversity damage risk index. The ecological impact assessment results of photovoltaic power plants are obtained based on the biodiversity damage risk index.

[0007] It should be understood that, since the proposed power station has not yet begun construction, its impact on bird ecological activities around the proposed power station's coordinates cannot be directly observed. To overcome this technical challenge, and to assess ecological risks in advance during the site selection phase, the aforementioned multi-source information photovoltaic power station ecological impact assessment method first matches bird observation data, data from existing power stations, and environmental data within the proposed site's assessment area with similar areas close to the proposed site. From the existing power station database, historical power station data matching the proposed power station are identified in similar areas. Based on historical bird observation data before and after the commissioning of historical power stations and historical data from existing power stations, the biodiversity damage risk index of constructing the power station under the proposed power station's parameters and coordinates is assessed, quantifying the impact on bird ecology within the assessment area.

[0008] Specifically, this invention first uses the difference-in-differences method to estimate the causal impact of constructing a power station under these conditions on the surrounding bird ecology, based on the parameters of the proposed power station, historical power station data, historical bird observation data, and historical data of already operational power stations. Since different bird species have varying sensitivities to the polarized lake effect, this invention also maps species to mutually exclusive functional groups such as shorebirds, waterbirds, waterfowl, and landbirds, and calculates the relative abundance percentage of each functional group within the entire species population. Then, combining multi-source information such as the relative abundance percentage of bird functional groups, the parameters of the proposed power station, historical power station data, historical bird observation data, and historical data of already operational power stations, it analyzes the functional group damage risk index for each bird functional group. This is used to quantify the ecological impact of adding a photovoltaic power station of a specific scale and technical parameters to the surrounding environment, on specific bird functional groups, in addition to existing operational power stations. Considering the cumulative interference caused by existing photovoltaic facilities to bird populations within the environmental area, the invention finally outputs a biodiversity damage risk index and assessment results by summing the risk indices of each functional group and incorporating historical data from already operational power stations for cumulative effect correction, thus quantifying the overall ecological impact of the proposed power station on the surrounding bird populations. According to the present invention, the ecological impact assessment results of photovoltaic power plants can be obtained, and construction sites that pose a high ecological risk to the surrounding habitats can be assessed, identified and avoided during the early planning stage of photovoltaic power plant projects, thereby reducing the adverse impact of photovoltaic power plants on aquatic birds and the overall biodiversity of the region from the source.

[0009] It should be noted that the similar area matching step of this invention, on the one hand, by introducing environmental data and bird observation data to match similar areas, can obtain and evaluate historical data on the environmental data of the assessment area itself that are close to the bird abundance characteristics for analysis, avoiding the misjudgment that the low bird abundance in the assessment area itself is a problem caused by the power plant construction; on the other hand, the bird observation data, data of the already commissioned power plants, and environmental data of the similar areas are close to the assessment area, and the historical power plant locations and parameters of the similar areas are close to the locations and parameters of the proposed power plant within the assessment area. This serves as the basis for transferring the historical patterns of the impact of historical power plants on the local bird ecology after commissioning in similar areas to the proposed power plant, providing a transferable causal inference basis for the prior assessment of the ecological impact of the proposed power plant.

[0010] Furthermore, based on the relative abundance of each of the bird functional groups, the parameters of the proposed power plant, the historical power plants, the historical bird observation data, and the historical data of the operational power plants, the functional group damage risk index for each of the bird functional groups is obtained, including: Based on the parameters of the proposed power station, the historical power stations, the historical bird observation data, and the historical data of the already operational power stations, a spatial decay function, a polarized light attraction index, and a technical adjustment factor are obtained respectively. The spatial decay function characterizes the spatial relationship of the impact of photovoltaic power stations on bird biodiversity decreasing with increasing distance; the polarized light attraction index characterizes the attraction intensity of horizontally linearly polarized light generated on the photovoltaic array surface to aquatic birds; and the technical adjustment factor characterizes the moderating effect of photovoltaic power station engineering technology configuration on ecological risk. For any of the aforementioned bird functional groups: Based on the historical data of the power plants already in operation, the bird functional group sensitivity weights are obtained. These bird functional group sensitivity weights are used to characterize the relative degree to which the bird functional group is affected by photovoltaic power plants due to polarized light pollution. The functional group damage risk index of the bird functional group is obtained based on the bird functional group sensitivity weight, the relative abundance of the bird functional group, the spatial decay function, the polarization attraction index, and the technology adjustment factor.

[0011] In this implementation, the spatial attenuation function, the polarization attraction index, and the technical adjustment factor characterize the harmful mechanisms and impact paths of photovoltaic power plants on bird functional groups from different dimensions. The spatial attenuation function reflects the law that the impact of polarized light signals generated by photovoltaic facilities on bird habitat activities gradually weakens with increasing spatial distance, enabling the assessment results to reflect the ecological impact risk level of the area corresponding to the target assessment range. The polarization attraction index is used to characterize the attraction intensity of horizontally linearly polarized light generated by specific photovoltaic panel materials to birds. The technical adjustment factor is used to reflect the different degrees of impact of different types of photovoltaic power plant engineering technology configurations on ecological risks. Based on this, for each bird functional group, the sensitivity weight of the bird functional group obtained through historical data of operational power plants and the relative abundance proportion of that functional group are combined with the above-mentioned multi-dimensional risk factors, the polarization sensitivity and population size proportion of that functional group, to obtain the functional group damage risk index. Through the above-mentioned technical means, this invention decomposes the harmful mechanism of photovoltaic power stations to bird functional groups into multiple independently quantifiable and physically meaningful dimensions such as spatial attenuation, polarized light attraction, technical parameter adjustment, and functional group sensitivity. It comprehensively assesses the impact of the proposed power station on the bird habitat ecological environment at different proximity distances, thereby improving the pertinence and accuracy of the ecological impact assessment of photovoltaic power stations.

[0012] Furthermore, the step of obtaining the bird functional group sensitivity weights based on the historical data of the operational power station includes: The similar regions are divided into multiple grids; Based on the historical bird observation data, historical data of the functional group abundance index of the bird functional group in each grid were obtained. A bird functional group sensitivity regression equation is constructed, with the historical data of the functional group abundance index in each grid as the dependent variable and the historical data of the operational power station as the independent variable. Then, the bird functional group sensitivity weight of the bird functional group is obtained according to the bird functional group sensitivity regression equation.

[0013] In this implementation, similar areas are divided into multiple grids. Historical data on the abundance index of each bird functional group in each grid are obtained based on bird observation data. A sensitivity regression equation for bird functional groups is constructed, with historical abundance index data of functional groups after the commissioning of historical power plants as the dependent variable and historical data of the commissioned power plants as the independent variable. Based on historical data of historical power plants and historical abundance index data of the surrounding area, treatment and control groups are generated, and the regression results of the regression equation are obtained to quantify the differences in sensitivity of different bird functional groups to the polarized lake effect. This regression equation uses the construction and operation of commissioned power plants in similar areas as the treatment condition of a natural experiment. By comparing the differences in the abundance of bird functional groups in areas affected by photovoltaic facilities and unaffected areas, the relative degree of influence of photovoltaic power plants on each functional group due to polarized light pollution is identified and quantified, thereby obtaining the sensitivity weight of bird functional groups. This ensures that the contribution weight of each functional group in the risk index calculation matches its true ecological sensitivity, improving the accuracy of the assessment results in representing the actual impact on biodiversity.

[0014] Furthermore, the step of obtaining the spatial attenuation function, polarization attraction index, and technical adjustment factor based on the parameters of the proposed power station, the historical power stations, the historical bird observation data, and the historical data of the already operational power stations includes: The similar regions are divided into multiple grids; Historical data on bird abundance indices for each grid are obtained based on the aforementioned historical bird observation data. The similar area is divided into several distance rings with the historical power station as the center. A spatial decay regression equation is constructed with the historical data of the bird abundance index in each grid as the dependent variable and the distance ring and the historical data of the operational power station as the independent variables. Then, the spatial decay function is obtained according to the spatial decay regression equation. The polarization attraction index is obtained based on the parameters of the proposed power station. A technology-modified regression equation is constructed with the historical data of the bird abundance index in each grid as the dependent variable and the parameters of the proposed power station and the historical data of the power stations already in operation as independent variables. Then, the technology-modified factor is obtained based on the technology-modified regression equation.

[0015] In this implementation, similar areas are divided into multiple grids. A spatial decay regression equation is constructed using historical bird abundance index data from each grid after the commissioning of the power station as the dependent variable, and distance loops and historical data from the already commissioned power station as independent variables. This regression equation utilizes the spatial distribution information of the already commissioned power station as a natural experimental condition. By comparing the changes in bird abundance within different distance loops relative to the distant control area, the difference-in-differences effect coefficients of each distance loop are extracted. After normalization, a spatial decay function is obtained to characterize the spatial relationship of the decrease in the impact of photovoltaic facilities on bird biodiversity with increasing distance. This allows the assessment results to reflect the intensity of the differentiated impact of the proposed power station on bird populations within the target assessment area. The polarization attraction index is obtained based on the parameters of the proposed power station, and is preset as a known constant input according to the material type and optical properties of the photovoltaic panel surface of the proposed power station.

[0016] The process of obtaining the technology moderating factors involved constructing a technology-moderating regression equation with historical bird abundance index data in each grid after the commissioning of historical power plants as the dependent variable and the parameters of the proposed power plant and data from already operational power plants as independent variables. In the process of generating treatment and control groups based on historical operating data of historical power plants and historical abundance index data of surrounding areas, multiple regression results were solved by dividing the historical operating data of historical power plants and historical abundance index data of surrounding areas into different groups according to different engineering technology configurations of historical power plants. This allowed for comparison of the inter-group differences in bird abundance response under different engineering technology configuration groups, quantifying the moderating effect of each technology dimension on ecological risk. In the actual assessment process, the corresponding regression results were matched as technology moderating factors according to the actual engineering technology configuration group corresponding to the proposed power plant.

[0017] Furthermore, the functional group damage risk index of all the aforementioned bird functional groups is summed to obtain a total risk index. Then, based on historical data from the operational power plants, the total risk index is corrected for the cumulative effect of the power plants to obtain a biodiversity damage risk index, including: The similar regions are divided into multiple grids; Historical data on bird abundance indices for each grid are obtained based on the aforementioned historical bird observation data. Historical data on the cumulative photovoltaic exposure intensity of each grid were obtained based on the historical data of the power plants already in operation. A cumulative clustering effect regression equation is constructed with the historical data of the bird abundance index in each grid as the dependent variable and the historical data of the cumulative photovoltaic exposure intensity of each grid as the independent variable. Then, the cumulative clustering effect penalty factor is obtained based on the cumulative clustering effect regression equation. The total risk index is corrected for power plant cumulative effect based on the cumulative clustering effect penalty factor to obtain the biodiversity damage risk index.

[0018] In this implementation, the similar area is divided into multiple grids. Historical data on the cumulative photovoltaic exposure intensity of each grid is obtained based on historical data of existing power plants. A cumulative clustering effect regression equation is constructed, with historical bird abundance index data in each grid as the dependent variable and historical photovoltaic cumulative exposure intensity data in each grid as the independent variable. Based on historical operating data of the power plants and historical abundance index data of the surrounding area, processing and control groups are generated, and the regression results of the regression equation are obtained. A cumulative clustering effect penalty factor is obtained based on the regression results of this equation to characterize the cumulative ecological harm caused by the spatial clustering of multiple power plants. Furthermore, the total risk index is corrected based on this cumulative clustering effect penalty factor, thereby considering the cumulative risk assessment of the superimposed interference of existing photovoltaic facilities in the region to correct the total risk index and obtain a biodiversity damage risk index. This ensures that the assessment results can reflect the increased marginal ecological risk of the proposed power plant in the context of existing facility clusters, improving the accuracy of the ecological impact assessment of photovoltaic power plants.

[0019] Furthermore, after summing the functional group damage risk indices of all the aforementioned bird functional groups to obtain a total risk index, and then correcting the total risk index for the cumulative effect of the power plants based on historical data of the operational power plants to obtain a biodiversity damage risk index, the process further includes: Based on the historical bird observation data and the historical data of the power plants already in operation, a regression equation for the changes in bird abundance before and after the historical power plants were put into operation in the similar area was constructed. Then, based on the regression equation for the changes in bird abundance, the time effect curve was obtained through the event study method. Based on the biodiversity damage risk index and the time effect curve, the population change trajectory curve is predicted, and then the ecological impact assessment results of the photovoltaic power station are obtained based on the population change trajectory curve.

[0020] In this implementation, a regression equation for bird abundance changes before and after the commissioning of historical power plants in the similar area is constructed based on historical bird observation data and historical data of already operational power plants. The commissioning time of the already operational power plants is used as the event point, and the changes in bird abundance in each year before and after the power plant's commissioning are incorporated into the regression analysis framework. This allows for the acquisition of a time-effect curve using the event study method, which characterizes the dynamic changes in bird abundance over time after the photovoltaic power plant's commissioning. Based on this, the steady-state risk level represented by the biodiversity damage risk index is combined with the dynamic temporal characteristics represented by the time-effect curve to predict the population change trajectory curve. This yields a prediction of annual population changes after the proposed power plant's commissioning, expanding the ecological impact assessment from the initial construction time of the photovoltaic power plant to a prediction of its ecological impact year by year after construction. This broadens the assessment dimensions of the ecological impact assessment results and ultimately enhances their comprehensiveness.

[0021] Furthermore, after summing the functional group damage risk indices of all the aforementioned bird functional groups to obtain a total risk index, and then correcting the total risk index for the cumulative effect of the power plants based on historical data of the operational power plants to obtain a biodiversity damage risk index, the process further includes: Obtain the coordinates of several candidate power plants; Obtain the biodiversity damage risk index for each of the candidate power station coordinates; A risk heat map is generated based on the biodiversity damage risk index of each candidate power station coordinate, and then the ecological impact assessment results of the photovoltaic power station are obtained based on the risk heat map.

[0022] In this implementation, by acquiring the coordinates of several candidate power stations and calculating the biodiversity damage risk index for each candidate coordinate, a risk heat map is generated based on the risk index of each candidate coordinate. This presents the differences in ecological risk of building new photovoltaic power stations at different coordinate locations as an ecological impact assessment of photovoltaic power stations. This allows decision-makers to intuitively identify spatial locations with lower and higher ecological risks within the same assessment area, quickly screen ecologically friendly candidate sites, avoid site selection schemes with high ecological risks, extend the risk assessment of a single site to the horizontal comparison of multiple candidate sites, expand the assessment dimensions of the ecological impact assessment results, and ultimately improve the comprehensiveness of the ecological impact assessment results.

[0023] A second aspect of this invention provides a photovoltaic power plant ecological impact assessment system based on multi-source information, comprising: The data acquisition module is used to acquire the parameters and coordinates of the proposed power station, and to determine the assessment range based on the coordinates of the proposed power station; the data acquisition module is also used to acquire bird observation data, operational power station data and environmental data within the assessment range, to acquire bird functional groups within the assessment range based on the bird observation data, and to acquire the relative abundance percentage of each bird functional group. The similar area data analysis module is used to match similar areas corresponding to the assessment range based on the bird observation data, the operational power station data, and the environmental data within the assessment range; the similar area data analysis module is also used to match historical power stations corresponding to the proposed power station in the similar areas according to the parameters of the proposed power station, the coordinates of the proposed power station, and the assessment range, and to obtain historical bird observation data and historical operational power station data of the similar areas; The functional group injury risk index analysis module is used to obtain the functional group injury risk index of each bird functional group based on the relative abundance ratio of each bird functional group, the parameters of the proposed power station, the historical power station, the historical bird observation data, and the historical data of the power station already in operation. The biodiversity damage risk index analysis module is used to iterate and sum the functional group damage risk indices of all the bird functional groups to obtain the total risk index. Then, based on the historical data of the power plants already in operation, the total risk index is corrected for the cumulative effect of the power plants to obtain the biodiversity damage risk index. The ecological impact assessment module is used to obtain the ecological impact assessment results of the photovoltaic power station based on the biodiversity damage risk index.

[0024] Furthermore, based on the relative abundance of each of the bird functional groups, the parameters of the proposed power plant, the historical power plants, the historical bird observation data, and the historical data of the operational power plants, the functional group damage risk index for each of the bird functional groups is obtained, including: Based on the parameters of the proposed power station, the historical power stations, the historical bird observation data, and the historical data of the already operational power stations, a spatial decay function, a polarized light attraction index, and a technical adjustment factor are obtained respectively. The spatial decay function characterizes the spatial relationship of the impact of photovoltaic power stations on bird biodiversity decreasing with increasing distance; the polarized light attraction index characterizes the attraction intensity of horizontally linearly polarized light generated on the photovoltaic array surface to aquatic birds; and the technical adjustment factor characterizes the moderating effect of photovoltaic power station engineering technology configuration on ecological risk. For any of the aforementioned bird functional groups: Based on the historical data of the power plants already in operation, the bird functional group sensitivity weights are obtained. These bird functional group sensitivity weights are used to characterize the relative degree to which the bird functional group is affected by photovoltaic power plants due to polarized light pollution. The functional group damage risk index of the bird functional group is obtained based on the bird functional group sensitivity weight, the relative abundance of the bird functional group, the spatial decay function, the polarization attraction index, and the technology adjustment factor.

[0025] Furthermore, the step of obtaining the bird functional group sensitivity weights based on the historical data of the operational power station includes: The similar regions are divided into multiple grids; Based on the historical bird observation data, historical data of the functional group abundance index of the bird functional group in each grid were obtained. A bird functional group sensitivity regression equation is constructed, with the historical data of the functional group abundance index in each grid as the dependent variable and the historical data of the operational power station as the independent variable. Then, the bird functional group sensitivity weight of the bird functional group is obtained according to the bird functional group sensitivity regression equation. Attached Figure Description

[0026] Figure 1 This is a flowchart illustrating a photovoltaic power plant ecological impact assessment method based on multi-source information provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of the time-effect curve of the event study method provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the heterogeneity analysis of avian functional groups provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the spatial distance attenuation curve provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of the structure of a photovoltaic power station ecological impact assessment system based on multi-source information provided in an embodiment of the present invention. Detailed Implementation

[0027] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that the following detailed descriptions are exemplary and intended to provide further detailed explanation of the present invention. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used herein in the specification is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings, are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings are used to distinguish different objects, not to describe a particular order.

[0028] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0029] Current technologies lack continuous quantitative biodiversity indicators. All site selection tools and assessment frameworks treat ecology as a binary exclusion condition, never using species richness indices, bird abundance, or habitat quality scores as continuously weighted decision inputs; the mechanisms of polarized light damage are not incorporated into the assessment system. The polarized light physical characteristics of photovoltaic panels are not converted into calculable ecological risk parameters, thus failing to quantify the damaging intensity of the photovoltaic panel lake effect of photovoltaic power plants; there is a lack of functional group differential sensitivity assessment based on causal inference evidence. Existing schemes do not differentiate the differential sensitivities of different bird functional groups to photovoltaic facilities based on empirical causal inference evidence, failing to identify the much higher polarized light damage risks faced by high-risk groups such as shorebirds, waterfowl, and waterfowl compared to resident terrestrial birds. In the temporal dimension, existing schemes can only provide static or short-term assessment results, unable to simulate the long-term population dynamic trajectory from the onset of effects in the second year to the peak in the seventh year; in the spatial dimension, existing schemes all adopt an independent assessment model for each facility, ignoring the superlinear cumulative ecological hazards generated by dense photovoltaic clusters.

[0030] Please refer to Figure 1 To address the aforementioned technical issues, this invention aims to provide a method and system for assessing the ecological impact of photovoltaic power plants based on multi-source information. This method quantifies the specific impact of photovoltaic power plant construction on the habitat and activities of surrounding birds, thereby avoiding photovoltaic site selection schemes that severely impact the surrounding ecology and reducing the adverse effects of large-scale photovoltaic industry development on the ecological environment.

[0031] To achieve the above objectives, the first embodiment of the present invention provides a method for assessing the ecological impact of photovoltaic power plants based on multi-source information, comprising the following steps: S1. Obtain the parameters and coordinates of the proposed power station, and determine the evaluation range based on the coordinates of the proposed power station; S2. Obtain bird observation data, operational power plant data, and environmental data within the assessment scope; based on the bird observation data, obtain bird functional groups within the assessment scope; and obtain the relative abundance percentage of each bird functional group. S3. Based on the bird observation data, the operational power plant data, and the environmental data within the assessment scope, match similar areas corresponding to the assessment scope; S4. Based on the parameters of the proposed power station, the coordinates of the proposed power station, and the evaluation range, match the historical power stations corresponding to the proposed power station in the similar area; S5. Obtain historical bird observation data and historical power plant data of the similar areas; S6. Based on the relative abundance ratio of each bird functional group, the parameters of the proposed power station, the historical power station, the historical bird observation data, and the historical data of the power station already in operation, obtain the functional group damage risk index for each bird functional group. S7. Sum the functional group damage risk indices of all the bird functional groups to obtain the total risk index. Then, based on the historical data of the power plants already in operation, the total risk index is corrected for the cumulative effect of the power plants to obtain the biodiversity damage risk index. S8. Obtain the ecological impact assessment results of the photovoltaic power station based on the biodiversity damage risk index.

[0032] It should be understood that, since the proposed power station has not yet begun construction, its impact on bird ecological activities around the proposed power station's coordinates cannot be directly observed. To overcome this technical challenge, the aforementioned multi-source information photovoltaic power station ecological impact assessment method, which assesses ecological risks in advance during the site selection phase, firstly matches similar areas close to the proposed site with bird observation data, data from existing power stations, and environmental data within the assessment area of ​​the proposed site. Then, from the database of existing power stations, it identifies historical power station data matching the proposed power station in similar areas. Based on historical bird observation data before and after the commissioning of historical power stations and historical data from existing power stations, it assesses the biodiversity damage risk index of constructing the power station under the proposed power station's parameters and coordinates, quantifying the impact on bird ecology within the assessment area.

[0033] Specifically, this invention first uses the difference-in-differences method to estimate the causal impact of constructing a power station under these conditions on the surrounding bird ecology, based on the parameters of the proposed power station, historical power station data, historical bird observation data, and historical data of already operational power stations. Since different bird species have varying sensitivities to the polarized lake effect, species are first mapped to mutually exclusive functional groups such as shorebirds, waterbirds, waterfowl, and landbirds, and the relative abundance percentage of each functional group within the entire species population is calculated. Then, combining multi-source information such as the relative abundance percentage of bird functional groups, the parameters of the proposed power station, historical power station data, historical bird observation data, and historical data of already operational power stations, the functional group damage risk index for each bird functional group is analyzed. This is used to quantify the ecological impact of adding a photovoltaic power station of a specific scale and technical parameters to the surrounding environment, on specific bird functional groups, in addition to existing operational power stations. Considering the cumulative interference caused by existing photovoltaic facilities to bird populations within the environmental area, the risk indices of each functional group are summed ergonomically, and historical data from already operational power stations are incorporated for cumulative effect correction. The resulting biodiversity damage risk index and assessment results are then output, quantifying the overall ecological impact of the proposed power station on the surrounding bird populations. According to the present invention, the ecological impact assessment results of photovoltaic power plants can be obtained, and construction sites that pose a high ecological risk to the surrounding habitats can be assessed, identified and avoided during the early planning stage of photovoltaic power plant projects, thereby reducing the adverse impact of photovoltaic power plants on aquatic birds and the overall biodiversity of the region from the source.

[0034] It should be noted that the similar area matching step of this invention, on the one hand, by introducing environmental data and bird observation data to match similar areas, can obtain and evaluate historical data on the environmental data of the assessment area itself that are close to the bird abundance characteristics for analysis, avoiding the misjudgment that the low bird abundance in the assessment area itself is a problem caused by the power plant construction; on the other hand, the bird observation data, data of the already commissioned power plants, and environmental data of the similar areas are close to the assessment area, and the historical power plant locations and parameters of the similar areas are close to the locations and parameters of the proposed power plant within the assessment area. This serves as the basis for transferring the historical patterns of the impact of historical power plants on the local bird ecology after commissioning in similar areas to the proposed power plant, providing a transferable causal inference basis for the prior assessment of the ecological impact of the proposed power plant.

[0035] The functional group classification refers to mapping species to mutually exclusive functional groups such as shorebirds, waterbirds, waterfowl, and landbirds, based on the Ornithological Standard Classification System (ACAD).

[0036] In a preferred embodiment, geospatial and environmental data for each grid is acquired using Google Earth Engine, including effective solar radiation intensity, land cover type, NDVI vegetation index, slope, aspect, land use, and nighttime light intensity. A propensity score is calculated using multidimensional covariates such as bird observation data, operational power plant data, geographical location, climate conditions, land use type, NDVI vegetation index, slope, aspect, land use, and nighttime light intensity within the assessment area. This score is then used to match similar areas corresponding to the assessment area, as well as historical power plants that match the proposed power plant.

[0037] Furthermore, based on the relative abundance of each of the bird functional groups, the parameters of the proposed power plant, the historical power plants, the historical bird observation data, and the historical data of the operational power plants, the functional group damage risk index for each of the bird functional groups is obtained, including: Based on the parameters of the proposed power station, the historical power stations, the historical bird observation data, and the historical data of the already operational power stations, a spatial decay function, a polarized light attraction index, and a technical adjustment factor are obtained respectively. The spatial decay function characterizes the spatial relationship of the impact of photovoltaic power stations on bird biodiversity decreasing with increasing distance; the polarized light attraction index characterizes the attraction intensity of horizontally linearly polarized light generated on the photovoltaic array surface to aquatic birds; and the technical adjustment factor characterizes the moderating effect of photovoltaic power station engineering technology configuration on ecological risk. For any of the aforementioned bird functional groups: Based on the historical data of the power plants already in operation, the bird functional group sensitivity weights are obtained. These bird functional group sensitivity weights are used to characterize the relative degree to which the bird functional group is affected by photovoltaic power plants due to polarized light pollution. The functional group damage risk index of the bird functional group is obtained based on the bird functional group sensitivity weight, the relative abundance of the bird functional group, the spatial decay function, the polarization attraction index, and the technology adjustment factor.

[0038] In this implementation, the spatial attenuation function, the polarization attraction index, and the technical adjustment factor characterize the harmful mechanisms and impact paths of photovoltaic power plants on bird functional groups from different dimensions. The spatial attenuation function reflects the law that the impact of polarized light signals generated by photovoltaic facilities on bird habitat activities gradually weakens with increasing spatial distance, enabling the assessment results to reflect the ecological impact risk level of the area corresponding to the target assessment range. The polarization attraction index is used to characterize the attraction intensity of horizontally linearly polarized light generated by specific photovoltaic panel materials to birds. The technical adjustment factor is used to reflect the different degrees of impact of different types of photovoltaic power plant engineering technology configurations on ecological risks. Based on this, for each bird functional group, the sensitivity weight of the bird functional group obtained through historical data of operational power plants and the relative abundance proportion of that functional group are combined with the above-mentioned multi-dimensional risk factors, the polarization sensitivity and population size proportion of that functional group, to obtain the functional group damage risk index. Through the above-mentioned technical means, this invention decomposes the harmful mechanism of photovoltaic power stations to bird functional groups into multiple independently quantifiable and physically meaningful dimensions such as spatial attenuation, polarized light attraction, technical parameter adjustment, and functional group sensitivity. It comprehensively assesses the impact of the proposed power station on the bird habitat ecological environment at different proximity distances, thereby improving the pertinence and accuracy of the ecological impact assessment of photovoltaic power stations.

[0039] In one specific embodiment, the overall calculation relationship of the biodiversity damage risk index is as follows: In the formula, The bird functional group sensitivity weights represent the bird functional group g. This indicates the relative abundance percentage of functional group g in birds. Indicates the attraction index of polarized light. Represents the spatial decay function. Indicates the technology adjustment factor. This represents the penalty factor for cumulative aggregation effects.

[0040] An empirical heterogeneity analysis showed that the degree of photovoltaic effect varies across regions due to different climatic conditions. For example, the photovoltaic effect in the southern part of a region (-0.104, p<0.05) was significantly greater than that in the western part (-0.059, not significant). Based on this, a preferred embodiment introduces a regional correction factor when calculating the biodiversity damage risk index. In one specific embodiment, the regional correction factor for the southern and northeastern regions of a certain area is set to a higher value (1.3–1.5), while the regional correction factor for the central and western regions is set to a lower value (0.8–1.0).

[0041] Preferably, if the assessment area of ​​the proposed power station's coordinates is located within a government-designated critical habitat for endangered species, an additional penalty factor is applied to the biodiversity damage risk index. (Preferred value: 1.5). Empirical data show that the facility effect located within key habitats (-0.085) is greater than the facility effect outside habitats (-0.060).

[0042] Furthermore, the step of obtaining the bird functional group sensitivity weights based on the historical data of the operational power station includes: The similar regions are divided into multiple grids; Based on the historical bird observation data, historical data of the functional group abundance index of the bird functional group in each grid were obtained. A bird functional group sensitivity regression equation is constructed, with the historical data of the functional group abundance index in each grid as the dependent variable and the historical data of the operational power station as the independent variable. Then, the bird functional group sensitivity weight of the bird functional group is obtained according to the bird functional group sensitivity regression equation.

[0043] In this implementation, similar areas are divided into multiple grids. Historical abundance data of bird functional groups in each grid are obtained based on bird observation data. A sensitivity regression equation for bird functional groups is constructed, with historical abundance data of functional groups after the commissioning of historical power plants as the dependent variable and historical data of the commissioned power plants as the independent variable. Based on historical operation data of historical power plants and historical abundance data of the surrounding area, treatment and control groups are generated, and the regression results of the regression equation are obtained to quantify the differences in sensitivity of different bird functional groups to the polarized lake effect. This regression equation uses the construction and operation of commissioned power plants in similar areas as the treatment condition of a natural experiment. By comparing the differences in bird functional group abundance between areas affected by photovoltaic facilities and unaffected areas, the relative degree of influence of photovoltaic power plants on each functional group due to polarized light pollution is identified and quantified, thereby obtaining the sensitivity weight of bird functional groups. This ensures that the contribution weight of each functional group in the risk index calculation matches its true ecological sensitivity, improving the accuracy of the assessment results in representing the actual impact on biodiversity.

[0044] In one specific embodiment, the historical bird observation data is obtained by calling the eBirdAPI to retrieve bird observation records within a specified radius around the planned site, preferably 25 miles. First, the raw abundance data of each bird functional group in each grid is directly obtained from the bird observation records. Then, a Poisson pseudo-maximum likelihood regression framework is applied to the raw abundance data to regress the raw counts to the observation duration and number of observers, while absorbing the fixed effects of time period and month, thereby outputting a bias-corrected bird abundance index. This yields historical data of the functional group abundance index for each bird functional group in each grid, with the historical data of the functional group abundance index in years.

[0045] After obtaining historical data on the abundance index of bird functional groups in each grid, the technical implementation logic of this invention is as follows: For similar areas with assessment range conditions close to the proposed construction site, a bird functional group sensitivity regression equation is first constructed, with historical data on the abundance index of the bird functional group in each grid after the power plant's commissioning as the dependent variable and the data of the commissioned power plant as the independent variable. Appropriate treatment and control groups are constructed using historical data to solve the regression equation and obtain the regression results. Based on the regression results, the bird functional group sensitivity weight for each functional group is obtained. Thus, when conducting actual ecological impact assessments of photovoltaic power plants, as long as the bird functional group species existing within the assessment range are determined, the corresponding bird functional group sensitivity weights can be obtained based on the above analysis results.

[0046] Specifically, the bird functional group sensitivity weights The calculation methods used to characterize the relative degree to which different bird functional groups are affected by photovoltaic facilities due to polarized light pollution include: Geospatial and environmental data for each grid were acquired using Google Earth Engine, including effective solar radiation intensity, land cover type, NDVI vegetation index, slope, aspect, land use, and nighttime light intensity. Propensity scores were calculated using multidimensional covariates such as geographic location, climate conditions, land use type, and NDVI vegetation index. Control areas with similar characteristics were matched to each grid to construct comparable treatment and control groups, thus controlling for endogeneity bias in site selection.

[0047] Based on historical data of functional group abundance indices in the treatment and control groups, each grid, and operational power plants, each functional group was analyzed. Construct panel datasets separately.

[0048] Based on the difference-in-differences (DID) causal inference method, the following avian functional group sensitivity regression equations were constructed and estimated: In the formula, For functional groups In the grid ,years The standardized deviation correction function group abundance index, where year t is the t-th year after the historical power plant was put into operation; This indicates that the grid center is no more than 4 miles from the nearest solar installation. This indicates that the grid center is 20 to 25 miles from the nearest photovoltaic facility, and excludes intermediate samples of 4 to 20 miles. This indicates a year that is greater than or equal to the year in which the facility was put into operation. To control for variables, it is preferable to include distance to key habitats and Interaction items, and whether they are located within key habitats. Interactive items; County and year fixed effects; , , The coefficients of the regression equation are determined by obtaining the regression results of the regression equation.

[0049] The distance from the grid center to the nearest photovoltaic facility, and the year the nearest facility was put into operation, are obtained from historical data of the power plants already in operation.

[0050] Based on panel datasets constructed from the treatment and control groups, the sensitivity regression equations for bird functional groups corresponding to different bird functional groups were solved to obtain the regression results.

[0051] Preferably, the standard error is clustered bidirectionally by county and year. Functional groups are extracted based on the regression results. Treatment effect coefficient The sensitivity weights of bird functional groups are obtained by normalizing them according to the following formula: Preferably, when a certain functional group Statistically insignificant season .

[0052] See Figure 3 , Figure 3This paper presents empirical results demonstrating the differentiated sensitivity of different bird functional groups to photovoltaic facilities. The vertical axis represents the classification of each bird functional group, including all birds, migratory birds, resident birds, landbirds, shorebirds, waterbirds, and waterfowl; the horizontal axis represents the estimated treatment coefficient (in Std. Dev.), expressed in standard deviation. Among these, shorebirds, waterbirds, waterfowl, and landbirds are classified according to their habitat-feeding type and are mutually exclusive functional groups as described in this invention; that is, the mutually exclusive functional groups used to calculate the functional group damage risk index for bird functional groups are shorebirds, waterbirds, waterfowl, and landbirds. Migratory birds and resident birds are treated as a separate orthogonal dimension and are not included in the calculations of this invention.

[0053] The length of the bars corresponding to each functional group represents the magnitude of the DID treatment effect coefficient, and the horizontal error bar represents the 95% confidence interval (95% CI). The figure shows that the absolute value of the DID treatment effect coefficient for shorebirds is the largest (-0.1257), reaching a very high statistical significance level (p<0.001); followed by migratory birds (-0.0768) and waterbirds (-0.0662), both statistically significant; the treatment effect coefficient for waterfowl is -0.0589, also significant; the absolute value of the treatment effect coefficient for land birds is the smallest (-0.0248), and it does not reach a statistical significance level. The treatment effect coefficient for resident birds is positive (0.025), indicating that photovoltaic facilities have no significant negative impact on the abundance of resident birds. The aggregate treatment effect coefficient for all bird species is -0.061, reaching a statistical significance level. The above results provide empirical evidence for assigning sensitivity weights to functional groups in birds: shorebirds, as the functional group most sensitive to polarized light, receive the highest weight, followed by migratory birds and waterbirds, then waterfowl, while land birds have a weight of zero due to the insignificant effect.

[0054] If the planned area for the proposed power station is located within the core area of ​​a major migratory bird corridor, the system applies an additional correction factor to the weights of migratory bird functional groups. The preferred value is 1.2–1.5. This correction is used to reflect the actual situation where bird populations during peak migration periods far exceed those of resident birds.

[0055] In another possible implementation, the sensitivity weights of bird functional groups can be dynamically fitted using machine learning models, such as gradient boosting trees, random forests, or neural networks. Specifically, the sensitivity weights of each functional group are predicted using local bird observation data and environmental characteristics of the planned area as input. This approach is suitable for areas with abundant data.

[0056] Furthermore, the step of obtaining the spatial attenuation function, polarization attraction index, and technical adjustment factor based on the parameters of the proposed power station, the historical power stations, the historical bird observation data, and the historical data of the already operational power stations includes: The similar regions are divided into multiple grids; Historical data on bird abundance indices for each grid are obtained based on the aforementioned historical bird observation data. The similar area is divided into several distance rings with the historical power station as the center. A spatial decay regression equation is constructed with the historical data of the bird abundance index in each grid as the dependent variable and the distance ring and the historical data of the operational power station as the independent variables. Then, the spatial decay function is obtained according to the spatial decay regression equation. The polarization attraction index is obtained based on the parameters of the proposed power station. A technology-modified regression equation is constructed with the historical data of the bird abundance index in each grid as the dependent variable and the parameters of the proposed power station and the historical data of the power stations already in operation as independent variables. Then, the technology-modified factor is obtained based on the technology-modified regression equation.

[0057] In this implementation, similar areas are divided into multiple grids. A spatial decay regression equation is constructed using historical bird abundance index data from each grid after the commissioning of the power station as the dependent variable, and distance loops and historical data from the already commissioned power station as independent variables. This regression equation utilizes the spatial distribution information of the already commissioned power station as a natural experimental condition. By comparing the changes in bird abundance within different distance loops relative to the distant control area, the difference-in-differences effect coefficients of each distance loop are extracted. After normalization, a spatial decay function is obtained to characterize the spatial relationship of the decrease in the impact of photovoltaic facilities on bird biodiversity with increasing distance. This allows the assessment results to reflect the intensity of the differentiated impact of the proposed power station on bird populations within the target assessment area. The polarization attraction index is obtained based on the parameters of the proposed power station, and is preset as a known constant input according to the material type and optical properties of the photovoltaic panel surface of the proposed power station.

[0058] The process of obtaining the technology moderating factors involved constructing a technology-moderating regression equation with historical bird abundance index data in each grid after the commissioning of historical power plants as the dependent variable and the parameters of the proposed power plant and data from already operational power plants as independent variables. In the process of generating treatment and control groups based on historical operating data of historical power plants and historical abundance index data of surrounding areas, multiple regression results were solved by dividing the historical operating data of historical power plants and historical abundance index data of surrounding areas into different groups according to different engineering technology configurations of historical power plants. This allowed for comparison of the inter-group differences in bird abundance response under different engineering technology configuration groups, quantifying the moderating effect of each technology dimension on ecological risk. In the actual assessment process, the corresponding regression results were matched as technology moderating factors according to the actual engineering technology configuration group corresponding to the proposed power plant.

[0059] In one specific embodiment, the spatial attenuation function is used to characterize the spatial relationship of the attenuation of the impact of photovoltaic facilities on bird biodiversity with increasing distance. The calculation method includes dividing the treatment area into distance rings, preferably one distance ring every 2 miles. Using a 20-25 mile region as a control group, the following spatial decay regression equation was estimated: In the formula, the control variables, fixed effects settings, and standard misclustering method are consistent with the estimation process of the sensitivity weights of bird functional groups; For in the grid ,years The standardized bias-corrected bird abundance index, where year t is the t-th year after the historical power station was put into operation. This indicates a year that is greater than or equal to the year in which the facility was put into operation. To control for variables, it is preferable to include distance to key habitats and Interaction items, and whether they are located within key habitats. Interactive items; For county and year fixed effects, Let m be the distance ring corresponding to grid i. For distance loop The difference-in-differences effect coefficient relative to the control group. , The coefficients of the regression equation are determined by obtaining the regression results of the regression equation.

[0060] The distance from the grid center to the nearest photovoltaic facility, and the year the nearest facility was put into operation, are obtained from historical data of the power plants already in operation.

[0061] Similarly, the technical implementation logic of this invention is as follows: for similar areas with conditions close to the proposed site assessment range, a spatial decay regression equation is first constructed. Appropriate processing and control groups are then constructed using historical data to solve this regression equation and obtain the regression results. Based on the regression results, the spatial decay function of the distance ring m corresponding to each grid i is obtained. Thus, when conducting actual ecological impact assessments of photovoltaic power plants, as long as the radius of the assessment range is determined, the distance ring m corresponding to the assessment range can be determined, thereby determining the spatial decay function value corresponding to the ecological impact assessment of this photovoltaic power plant based on the aforementioned regression results.

[0062] Furthermore, after obtaining the regression results of the above regression equation, the difference-in-differences treatment effect coefficients are extracted based on the regression results. The spatial decay function is normalized by the following formula: The decay function for the core area from 0 to 4 miles is 1.0; when a certain distance ring corresponds to Statistically insignificant, i.e. At that time, the attenuation function of the distance loop is set to 0; the normalized coefficient ratios are used for the remaining distance loops. Alternatively, if data supports it, each functional group can be... Estimate separately .

[0063] See Figure 4 , Figure 4 This paper presents empirical results demonstrating the spatial pattern of how the impact of photovoltaic (PV) facilities on bird abundance decreases with increasing distance. The horizontal axis represents the distance from the PV facility (in miles), divided into distance rings at 2-mile intervals: [0,2), [2,4), [4,6), [6,8), [8,10), [10,12), [12,14), [14,16), [16,18), [18,20) miles. The vertical axis represents the effect on bird abundance. The figure presents the difference-in-differences effect coefficients for each distance ring using point estimates (blue dotted lines) and 95% confidence intervals (95% CI, light gray shaded areas). The zero-value reference line is marked with a red dashed line. The figure shows that the core area of ​​influence is approximately 0 to 4 miles, within which the absolute value of the effect coefficient is largest. The effect gradually decays with increasing distance, approaching zero at 8 to 10 miles. Beyond 10 miles, the confidence intervals of the effect coefficient all cover zero, indicating that the effect is no longer statistically significant. This decay pattern is a spatial decay function. The parameterization provides an empirical basis: the decay function is normalized to 1.0 for the core area from 0 to 4 miles, the decay transition area is from 4 to 8 miles, and the area beyond 8 miles is the area with no significant effect.

[0064] With data support, the spatial decay function This can be further refined to the species level. For water-obligate birds such as loons and grebes, which must take off from the water's surface, the distance decay parameter is set to a more conservative value than that for shorebirds, meaning a larger radius of influence. This is because these species cannot take off again once they land on the photovoltaic array, resulting in an extremely high mortality rate.

[0065] In another possible embodiment, a piecewise linear decay function can be used instead of the distance loop method based on regression to obtain the spatial decay function. Specifically, the value is 1.0 when the evaluation range is within the core area (0-4 miles from the proposed power station), linearly decreasing to 0 within the decay zone, and set to 0 in the unaffected zone far from the proposed power station. This scheme is simpler to calculate and can serve as a conservative approximation when sufficient data is lacking to estimate the distance loop regression.

[0066] In one specific embodiment, the polarization attraction index This index is used to characterize the attraction intensity of horizontally linearly polarized light generated on the surface of a photovoltaic array to aquatic birds. The polarization attraction index can be preset as a known constant based on the optical properties of the panel surface. Therefore, in actual ecological impact assessments of photovoltaic power plants, as long as the photovoltaic panel type of the proposed power plant is obtained based on the parameters of the proposed power plant, the corresponding polarization attraction index can be determined. Specifically, a standard crystalline silicon panel without an anti-reflective coating is used. Panels equipped with anti-reflective coating Therefore, the polarization attraction index is directly used as a model input parameter in the calculation, without the need for additional solution.

[0067] Preferably, a meteorological correction factor can be further introduced based on the polarization attraction index. and seasonal correction factor This allows the assessment of the polarized light attraction intensity to possess spatiotemporal dynamic characteristics. Among them: In the formula, CC is the cloud cover coefficient, where a value of 0 represents clear skies and a value of 1 represents complete sun cover. This is the normalized excess value after the relative humidity exceeds the 50% threshold. To correct the parameters based on the seasonal patterns of local bird migration, the seasonal abundance changes of aquatic-related birds in historical monthly eBird observation data can be analyzed, and the relative abundance of aquatic-related birds in each month can be normalized to the [0,1] interval.

[0068] In another possible embodiment, a polarization attraction lookup table can be pre-built, using panel material type and geographical region as index keys to store pre-calculated polarization attraction index values, which can reduce computational complexity and is suitable for mobile application scenarios.

[0069] In one specific embodiment, the technology adjustment factor The calculation methods used to characterize the moderating effect of photovoltaic facility engineering technology configuration on ecological risk include: The technical dimensions of the proposed power plant are obtained based on the parameters of the proposed power plant. These technical dimensions include three types: panel movement type, installed capacity, and land use type.

[0070] For any technical dimension The historical data of the treatment and control groups of historical power plants were divided into two groups based on binary indicator variables. Groups and Groups, so that a set of data includes only The photovoltaic facilities, another set of data only includes Photovoltaic facilities with a value of 0. Specifically, regarding panel motion types, this includes tracking system motion types and fixed-tilt motion types. The tracking system type, relative to the fixed-tilt type, constitutes a risk amplification group in the panel motion type dimension; therefore, the tracking system type is taken as... For fixed tilt angle types, k=0. Regarding installed capacity, facilities larger than the median installed capacity constitute a risk amplification group in terms of scale compared to smaller facilities; therefore, facilities larger than the median installed capacity are... For small facilities, k=0. Regarding land use types, green space, relative to brownfield, constitutes a risk amplification group in the land use dimension; therefore, green space is taken as... For brownfield types, k=0.

[0071] For any technical dimension respectively adopt Groups and The group estimates the following technically moderated regression equation, and obtains... Groups and The regression results for each group: In the formula, the control variables, fixed effects settings, and standard misclustering method are consistent with the estimation process of the sensitivity weights of bird functional groups; For in the grid ,years The standardized bias-corrected bird abundance index, where year t is the t-th year after the historical power station was put into operation. This indicates that the grid center is no more than 4 miles from the nearest solar installation. This indicates that the grid center is 20 to 25 miles from the nearest photovoltaic facility, and excludes intermediate samples of 4 to 20 miles. This indicates a year that is greater than or equal to the year in which the facility was put into operation. To control for variables, it is preferable to include distance to key habitats and Interaction items, and whether they are located within key habitats. Interactive items; For county and year fixed effects.

[0072] To process the effect coefficients, preferably, the standard error is clustered bidirectionally by county and year. , , The coefficients of the regression equation are determined by obtaining the regression results of the regression equation.

[0073] according to Groups and The regression results for each group yielded the treatment effect coefficients for each group. and The statistical significance of the differences between groups was verified by the following Z-test: Furthermore, with The group serves as the baseline group, and the technical dimensions are calculated using the following formula. Regulatory factors: Ultimately, adjustment factors for three technical dimensions—panel motion type, installed capacity, and land use type—were obtained. , and .

[0074] Preferably, when When the statistical significance is not significant, the baseline value of 1.0 is used instead; when When it is not significant, let Preferably, the technology adjustment factor The product is expressed in the following form: Furthermore, the functional group damage risk index of all the aforementioned bird functional groups is summed to obtain a total risk index. Then, based on historical data from the operational power plants, the total risk index is corrected for the cumulative effect of the power plants to obtain a biodiversity damage risk index, including: The similar regions are divided into multiple grids; Historical data on bird abundance indices for each grid are obtained based on the aforementioned historical bird observation data. Historical data on the cumulative photovoltaic exposure intensity of each grid were obtained based on the historical data of the power plants already in operation. A cumulative clustering effect regression equation is constructed with the historical data of the bird abundance index in each grid as the dependent variable and the historical data of the cumulative photovoltaic exposure intensity of each grid as the independent variable. Then, the cumulative clustering effect penalty factor is obtained based on the cumulative clustering effect regression equation. The total risk index is corrected for power plant cumulative effect based on the cumulative clustering effect penalty factor to obtain the biodiversity damage risk index.

[0075] In this implementation, the similar area is divided into multiple grids. Historical data on the cumulative photovoltaic exposure intensity of each grid is obtained based on historical data of existing power plants. A cumulative clustering effect regression equation is constructed, with historical bird abundance index data in each grid as the dependent variable and historical photovoltaic cumulative exposure intensity data in each grid as the independent variable. Based on historical operating data of the power plants and historical abundance index data of the surrounding area, processing and control groups are generated, and the regression results of the regression equation are obtained. A cumulative clustering effect penalty factor is obtained based on the regression results of this equation to characterize the cumulative ecological harm caused by the spatial clustering of multiple power plants. Furthermore, the total risk index is corrected based on this cumulative clustering effect penalty factor, thereby considering the cumulative risk assessment of the superimposed interference of existing photovoltaic facilities in the region to correct the total risk index and obtain a biodiversity damage risk index. This ensures that the assessment results can reflect the increased marginal ecological risk of the proposed power plant in the context of existing facility clusters, improving the accuracy of the ecological impact assessment of photovoltaic power plants.

[0076] In one specific embodiment, the cumulative aggregation effect penalty factor The calculation method used to characterize the superlinear cumulative ecological harm to birds caused by multiple photovoltaic facilities densely clustered in space includes the following steps: First, based on historical data of operational power plants and historical data of bird abundance indices, each grid was analyzed. In the year Calculate the cumulative photovoltaic exposure intensity it receives: In the formula, For grid to facilities distance, For the distance decay weighting function, it is preferred to use Alternatively, it can be adopted. or As a form of robustness test.

[0077] Secondly, based on the cumulative exposure intensity, its marginal effect on bird abundance is estimated, and the cumulative aggregation effect regression equation is as follows: In the formula, For in the grid ,years Standardized bias corrected bird abundance index, To control variables, For county and year fixed effects, This is due to the grid fixation effect.

[0078] Finally, based on the regression estimation... and the estimated cumulative exposure values ​​of the proposed site Construct the cumulative aggregation effect penalty factor according to the following formula: in, The cumulative exposure index is automatically calculated by the system based on the coordinates of the planned site and existing surrounding facilities; when there are no surrounding facilities or season .

[0079] In another possible embodiment, a fixed radius can be used as the search range to directly count the number N of existing facilities within the range. If N exceeds a preset threshold, the total risk index is multiplied by a fixed penalty coefficient to obtain the biodiversity damage risk index.

[0080] Furthermore, after summing the functional group damage risk indices of all the aforementioned bird functional groups to obtain a total risk index, and then correcting the total risk index for the cumulative effect of the power plants based on historical data of the operational power plants to obtain a biodiversity damage risk index, the process further includes: Based on the historical bird observation data and the historical data of the power plants already in operation, a regression equation for the changes in bird abundance before and after the historical power plants were put into operation in the similar area was constructed. Then, based on the regression equation for the changes in bird abundance, the time effect curve was obtained through the event study method. Based on the biodiversity damage risk index and the time effect curve, the population change trajectory curve is predicted, and then the ecological impact assessment results of the photovoltaic power station are obtained based on the population change trajectory curve.

[0081] In this implementation, a regression equation for bird abundance changes before and after the commissioning of historical power plants in the similar area is constructed based on historical bird observation data and historical data of already operational power plants. The commissioning time of the already operational power plants is used as the event point, and the changes in bird abundance in each year before and after the power plant's commissioning are incorporated into the regression analysis framework. This allows for the acquisition of a time-effect curve using the event study method, which characterizes the dynamic changes in bird abundance over time after the photovoltaic power plant's commissioning. Based on this, the steady-state risk level represented by the biodiversity damage risk index is combined with the dynamic temporal characteristics represented by the time-effect curve to predict the population change trajectory curve. This yields a prediction of annual population changes after the proposed power plant's commissioning, expanding the ecological impact assessment from the initial construction time of the photovoltaic power plant to a prediction of its ecological impact year by year after construction. This broadens the assessment dimensions of the ecological impact assessment results and ultimately enhances their comprehensiveness.

[0082] In one specific embodiment, the time-dynamic assessment constructs a time-effect curve using the event study method. To simulate the long-term bird abundance change trajectory after the power station was put into operation, the regression equation for the bird abundance change is as follows: In the formula, ( The event time is relative; preferably, the year before the historical power plant was put into operation is used as the base period, i.e. and The event window is preferably 6 years before and 9 years after the historical power plant's commissioning; the standard error is preferably determined by bidirectional clustering based on county and year. The coefficient sequence is obtained based on the regression results. And normalize it according to the following formula: in, , representing the peak effect coefficient after commissioning.

[0083] Please refer to Figure 2 , Figure 2 The normalized time effect curves obtained based on the event study method are shown. The empirical form is shown. The horizontal axis represents the event time (Year) relative to the historical power plant commissioning year, covering 6 years before commissioning (prior year 6 to prior year 1) to 9 years after commissioning (year 0 to year 9); the vertical axis represents the PV effect on bird abundance (Std. Dev.), expressed in standard deviation. The figure uses the year before commissioning (prior year 1) as the baseline period, marked with a red dashed line, where the effect coefficient is zero. Before commissioning, the point estimates (marked by blue squares) fluctuated around zero, and the 95% confidence interval (95% CI event study, light orange shaded area) covered zero, validating the parallel trend hypothesis. Starting in year 2 after commissioning, the point estimates began to decline continuously, indicating that the negative effects began to appear. By year 7, the negative effects reached their peak, with a difference-in-differences coefficient of −0.0852 (p<0.01). Thereafter, the effects declined somewhat but remained significantly negative for at least seven years. This time dynamic characteristic indicates that the negative impact of photovoltaic facilities on bird abundance is not immediate, but rather has a latency period of approximately two years before peaking in year 7. This long-term dynamic trajectory far exceeds the coverage of the current standard monitoring window of 1 to 2 years.

[0084] Furthermore, based on the aforementioned biodiversity damage risk index, the population change curve trajectory for at least 10 years can be predicted using the following formula: Preferably, when the predicted population decline in the 7th year exceeds 5% of the baseline abundance, an early warning is triggered, and a high-risk marker, technical optimization suggestions, and a prompt to extend the monitoring window are output.

[0085] Furthermore, after summing the functional group damage risk indices of all the aforementioned bird functional groups to obtain a total risk index, and then correcting the total risk index for the cumulative effect of the power plants based on historical data of the operational power plants to obtain a biodiversity damage risk index, the process further includes: Obtain the coordinates of several candidate power plants; Obtain the biodiversity damage risk index for each of the candidate power station coordinates; A risk heat map is generated based on the biodiversity damage risk index of each candidate power station coordinate, and then the ecological impact assessment results of the photovoltaic power station are obtained based on the risk heat map.

[0086] In this implementation, by acquiring the coordinates of several candidate power stations and calculating the biodiversity damage risk index for each candidate coordinate, a risk heat map is generated based on the risk index of each candidate coordinate. This presents the differences in ecological risk of building new photovoltaic power stations at different coordinate locations as an ecological impact assessment of photovoltaic power stations. This allows decision-makers to intuitively identify spatial locations with lower and higher ecological risks within the same assessment area, quickly screen ecologically friendly candidate sites, avoid site selection schemes with high ecological risks, extend the risk assessment of a single site to the horizontal comparison of multiple candidate sites, expand the assessment dimensions of the ecological impact assessment results, and ultimately improve the comprehensiveness of the ecological impact assessment results.

[0087] In one specific embodiment, after obtaining the biodiversity damage risk index, the risk level of the proposed power station is classified according to the obtained biodiversity damage risk index. Preferably, the risk level is classified as follows: low risk when the biodiversity damage risk index is less than 0.3; medium risk when the biodiversity damage risk index is greater than or equal to 0.3 and less than 0.6; high risk when the biodiversity damage risk index is greater than or equal to 0.6 and less than 0.8; and extremely high risk when the biodiversity damage risk index is greater than or equal to 0.8.

[0088] Furthermore, the ecological impact assessment results of the photovoltaic power station shall include at least a biodiversity damage risk index score and risk level color code, details of the contribution of each bird functional group, risk heat map, 10-year population change trajectory curve, and technical optimization suggestions; wherein, the technical optimization suggestions shall include at least adjusting the photovoltaic panel motion type in the parameters of the proposed power station from the tracking system type to the fixed tilt angle type, adding an anti-reflection coating to the photovoltaic panel, increasing the distance between the coordinates of the proposed power station and the most recently commissioned power station, or adjusting the coordinates of the proposed power station.

[0089] Furthermore, the candidate site area can be scanned in a grid and the biodiversity damage risk index of each candidate location can be calculated to generate a risk heat map and output alternative site selection schemes for the proposed power plant.

[0090] Furthermore, based on literature on ecosystem service value, the system converts the biodiversity damage risk index into a monetized environmental cost estimate. Specifically, using literature on bird ecosystem service value as a reference, and combining the ecological impact reflected by the biodiversity damage risk index, the system estimates the average annual environmental cost of the proposed power plant to the surrounding ecological environment. This allows the non-market-based implicit environmental costs to be compared and weighed with explicit economic costs such as land lease costs, infrastructure costs, and power generation revenue on the same scale.

[0091] The second embodiment of the present invention provides a photovoltaic power plant ecological impact assessment system based on multi-source information. The system includes a data input and preprocessing module, a core risk assessment calculation module, a long-term population dynamics simulation and early warning module, and a report generation and site selection optimization module. Each module is connected in sequence through a standardized data interface to form a complete assessment calculation pipeline.

[0092] The data input and preprocessing module is responsible for receiving user input parameters and fusing multi-source external data. The technical implementation is as follows: User parameter reception: Receive parameters (panel type, coating status, installed capacity, land type) and coordinates of the proposed power station through a natural language interaction interface based on a large language model or a standardized API interface.

[0093] Bird ecological data acquisition: Bird observation records within a specified radius around the planned site are obtained by calling the eBirdAPI (a RESTful interface provided by the Cornell Lab of Ornithology) or similar publicly available bird observation data interfaces in China, such as birdwatching record centers and synchronous wetland waterbird surveys. The system performs bias correction processing on the raw observation data, using a Poisson pseudo-maximum likelihood regression framework to regress the original inventory-level counts to the observation duration and number of observers, absorbing the fixed effects of time period and month, and outputting the historical data of bird abundance index in each grid after bias correction, obtaining the grid-year bird abundance index. Subsequently, a functional group classifier (based on the ACAD classification system) is run to map each species to four mutually exclusive functional groups: shorebirds, waterbirds, waterfowl, and landbirds, obtaining the historical data of functional group abundance index in each grid for each bird functional group, and calculating the relative abundance percentage (Ag) of each functional group in the planned area.

[0094] Photovoltaic facility data acquisition: By connecting to the USPVDB database, we can obtain the location coordinates, installed capacity, and commissioning time of power plants already in operation within a 30-mile radius of the planned site.

[0095] Geospatial and environmental data acquisition: Obtain effective solar radiation intensity, land cover type, NDVI vegetation index, slope, aspect, land use, and nighttime light intensity through Google Earth Engine.

[0096] Preprocessing output: This module outputs a structured data packet containing the abundance ratio vector of each functional group, a list of surrounding facility locations, and the technical parameters input by the user, which is then passed to the core risk assessment calculation module.

[0097] The core risk assessment calculation module receives the output data from the preprocessing module and executes the BIRS core formula calculation. The technical implementation is as follows: The parameter calculation submodule sequentially calls five core parameters: bird function group sensitivity weights. (The regression results were obtained from the pre-set bird functional group sensitivity DID regression equation and normalized), polarized light attraction index. (Select a preset constant based on the panel coating condition), spatial attenuation function (The regression results are read and normalized from the preset spatial decay DID regression equation), technical adjustment factor. (Calculate the coefficient ratio from the regression results read from the preset technically adjusted DID regression equation) and the cumulative aggregation effect penalty factor. (Calculated based on the regression results of the cumulative agglomeration effect regression equation and the distribution of existing power plants around the proposed site).

[0098] The biodiversity damage risk index calculation submodule iterates through all functional groups and sums the results to calculate... Multiply by Obtain the biodiversity damage risk index .

[0099] This module outputs a biodiversity damage risk index score and details of the contributions of each bird functional group.

[0100] The long-term population dynamics simulation and early warning module receives biodiversity damage risk index scores and time-effect curves obtained based on event study regression. A 10-year population change trajectory simulation was performed.

[0101] Trajectory simulation submodule: Built-in normalized time effect curve ( (up to 10 years), calculated year by year. Output the 10-year population change trajectory curve.

[0102] Early warning submodule: Determines whether the predicted population decline in year 7 (peak effect year) exceeds a preset threshold. If it does, an early warning is triggered, and risk level markers, technical optimization suggestions, and monitoring window suggestions are output.

[0103] The report generation and site selection optimization module summarizes all the above calculation results and outputs a standardized assessment report, including a biodiversity damage risk index score and risk level color code, risk heat map, 10-year population change trajectory curve, and a list of technical optimization suggestions.

[0104] Optional site selection optimization submodule: Calculates the biodiversity damage risk index value of each candidate power plant site grid in the region using a grid scanning method, generates a risk heat map, marks the lowest risk location, and provides technical optimization suggestions.

[0105] The third embodiment of this invention provides a specific application case of the photovoltaic power station ecological impact assessment method based on multi-source information, using a proposed large-scale photovoltaic power station in a certain area as the assessment object. This proposed power station uses a single-axis tracking system with an installed capacity of 30MW. No anti-reflective coating is planned, and the site is an undeveloped plateau desert grassland (green space). The planned site is located in the core area of ​​a bird migration corridor. Within a 10km radius (approximately 6 miles), there are already three operational photovoltaic facilities, the closest of which is about 4km from the planned site. Local bird observation data comes from birdwatching record centers, synchronous wetland waterbird surveys, and eBird data, showing that shorebirds (including black-winged stilts, ringed plovers, and common greenshanks) account for approximately 35% of the bird community in this area; migratory birds account for approximately 25%; waterbirds account for approximately 15%; waterfowl account for approximately 10%; and land birds account for approximately 15%.

[0106] During the data input and preprocessing stage, users input the coordinates of the proposed power station (36.2°N, 100.5°E) and parameters such as panel type (tracking system), installed capacity (30MW), coating type (non-anti-reflective coating), and land type (green space) through the system's natural language interface. The system automatically connects to a bird observation data interface to obtain local bird functional group composition and abundance data. This bird observation data interface can interface with data sources such as eBirdAPI, birdwatching record centers, and wetland waterbird synchronous surveys. Simultaneously, it connects to a photovoltaic power station data interface to obtain information on surrounding operational facilities. This photovoltaic power station data interface can interface with the USPVDB, the National Photovoltaic Power Generation Information Management Platform, or local renewable energy databases. The preprocessing module completes bird abundance bias correction, functional group classification, and divides the space into spatial grids in 2-mile (approximately 3.2 km) distance loops.

[0107] In the core calculation stage of the functional group injury risk index, the polarized light attraction index is determined based on a standard crystalline silicon panel without anti-reflection coating, using a preset constant value. The sensitivity weights for the bird functional group are extracted and normalized using pre-set difference-in-differences regression results to obtain the treatment effect coefficients for each functional group. The absolute value of the difference-in-differences coefficient is largest for shorebirds, normalized to 1.0; for waterfowl, it is normalized to 0.53; for waterfowl, it is normalized to 0.47; and for land birds, the coefficient is insignificant and is set to 0. The spatial attenuation function is calculated based on distance. Since the planned site is adjacent to the boundary of existing facilities and located within the 0-4 mile core area, the spatial attenuation function is set to 1.0 and fully included. The technology adjustment factor is calculated based on the ratio of heterogeneous difference-in-differences regression coefficients of the technology adjustment regression equation. The adjustment factor for the panel motion type dimension takes the tracking system as the risk amplification group and is normalized to a value greater than 1.0. In actual implementation, the corresponding adjustment factor value is taken with the tracking system as the risk amplification group. Similarly, the adjustment factors for the installed capacity dimension and the land use type dimension are also determined by the ratio of heterogeneous regression coefficients. Based on the above parameters, the functional group injury risk index for each bird group was calculated. The contribution results for each functional group are as follows: For shorebirds, the functional group sensitivity weight is 1.00, relative abundance is 0.35, polarized light attraction index is 0.84, and spatial decay function is 1.0, indicating a relatively high contribution value to the functional group injury risk index. For waterbirds, the functional group sensitivity weight is 0.53, relative abundance is 0.15, polarized light attraction index is 0.84, and spatial decay function is 1.0. For waterfowl, the functional group sensitivity weight is 0.47, relative abundance is 0.10, polarized light attraction index is 0.84, and spatial decay function is 1.0. For land birds, the functional group sensitivity weight is 0, and the contribution value is 0. The total risk index is obtained by summing the results for all functional groups.

[0108] During the cumulative effect correction phase, the system calculates the cumulative exposure index of the planned site by performing a distance-weighted summation based on the distances between the planned site and the three surrounding operational facilities. The cumulative clustering effect penalty factor is calculated based on the regression coefficients of the pre-solved cumulative clustering effect regression equation. Due to the dense surrounding facilities, the cumulative clustering effect penalty factor is greater than 1.0, thus amplifying the overall risk index.

[0109] During the dynamic simulation and early warning phase, the system simulated a 10-year population trajectory based on the regression coefficient sequence of the event study method. The prediction results showed that the predicted bird abundance decline exceeded the 5% warning threshold in the 7th year. The system output the following assessment results: the biodiversity damage risk index for this site is 0.72, corresponding to a high-risk level; if construction at this location is necessary, it is recommended to change the panel movement type from a tracking system to a fixed-tilt system and install an anti-reflective coating. It is also recommended to increase the distance from the most recently operational facilities. Based on ecosystem service value literature, the estimated annual ecosystem service loss corresponding to this biodiversity damage risk index of 0.72 is approximately US$230 per acre. This environmental cost estimate can be directly compared with the site's explicit economic benefits, such as power generation revenue and land rental income, providing a cost-benefit analysis basis for site selection decisions.

[0110] Preferably, if the vegetation dimension is evaluated simultaneously, the system can replace the dependent variable with the NDVI vegetation cover index, obtain long-term time-series NDVI data around the proposed site through Google Earth Engine, use the same propensity score matching-difference-differences framework to estimate the causal impact of photovoltaic construction on the surrounding vegetation, and incorporate the results into the comprehensive ecological impact score.

[0111] The fourth embodiment of this invention provides another specific application case of the photovoltaic power station ecological impact assessment method based on multi-source information, using a proposed photovoltaic power station on a former chemical mining site (brownfield) as the assessment object. The proposed power station uses a fixed-tilt system with an installed capacity of 8MW, and plans to install anti-reflective coatings. The site is a former chemical mining site (brownfield), which is hardened and disturbed land with no ecological protection value after the end of mining activities. Within a 15km (approximately 9.3 miles) radius, there is only one operational photovoltaic facility, located approximately 19km (approximately 12 miles) away. Local birdwatching data shows that the area is dominated by resident terrestrial birds (approximately 65%), shorebirds (approximately 3%), migratory birds (approximately 20%), and waterfowl and waterfowl (approximately 12%).

[0112] During the data input and preprocessing stage, users input planning parameters through the system interface. The system automatically connects to data sources such as the birdwatching record center, eBird data, wetland waterbird synchronous survey, and the national photovoltaic power generation information management platform to obtain bird data and information on surrounding facilities in the region.

[0113] In the core calculation phase of the functional group injury risk index, the polarized light attraction index was determined based on a panel equipped with an anti-reflection coating, with a preset constant value of 0.50, significantly reducing the degree of polarization. In the sensitivity weights for the bird functional group, the values ​​were normalized to 1.00 for shorebirds, 0.53 for waterfowl, and 0.47 for waterfowl; the value for land birds was 0 due to the insignificant difference-in-differences coefficient. Regarding the spatial attenuation function, since the nearest operational facility is approximately 12 miles away, the site is located in a remote area, and the spatial attenuation function was set to 0.50. In the technology adjustment factors, the panel motion type dimension used a fixed tilt angle as the baseline value, the installed capacity dimension used a baseline value due to the small facility size, and the land use type dimension used a baseline value due to brownfield status; all sub-factors were close to 1.0. Based on the above parameters, the functional group injury risk index for each bird functional group was calculated. The contribution results for each functional group are as follows: for shorebirds, the functional group sensitivity weight is 1.00, the relative abundance percentage is 0.03, the polarization attraction index is 0.50, the spatial attenuation function is 0.50, the technical adjustment factor is approximately 1.0, and the contribution value is 0.0075; for waterbirds, the contribution value is 0.008; for waterfowl, the contribution value is 0.007; and for land birds, the functional group sensitivity weight is 0, and the contribution value is 0. The total risk index is obtained by summing all functional groups.

[0114] During the cumulative effect correction phase, there were no operational facilities within a 10-mile radius, and the nearest facility was approximately 12 miles away. The cumulative exposure index was extremely low, and the cumulative aggregation effect penalty factor was set to 1.0. The final calculated biodiversity damage risk index was below 0.3, corresponding to a low-risk level.

[0115] During the dynamic simulation and early warning phase, the predicted abundance decline in year 7 was far below the 5% warning threshold. The system output ecological impact assessment result was: the biodiversity risk at this site is extremely low, making it suitable for construction.

[0116] Please refer to Figure 5 The fifth embodiment of the present invention provides a photovoltaic power plant ecological impact assessment system based on multi-source information, comprising: The data acquisition module 100 is used to acquire the parameters and coordinates of the proposed power station, and to determine the assessment range based on the coordinates of the proposed power station; the data acquisition module is also used to acquire bird observation data, operational power station data and environmental data within the assessment range, to acquire bird functional groups within the assessment range based on the bird observation data, and to acquire the relative abundance percentage of each bird functional group. The similar area data analysis module 200 is used to match similar areas corresponding to the assessment range based on the bird observation data, the operational power station data, and the environmental data within the assessment range; the similar area data analysis module is also used to match historical power stations corresponding to the proposed power station within the similar areas according to the parameters of the proposed power station, the coordinates of the proposed power station, and the assessment range, and to obtain historical bird observation data and historical operational power station data of the similar areas; The functional group injury risk index analysis module 300 is used to obtain the functional group injury risk index of each bird functional group based on the relative abundance ratio of each bird functional group, the parameters of the proposed power station, the historical power station, the historical bird observation data and the historical data of the power station already in operation. The biodiversity damage risk index analysis module 400 is used to iterate and sum the functional group damage risk indices of all the bird functional groups to obtain the total risk index, and then correct the total risk index for the cumulative effect of the power station based on the historical data of the power station already in operation to obtain the biodiversity damage risk index. The ecological impact assessment module 500 is used to obtain the ecological impact assessment results of the photovoltaic power station based on the biodiversity damage risk index.

[0117] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0118] The term "embodiment" as used herein means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described; however, any combination of these technical features that does not contradict each other should be considered within the scope of this specification.

[0119] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various improvements and substitutions without departing from the concept of this application, and these improvements and substitutions should also be considered within the scope of protection of this invention. Therefore, the scope of protection of this application should be determined by the appended claims.

Claims

1. A method for assessing the ecological impact of photovoltaic power plants based on multi-source information, characterized in that, include: Obtain the parameters and coordinates of the proposed power station, and determine the evaluation range based on the coordinates of the proposed power station; Obtain bird observation data, operational power plant data, and environmental data within the assessment scope; based on the bird observation data, obtain bird functional groups within the assessment scope; and obtain the relative abundance percentage of each bird functional group. Based on the bird observation data, the operational power plant data, and the environmental data within the assessment scope, similar areas corresponding to the assessment scope are matched. Based on the parameters of the proposed power station, the coordinates of the proposed power station, and the evaluation range, historical power stations corresponding to the proposed power station are matched in the similar areas; Obtain historical bird observation data and historical data of operational power plants in the similar areas; Based on the relative abundance of each bird functional group, the parameters of the proposed power plant, the historical power plants, the historical bird observation data, and the historical data of the operational power plants, the functional group damage risk index for each bird functional group is obtained, specifically as follows: The similar regions are divided into multiple grids; Historical data on bird abundance indices for each grid are obtained based on the aforementioned historical bird observation data. The similar area is divided into several distance rings with the historical power station as the center. A spatial decay regression equation is constructed with the historical data of the bird abundance index in each grid as the dependent variable and the distance ring and the historical data of the operational power station as the independent variables. Then, the spatial decay function is obtained according to the spatial decay regression equation. The polarization attraction index is obtained based on the parameters of the proposed power station. A technically modulated regression equation is constructed with the historical data of the bird abundance index in each grid as the dependent variable and the parameters of the proposed power station and the historical data of the power station already in operation as independent variables. Then, the technically modulated factor is obtained based on the technically modulated regression equation. For any of the aforementioned bird functional groups: Based on the historical bird observation data, historical data of the functional group abundance index of the bird functional group in each grid were obtained. A bird functional group sensitivity regression equation is constructed with the historical data of the functional group abundance index in each grid as the dependent variable and the historical data of the operational power station as the independent variable. Then, the bird functional group sensitivity weight of the bird functional group is obtained according to the bird functional group sensitivity regression equation. The functional group damage risk index of the bird functional group is obtained based on the bird functional group sensitivity weight, the relative abundance of the bird functional group, the spatial decay function, the polarization attraction index, and the technology adjustment factor. The functional group damage risk index of all the bird functional groups is summed to obtain the total risk index. Then, the total risk index is corrected for the cumulative effect of the power station based on the historical data of the power station already in operation to obtain the biodiversity damage risk index. The ecological impact assessment results of photovoltaic power plants are obtained based on the biodiversity damage risk index.

2. The method for assessing the ecological impact of photovoltaic power plants based on multi-source information according to claim 1, characterized in that, The functional group damage risk index of all the aforementioned bird functional groups is summed to obtain a total risk index. Then, based on historical data from the operational power plants, the total risk index is corrected for the cumulative effect of the power plants to obtain a biodiversity damage risk index, including: The similar regions are divided into multiple grids; Historical data on bird abundance indices for each grid are obtained based on the aforementioned historical bird observation data. Historical data on the cumulative photovoltaic exposure intensity of each grid were obtained based on the historical data of the power plants already in operation. A cumulative clustering effect regression equation is constructed with the historical data of the bird abundance index in each grid as the dependent variable and the historical data of the cumulative photovoltaic exposure intensity of each grid as the independent variable. Then, the cumulative clustering effect penalty factor is obtained based on the cumulative clustering effect regression equation. The total risk index is corrected for power plant cumulative effect based on the cumulative clustering effect penalty factor to obtain the biodiversity damage risk index.

3. The method for assessing the ecological impact of photovoltaic power plants based on multi-source information according to claim 1, characterized in that, The process of summing the functional group damage risk indices of all the aforementioned bird functional groups to obtain a total risk index, and then correcting the total risk index for the cumulative effect of the power plants based on historical data of the operational power plants to obtain a biodiversity damage risk index, further includes: Based on the historical bird observation data and the historical data of the power plants already in operation, a regression equation for the changes in bird abundance before and after the historical power plants were put into operation in the similar area was constructed. Then, based on the regression equation for the changes in bird abundance, the time effect curve was obtained through the event study method. Based on the biodiversity damage risk index and the time effect curve, the population change trajectory curve is predicted, and then the ecological impact assessment results of the photovoltaic power station are obtained based on the population change trajectory curve.

4. The method for assessing the ecological impact of photovoltaic power plants based on multi-source information according to claim 1, characterized in that, The process of summing the functional group damage risk indices of all the aforementioned bird functional groups to obtain a total risk index, and then correcting the total risk index for the cumulative effect of the power plants based on historical data of the operational power plants to obtain a biodiversity damage risk index, further includes: Obtain the coordinates of several candidate power plants; Obtain the biodiversity damage risk index for each of the candidate power station coordinates; A risk heat map is generated based on the biodiversity damage risk index of each candidate power station coordinate, and then the ecological impact assessment results of the photovoltaic power station are obtained based on the risk heat map.

5. A photovoltaic power plant ecological impact assessment system based on multi-source information, characterized in that, include: The data acquisition module is used to acquire the parameters and coordinates of the proposed power station, and to determine the assessment range based on the coordinates of the proposed power station; the data acquisition module is also used to acquire bird observation data, operational power station data and environmental data within the assessment range, to acquire bird functional groups within the assessment range based on the bird observation data, and to acquire the relative abundance percentage of each bird functional group. The similar area data analysis module is used to match similar areas corresponding to the assessment range based on the bird observation data, the operational power station data, and the environmental data within the assessment range. The similar area data analysis module is also used to match historical power stations corresponding to the proposed power station in the similar area based on the parameters of the proposed power station, the coordinates of the proposed power station, and the evaluation range, and to obtain historical bird observation data and historical data of the power stations already in operation in the similar area; The functional group injury risk index analysis module is used to obtain the functional group injury risk index for each of the bird functional groups based on the relative abundance ratio of each bird functional group, the parameters of the proposed power station, the historical power station, the historical bird observation data, and the historical data of the operational power station. Specifically: The similar regions are divided into multiple grids; Historical data on bird abundance indices for each grid are obtained based on the aforementioned historical bird observation data. The similar area is divided into several distance rings with the historical power station as the center. A spatial decay regression equation is constructed with the historical data of the bird abundance index in each grid as the dependent variable and the distance ring and the historical data of the operational power station as the independent variables. Then, the spatial decay function is obtained according to the spatial decay regression equation. The polarization attraction index is obtained based on the parameters of the proposed power station. A technically modulated regression equation is constructed with the historical data of the bird abundance index in each grid as the dependent variable and the parameters of the proposed power station and the historical data of the power station already in operation as independent variables. Then, the technically modulated factor is obtained based on the technically modulated regression equation. For any of the aforementioned bird functional groups: Based on the historical bird observation data, historical data of the functional group abundance index of the bird functional group in each grid were obtained. A bird functional group sensitivity regression equation is constructed with the historical data of the functional group abundance index in each grid as the dependent variable and the historical data of the operational power station as the independent variable. Then, the bird functional group sensitivity weight of the bird functional group is obtained according to the bird functional group sensitivity regression equation. The functional group damage risk index of the bird functional group is obtained based on the bird functional group sensitivity weight, the relative abundance of the bird functional group, the spatial decay function, the polarization attraction index, and the technology adjustment factor. The biodiversity damage risk index analysis module is used to iterate and sum the functional group damage risk indices of all the bird functional groups to obtain the total risk index. Then, based on the historical data of the power plants already in operation, the total risk index is corrected for the cumulative effect of the power plants to obtain the biodiversity damage risk index. The ecological impact assessment module is used to obtain the ecological impact assessment results of the photovoltaic power station based on the biodiversity damage risk index.

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