Method, apparatus, product, and medium for assessing the impact of photovoltaic installations on grassland ecosystems

Abstract

This invention relates to a method, equipment, product, and medium for assessing the impact of photovoltaic (PV) installations on grassland ecosystems, and pertains to the field of ecological and environmental monitoring technology. The method involves acquiring solar-induced chlorophyll fluorescence (SEL) remote sensing data of the PV installation area over a preset time span; constructing a chlorophyll fluorescence remote sensing time series of the PV installation area based on the SEL data; processing the chlorophyll fluorescence time series using a time series breakpoint detection algorithm to identify target mutation points caused by PV installation activities; determining photosynthetic disturbance intensity parameters and photosynthetic recovery rate parameters based on the chlorophyll fluorescence time series data before and after the target mutation points; and determining the assessment results of the ecosystem impact on the PV installation area based on the photosynthetic disturbance intensity parameters and photosynthetic recovery rate parameters. This method improves the accuracy of assessing the impact of PV installations on grassland ecosystems.

Description

Technical Field

[0001] This application relates to the field of ecological environment monitoring technology, specifically to an assessment method, equipment, product, and medium for the impact of photovoltaic installation on grassland ecosystems. Background Technology

[0002] With the accelerated global energy transition, solar energy, as a clean and renewable energy source, has experienced explosive growth in its development and utilization. To access more solar energy resources, large-scale photovoltaic (PV) power plants often require vast areas of land for installing photovoltaic panels. However, the coverage of PV modules significantly alters the microclimate environment, including surface radiation balance, temperature, humidity, and wind speed, thus having a considerable impact on soil properties and vegetation growth within the installation area. Therefore, scientifically and accurately assessing the impact of PV installations on the local ecosystem is crucial for the green and sustainable development of the PV industry and the protection of the ecological environment.

[0003] Currently, in the field of photovoltaic (PV) ecological impact assessment, comparative analysis or model prediction methods are commonly used. For example, a Chinese patent application with publication number CN117217558A discloses a method for assessing the environmental impact of PV systems. This method obtains design parameters (such as soil conductivity and atmospheric humidity), structural parameters (such as PV panel tilt angle and height), and assessment parameters at both in-station and out-of-station points of the PV system. It calculates the absolute value of the relative differences between the assessment parameters at in-station and out-of-station points and uses the CRITIC weighting method to derive the assessment results. Simultaneously, this method also utilizes machine learning to construct a predictive model, predicting the environmental impact of the PV system by inputting design and structural parameters.

[0004] However, existing technologies lack direct monitoring methods for the physiological function of vegetation, making it difficult to accurately identify and quantify the actual impact of photovoltaic installation activities on the core functions of the ecosystem. It is also difficult to distinguish whether ecosystem changes are caused by photovoltaic installation or by other environmental factors, which can easily lead to insufficient accuracy in the assessment of ecosystem impact. Summary of the Invention

[0005] This application provides a method, equipment, product, and medium for assessing the impact of photovoltaic installation on grassland ecosystems, which improves the accuracy of the assessment of the impact of photovoltaic installation on grassland ecosystems.

[0006] The first aspect of this application provides a method for assessing the impact of photovoltaic installations on grassland ecosystems, specifically including: Acquire solar-induced chlorophyll fluorescence remote sensing data of the photovoltaic installation area within a preset time span; Construct a time series of chlorophyll fluorescence remote sensing data for photovoltaic installation areas; A time-series breakpoint detection algorithm was used to process chlorophyll fluorescence remote sensing time series to identify target mutation points caused by photovoltaic installation activities. Based on chlorophyll fluorescence remote sensing time series data before and after the target mutation point, the photosynthetic disturbance intensity parameter and photosynthetic recovery rate parameter were determined. The assessment results of the impact on the ecosystem of the photovoltaic installation area were determined based on the photosynthetic disturbance intensity parameter and the photosynthetic recovery rate parameter.

[0007] By employing the aforementioned technical solution, solar-induced chlorophyll fluorescence remote sensing data of the photovoltaic (PV) installation area within a preset time span is first acquired. This data directly reflects the actual physiological state of vegetation photosynthesis, providing a direct quantitative basis for assessing changes in ecosystem function. Subsequently, a chlorophyll fluorescence remote sensing time series is constructed based on the acquired solar-induced chlorophyll fluorescence remote sensing data, enabling dynamic tracking of the continuous changes in photosynthetic activity of the ecosystem in the PV installation area and fully capturing its dynamic response trajectory before and after PV installation. Next, a time series breakpoint detection algorithm is used to process the chlorophyll fluorescence remote sensing time series, objectively and accurately identifying the target mutation point caused by PV installation activities, effectively separating the drastic changes in photosynthetic function caused by human interference from the natural growth rhythm and seasonal fluctuations of vegetation. After identifying the target mutation point, based on the chlorophyll fluorescence remote sensing time series data before and after the mutation point, the photosynthetic disturbance intensity parameter and photosynthetic recovery rate parameter are determined. The former quantifies the instantaneous impact of PV installation on the photosynthetic function of the grassland ecosystem, while the latter quantifies the ecosystem's self-repair ability and recovery speed after being disturbed. Finally, the assessment results were determined by integrating the photosynthetic disturbance intensity parameters and photosynthetic recovery rate parameters. This ensured that the final assessment conclusion not only reflected the magnitude of the negative impact of the disturbance but also included positive information about the ecosystem's recovery capacity, forming a comprehensive and multi-dimensional evaluation of the ecosystem's response process. Thus, by starting with remote sensing data that directly reflects ecosystem function, combining it with time-series analysis for dynamic tracking and precise attribution, and performing bidirectional quantification of the disturbance and recovery processes, the accuracy of the assessment of the impact on the ecosystem in photovoltaic installation areas was significantly improved.

[0008] Optionally, the construction of a chlorophyll fluorescence remote sensing time series of the photovoltaic installation area based on solar-induced chlorophyll fluorescence remote sensing data specifically includes: Spatial matching was performed on solar-induced chlorophyll fluorescence remote sensing data to extract a subset of data covering the photovoltaic installation area and a preset surrounding area; Perform time resampling on a subset of data to convert the original time resolution into a uniform preset time scale; The resampled data was filtered and smoothed to obtain a continuous chlorophyll fluorescence remote sensing time series.

[0009] By employing the aforementioned technical solutions, spatial matching was performed on solar-induced chlorophyll fluorescence remote sensing data, and a data subset covering the photovoltaic installation area and a preset surrounding range was extracted. This accurately defined the analysis area, reduced data interference, and improved data processing efficiency. Temporal resampling converted the original temporal resolution to a uniform preset time scale, resolving the uneven time intervals caused by revisit cycles and cloud cover in remote sensing images, thus achieving standardization and comparability of the time series. Filtering and smoothing eliminated atmospheric and sensor-introduced noise, supplemented missing data, and transformed discrete data into a continuous and smooth time series curve. This multi-dimensional feature fusion processing method constructed a complete and continuous chlorophyll fluorescence remote sensing time series, accurately revealing the dynamic changes in the photovoltaic installation area and surrounding vegetation.

[0010] Optionally, the step of filtering and smoothing the resampled data to obtain a continuous chlorophyll fluorescence remote sensing time series specifically includes: Calculate the statistical mean and standard deviation of the resampled data; An effective data distribution interval is constructed based on the statistical mean, standard deviation, and a preset deviation factor; Identify outlier observations in the resampled data whose values ​​are outside the effective data distribution range, and remove outlier observations from the resampled data; The preset time series smoothing algorithm is invoked to smooth the data after removing abnormal observation points, eliminating short-term random fluctuations in the data and generating a continuous chlorophyll fluorescence remote sensing time series.

[0011] By employing the aforementioned technical solution, the basic statistical characteristics of the data distribution were established by calculating the statistical mean and standard deviation of the resampled data. Based on the statistical mean, standard deviation, and a preset deviation factor, an effective data distribution interval was constructed, forming a quantitative standard for data quality control. Abnormal observation points whose values ​​exceeded the effective distribution interval were identified and removed, eliminating obvious outliers in the data. A preset time-series smoothing algorithm was applied to smooth the data after outlier removal, eliminating short-term random fluctuations and generating a continuous chlorophyll fluorescence remote sensing time series reflecting the patterns of vegetation growth and change. This multi-step data optimization method achieved accurate identification and removal of outliers, ensuring the continuity and reliability of the time-series data.

[0012] Optionally, the step of using a time-series breakpoint detection algorithm to process the chlorophyll fluorescence remote sensing time series and identify target abrupt change points caused by photovoltaic installation activities specifically includes: Seasonal effect correction was applied to the chlorophyll fluorescence remote sensing time series to obtain the deseasonalized time series. By using a time series abrupt change detection algorithm, the deseasonalized time series is scanned to identify the points in time when structural changes occur in the data; Obtain the construction cycle information of the photovoltaic installation area, filter the time points that fall within the time window corresponding to the construction cycle information, and determine them as the target mutation points.

[0013] By employing the aforementioned technical solutions, seasonal effect correction was applied to the chlorophyll fluorescence remote sensing time series, eliminating the influence of periodic changes in vegetation growth and highlighting the abnormal changes caused by human interference. A time series mutation detection algorithm was used to scan the deseasonalized time series, identifying moments of significant data structure changes and effectively capturing drastic fluctuations in vegetation growth. Combined with construction cycle information of the photovoltaic (PV) installation area, moments falling within the construction time window were selected as target mutation points, accurately pinpointing the time nodes of vegetation changes caused by PV construction activities. This analytical method, combining seasonal correction and mutation detection, achieves precise localization of vegetation changes caused by PV construction.

[0014] Optionally, the seasonality correction processing of the chlorophyll fluorescence remote sensing time series to obtain a deseasonalized time series specifically includes: Calculate the multi-year climatological mean and standard deviation for each time point in the time series; Subtract the multi-year climatological mean from the original chlorophyll fluorescence value and divide by the standard deviation to obtain the standardized sequence value; Deseasonalized time series are constructed based on the standardized sequence values ​​at all time points.

[0015] By employing the aforementioned technical solution, the multi-year climatological mean and standard deviation corresponding to each time point in the time series were calculated, establishing a benchmark characteristic reflecting the normal seasonal variation pattern of vegetation. The original chlorophyll fluorescence values ​​were standardized by subtracting the multi-year climatological mean and dividing by the standard deviation, eliminating the influence of seasonal fluctuations in vegetation growth. A deseasonalized time series was constructed based on the standardized sequence values ​​of all time points, highlighting the abnormal fluctuations in vegetation growth relative to normal seasonal changes. This statistically standardized seasonal effect correction method effectively isolates the natural seasonal variations in vegetation growth, making the vegetation change characteristics caused by human interference such as photovoltaic construction more apparent.

[0016] Optionally, determining the photosynthetic perturbation intensity parameter and photosynthetic recovery rate parameter based on chlorophyll fluorescence remote sensing time series data before and after the target mutation point specifically includes: The difference in chlorophyll fluorescence characteristic values ​​before and after the occurrence of the target mutation point is calculated, and the difference is determined as the photosynthetic disturbance intensity parameter characterizing the degree of photosynthetic function impairment. The recovery observation period after identifying the target mutation point; The rate of change of chlorophyll fluorescence value during the recovery observation period was calculated, and the rate of change was determined as the photosynthetic recovery rate parameter characterizing the ecological self-repair capacity.

[0017] By employing the aforementioned technical solution, the difference in chlorophyll fluorescence characteristic values ​​before and after the target mutation point is calculated and determined as the photosynthetic disturbance intensity parameter. This directly reflects the specific magnitude of the decline in vegetation photosynthetic capacity caused by photovoltaic construction, elevating the impact assessment from qualitative description to quantitative measurement. By determining the recovery observation period and calculating the rate of change in chlorophyll fluorescence values ​​as the photosynthetic recovery rate parameter, the self-repair process of vegetation is transformed into a measurable numerical indicator, revealing the differences in the adaptability of different regions and vegetation types to photovoltaic construction interference. This parameterized assessment method based on key time nodes simplifies the complex ecological impact process into two intuitive numerical indicators, quantifying both the initial degree of damage and characterizing the later recovery trend, providing accurate judgment criteria for the ecological impact assessment and site selection optimization of photovoltaic projects.

[0018] Optionally, the assessment results of determining the impact on the ecosystem of the photovoltaic installation area based on the photosynthetic disturbance intensity parameter and the photosynthetic recovery rate parameter specifically include: The photosynthetic disturbance response index is determined based on the photosynthetic recovery rate parameter and the photosynthetic disturbance intensity parameter; If the photosynthetic disturbance response index is greater than or equal to 1, the assessment result is that the region has a high photosynthetic recovery capacity and the recovery rate exceeds the disturbance amplitude. If the photosynthetic disturbance response index is less than 1 and greater than or equal to 0, the assessment result is that the photovoltaic interference effect persists. If the photosynthetic disturbance response index is less than 0, the assessment result is that the photovoltaic installation causes long-term photosynthetic decline.

[0019] By employing the aforementioned technical solution, a photosynthetic disturbance response index was constructed using photosynthetic recovery rate parameters and photosynthetic disturbance intensity parameters, thus establishing a quantitative evaluation index that comprehensively reflects the degree of vegetation damage and its recovery capacity. When the photosynthetic disturbance response index is greater than or equal to 1, it indicates that the vegetation possesses a strong self-repair capability, with the recovery rate exceeding the damage caused by the initial disturbance. When the index is between 0 and 1, it reflects that while the vegetation shows a recovery trend, the negative impacts of photovoltaic construction have not been completely eliminated. When the index is less than 0, it reveals a serious ecological risk of a continuous decline in vegetation photosynthetic capacity due to photovoltaic installation. This ratio-based hierarchical assessment method transforms vegetation response characteristics into clearly defined numerical ranges, achieving objective quantification and risk classification of the ecological impact of photovoltaic projects.

[0020] In a second aspect, this application provides an electronic device for an assessment method of the impact of photovoltaic installation on grassland ecosystems. The electronic device includes: one or more processors and a memory; the memory is coupled to the one or more processors and is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the electronic device for the assessment method of the impact of photovoltaic installation on grassland ecosystems to perform the method as described in the first aspect and any possible implementation thereof.

[0021] Thirdly, this application provides a computer program product containing instructions that, when run on an electronic device for an assessment method of the impact of photovoltaic installation on grassland ecosystems, cause the electronic device to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, this application provides a computer-readable storage medium including instructions that, when executed on a device for assessing the impact of photovoltaic installations on grassland ecosystems, cause the electronic device to perform the methods described in the first aspect and any possible implementation thereof. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the architecture of an assessment system for the impact of photovoltaic installation on grassland ecosystems provided in an embodiment of this application; Figure 2 This is a flowchart illustrating a method for assessing the impact of photovoltaic installation on grassland ecosystems, as provided in an embodiment of this application. Figure 3 This is an exemplary hardware structure diagram of an electronic device for assessing the impact of photovoltaic installation on grassland ecosystems, provided in an embodiment of this application. Detailed Implementation

[0024] Figure 1 An exemplary system architecture 10 for an assessment system of the impact of photovoltaic installation on grassland ecosystems is shown.

[0025] like Figure 1 As shown, system architecture 10 may include electronic device 11, network 12, and monitoring device 13. Network 12 serves as the medium for providing a communication link between electronic device 11 and monitoring device 13. Network 12 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0026] Staff can use electronic device 11 to interact with monitoring device 13 via network 12 to receive or send data. Various data processing applications can be installed on electronic device 11, such as chlorophyll fluorescence data analysis applications and ecological assessment applications.

[0027] Electronic device 11 is hardware and can be various electronic devices with a display screen, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0028] Monitoring device 13 can be a device that provides remote sensing data acquisition of chlorophyll fluorescence, such as a remote sensor equipped with a spectrometer, a satellite, or other monitoring equipment. The monitoring device can continuously monitor the photovoltaic installation area, collect solar-induced chlorophyll fluorescence data, and transmit the data to electronic equipment for analysis and processing.

[0029] The monitoring device 13 here corresponds to a key step in the original plan: acquiring remote sensing data on solar-induced chlorophyll fluorescence in the photovoltaic installation area. It is an important component responsible for data acquisition in the entire evaluation system.

[0030] The following detailed explanation uses the electronic device side as an example.

[0031] This embodiment provides a method for assessing the impact of photovoltaic installation on grassland ecosystems. Figure 2 This is a flowchart illustrating a method for assessing the impact of photovoltaic installation on grassland ecosystems, as provided in an embodiment of this application. Figure 2 As shown, the method includes steps S101 to S105: S101: Acquire solar-induced chlorophyll fluorescence remote sensing data of the photovoltaic installation area within a preset time span.

[0032] In this embodiment, solar-induced chlorophyll fluorescence remote sensing data refers to fluorescence signal data reflecting the photosynthetic state of vegetation acquired through satellites or other remote sensing platforms. This data can reflect the real-time activity level of the plant's photosynthetic system. When photovoltaic facilities are installed and block sunlight or alter the surface environment, the chlorophyll fluorescence signal of the surrounding vegetation will change accordingly. By monitoring the temporal changes in fluorescence signals, the impact of photovoltaic installation on the photosynthetic function of the grassland ecosystem can be quantified. The photovoltaic installation area refers to the surface area occupied by the photovoltaic power station construction and the surrounding affected area.

[0033] Specifically, the electronic device retrieves the geographic coordinate boundary information of the target photovoltaic installation area from the remote sensing data service platform, submits the coordinate boundary information as a spatial query condition to the satellite remote sensing database, and the remote sensing database returns all available solar-induced chlorophyll fluorescence remote sensing data records covering the target area. The electronic device performs time filtering on the returned data records according to the start and end time nodes of a preset time span, retaining all data records whose timestamps fall within the preset time span. The filtered data records contain attribute information such as fluorescence intensity value, spatial resolution, and cloud coverage corresponding to each time point. The electronic device downloads the filtered data records to the local storage system and completes the format conversion. The converted data is stored in the form of standard raster images, with each pixel carrying the chlorophyll fluorescence intensity value of the corresponding location, thereby completing the acquisition of solar-induced chlorophyll fluorescence remote sensing data.

[0034] S102: Constructing a time series of chlorophyll fluorescence remote sensing data for photovoltaic installation areas based on solar-induced chlorophyll fluorescence remote sensing data.

[0035] Specifically, the electronic device reads the spatial coordinate information of each raster image in the acquired solar-induced chlorophyll fluorescence remote sensing data, matches the boundary coordinates of the photovoltaic laying area with the spatial coordinates of the raster image, identifies all pixel positions within the boundary coordinate range, extracts the matched pixels and their fluorescence intensity values ​​to form a spatial subset, and performs spatial averaging on the fluorescence intensity values ​​of multiple pixels in the spatial subset at the same time point. The averaged value is used as the representative fluorescence value of the photovoltaic laying area at the current time point. The electronic device iterates through all time nodes within a preset time span, repeats the spatial averaging operation, and organizes the representative fluorescence values ​​calculated at each time node into a one-dimensional array in chronological order. Each element in the array corresponds to a time node and its fluorescence intensity value. The electronic device performs time resampling on the one-dimensional array, converting the original irregular time interval data into data points with a uniform time interval. The resampled data points maintain an equidistant distribution over time. The electronic device applies a filtering and smoothing algorithm to the resampled data points to eliminate short-term random fluctuations and noise interference in the data. The filtered data retains long-term trends and periodic characteristics, thus completing the construction of the chlorophyll fluorescence remote sensing time series of the photovoltaic laying area.

[0036] Based on the above embodiments, as an optional embodiment, the step of constructing a chlorophyll fluorescence remote sensing time series of the photovoltaic installation area based on solar-induced chlorophyll fluorescence remote sensing data includes steps S201 to S203: S201: Spatial matching of solar-induced chlorophyll fluorescence remote sensing data to extract a subset of data covering the photovoltaic installation area and a preset surrounding area.

[0037] In this embodiment, the data subset refers to a portion of the data extracted from the complete solar-induced chlorophyll fluorescence remote sensing data that meets the spatial range requirements. This subset includes both fluorescence data from the photovoltaic installation area itself and fluorescence data within a predetermined perimeter. The predetermined perimeter represents the buffer distance extending outward from the boundary of the photovoltaic installation area.

[0038] Specifically, the electronic device acquires the boundary coordinates of the photovoltaic installation area and extends these boundary coordinates outwards along the four cardinal directions (north, south, east, and west) by a predetermined distance to form an expanded spatial query range. The electronic device then reads the georeferenced information of each raster image in the solar-induced chlorophyll fluorescence remote sensing data. It compares the center point coordinates of each pixel in the raster image with the expanded spatial query range to determine if the pixel's center point falls within the latitude and longitude boundaries of the spatial query range. The electronic device marks all pixels whose center points fall within the spatial query range and separates the marked pixels and their associated fluorescence intensity values, timestamps, quality identifiers, and other attribute information from the original remote sensing data. The separated data forms an independent dataset. The electronic device reassembles this dataset according to the spatial topology of the original raster image, maintaining the relative positional relationships between pixels. The reassembled data retains complete spatial structure and time series information, thus completing the extraction of a subset of data covering the photovoltaic installation area and the predetermined surrounding range.

[0039] S202: Perform time resampling on a subset of data to convert the original time resolution into a uniform preset time scale.

[0040] In this embodiment, temporal resampling refers to the process of converting remote sensing data with different time intervals or irregular time distributions into data with fixed time intervals. Temporal resampling can eliminate the uneven time distribution of the original data caused by factors such as the unpredictable satellite transit time and data loss due to cloud cover. The converted data exhibits a regular, evenly spaced distribution on the time axis, which facilitates subsequent time series analysis and abrupt change detection.

[0041] Specifically, the electronic device reads the timestamp information of all data records in the data subset, statistically analyzes the time interval distribution characteristics between adjacent data records, identifies data segments with irregular time interval changes, and generates a standard time node sequence with equal spacing within a preset time span according to a preset time scale. The time interval between any two adjacent nodes in the standard time node sequence remains constant. The electronic device traverses each node in the standard time node sequence, searches for several records in the original data records whose timestamps are closest to the current standard node, and performs a time-weighted average calculation on the fluorescence intensity values ​​carried by the found records. The weighting coefficient is set in reverse according to the time distance between the record timestamp and the standard node, with records that are closer in time receiving a greater weight. The electronic device assigns the weighted average fluorescence intensity value to the current standard time node. After traversal, each standard time node obtains a resampled fluorescence intensity value. The electronic device organizes all standard time nodes and their corresponding fluorescence intensity values ​​into a new time series data in chronological order. The data points in the new time series data are evenly distributed on the time axis, thus completing the conversion of the data subset from the original time resolution to a unified preset time scale.

[0042] S203: The resampled data is filtered and smoothed to obtain a continuous chlorophyll fluorescence remote sensing time series.

[0043] In this embodiment, filtering and smoothing refers to the process of eliminating random noise and short-term fluctuations in time series data through mathematical algorithms. This process can preserve the long-term trend and periodicity of the data while suppressing high-frequency noise introduced by factors such as changes in atmospheric conditions, sensor errors, and cloud residue. The processed data curves are smoother and more continuous, and can truly reflect the essential changes in vegetation photosynthetic function.

[0044] Specifically, the electronic device reads the fluorescence intensity values ​​of all data points in the resampled time series data, calculates the statistical mean and statistical standard deviation of the fluorescence intensity values ​​of all data points, multiplies the statistical standard deviation by a preset deviation factor to obtain the maximum allowable deviation threshold, constructs an effective value interval centered on the statistical mean and with the maximum deviation threshold as the radius, traverses each data point in the time series, and determines whether the fluorescence intensity value of the data point falls within the effective value interval. The electronic device marks data points outside the effective value interval as outliers and removes them from the time series. The remaining time series retains normal data points that conform to the statistical distribution. After outlier removal, the time series is smoothed using a sliding window algorithm. A fixed-width sliding window moves along the time axis. Each time the window moves to a new position, the weighted average of the fluorescence intensity values ​​of all data points within the window is calculated. The electronic device uses the calculated weighted average as the smoothed fluorescence intensity value corresponding to the time point at the center of the window. After traversal, a smoothed fluorescence intensity value is obtained for each time point. The electronic device organizes all time points and their corresponding smoothed fluorescence intensity values ​​in chronological order into the final data series. The fluorescence intensity values ​​in the final data series show a smooth and continuous trend on the time axis, thus completing the generation of a continuous chlorophyll fluorescence remote sensing time series.

[0045] Based on the above embodiments, as an optional embodiment, the step of filtering and smoothing the resampled data to obtain a continuous chlorophyll fluorescence remote sensing time series includes steps S301 to S304: S301: Calculate the statistical mean and standard deviation of the resampled data.

[0046] Specifically, the electronic device reads the fluorescence intensity values ​​of all time points in the resampled time series data and stores the read fluorescence intensity values ​​as a numerical array. The electronic device iterates through each element in the numerical array, sums the values ​​of all elements, counts the total number of elements in the numerical array, divides the sum by the total number of elements to obtain the statistical mean of the resampled data, iterates through each element in the numerical array again, calculates the difference between each element's value and the statistical mean, squares the difference and sums them, divides the sum of squares by the total number of elements minus 1, and performs a square root operation on the division result to obtain the standard deviation of the resampled data. The electronic device stores the calculated statistical mean and standard deviation as reference parameters for subsequent anomaly detection.

[0047] S302: Construct an effective data distribution interval based on the statistical mean, standard deviation, and preset deviation multiple.

[0048] In this embodiment, the effective data distribution interval refers to the numerical boundary used to determine whether a data point belongs to the normal range. The effective data distribution interval determines a reasonable range of data fluctuations based on statistical principles. Data points falling within the distribution interval are considered valid observations conforming to normal statistical laws, while data points falling outside the distribution interval are considered invalid observations due to abnormal interference. By dividing the distribution interval, noisy data in the time series can be systematically identified and removed. A preset deviation factor is used to represent the amplification factor of the standard deviation, and the deviation factor determines the width of the effective data distribution interval.

[0049] Specifically, the electronic device reads the calculated statistical mean and standard deviation values, retrieves the preset deviation factor parameter from the configuration file, multiplies the standard deviation value by the deviation factor parameter to obtain the maximum allowable deviation range, subtracts the maximum deviation range from the statistical mean value, and uses the result as the lower boundary value of the effective data distribution interval. The electronic device adds the maximum deviation range to the statistical mean value, and uses the result as the upper boundary value of the effective data distribution interval. The electronic device combines the lower and upper boundary values ​​to form a closed interval. The two endpoints of the closed interval limit the normal fluctuation range of the fluorescence intensity data. The electronic device stores the constructed effective data distribution interval as the basis for subsequent data filtering, thus completing the construction of an effective data distribution interval based on the statistical mean, standard deviation, and preset deviation factor.

[0050] S303: Identify outlier observations in the resampled data whose values ​​are outside the valid data distribution range, and remove outlier observations from the resampled data.

[0051] Specifically, the electronic device reads the upper and lower boundary values ​​of the constructed effective data distribution interval, creates a blank outlier index list to record the location information of outlier observation points, traverses each data point in the resampled time series data, reads the fluorescence intensity value corresponding to the current data point, determines whether the fluorescence intensity value is less than the lower boundary value of the effective data distribution interval, and if the result is yes, adds the time index of the current data point to the outlier index list. The electronic device then determines whether the fluorescence intensity value is greater than the upper boundary value of the effective data distribution interval, and if the result is yes, adds the time index of the current data point to the outlier index list. After the electronic device completes the traversal, the outlier index list contains the locations of all data points that exceed the effective data distribution interval. Based on the time index recorded in the outlier index list, the electronic device deletes the corresponding data points from the resampled time series data. After the deletion operation, only data points whose fluorescence intensity values ​​fall within the effective data distribution interval are retained in the time series data. The electronic device re-arranges the time index of the deleted time series data, maintaining the time order relationship of the remaining data points unchanged, thereby completing the identification and removal of outlier observation points.

[0052] S304: Call the preset time series smoothing algorithm to smooth the data after removing abnormal observation points, eliminate short-term random fluctuations in the data, and generate a continuous chlorophyll fluorescence remote sensing time series.

[0053] Specifically, the electronic device reads the time series data after removing outlier observation points, obtains the fluorescence intensity values ​​and corresponding time markers of all data points in the time series, loads a preset time series smoothing algorithm from the algorithm configuration library, sets the width parameter of the sliding window according to the algorithm requirements, and determines the number of data points involved in each smoothing operation. The electronic device positions the sliding window at the beginning of the time series, reads the fluorescence intensity values ​​of all data points within the window, performs a weighted average calculation on the fluorescence intensity values ​​within the window, and assigns weighting coefficients according to the distance between the data points and the center of the window, with data points closer to the center of the window receiving higher weights. The electronic device uses the weighted average calculation result as the smoothed fluorescence intensity value corresponding to the time point at the center of the window, stores the smoothing result in a new time series array, moves the sliding window along the time axis to the next position, and repeats the weighted average calculation and result storage operation until the sliding window has traversed all data points in the time series. The electronic device arranges the stored smoothed fluorescence intensity values ​​in chronological order to form a complete smoothed time series. The numerical changes in the smoothed time series exhibit continuous and gradual characteristics, thereby completing the elimination of short-term random fluctuations and the generation of a continuous chlorophyll fluorescence remote sensing time series.

[0054] S103: The time series breakpoint detection algorithm is used to process the chlorophyll fluorescence remote sensing time series to identify the target mutation points caused by photovoltaic installation activities.

[0055] In this embodiment, the time series breakpoint detection algorithm refers to a calculation method used to identify locations in time series data where statistical characteristics undergo significant changes. The time series breakpoint detection algorithm analyzes the statistical characteristics of the data series, such as the mean, variance, and trend, to locate time points where the data distribution pattern undergoes abrupt changes. These abrupt changes may correspond to key periods such as the construction of photovoltaic power plants and the large-scale installation of photovoltaic modules. Accurately identifying these abrupt changes can provide a temporal basis for assessing the impact of photovoltaic installation on the vegetation ecosystem. The target abrupt change point represents a time series breakpoint with a causal relationship to the photovoltaic installation activities, and the time location of the target abrupt change point should coincide with the actual construction progress of the photovoltaic project.

[0056] Specifically, the electronic device reads the generated continuous chlorophyll fluorescence remote sensing time series data, obtains the fluorescence intensity values ​​and time markers of all data points in the time series, loads the time series breakpoint detection algorithm from the algorithm library, sets the parameters required for the algorithm to run, including minimum breakpoint interval, significance test threshold, penalty coefficient, and other configuration items, inputs the time series data into the breakpoint detection algorithm, the algorithm performs segmented fitting on the time series, calculates the fitting error at each possible segment position, and the electronic device searches for the segmentation scheme that minimizes the global fitting error through the optimization algorithm. During the search process, the balance between the number of segments and the fitting accuracy is comprehensively considered. The electronic device extracts the boundary points between each segment in the optimized segmentation scheme. These boundary points correspond to the moments when the statistical characteristics of the time series change abruptly. The electronic device reads the construction records and construction progress information of the photovoltaic installation project, compares and matches the detected abrupt change point time with the project construction time, and filters out abrupt change points whose time positions coincide with or are closely adjacent to the photovoltaic installation activities. The filtered results are marked as target abrupt change points. The time information and fluorescence intensity change range of the target abrupt change points are recorded for subsequent impact assessment, thereby completing the identification of target abrupt change points caused by photovoltaic installation activities.

[0057] Based on the above embodiments, as an optional embodiment, the step of using a time series breakpoint detection algorithm to process the chlorophyll fluorescence remote sensing time series and identifying the target mutation point caused by photovoltaic installation activities includes steps S401 to S403: S401: Seasonal effect correction is performed on the chlorophyll fluorescence remote sensing time series to obtain the deseasonalized time series.

[0058] Specifically, the electronic device reads continuous chlorophyll fluorescence remote sensing time series data, counts the number of complete years covered by the time series, groups the time series by month or quarter, and assigns data points from the same month or quarter to the same group. The electronic device calculates the average fluorescence intensity value of all data points within each group to obtain the seasonal baseline level for each month or quarter. The electronic device organizes all the seasonal baseline levels for all months or quarters into a seasonal effect pattern, which reflects the natural periodic variation of vegetation photosynthetic function. The electronic device traverses each data point in the original time series, and finds the corresponding seasonal baseline level from the seasonal effect pattern according to the month or quarter to which the data point belongs. The electronic device subtracts the corresponding seasonal baseline level from the fluorescence intensity value of the data point, and the difference is used as the fluorescence intensity value after removing the seasonal effect. The electronic device reorganizes the deseasoned fluorescence intensity values ​​of all data points in chronological order to form a deseasoned time series. The numerical fluctuations in the deseasoned time series mainly reflect long-term trends and abrupt change signals, and the periodic seasonal variation characteristics are effectively eliminated, thus completing the seasonal effect correction process and generating the deseasoned time series.

[0059] Based on the above embodiments, as an optional embodiment, the step of performing seasonal effect correction processing on the chlorophyll fluorescence remote sensing time series to obtain a deseasonalized time series includes steps S501 to S503: S501: Calculate the multi-year climatological mean and standard deviation for each time point in the time series.

[0060] In this embodiment, the multi-year climatological mean refers to the long-term average level of fluorescence intensity values ​​for a specific month or quarter across multiple years. The multi-year climatological mean reflects the baseline state of normal photosynthetic function of vegetation at that time point, eliminating abnormal fluctuations caused by occasional weather conditions in a single year, and obtaining a stable and reliable seasonal reference benchmark through statistical averaging of multi-year data. The multi-year climatological standard deviation is used to represent the dispersion of fluorescence intensity values ​​for a specific month or quarter across multiple years. The standard deviation value reflects the interannual fluctuation range of the data at that time point; a smaller standard deviation indicates stable seasonal characteristics at that time point, while a larger standard deviation indicates that that time point is easily affected by interannual climate anomalies.

[0061] Specifically, the electronic device reads continuous chlorophyll fluorescence remote sensing time series data, identifies all different month or quarter types within the time series, and for the first month or quarter type, filters out all data points belonging to that month or quarter from the time series. The fluorescence intensity values ​​of the filtered data points are stored as a temporary array. The electronic device iterates through all elements in the temporary array, sums the element values, and divides the sum by the total number of elements to obtain the multi-year climatological mean for that month or quarter. The electronic device then iterates through all elements in the temporary array again, calculates the difference between each element's value and the multi-year climatological mean, squares the difference, and accumulates the sum. The electronic device divides the sum of squares by the total number of elements minus 1, and performs a square root operation on the result to obtain the multi-year climatological standard deviation for that month or quarter. The electronic device associates and stores the calculated multi-year climatological mean and standard deviation with the corresponding month or quarter identifier. The electronic device processes all different month or quarter types in sequence, repeatedly performing data filtering, mean calculation, and standard deviation calculation operations. The electronic device organizes the multi-year climatological mean and standard deviation of all months or quarters into a complete climatological statistical table. The climatological statistical table provides a reference benchmark for subsequent deseasonalization processing, thereby completing the calculation of the multi-year climatological mean and standard deviation corresponding to each time point in the time series.

[0062] S502: Subtract the multi-year climatological mean from the original chlorophyll fluorescence value and divide by the standard deviation to obtain the standardized sequence value.

[0063] Specifically, the electronic device reads continuous chlorophyll fluorescence remote sensing time series data, obtains the raw fluorescence intensity value and time identifier of each data point in the time series, reads the pre-calculated multi-year climatological statistical table, which contains the multi-year climatological mean and standard deviation for all months or quarters, creates a new array to store the standardized series values, iterates through the first data point in the original time series, extracts the raw fluorescence intensity value and the month or quarter information of that data point, and queries the corresponding multi-year climatological mean and standard deviation values ​​from the multi-year climatological statistical table according to the month or quarter to which the data point belongs. The electronic device performs a subtraction operation to return the original value to the standard deviation. The initial fluorescence intensity value is subtracted from the multi-year climatological mean obtained from the query to obtain the mean-reduced deviation value. The electronic device performs a division operation to divide the deviation value by the standard deviation obtained from the query, and obtains the standardized sequence value of the data point. The electronic device stores the standardized sequence value in a new array at the position corresponding to the original data point, keeping the time identifier unchanged. The electronic device processes all data points in the original time series in sequence, repeating the monthly or quarterly query, mean subtraction, standard deviation division and result storage operations. After the electronic device completes the processing, the new array contains standardized sequence values ​​of the same length as the original time series. The standardized time series eliminates seasonal baseline differences, thus completing the calculation of the standardized sequence value.

[0064] Specifically, the above standardization process can be represented by the following formula (1): (1) in, This refers to the standardized sequence value (also known as the deseasoned outlier) described in this embodiment. For time points The corresponding original chlorophyll fluorescence value, This represents the multi-year climatological standard deviation for the month or quarter to which this point in time belongs. This is the multi-year climatological average for the month or quarter to which this point in time belongs.

[0065] S503: Construct a deseasonalized time series based on the standardized sequence values ​​of all time points.

[0066] Specifically, the electronic device reads all the standardized sequence values ​​that have been calculated, obtains the original time stamp information corresponding to each sequence value, and creates a new time series data structure to store the deseasoned time series. This data structure includes a time stamp field and a standardized sequence value field. The electronic device sorts the standardized sequence values ​​according to their chronological order, ensuring that the sequence values ​​are arranged in ascending order of time. The electronic device iterates through the sorted standardized sequence values, writing the first sequence value and its time stamp as a record into the deseasoned time series data structure. The electronic device continues to iterate through the subsequent standardized sequence values, appending each sequence value and its time stamp as an independent record to the deseasoned time series data structure. The electronic device checks for time order errors or duplicate records in the deseasoned time series, and corrects any anomalies found. The electronic device adds metadata information to the deseasoned time series, including descriptive attributes such as time range, number of data points, and standardization method. The electronic device stores the constructed deseasoned time series as an independent data object, which can be directly called by subsequent breakpoint detection algorithms. The numerical fluctuations in the deseasoned time series mainly reflect the influence of non-seasonal factors, thus completing the construction of a deseasoned time series based on the standardized sequence values.

[0067] S402: Using a time series abrupt change detection algorithm, the deseasonalized time series is scanned to identify the points in time when structural changes occur in the data.

[0068] Specifically, the electronic device reads the constructed deseasoned time series data, obtains the standardized sequence values ​​and time identifiers of all data points in the series, loads a time series mutation detection algorithm from the algorithm library, sets the parameters required for the algorithm to run, including minimum segment length, penalty function type, significance level threshold, etc., initializes the candidate mutation point set to an empty set, sets the start and end points of the time series as fixed segment boundaries, and uses dynamic programming or iterative search methods to calculate the segmentation fitting cost at each possible position of the time series when assuming that position is a mutation point. The electronic device evaluates the overall fit quality of different segmentation schemes through a cost function. The electronic device searches for a segmentation scheme that minimizes the cost function, taking into account both the fitting error and the number of segments as penalties. The segmentation boundary points corresponding to this scheme are the detected mutation points. The electronic device performs a significance test on each detected mutation point, calculating the statistical significance of the difference between the data segments before and after the mutation point. The electronic device removes pseudo-mutation points that fail the significance test, retaining only the real mutation points with statistically significant differences. The electronic device arranges the mutation points that pass the test in chronological order, recording the time position of each mutation point, the magnitude of the change in the mean before and after the mutation, confidence level, and other attribute information, thereby completing the scanning of the deseasoned time series and the identification of the time points of structural changes.

[0069] For example, the electronic device reads the constructed deseasoned time series data, obtains the standardized sequence values ​​of 67 data points in the series and the time identifiers from March 2018 to September 2023. The electronic device loads the PELT time series mutation detection algorithm from the algorithm library, sets the parameters required for the algorithm to run, including a minimum segment length of 6 months, a penalty function type of BIC, and a significance level threshold of 0.01. The electronic device initializes the candidate mutation point set to an empty set, sets the start point of the time series (March 2018) and the end point (September 2023) as fixed segment boundaries. The electronic device uses a dynamic programming optimization method to calculate the segment fitting costs at each possible position in the time series (August 2019, November 2020, and March 2022) assuming that the position is a mutation point, which are 15.2, 12.8, and 18.5 respectively. The electronic device evaluates the overall fitting quality of different segmentation schemes through the BIC cost function, which comprehensively considers the fitting error and segmentation. The electronic device uses a segmentation penalty term of 5.0 to search for a segmentation scheme that minimizes the cost function to 32.8. The segmentation boundary points corresponding to this scheme, August 2019 and November 2020, are the detected mutation points. The electronic device performs a t-test on each mutation point detected in August 2019 and November 2020 to test the significance. The statistical significance p-values ​​of the data segments before and after the mutation point are calculated to be 0.003 and 0.005, respectively. The electronic device removes pseudo-mutation points that fail the significance test threshold of 0.01, and only retains the statistically significant true mutation points in August 2019 and November 2020. The electronic device arranges the two mutation points that pass the test in chronological order and records the time position of each mutation point in August 2019 and November 2020, the magnitude of the change in the mean before and after the mutation from 0.85 to -0.42, the confidence level of 99%, and other attribute information, thereby completing the scanning of the deseasonalized time series and the identification of the time points of structural change.

[0070] To more accurately identify abrupt change points, the algorithm used in this embodiment (such as the BFAST algorithm) decomposes the time series based on an additive model, and its mathematical model can be expressed as formula (2): (2) in, The observed chlorophyll fluorescence remote sensing time series, For seasonal terms that change periodically, For the residual term, This represents the trend.

[0071] S403: Obtain the construction cycle information of the photovoltaic installation area, filter the time points that fall within the time window corresponding to the construction cycle information, and determine them as the target mutation points.

[0072] In this embodiment, construction period information refers to the complete time record from the commencement of construction to the formal commissioning and operation of a photovoltaic project. Construction period information typically includes key time nodes such as project start-up time, main construction time, equipment installation time, and grid connection time. These time nodes define the time range within which photovoltaic installation activities directly impact the vegetation ecosystem. A time window represents a time interval extended by a certain tolerance range before and after the construction period. The time window considers the preparation period and impact lag period of the construction activities to ensure that all abrupt change signals related to photovoltaic installation can be captured. The target abrupt change point refers to the point in time within the time window where a structural change with a clear causal relationship to the photovoltaic installation activities is detected.

[0073] Specifically, the electronic device queries the construction cycle information of the target photovoltaic installation area from the photovoltaic project management system or engineering file database, reading key time nodes such as the project start date and completion date. Based on the read start date, the electronic device extends forward by a preset preparation period and backward by a preset impact lag period based on the completion date. The electronic device combines the extended start and end times to form a time window corresponding to the construction cycle, covering the complete period during which photovoltaic installation activities may impact vegetation. The electronic device reads a list of all identified structural change time points, creates an empty list of target mutation points to store the filtering results, and iterates through the list of structural change time points. At the first point in time, the time identifier of that point is extracted. The electronic device determines whether the time identifier of that point is greater than or equal to the start time of the time window. If the result is yes, otherwise, the point is skipped and the next point is processed. The electronic device then determines whether the time identifier of that point is less than or equal to the end time of the time window. If the result is yes, the point is added to the target mutation point list. The electronic device processes all structural change point in time in sequence, repeating the time comparison and filtering operations. After the electronic device completes the filtering, the target mutation point list contains all mutation points that fall within the construction cycle time window. These mutation points have a temporal correspondence with the photovoltaic installation activities, thus completing the determination of the target mutation points.

[0074] S104: Based on chlorophyll fluorescence remote sensing time series data before and after the target mutation point, determine the photosynthetic disturbance intensity parameter and photosynthetic recovery rate parameter.

[0075] In this embodiment, the photosynthetic disturbance intensity parameter refers to the magnitude or change in the chlorophyll fluorescence intensity of vegetation relative to normal levels caused by photovoltaic installation activities. The photosynthetic disturbance intensity parameter quantitatively characterizes the immediate impact of photovoltaic project construction on vegetation photosynthetic function. A larger parameter value indicates a more severe inhibition of vegetation photosynthetic activity, reflecting the combined impact of factors such as shading effects, soil disturbance, and vegetation removal on the ecosystem. The photosynthetic recovery rate parameter represents the speed at which vegetation photosynthetic function returns to normal levels after being disturbed. The photosynthetic recovery rate parameter reflects the ecosystem's self-repair capability and adaptability to photovoltaic facilities. A faster recovery rate indicates that vegetation can adapt to new light and soil conditions in a shorter time, while a slower recovery rate or no recovery suggests that the photovoltaic facilities have caused a sustained ecological impact.

[0076] Specifically, the electronic device reads the identified list of target mutation points, obtains the temporal location information of each target mutation point, and for the first target mutation point, extracts data from the chlorophyll fluorescence remote sensing time series for a preset time period before the mutation point, calculates the average fluorescence intensity value within that time period as the baseline value before the perturbation, extracts data from the time series for the immediately following time period after the mutation point, calculates the average fluorescence intensity value within that time period as the initial value after the perturbation, and calculates the difference between the baseline value before the perturbation and the initial value after the perturbation. The absolute value or relative percentage of the difference is used as the photosynthetic perturbation intensity parameter. The photosynthetic perturbation intensity parameter quantifies the decline in vegetation photosynthetic function at the mutation point. The electronic device extracts data from the time series after the mutation point for a longer time range... The electronic device performs linear or nonlinear trend fitting on the recovery period data, calculates the slope of the fitted curve or the characteristic recovery time constant, and uses the slope value or the reciprocal of the time constant as the photosynthetic recovery rate parameter. The photosynthetic recovery rate parameter reflects the speed at which the fluorescence intensity regresses to the baseline level. The electronic device determines whether the recovery period data shows a significant upward trend. If there is no upward trend, the photosynthetic recovery rate parameter is marked as zero or negative to indicate no recovery. The electronic device processes all target mutation points in sequence, repeating the baseline value calculation, disturbance intensity calculation, and recovery rate calculation operations. The electronic device organizes the photosynthetic disturbance intensity parameters and photosynthetic recovery rate parameters corresponding to all target mutation points into an evaluation result table, thereby completing the parameter determination based on the data before and after the target mutation point.

[0077] Based on the above embodiments, as an optional embodiment, the step of determining the photosynthetic perturbation intensity parameter and photosynthetic recovery rate parameter based on chlorophyll fluorescence remote sensing time series data before and after the target mutation point includes steps S601 to S603: S601: Calculate the difference in chlorophyll fluorescence characteristic values ​​before and after the occurrence of the target mutation point, and determine the difference as the photosynthetic disturbance intensity parameter characterizing the degree of photosynthetic function impairment.

[0078] In this embodiment, the chlorophyll fluorescence characteristic value refers to a representative statistical quantity of chlorophyll fluorescence intensity data within a specific time period. It can be the arithmetic mean, median, weighted average, or other statistical indicators that reflect the overall level of vegetation photosynthetic activity during that time period. The selection of the characteristic value should ensure that it can stably and reliably represent the photosynthetic function state of the vegetation before and after disturbance. The degree of photosynthetic function impairment is used to represent the proportion of decline in vegetation photosynthetic capacity relative to the normal state.

[0079] Specifically, the electronic device reads the time location of the identified target mutation point, sets the length of the reference time window before the mutation point and the length of the comparison time window after the mutation point, extracts all data points within the reference time window range before the mutation point from the chlorophyll fluorescence remote sensing time series, and stores the fluorescence intensity values ​​of these data points as the pre-maturity dataset. The electronic device sums all fluorescence intensity values ​​in the pre-maturity dataset and divides the sum by the number of data points to obtain the chlorophyll fluorescence characteristic value before the mutation point, which represents the normal photosynthetic activity level of the vegetation before the disturbance. The electronic device then extracts all data points within the comparison time window range after the mutation point from the time series, stores the fluorescence intensity values ​​of these data points as the post-maturity dataset. The device sums all fluorescence intensity values ​​in the later dataset, divides the sum by the number of data points, and obtains the chlorophyll fluorescence characteristic value after the mutation point. This characteristic value represents the photosynthetic activity level of the vegetation after the disturbance. The electronic device performs a subtraction operation to subtract the chlorophyll fluorescence characteristic value after the mutation point from the chlorophyll fluorescence characteristic value before the mutation point, and calculates the decrease in fluorescence intensity. The electronic device can optionally divide the decrease in fluorescence intensity by the chlorophyll fluorescence characteristic value before the mutation point to calculate the relative decrease ratio. The electronic device determines the absolute value or relative decrease ratio of the fluorescence intensity as the photosynthetic disturbance intensity parameter. The larger the value of the photosynthetic disturbance intensity parameter, the more severe the inhibition of the photosynthetic function of the vegetation by the photovoltaic installation is, thus completing the calculation and determination of the photosynthetic disturbance intensity parameter.

[0080] Specifically, the photosynthetic disturbance intensity parameter (denoted as...) The calculation of ) is shown in formula (3): (3) in, The chlorophyll fluorescence characteristic values ​​(e.g., mean) within a preset time window before the occurrence of the target mutation point. The chlorophyll fluorescence characteristic value is the value of chlorophyll fluorescence in the time period immediately following the occurrence of the target mutation point.

[0081] Typically, the installation of photovoltaic systems leads to a short-term decrease in photosynthesis. To facilitate subsequent exponent calculations, the absolute value can be directly taken in this embodiment. To characterize the degree of impairment of photosynthetic function.

[0082] S602: The recovery observation period after the target mutation point is determined.

[0083] Specifically, the electronic device reads the time and location information of the identified target mutation point, obtaining the specific year and month of the mutation. It then queries the vegetation type database to determine the main vegetation type of the photovoltaic installation area, identifying whether it is grassland, shrubs, or forest. Based on the vegetation type, it retrieves the corresponding typical restoration cycle from the ecological restoration parameter database. Grassland vegetation typically has a shorter restoration cycle, shrub vegetation a medium cycle, and forest vegetation a longer cycle. The device also reads the construction method information of the photovoltaic project to determine whether vegetation protection or ecological restoration measures have been adopted. If ecological restoration measures have been adopted, the restoration cycle is appropriately shortened. Finally, the device queries the time range of available remote sensing data to obtain the time point of the last available observation data. The sub-device calculates the number of months between the target mutation point and the last available data point, using this number of months as a data availability constraint. The electronic device takes the smaller value between the vegetation type recovery cycle and the data availability constraint to determine the initial recovery observation period length. The electronic device checks whether the initially determined period length is less than the preset minimum observation threshold. If it is less than the minimum threshold, the period length is adjusted to the minimum threshold. The electronic device uses the target mutation point as the start time, adds the determined period length to the start time to obtain the end time, and combines the start and end times to form the recovery observation period. It records the start and end dates, period length, vegetation type, and other attribute information of this period, thus completing the determination of the recovery observation period after the target mutation point.

[0084] S603: Calculate the rate of change of chlorophyll fluorescence value during the recovery observation period, and determine the rate of change as the photosynthetic recovery rate parameter characterizing the ecological self-repair capacity.

[0085] In this embodiment, the rate of change of chlorophyll fluorescence value refers to how quickly the chlorophyll fluorescence intensity changes over time during the recovery observation period. The rate of change is obtained by trend fitting of the chlorophyll fluorescence time series data during the recovery observation period. A positive rate of change indicates an upward trend in chlorophyll fluorescence intensity, meaning that vegetation photosynthetic function is gradually recovering; a negative rate of change indicates a downward trend in chlorophyll fluorescence intensity, meaning that vegetation photosynthetic function is continuously deteriorating. The absolute value of the rate of change reflects the speed of the recovery or degradation process. Ecological self-repair capacity refers to the ability of an ecosystem to spontaneously recover to its original state or form a new equilibrium state after being disturbed by external factors. This capacity is quantitatively characterized by the photosynthetic recovery rate parameter.

[0086] Specifically, the electronic device reads the start and end time information of the determined recovery observation period, extracts all data points within the recovery observation period from the chlorophyll fluorescence remote sensing time series, arranges the extracted data points in chronological order, obtains the time coordinates and chlorophyll fluorescence intensity values ​​corresponding to each data point, establishes a dataset with time as the independent variable and chlorophyll fluorescence intensity as the dependent variable, and calls a linear regression algorithm to fit the dataset. The electronic device calculates the slope parameter of the best-fit line through the linear regression algorithm. This slope parameter represents the average increment or decrease of chlorophyll fluorescence intensity with time unit. The electronic device obtains the goodness-of-fit index output by the linear regression algorithm and judges whether the degree of explanation of the actual data by the fitted line meets the preset confidence requirements. If the goodness-of-fit index meets the confidence requirements, the electronic device determines the calculated slope parameter as the rate of change of chlorophyll fluorescence value. If the goodness-of-fit index does not meet the confidence requirements, the electronic device calls a nonlinear regression algorithm to perform a second fitting on the dataset, extracts the derivative value of the nonlinear curve at the midpoint of the recovery observation period as the rate of change, and stores the determined rate of change value and its unit as the photosynthetic recovery rate.

[0087] During the recovery observation period, the trend of chlorophyll fluorescence value can be described by the linear regression model formula (4): (4) in, For the recovery period, time is a variable. The fluorescence intensity at that moment, The intercept, For random error, This is the slope of the regression line.

[0088] In this embodiment, the slope This is determined to be a parameter for the photosynthetic recovery rate. If This indicates that the photosynthetic function of vegetation is showing a recovery and upward trend; The higher the value, the stronger the ecological self-repair ability. S105: Determine the assessment results of the impact on the ecosystem of the photovoltaic installation area based on the photosynthetic disturbance intensity parameter and the photosynthetic recovery rate parameter.

[0089] In this embodiment, the assessment result of ecosystem impact refers to the conclusion of the ecological impact type caused by photovoltaic (PV) installation activities based on the quantitative relationship between the photosynthetic disturbance intensity parameter and the photosynthetic recovery rate parameter. The assessment results are divided into different impact types according to the numerical range of the photosynthetic disturbance response index, including categories such as high photosynthetic recovery capacity, persistent PV interference effects, and long-term photosynthetic decline caused by PV installation. The assessment results provide a scientific basis for ecological management decisions for PV projects. The photosynthetic disturbance response index is the ratio of the photosynthetic recovery rate parameter to the photosynthetic disturbance intensity parameter; this index reflects the relative strength of the ecosystem's recovery capacity relative to the degree of damage.

[0090] Specifically, the electronic device reads the determined photosynthetic recovery rate parameter and photosynthetic disturbance intensity parameter. It performs a division operation, dividing the photosynthetic recovery rate parameter by the photosynthetic disturbance intensity parameter to calculate the photosynthetic disturbance response index. The electronic device compares the calculated photosynthetic disturbance response index with a first preset threshold to determine if the index is greater than or equal to the first preset threshold. If the electronic device determines that the photosynthetic disturbance response index is greater than or equal to the first preset threshold, it determines the assessment result as indicating that the region has a high photosynthetic recovery capacity and the recovery rate exceeds the disturbance amplitude, indicating that the ecosystem can quickly recover from the photovoltaic installation interference. If the electronic device determines that the photosynthetic disturbance response index is less than the first preset threshold, it compares the photosynthetic disturbance response index with a second preset threshold. The electronic device compares the numerical values ​​to determine whether the photosynthetic disturbance response index is greater than or equal to the second preset threshold. If the electronic device determines that the photosynthetic disturbance response index is less than the first preset threshold but greater than or equal to the second preset threshold, the assessment result is determined to be that the photovoltaic interference effect persists, indicating that although the ecosystem has a recovery trend, the recovery speed is insufficient to offset the disturbance impact. If the electronic device determines that the photosynthetic disturbance response index is less than the second preset threshold, the assessment result is determined to be that the photovoltaic installation has caused long-term photosynthetic decline, indicating that the ecosystem has not only failed to recover but has continued to degrade. The electronic device records the determined assessment result together with the corresponding photosynthetic disturbance response index value, photosynthetic recovery rate parameter value, and photosynthetic disturbance intensity parameter value and generates an assessment report, thereby completing the determination of the assessment result of the impact on the ecosystem of the photovoltaic installation area.

[0091] Based on the above embodiments, as an optional embodiment, the step of determining the assessment results of the impact on the ecosystem of the photovoltaic installation area according to the photosynthetic disturbance intensity parameter and the photosynthetic recovery rate parameter includes steps S701 to S704: S701: Determine the photosynthetic disturbance response index based on the photosynthetic recovery rate parameter and the photosynthetic disturbance intensity parameter.

[0092] In this embodiment, the photosynthetic disturbance response index refers to the ratio of the photosynthetic recovery rate parameter to the photosynthetic disturbance intensity parameter. This index comprehensively reflects the relative strength of the ecosystem's recovery capacity relative to the degree of damage. A larger index value indicates that the recovery rate is significantly higher than the disturbance intensity, meaning the ecosystem has a strong recovery capacity. An index value close to zero indicates that the recovery rate is comparable to the disturbance intensity, meaning the recovery process is slow. A negative index value indicates that the ecosystem has not only failed to recover but has continued to degrade. As a dimensionless comprehensive evaluation index, the photosynthetic disturbance response index eliminates the influence of different parameter dimensions, facilitating horizontal comparisons of the ecological impacts of different photovoltaic projects or different regions.

[0093] Specifically, the electronic device reads the determined photosynthetic recovery rate parameter value, which characterizes the rate of change of chlorophyll fluorescence intensity over time during the recovery observation period. It also reads the determined photosynthetic disturbance intensity parameter value, which characterizes the difference in chlorophyll fluorescence characteristic values ​​before and after the target mutation point. The electronic device checks whether the units of measurement of the photosynthetic recovery rate parameter and the photosynthetic disturbance intensity parameter are consistent. If the units are inconsistent, a unit conversion operation is performed to make the two parameters comparable. The electronic device calculates the photosynthetic disturbance response index using a specific formula, stores the obtained photosynthetic disturbance response index value, and records the source data information such as the photosynthetic recovery rate parameter and photosynthetic disturbance intensity parameter corresponding to the index. The electronic device can optionally normalize or standardize the photosynthetic disturbance response index to provide a unified reference benchmark for the index values ​​of different items, thereby completing the calculation and determination of the photosynthetic disturbance response index.

[0094] In this embodiment, the photosynthetic disturbance response index (denoted as ) The specific calculation formula for ) is shown in formula (5): (5) in, For the aforementioned determined photosynthetic recovery rate parameters, The absolute value of the photosynthetic disturbance intensity parameter determined above. It is a preset small constant (e.g., 0.0001).

[0095] Introducing constants The purpose is to prevent the denominator from becoming invalid when the photosynthetic disturbance intensity parameter approaches zero, thus ensuring the stability of the calculation process. This index... The relationship between the recovery rate of the ecosystem under the disturbance of photovoltaic installation and the magnitude of the disturbance was comprehensively quantified.

[0096] S702: If the photosynthetic disturbance response index is greater than or equal to 1, the assessment result is that the region has a high photosynthetic recovery capacity and the recovery rate exceeds the disturbance amplitude.

[0097] In this embodiment, the first preset threshold is a value of 1, which serves as a critical value for determining whether the ecosystem's recovery capacity exceeds the degree of disturbance. When the photosynthetic disturbance response index is greater than or equal to 1, it indicates that the value of the photosynthetic recovery rate parameter is greater than or equal to the value of the photosynthetic disturbance intensity parameter. That is, the recovery speed of the ecosystem's photosynthetic function during the recovery observation period is equal to or exceeds the degree of photosynthetic damage caused by photovoltaic installation activities, and the region has a high photosynthetic recovery capacity. The recovery rate exceeding the disturbance amplitude means that the increase in chlorophyll fluorescence intensity per unit time reaches or exceeds the decrease in chlorophyll fluorescence intensity caused by photovoltaic installation. In this case, the ecosystem can recover from the photovoltaic installation disturbance to its original state or close to its original state in a relatively short period of time.

[0098] Specifically, the electronic device reads the calculated photosynthetic disturbance response index value, obtains the value of the first preset threshold (set to 1), performs a numerical comparison operation, and determines whether the photosynthetic disturbance response index is greater than or equal to the first preset threshold of 1. If the electronic device determines that the photosynthetic disturbance response index is greater than or equal to 1, it determines that the ecosystem recovery capacity of the photovoltaic installation area is at a high level. The electronic device generates an assessment result description text, which states that the area has a high photosynthetic recovery capacity and the recovery rate exceeds the disturbance amplitude. The electronic device associates and stores this assessment result with the photosynthetic disturbance response index value, the photosynthetic recovery rate parameter value, and the photosynthetic disturbance intensity parameter value. The electronic device marks in the assessment result that the ecosystem in the area can recover quickly from the photovoltaic installation disturbance and suggests that the photovoltaic project can operate normally on the basis of taking conventional ecological protection measures. The electronic device outputs the assessment result to the display interface or generates an assessment report document, thereby completing the determination of the assessment result when the photosynthetic disturbance response index is greater than or equal to 1.

[0099] S703: If the photosynthetic disturbance response index is less than 1 and greater than or equal to 0, the evaluation result is that the photovoltaic interference effect persists.

[0100] Specifically, the electronic device reads the calculated photosynthetic disturbance response index value. It determines that the index is less than a first preset threshold of 1. The electronic device then obtains the value of a second preset threshold, which is set to 0. It performs a numerical comparison operation to determine if the photosynthetic disturbance response index is greater than or equal to the second preset threshold of 0. If the electronic device determines that the index is less than 1 but greater than or equal to 0, it determines that the ecosystem of the photovoltaic installation area is in a slow recovery state but with insufficient recovery capacity. The electronic device generates an assessment result description text stating that the photovoltaic interference effect persists. The electronic device associates and stores this assessment result with the photosynthetic disturbance response index value, the photosynthetic recovery rate parameter value, and the photosynthetic disturbance intensity parameter value. The electronic device notes in the assessment result that although the ecosystem in this area shows a recovery trend, the recovery speed is insufficient to offset the disturbance impact. It recommends that the photovoltaic project take enhanced ecological restoration measures, such as increasing the vegetation restoration area or optimizing the photovoltaic panel layout to reduce shading impact. The electronic device outputs the assessment result to the display interface or generates an assessment report document, thus completing the assessment result determination for the case where the photosynthetic disturbance response index is less than 1 but greater than or equal to 0.

[0101] S704: If the photosynthetic disturbance response index is less than 0, the assessment result is that the photovoltaic installation causes long-term photosynthetic degradation.

[0102] Specifically, the electronic device reads the calculated photosynthetic disturbance response index value. If the electronic device determines that the photosynthetic disturbance response index is less than the first preset threshold of 1, or less than the second preset threshold of 0, the electronic device determines that the ecosystem of the photovoltaic installation area is in a state of continuous degradation. The electronic device generates an assessment result description text, which states that the photovoltaic installation has caused long-term photosynthetic decline. The electronic device associates and stores this assessment result with the photosynthetic disturbance response index value, photosynthetic recovery rate parameter value, and photosynthetic disturbance intensity parameter value. The electronic device marks in the assessment result that the ecosystem of the area has not only failed to recover but has continued to degrade, and the photosynthetic function shows a long-term declining trend. The electronic device adds early warning information to the assessment result, recommending that the photovoltaic project take immediate emergency ecological protection measures, including but not limited to adjusting the installation angle and spacing of photovoltaic panels to increase light transmittance, implementing large-scale vegetation restoration projects, or assessing whether it is necessary to partially dismantle photovoltaic facilities to reduce ecological pressure. The electronic device outputs the assessment result to the display interface with high priority or generates an assessment report document containing early warning indicators. The electronic device can optionally trigger an automatic alarm mechanism to notify relevant management personnel, thereby completing the determination of the assessment result when the photosynthetic disturbance response index is less than 0.

[0103] The following describes an electronic device for assessing the impact of exemplary photovoltaic installations on grassland ecosystems, provided in an embodiment of this application. Figure 3This is an exemplary hardware structure diagram of an electronic device for assessing the impact of photovoltaic installation on grassland ecosystems, provided in an embodiment of this application.

[0104] In some embodiments, the electronic device for assessing the impact of photovoltaic installation on the grassland ecosystem is a computer device, or the electronic device for assessing the impact of photovoltaic installation on the grassland ecosystem includes a computer device. The computer device includes a processor, memory, and a network interface connected via a system bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device stores data. The network interface of the computer device is used to communicate with other external terminals or servers via a network connection. In some embodiments, the network interface can be a wired network interface; in some embodiments, the network interface can also be a wireless network interface. When the computer program is executed by the processor, it implements the methods in the embodiments of this application.

[0105] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

Claims

1. A method for assessing the impact of photovoltaic installation on grassland ecosystems, characterized in that, The method includes: Acquire solar-induced chlorophyll fluorescence remote sensing data of the photovoltaic installation area within a preset time span; Construct a time series of chlorophyll fluorescence remote sensing data for photovoltaic installation areas; A time-series breakpoint detection algorithm was used to process chlorophyll fluorescence remote sensing time series to identify target mutation points caused by photovoltaic installation activities. Based on chlorophyll fluorescence remote sensing time series data before and after the target mutation point, the photosynthetic disturbance intensity parameter and photosynthetic recovery rate parameter were determined. The assessment results of the impact on the ecosystem of the photovoltaic installation area were determined based on the photosynthetic disturbance intensity parameter and the photosynthetic recovery rate parameter.

2. The method for assessing the impact of photovoltaic installation on grassland ecosystems according to claim 1, characterized in that, The construction of the chlorophyll fluorescence remote sensing time series of the photovoltaic laying area based on solar-induced chlorophyll fluorescence remote sensing data specifically includes: Spatial matching was performed on solar-induced chlorophyll fluorescence remote sensing data to extract a subset of data covering the photovoltaic installation area and a preset surrounding area; Perform time resampling on a subset of data to convert the original time resolution into a uniform preset time scale; The resampled data was filtered and smoothed to obtain a continuous chlorophyll fluorescence remote sensing time series.

3. The method for assessing the impact of photovoltaic installation on grassland ecosystems according to claim 2, characterized in that, The process of filtering and smoothing the resampled data to obtain a continuous chlorophyll fluorescence remote sensing time series specifically includes: Calculate the statistical mean and standard deviation of the resampled data; An effective data distribution interval is constructed based on the statistical mean, standard deviation, and a preset deviation factor; Identify outlier observations in the resampled data whose values ​​are outside the effective data distribution range, and remove outlier observations from the resampled data; The preset time series smoothing algorithm is invoked to smooth the data after removing abnormal observation points, eliminating short-term random fluctuations in the data and generating a continuous chlorophyll fluorescence remote sensing time series.

4. The method for assessing the impact of photovoltaic installation on grassland ecosystems according to claim 1, characterized in that, The process of using a time-series breakpoint detection algorithm to process chlorophyll fluorescence remote sensing time series and identify target mutation points caused by photovoltaic installation activities specifically includes: Seasonal effect correction was applied to the chlorophyll fluorescence remote sensing time series to obtain the deseasonalized time series. By using a time series abrupt change detection algorithm, the deseasonalized time series is scanned to identify the points in time when structural changes occur in the data; Obtain the construction cycle information of the photovoltaic installation area, filter the time points that fall within the time window corresponding to the construction cycle information, and determine them as the target mutation points.

5. The method for assessing the impact of photovoltaic installation on grassland ecosystems according to claim 4, characterized in that, The seasonality correction process performed on the chlorophyll fluorescence remote sensing time series to obtain the deseasonalized time series specifically includes: Calculate the multi-year climatological mean and standard deviation for each time point in the time series; Subtract the multi-year climatological mean from the original chlorophyll fluorescence value and divide by the standard deviation to obtain the standardized sequence value; Deseasonalized time series are constructed based on the standardized sequence values ​​at all time points.

6. The method for assessing the impact of photovoltaic installation on grassland ecosystems according to claim 1, characterized in that, The determination of photosynthetic perturbation intensity parameters and photosynthetic recovery rate parameters based on chlorophyll fluorescence remote sensing time series data before and after the target mutation point specifically includes: The difference in chlorophyll fluorescence characteristic values ​​before and after the occurrence of the target mutation point is calculated, and the difference is determined as the photosynthetic disturbance intensity parameter characterizing the degree of photosynthetic function impairment. The recovery observation period after identifying the target mutation point; The rate of change of chlorophyll fluorescence value during the recovery observation period was calculated, and the rate of change was determined as the photosynthetic recovery rate parameter characterizing the ecological self-repair capacity.

7. The method for assessing the impact of photovoltaic installation on grassland ecosystems according to claim 1, characterized in that, The assessment results regarding the impact on the ecosystem of the photovoltaic installation area, determined based on photosynthetic disturbance intensity parameters and photosynthetic recovery rate parameters, specifically include: The photosynthetic disturbance response index is determined based on the photosynthetic recovery rate parameter and the photosynthetic disturbance intensity parameter; If the photosynthetic disturbance response index is greater than or equal to 1, the assessment result is that the region has a high photosynthetic recovery capacity and the recovery rate exceeds the disturbance amplitude. If the photosynthetic disturbance response index is less than 1 and greater than or equal to 0, the assessment result is that the photovoltaic interference effect persists. If the photosynthetic disturbance response index is less than 0, the assessment result is that the photovoltaic installation causes long-term photosynthetic decline.

8. An electronic device for assessing the impact of photovoltaic installation on grassland ecosystems, characterized in that, The electronic device includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the electronic device to perform the method as described in any one of claims 1-7.

9. A computer program product containing instructions, characterized in that, When the computer program product is run on an electronic device for assessing the impact of photovoltaic installations on grassland ecosystems, the electronic device causes the electronic device to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium comprising instructions, characterized in that, When the instructions are run on an electronic device for assessing the impact of photovoltaic installations on grassland ecosystems, the electronic device causes the electronic device to perform the method as described in any one of claims 1-7.

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