Alpine grassland rapid restoration strategy analysis method, system, equipment and medium

Abstract

The invention relates to the technical field of strategy analysis, and discloses an alpine grassland rapid restoration strategy analysis method, system and device and a medium. The method comprises the following steps: performing quality evaluation and restoration on an alpine grassland multispectral image to obtain a standard image set; spectral texture features are extracted, degradation classification is carried out, and a feature set and a score table are obtained; evaluating the degradation degree in combination with multi-dimensional indexes, and generating a distribution matrix and an index set; forming a repair scheme library and a parameter list through case analysis and parameter optimization; analyzing a restoration effect in combination with environmental data to obtain an index matrix and an evaluation table; and optimizing the monitoring data to obtain a strategy scheme and an adjustment parameter set. According to the method and the device, the degradation conditions of alpine grassland of different types and different areas are accurately identified, the restoration strategy can be dynamically adjusted according to the real-time monitoring data, and the restoration effect is improved.

Description

Technical Field

[0001] This application relates to the field of strategy analysis, and particularly to a method, system, device and medium for analyzing rapid restoration strategies for alpine grasslands. Background Art

[0002] The current degradation problem of alpine grasslands is becoming increasingly serious, and a variety of restoration methods and technical means have been developed. Traditional restoration methods are mainly based on field investigations and empirical judgments. Information on the degradation status of grasslands is obtained by setting quadrats, collecting samples, etc., and then restoration measures are selected based on historical experience. At the same time, the development of remote sensing technology has provided new means for large-scale grassland monitoring. Information such as vegetation indices and coverage can be obtained through multi-spectral remote sensing images, and spatial analysis is carried out in combination with geographic information systems. The formulation of restoration measures usually adopts a single-index evaluation method or a comprehensive evaluation method. The degree of grassland degradation is evaluated by setting up an evaluation index system, and then the corresponding restoration plan is selected.

[0003] However, the existing alpine grassland restoration technologies have obvious deficiencies. First, the traditional experience-based restoration plans lack data support and are difficult to adapt to the complex and changeable alpine environment. Second, although remote sensing technology can obtain large-scale data, the analysis and processing of the data still remain at a simple statistical level, and the deep associations between data have not been fully explored. Third, the existing evaluation methods often treat degraded grasslands in different regions and of different types uniformly, ignoring the regional differences and temporal characteristics of alpine grassland restoration. Finally, the formulation of restoration plans lacks a dynamic adjustment mechanism and cannot optimize the restoration strategies in a timely manner according to real-time monitoring data, resulting in unsatisfactory restoration effects. Summary of the Invention

[0004] This application provides a method, system, device and medium for analyzing rapid restoration strategies for alpine grasslands, which is used to accurately identify the degradation status of alpine grasslands of different types and in different regions, and can dynamically adjust the restoration strategies according to real-time monitoring data to improve the restoration effect.

[0005] In a first aspect, the present application provides a method for analyzing a rapid restoration strategy for alpine grasslands. The method for analyzing a rapid restoration strategy for alpine grasslands includes: performing quality scoring and image restoration processing on the collected multi-spectral images of alpine grasslands to obtain a standardized image data set through terrain correction; extracting spectral and texture features according to the standardized image data set, classifying the features for degradation types to obtain a degradation type distribution feature set and a grassland quality scoring table; based on the degradation type distribution feature set and the grassland quality scoring table, conducting multi-dimensional evaluation by combining remote sensing indicators, terrain indicators, and soil sampling data to generate a spatial distribution matrix of degradation degrees and a grassland status index set; according to the spatial distribution matrix of degradation degrees and the grassland status index set, forming a regional restoration plan library and a technical parameter list through historical restoration case analysis and parameter optimization; according to the regional restoration plan library and the technical parameter list, conducting spatio-temporal response analysis by combining meteorological data and terrain data to obtain a restoration expected index matrix and an effect evaluation table; based on the restoration expected index matrix and the effect evaluation table, obtaining an optimized restoration strategy plan and a dynamic adjustment parameter set through multi-objective optimization and online monitoring data analysis.

[0006] In a second aspect, the present application provides a system for analyzing a rapid restoration strategy for alpine grasslands. The system for analyzing a rapid restoration strategy for alpine grasslands includes: A collection module, configured to perform quality scoring and image restoration processing on the collected multi-spectral images of alpine grasslands to obtain a standardized image data set through terrain correction; A classification module, configured to extract spectral and texture features according to the standardized image data set, classify the features for degradation types to obtain a degradation type distribution feature set and a grassland quality scoring table; An evaluation module, configured to conduct multi-dimensional evaluation based on the degradation type distribution feature set and the grassland quality scoring table, by combining remote sensing indicators, terrain indicators, and soil sampling data to generate a spatial distribution matrix of degradation degrees and a grassland status index set; An optimization module, configured to form a regional restoration plan library and a technical parameter list through historical restoration case analysis and parameter optimization according to the spatial distribution matrix of degradation degrees and the grassland status index set; An analysis module, configured to conduct spatio-temporal response analysis according to the regional restoration plan library and the technical parameter list, by combining meteorological data and terrain data to obtain a restoration expected index matrix and an effect evaluation table; A monitoring module, configured to obtain an optimized restoration strategy plan and a dynamic adjustment parameter set through multi-objective optimization and online monitoring data analysis based on the restoration expected index matrix and the effect evaluation table.

[0007] A third aspect of the present application provides a computer device. The memory stores machine-readable instructions executable by the processor. When the computer device runs, the processor communicates with the memory via a bus. When the machine-readable instructions are executed by the processor, the steps of the above-mentioned rapid restoration strategy analysis method for alpine grasslands are executed.

[0008] A fourth aspect of the present application provides a computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When it runs on a computer, the computer is caused to execute the above-mentioned rapid restoration strategy analysis method for alpine grasslands.

[0009] In the technical solution provided by the present application, by performing quality scoring and image restoration processing on multi-spectral images of alpine grasslands, and combining terrain correction to obtain a standardized image data set, the problem of unstable image quality in traditional methods is overcome; by extracting spectral and texture features for degradation type classification processing, a degradation type distribution feature set and a grassland quality scoring table are obtained, realizing accurate identification and scoring of degradation types; based on the degradation type distribution feature set and the grassland quality scoring table, combined with remote sensing indicators, terrain indicators, and soil sampling data for multi-dimensional evaluation, a degradation degree spatial distribution matrix and a grassland state index set are generated, providing a comprehensive evaluation result of the degradation status; through historical restoration case analysis and parameter optimization, a regional restoration plan library and a technical parameter list are formed, formulating targeted restoration strategies for different regions; combining meteorological data and terrain data for spatio-temporal response analysis, obtaining a restoration expectation index matrix and an effect evaluation table, realizing scientific prediction of the restoration effect; finally, through multi-objective optimization and online monitoring data analysis, an optimized restoration strategy plan and a dynamic adjustment parameter set are obtained, establishing a dynamic optimization mechanism for the restoration strategy, which not only improves the accuracy of alpine grassland degradation identification and the scientificity of restoration plan formulation, but also ensures the adaptability and effectiveness of the restoration strategy through the dynamic adjustment mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for description in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0011] Figure 1 It is a schematic diagram of an embodiment of the rapid restoration strategy analysis method for alpine grasslands in an embodiment of the present application; Figure 2 It is a schematic diagram of an embodiment of the rapid restoration strategy analysis system for alpine grasslands in an embodiment of the present application; Figure 3 It is a schematic diagram of the structure of a computer device in an embodiment of the present application. Detailed implementation manners

[0012] The embodiments of the present application provide a method, a system, a device and a medium for analyzing a rapid restoration strategy of alpine grasslands. Terms such as "first", "second", "third", "fourth", etc. (if any) in the specification, claims and the above-mentioned drawings of the present application are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" or "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0013] For ease of understanding, the specific process of the embodiments of the present application will be described below. Please refer to Figure 1 , an embodiment of the method for analyzing a rapid restoration strategy of alpine grasslands in the embodiments of the present application includes: Step S101: Perform quality scoring and image restoration processing on the collected multi-spectral images of alpine grasslands, and obtain a standardized image data set through terrain correction; Step S102: Extract spectral and texture features according to the standardized image data set, perform degradation type classification processing on the features, and obtain a degradation type distribution feature set and a grassland quality scoring table; Step S103: Based on the degradation type distribution feature set and the grassland quality scoring table, perform multi-dimensional evaluation in combination with remote sensing indicators, terrain indicators and soil sampling data, and generate a degradation degree spatial distribution matrix and a grassland state index set; Step S104: According to the degradation degree spatial distribution matrix and the grassland state index set, form a sub-region restoration plan library and a technical parameter list through historical restoration case analysis and parameter optimization; Step S105: According to the sub-region restoration plan library and the technical parameter list, perform spatio-temporal response analysis in combination with meteorological data and terrain data, and obtain a restoration expected index matrix and an effect evaluation table; Step S106: Based on the restoration expected index matrix and the effect evaluation table, obtain a restoration strategy optimization plan and a dynamic adjustment parameter set through multi-objective optimization and online monitoring data analysis.

[0014] It can be understood that the execution subject of the present application can be a system for analyzing a rapid restoration strategy of alpine grasslands, or a terminal or a server. Specifically, it is not limited here. The embodiments of the present application will be described by taking the server as the execution subject as an example.

[0015] Specifically, the multi-spectral images of alpine grasslands collected are processed. During the processing of the multi-spectral images of alpine grasslands, the image data is cut into image blocks of a fixed size, and the clarity index and signal-to-noise ratio are calculated for each image block. The clarity index adopts a combined calculation method of image gradient and Laplacian operator, and the signal-to-noise ratio is based on the ratio of the variance to the mean of the pixel values of the image block. When the clarity index is lower than 0.6 or the signal-to-noise ratio is lower than 10 dB, the corresponding image block is marked as a region to be repaired. For the marked regions to be repaired, the interference types are further identified. Through the band combination analysis method, the reflectance differences of different bands are calculated to identify cloud and shadow regions. Subsequently, image repair is performed on the identified interference regions, using the spectral information of adjacent non-interference regions as a reference to reconstruct the pixel values of the interfered regions. After obtaining the repaired image, terrain correction is performed in combination with digital elevation model data to eliminate the radiation differences caused by terrain undulations, thereby obtaining a standardized image dataset.

[0016] Based on the obtained standardized image dataset, spectral and texture feature extraction is carried out. During spectral feature extraction, the data of each band is normalized, and band combination indices such as the normalized difference vegetation index and soil-adjusted vegetation index are calculated. Texture feature extraction is achieved by calculating the eigenvalues of the gray-level co-occurrence matrix to obtain statistics such as contrast, entropy, and correlation. The extracted spectral and texture features are combined to form a feature matrix, which is processed through a clustering analysis method to identify the characteristic patterns of different degradation types, resulting in a degradation type distribution feature set. At the same time, a scoring standard is established based on historical sample data to construct a grassland quality scoring table. After obtaining the degradation type distribution feature set and the grassland quality scoring table, multi-dimensional evaluation is carried out in combination with remote sensing indicators, terrain indicators, and soil sampling data. During the evaluation process, an evaluation unit grid is established, and the grassland quality score is mapped into the grid to form a basic score. Spatial interpolation is performed on the vegetation index and coverage data in the remote sensing indicators, combined with the slope and aspect values calculated from the terrain indicators, and parameters such as the organic matter content and nutrient index in the soil sampling data. Through the weighted overlay method, a spatial distribution matrix of degradation degree is generated. Based on this matrix, regional statistics and classification are carried out to obtain a grassland status index set.

[0017] Based on the spatial distribution matrix of degradation degree and the grassland status index set, formulate restoration strategies by analyzing historical restoration cases. Conduct spatial clustering on the spatial distribution matrix of degradation degree to divide the restoration area units. Determine the restoration priorities according to the grassland status index set, and extract relevant restoration measure parameters and effect records from the historical restoration case data. Through parameter matching and optimization, determine specific technical parameters such as fence height, overseeding density, and fertilization amount, and form a regional restoration plan library and a technical parameter list. According to the regional restoration plan library and the technical parameter list, conduct spatio-temporal response analysis in combination with meteorological data and topographic data. During the analysis process, construct a spatio-temporal response analysis grid and map the restoration parameters into the grid. Decompose the precipitation and temperature data in the meteorological data by time series to obtain the change trend. Combine the topographic data to calculate the water accumulation index and radiation amount, and obtain the restoration expected index matrix through spatial overlay analysis, and generate an effect evaluation table.

[0018] Optimize the strategy based on the restoration expected index matrix and the effect evaluation table. Extract the target values from the restoration expected index matrix to construct the target parameter space, and calculate the weights of the indicators in the effect evaluation table. Conduct a time series comparison of the online monitored vegetation coverage and soil moisture data to obtain the monitoring deviation. Adjust the restoration parameters within an interval according to the deviation to form parameter constraint conditions. Analyze based on the target parameter space and the indicator weights, generate an optimized restoration strategy plan, and output a dynamic adjustment parameter set.

[0019] For example, in the analysis of a 100-square-kilometer alpine grassland restoration area. In the image processing stage, the multispectral image is cut into 100 image blocks according to the size of 1 square kilometer. By calculating the clarity index and signal-to-noise ratio of each image block, it is found that 15 image blocks are interfered by clouds and 8 image blocks are interfered by shadows. Repair these interference areas, and use the spectral information of 8 adjacent non-interfered areas as reference values to calculate the average spectral characteristics for reconstruction. During the topographic correction process, combine the digital elevation model data, calculate the slope and aspect of each pixel point, and adjust the reflectance value according to the solar incidence angle to generate a standardized image data set. In the feature extraction stage, calculate the normalized difference vegetation index and soil-adjusted vegetation index for each pixel point of the standardized image data set. The texture feature calculation uses a 7×7 pixel window to calculate the contrast, entropy, and correlation of the gray-level co-occurrence matrix. Cluster analysis divides the grassland degradation types into quantity degradation type and quality degradation type, and each type is further divided into three levels: mild, moderate, and severe. The grassland quality score uses a 0-100 point system, and the score intervals for different degradation types and degrees are determined based on historical sample data.

[0020] In the multi-dimensional assessment stage, the research area is divided into a 10×10 grid of assessment units. The vegetation index is spatially interpolated using the inverse distance weighting method, and the slope and aspect in the topographic indicators are calculated from the digital elevation model. Soil sampling points are located at the center of each assessment unit, and data on soil organic matter content and nutrient index are collected. The weights of each indicator are determined by the analytic hierarchy process, and the weighted overlay method is used to generate the degradation degree score, which is divided into five grades according to the score, forming a spatial distribution matrix of degradation degree. During the formulation of the restoration strategy, the research area is divided into 20 restoration area units based on the spatial distribution matrix of degradation degree. The data of historical restoration cases are analyzed, and the degradation characteristics, restoration measure parameters, and restoration effects of each case are extracted. For different degradation types and degrees, the parameter ranges of the fence height between 1.2 - 1.8 m, the reseeding density between 15 - 30 kg / ha, and the fertilization amount between 150 - 300 kg / ha are determined. In the spatio-temporal response analysis stage, the restoration area units are used as the basic analysis units, and the monthly precipitation and temperature data for the past 5 years are collected. The water accumulation index and radiation amount of each unit are calculated, and the response analysis is carried out in combination with the restoration measure parameters. The expected vegetation coverage increase value, biomass increase amount, and species diversity change index of each unit are calculated by spatial statistical methods, generating a restoration expectation index matrix. In the strategy optimization stage, based on the online monitoring data, it is found that there is a deviation between the actual restoration effect and the expectation, such as the vegetation coverage increase in some areas not reaching the expectation. Through comparative analysis, the value range of the restoration parameters is adjusted, such as increasing the reseeding density to 20 - 35 kg / ha. According to the weights and constraint conditions of each indicator, the optimized restoration strategy is generated, and a dynamic adjustment parameter set is formed.

[0021] In the embodiments of the present application, by performing quality scoring and image restoration processing on the multi-spectral images of alpine grasslands, and combining terrain correction to obtain a standardized image dataset, the problem of unstable image quality in traditional methods is overcome; by extracting spectral and texture features for degradation type classification processing, a degradation type distribution feature set and a grassland quality scoring table are obtained, realizing the accurate identification and scoring of degradation types; based on the degradation type distribution feature set and the grassland quality scoring table, combined with remote sensing indicators, terrain indicators, and soil sampling data for multi-dimensional evaluation, a spatial distribution matrix of degradation degrees and a grassland state index set are generated, providing a comprehensive evaluation result of the degradation status; through historical restoration case analysis and parameter optimization, a regional restoration plan library and a technical parameter list are formed, formulating targeted restoration strategies for different regions; by combining meteorological data and terrain data for spatio-temporal response analysis, a restoration expectation index matrix and an effect evaluation table are obtained, realizing the scientific prediction of restoration effects; finally, through multi-objective optimization and online monitoring data analysis, an optimized restoration strategy plan and a dynamic adjustment parameter set are obtained, establishing a dynamic optimization mechanism for restoration strategies, not only improving the accuracy of alpine grassland degradation identification and the scientificity of restoration plan formulation, but also ensuring the adaptability and effectiveness of restoration strategies through a dynamic adjustment mechanism.

[0022] In a specific embodiment, the process of executing step S101 may specifically include the following steps: (1) Perform block segmentation on the multi-spectral images of alpine grasslands to obtain an image block sequence; (2) Calculate the clarity coefficient and signal-to-noise ratio parameter of each image block according to the image block sequence to generate a quality scoring result; (3) Compare the quality scoring result with the quality threshold, and mark the area below the threshold as the area to be restored; (4) Identify clouds and shadows in the area to be restored through band combination analysis to obtain an interference location map; (5) Perform image reconstruction according to the interference location map and the spectral information of adjacent regions to generate a restored image; (6) Register the restored image with the terrain elevation data and extract the terrain undulation coefficient; (7) Perform radiometric correction on the restored image based on the terrain undulation coefficient and the incident angle parameter to generate a standardized image dataset.

[0023] Specifically, the obtained original image is segmented into blocks according to a fixed size. The block segmentation adopts a non-overlapping sliding window method, with each window size of 1024×1024 pixels and a sliding step of 1024 pixels. Scanning line by line from the upper left corner of the image, an image block sequence is generated. The image block sequence is numbered by a unique identifier to record the spatial position information of each image block.

[0024] For each image block, calculate the sharpness coefficient and the signal-to-noise ratio parameter. The sharpness coefficient is calculated using the following formula:

[0025] where represents the sharpness coefficient, represents the gray value of the pixel point (x, y), M and N are the number of rows and columns of the image block, is the terrain complexity parameter of the alpine grassland.

[0026] The cloud and shadow recognition process uses the following band combination analysis formula:

[0027] where is the cloud recognition index, , , are the reflectances of the near-infrared, short-wave infrared, and visible light bands respectively, , , are the corresponding weight coefficients, is the altitude correction coefficient.

[0028] The terrain undulation coefficient is extracted using the following calculation formula:

[0029] where is the terrain undulation coefficient, is the relative elevation difference at the grid point (i, j), P and Q are the number of rows and columns of the elevation data, is the slope factor, is the vegetation coverage correction coefficient.

[0030] For example, taking a certain alpine grassland as an example, the size of the original multi-spectral image is 10240×10240 pixels. After block segmentation, 100 image blocks are obtained, and the size of each image block is 1024×1024 pixels. Calculate the sharpness coefficient for each image block, and the result is between 0 and 1. When the sharpness coefficient is less than 0.6, mark the image block as the area to be repaired. In the band combination analysis, the weight coefficients of the near-infrared, short-wave infrared, and visible light bands are set to 0.4, 0.35, and 0.25 respectively. For the identified cloud areas, use the spectral information of the adjacent 8 cloud-free areas for spatial interpolation and reconstruction. In the calculation of the terrain undulation coefficient, the elevation data uses a digital elevation model with a resolution of 10 meters, and is registered with the repaired image through bilinear interpolation. Finally, according to the terrain undulation coefficient and the solar incidence angle, perform radiometric correction on the repaired image to generate a standardized image dataset.

[0031] In a specific embodiment, the process of executing step S102 may specifically include the following steps: (1) Separate the standardized image dataset by band and calculate the reflectance values of each band; (2) Normalize the separated band data to generate a band feature matrix; (3) Calculate the vegetation index and water index based on the band feature matrix to form a spectral feature vector; (4) Calculate the gray-level co-occurrence matrix parameters and local image gradient values for the standardized image dataset to obtain a texture parameter set; (5) Fuse the spectral feature vector and the texture parameter set to construct a degradation feature table; (6) Analyze the degradation feature table through classification discrimination to generate a degradation type distribution feature set; (7) Combine historical sample data to calculate the scores of the degradation type distribution feature set to obtain a grassland quality score table.

[0032] Specifically, use the band separation technology to process the standardized image dataset. The band separation technology divides the multispectral image according to the spectral ranges of different bands, including the blue band (450 - 520 nm), the green band (520 - 600 nm), the red band (630 - 690 nm), and the near-infrared band (760 - 900 nm). Calculate the reflectance value of each band through radiometric calibration, and convert the digital quantization value into the actual reflectance value. Normalize the separated band data, and use the maximum-minimum normalization method to map the reflectance values of each band into the interval [0, 1]. The normalized data constitutes the band feature matrix, where each row of the matrix represents a pixel point, and each column represents the normalized reflectance values of different bands.

[0033] Calculate the vegetation index and water index based on the band feature matrix to form a spectral feature vector. The calculation formula is:

[0034] Among them, is the comprehensive vegetation index, is the weight coefficient of alpine vegetation type, and are the near-infrared and red band reflectances of the k-th type of vegetation respectively, is the soil background adjustment factor, is the atmospheric attenuation coefficient, is the altitude.

[0035] Conduct texture analysis on the standardized image dataset, and calculate the gray-level co-occurrence matrix parameters and local image gradient values. The texture feature extraction formula is:

[0036] Among them, is the texture feature value, d is the pixel spacing, θ is the direction angle, G is the number of gray levels, is the element of the gray-level co-occurrence matrix, is the distance weight, is the vegetation coverage correction factor, is the soil background variance.

[0037] The spectral feature vector and the texture parameter set are subjected to feature fusion, and a degradation feature table is constructed by means of feature concatenation. The degradation feature table contains multi-dimensional information such as spectral features, texture features, and terrain features. The degradation feature table is processed through classification discriminant analysis, and the Mahalanobis distance method is used to calculate the distance between the feature vector and the feature centers of each type to determine the attribution of the degradation type and generate a degradation type distribution feature set.

[0038] Combined with historical sample data, a scoring standard system is established. The feature values of typical sample points are extracted from the historical data as the reference standards for each degradation level. Then, for each region in the current degradation type distribution feature set, the similarity between its feature value and the reference standard is calculated, and the scoring value is determined according to the similarity size to form a grassland quality scoring table.

[0039] For example, for an alpine grassland area, a multi-spectral remote sensing image of this area is obtained. Four single-band images are obtained through band separation, and the size of each band image is 2048×2048 pixels. The separated band data is normalized to generate a band feature matrix. The vegetation index is calculated based on this matrix, and different weight coefficients are set considering the special vegetation types and environmental conditions in alpine regions. The texture feature calculation uses a 7×7 pixel window to calculate the gray-level co-occurrence matrix in four directions (0°, 45°, 90°, 135°). The obtained spectral features and texture features are fused to construct a degradation feature table containing multi-dimensional features. Finally, through comparison with historical sample data, the degradation degree scoring is completed to generate a grassland quality scoring table.

[0040] In a specific embodiment, the process of executing step S103 may specifically include the following steps: (1) Extract the spatial position information from the degradation type distribution feature set and establish an evaluation unit grid; (2) Map the scoring values in the grassland quality scoring table to the evaluation unit grid to form a basic scoring matrix; (3) Perform spatial interpolation on the vegetation index and coverage data in the remote sensing indicators to obtain a vegetation state matrix; (4) Calculate the slope and aspect combination value from the terrain indicators to construct a terrain influence matrix; (5) Calculate the organic matter content and nutrient index based on soil sampling data to generate a soil property matrix; (6) Perform weighted overlay on the basic score matrix, vegetation status matrix, terrain impact matrix, and soil property matrix to generate a spatial distribution matrix of degradation degree; (7) Conduct regional statistics and classification on the spatial distribution matrix of degradation degree to obtain a set of grassland status indices.

[0041] Specifically, the degradation assessment link extracts spatial location information from the degradation type distribution feature set, including data such as geographical coordinates, altitude, and block boundaries. Based on the extracted spatial information, an evaluation unit grid is established according to a grid size of 1 square kilometer. Each grid unit contains a unique identifier and spatial attribute information. When mapping the score values in the grassland quality score table to the evaluation unit grid, a spatial location matching method is used to map each score value to the corresponding grid position to form a basic score matrix. The mapping process considers the consistency of spatial location to ensure that the score values are accurately corresponding to the actual geographical locations.

[0042] Process the remote sensing index data, mainly including spatial interpolation of vegetation index and coverage data. Interpolate the discrete observation point data by the inverse distance weighting method to generate a continuous vegetation status matrix. The spatial distribution characteristics of the sampling points are considered during the interpolation process to ensure the spatial continuity and rationality of the interpolation results. In the terrain index processing, use the digital elevation model data to calculate the slope and aspect of each grid unit. The slope calculation is based on the elevation difference between adjacent grid points, and the aspect represents the orientation angle of the slope surface. Combine and calculate the slope and aspect data to construct a terrain impact matrix.

[0043] The processing process of soil sampling data involves the calculation of organic matter content and nutrient index. Standardize the original data of the sampling points and generate a continuously distributed soil property matrix by the Kriging interpolation method. The spatial autocorrelation of the sampling points is considered during the interpolation process to improve the accuracy of the interpolation results.

[0044] The following calculation method is used to generate the spatial distribution matrix of degradation degree:

[0045] where, is the spatial distribution matrix of degradation degree, is an element of the basic score matrix, is an element of the vegetation status matrix, is an element of the terrain impact matrix, is an element of the soil property matrix, 、 、 、 are the corresponding weight coefficients respectively, and m and n are the number of rows and columns of the matrix.

[0046] Perform regional statistics and classification processing on the spatial distribution matrix of degradation degree. Statistical analysis includes calculating indicators such as the average value, standard deviation, and spatial distribution characteristics of each region, and classifying the degradation degree according to the statistical results to generate a grassland status index set. The classification criteria are determined based on expert knowledge and historical data to ensure the scientificity and practicality of the classification results.

[0047] For example, in a study area of an alpine grassland with an area of 100 square kilometers, a 10×10 grid of evaluation units is established. Each grid unit has a size of 1 square kilometer, and the grassland quality score values are mapped into the grid through spatial matching. The vegetation index data is sourced from multi-spectral remote sensing images, and the coverage data is obtained through field sampling points, and inverse distance weighting method is used for spatial interpolation. The terrain data is used to calculate the slope and aspect values using a digital elevation model with a resolution of 10 meters. 50 sampling points are set in the study area for soil sampling, and continuous soil property data is generated through Kriging interpolation. Through the weighted overlay method, a spatial distribution matrix of degradation degree is generated and the classification evaluation is completed.

[0048] In a specific embodiment, the process of executing step S104 may specifically include the following steps: (1) Perform spatial clustering on the spatial distribution matrix of degradation degree to divide the restoration area units; (2) Classify the restoration area units according to the grassland status index set to obtain a restoration priority ranking list; (3) Extract the restoration measure parameters and effect records from the historical restoration case data to establish a restoration experience database; (4) Match the parameters in the restoration experience database with the characteristics of the restoration area units to generate a preliminary restoration measure set; (5) Numerically optimize the parameters of the fence height, reseeding density, and fertilization amount in the preliminary restoration measure set to form a regional restoration plan library; (6) Summarize and organize the restoration measure parameters in the regional restoration plan library to generate a technical parameter list.

[0049] Specifically, the spatial distribution matrix of the degree of degradation is subjected to spatial clustering. The spatial clustering adopts the K-means clustering algorithm, which considers the data characteristics of the two dimensions of spatial position and degree of degradation. In the specific operation, each grid unit in the spatial distribution matrix of the degree of degradation is used as a clustering object, and the degree of degradation value in the matrix and the spatial coordinates of the grid unit are used as clustering features. The Euclidean distance between each type of center point and the sample point is iteratively calculated until the intra-class difference is the smallest and the inter-class difference is the largest. The iteration is stopped to complete the division of the restoration area unit. The divided restoration area units are graded in combination with the grassland state index set. The grading process is based on the values ​​of various indicators in the grassland state index set, including vegetation coverage, biomass, species diversity and other indicators. The comprehensive index value of each restoration area unit is calculated, and the restoration area units are divided into different priority levels according to the order of the index values. Priority division takes into account factors such as the severity of degradation, restoration difficulty and ecological importance to form a restoration priority ranking table.

[0050] The processing process of historical restoration case data includes data extraction, cleaning and standardization. The restoration measure parameters, such as fence height, reseeding density, fertilizer application and other specific parameter values, are extracted from historical cases, and the corresponding restoration effect records, including vegetation recovery rate, biomass increase and other indicators, are extracted at the same time. The extracted data are cleaned, outliers and incomplete records are removed, and the data are standardized to establish a restoration experience database in a unified format. When matching the parameters in the restoration experience database with the characteristics of the restoration area unit, a similarity calculation method is used. The similarity between the characteristics of each restoration area unit and the characteristics in the historical case is calculated, and the restoration measure parameters in the case with the highest similarity are selected as the preliminary restoration plan for the area. The similarity calculation considers multiple feature dimensions, including terrain conditions, soil characteristics, climate factors, etc. The specific parameters in the preliminary restoration measures are optimized. The optimization process considers three key parameters: fence height, reseeding density and fertilizer application. The value range of these parameters is set, and the optimal parameter combination is determined by numerical optimization method in combination with regional characteristics and historical experience. In the numerical optimization process, multiple goals such as restoration effect, resource input and ecological impact are comprehensively considered to generate a regional restoration plan library.

[0051] Finally, the parameters of the restoration measures in the regional restoration solution library are summarized and sorted. The summary process includes statistical parameter distribution characteristics of different regions, analysis of the relationship between parameters, and sorting out a standard format of technical parameter list. The technical parameter list contains the specific implementation parameters and corresponding implementation requirements for each restoration area.

[0052] For example, a restoration analysis is carried out on a certain alpine grassland. The study area is divided into 20 restoration area units through spatial clustering, and the area of each unit is about 5 square kilometers. Priority ranking is carried out according to the vegetation coverage index (obtained through remote sensing data), biomass index (obtained through field sampling), and species diversity index (obtained through quadrat surveys) in the grassland status index. 50 successful cases are selected from the historical restoration case library, and the restoration parameters and effect records are extracted. During the similarity matching process, three characteristic dimensions of altitude (obtained through digital elevation model), average annual precipitation (obtained through meteorological station data), and soil type (obtained through soil sampling) are mainly considered. Parameter optimization determines the fence height, reseeding density, and fertilization amount in different regions, and generates a technical list containing specific implementation parameters.

[0053] In a specific embodiment, the process of executing step S105 may specifically include the following steps: (1) Extract the spatial distribution information of the restoration measures from the sub-region restoration plan library and construct a spatio-temporal response analysis grid; (2) Map the parameter values in the technical parameter list to the spatio-temporal response analysis grid to obtain a parameter spatial distribution map; (3) Perform time series decomposition on the precipitation and temperature data in the meteorological data to generate a meteorological change trend map; (4) Calculate the water accumulation index and radiation amount from the terrain data to form an environmental condition matrix; (5) Perform spatial overlay analysis on the parameter spatial distribution map and the environmental condition matrix to obtain a restoration expected index matrix; (6) Perform spatio-temporal statistics and index calculation on the restoration expected index matrix to generate an effect evaluation table.

[0054] Specifically, extract the spatial distribution information of the restoration measures in the sub-region restoration plan library. The extraction process includes obtaining the geographical scope, restoration measure type, and specific parameters of each restoration area. Based on the extracted spatial information, construct a spatio-temporal response analysis grid with a size of 250 meters × 250 meters to ensure that the analysis accuracy meets the requirements of restoration effect evaluation. Mapping the parameter values in the technical parameter list to the spatio-temporal response analysis grid requires establishing a spatial correspondence relationship. Each grid cell obtains the corresponding restoration measure parameters according to its geographical location, including fence height, reseeding density, and fertilization amount, etc. Through spatial interpolation methods, the discrete parameter values are converted into a continuously distributed parameter spatial distribution map.

[0055] The processing of meteorological data includes the time series decomposition of precipitation and temperature data. Taking 12 months as a cycle unit, the monthly average temperature and monthly precipitation data are seasonally decomposed to extract the long-term trend term, seasonal term, and random term. The trend term obtained through decomposition reflects the long-term change characteristics of meteorological elements, and the seasonal term represents the periodic change law, generating a meteorological change trend chart.

[0056] The following calculation formula is used for the processing of terrain data:

[0057] Among them, is the environmental condition matrix, is the slope runoff coefficient, is the runoff parameter, is the slope direction coefficient, is the solar radiation intensity, is the altitude correction factor, g and h are the grid row and column numbers, and G and H are the matrix dimensions.

[0058] When performing spatial overlay analysis on the parameter spatial distribution map and the environmental condition matrix, a weighted overlay method is used. The expected repair index value of each grid cell is jointly determined by the repair measure parameters and the environmental conditions, considering the mutual influence relationship between the parameters, generating an expected repair index matrix. Finally, spatio-temporal statistical analysis is performed on the expected repair index matrix to calculate statistics such as the index mean, variance, and spatial autocorrelation of each region. Based on the statistical analysis results, a quantitative evaluation of the expected repair effect of each region is carried out, generating an effect evaluation table containing multiple evaluation indicators.

[0059] For example, a certain alpine grassland research area has an area of 100 square kilometers and is divided into 1600 spatio-temporal response analysis grid cells. The fence height parameter in the technical parameter list is spatially interpolated by the inverse distance weighting method to generate a continuously distributed parameter spatial distribution map. The meteorological data is sourced from automatic weather stations within the research area, collecting monthly observation data for the past 5 years. The terrain data uses a digital elevation model with a resolution of 30 meters to calculate the moisture accumulation index and solar radiation amount of each grid cell. During the overlay analysis process, the environmental condition weight is 0.4, and the repair measure parameter weight is 0.6. The spatio-temporal statistical analysis includes calculating the expected repair indicators for the short term (within 1 year) and long term (3 years), generating the effect evaluation results by time period and region.

[0060] In a specific embodiment, the process of executing step S106 may specifically include the following steps: (1) Extract the repair target value from the expected repair index matrix to construct the target parameter space; (2) Calculate the weights of the evaluation indicators in the effect evaluation table to generate an index weight vector; (3) Conduct a temporal comparison of the vegetation coverage and soil moisture data in the online monitoring data to obtain a monitoring deviation table; (4) Based on the monitoring deviation table, perform interval adjustment on the restoration parameters to form a set of parameter constraint conditions; (5) Analyze the restoration effect based on the target parameter space and the index weight vector to generate an optimized restoration strategy plan; (6) Combine the set of parameter constraint conditions to adjust the parameters of the restoration strategy and output a dynamically adjusted parameter set.

[0061] Specifically, extract the key restoration target values from the restoration expectation index matrix. The extraction process conducts data analysis on each grid cell in the matrix to identify the expected values of indicators such as vegetation coverage, biomass, and species diversity. For each indicator, set a threshold range and perform numerical normalization to convert indicator values with different dimensions to a unified scale. The extracted target values are reorganized according to the spatial location information to construct the target parameter space. When calculating the weights of the evaluation indicators in the effect evaluation table, use the analytic hierarchy process to determine the indicator weights. Construct the hierarchical structure of the indicator system, including ecological benefit indicators (vegetation coverage, biomass), environmental benefit indicators (soil and water conservation, biodiversity), and economic benefit indicators (input-output ratio). Establish a judgment matrix through expert scoring, calculate the eigenvector, and conduct a consistency test to finally obtain the weight values of each indicator and form the indicator weight vector.

[0062] The processing of online monitoring data involves the temporal comparison of vegetation coverage and soil moisture. Arrange the monitoring data in a time series, and the data of each monitoring point includes information such as monitoring time, spatial location, and monitoring value. Compare the actual monitoring value of each monitoring point with the restoration expectation value to calculate the deviation value. The deviation calculation considers the influence of time factors and assigns different weights to the deviations in different periods, and finally generates a monitoring deviation table. Based on the monitoring deviation table, perform interval adjustment on the restoration parameters. The adjustment process analyzes the spatial distribution characteristics and temporal variation trends of the deviations, and identifies the regions and periods with larger deviations. For different types of deviations, set parameter adjustment rules, such as increasing the seeding density if the vegetation coverage is low, and adjusting the irrigation parameters if the soil moisture is low. Generate the adjustment interval of the parameters through rule operations to form a set of parameter constraint conditions.

[0063] When conducting the restoration effect analysis based on the target parameter space and the index weight vector, a multi-objective optimization method is adopted. The restoration target value is used as the optimization objective, and the index weight is used as the optimization weight to construct an optimization objective function. During the optimization process, the mutual influence and restrictive relationships among various indicators are considered, and the optimal parameter combination is found through iterative calculations to generate an optimized restoration strategy plan. Finally, the parameters of the restoration strategy are adjusted in combination with the parameter constraint condition set. During the adjustment process, the limitation range of the constraint conditions is strictly followed to ensure that the adjusted parameters meet the actual requirements. The parameters of each restoration area are adjusted one by one, considering the relevance among the parameters to avoid parameter conflicts. The feasibility of the adjustment results is verified, and finally, a dynamically adjusted parameter set is output.

[0064] For example, in a high-cold grassland restoration project, the target values of three key indicators, namely vegetation coverage, biomass, and species diversity, are extracted from the restoration expected index matrix. The weights of the three indicators are calculated to be 0.4, 0.35, and 0.25 respectively through the analytic hierarchy process. The online monitoring data comes from 30 automatic monitoring stations deployed in the study area, and each monitoring station records the vegetation coverage and soil moisture content data every day. Through time series comparison, it is found that the vegetation coverage in some areas is lower than the expected value, and the soil moisture fluctuates greatly. Based on the monitoring deviation, the seeding density and irrigation frequency are adjusted to form a new parameter constraint range. Finally, through multi-objective optimization calculation, the optimal parameter combination that meets the requirements of each indicator is obtained, and a dynamically adjusted plan including specific implementation parameters is generated.

[0065] The above describes the high-cold grassland rapid restoration strategy analysis method in the embodiments of the present application. Next, the high-cold grassland rapid restoration strategy analysis system in the embodiments of the present application will be described. Please refer to Figure 2 , an embodiment of the high-cold grassland rapid restoration strategy analysis system in the embodiments of the present application includes: A collection module 201, configured to perform quality scoring and image restoration processing on the collected multi-spectral images of the high-cold grassland, and obtain a standardized image data set through terrain correction; A classification module 202, configured to extract spectral and texture features according to the standardized image data set, perform degradation type classification processing on the features, and obtain a degradation type distribution feature set and a grassland quality scoring table; An evaluation module 203, configured to perform multi-dimensional evaluation based on the degradation type distribution feature set and the grassland quality scoring table, in combination with remote sensing indicators, terrain indicators, and soil sampling data, to generate a spatial distribution matrix of degradation degree and a grassland state index set; An optimization module 204, configured to form a regional restoration plan library and a technical parameter list through historical restoration case analysis and parameter optimization based on the spatial distribution matrix of degradation degree and the grassland state index set; An analysis module 205, configured to perform spatio-temporal response analysis based on the sub-region repair solution library and the technical parameter list, in combination with meteorological data and terrain data, to obtain a repair expectation index matrix and an effect evaluation form; A monitoring module 206, configured to obtain an optimized repair strategy plan and a dynamic adjustment parameter set through multi-objective optimization and online monitoring data analysis based on the repair expectation index matrix and the effect evaluation form.

[0066] Through the collaborative cooperation of the above-mentioned various components, by performing quality scoring and image repair processing on the multi-spectral images of alpine grasslands, and combining terrain correction to obtain a standardized image data set, the problem of unstable image quality in traditional methods is overcome; by extracting spectral and texture features for degradation type classification processing, a degradation type distribution feature set and a grassland quality scoring form are obtained, realizing the accurate identification and scoring of degradation types; based on the degradation type distribution feature set and the grassland quality scoring form, combined with remote sensing indicators, terrain indicators and soil sampling data for multi-dimensional evaluation, a degradation degree spatial distribution matrix and a grassland state index set are generated, providing a comprehensive evaluation result of the degradation status; through historical repair case analysis and parameter optimization, a sub-region repair solution library and a technical parameter list are formed, formulating targeted repair strategies for different regions; spatio-temporal response analysis is performed in combination with meteorological data and terrain data to obtain a repair expectation index matrix and an effect evaluation form, realizing the scientific prediction of the repair effect; finally, through multi-objective optimization and online monitoring data analysis, an optimized repair strategy plan and a dynamic adjustment parameter set are obtained, establishing a dynamic optimization mechanism for the repair strategy, not only improving the accuracy of alpine grassland degradation identification and the scientificity of repair plan formulation, but also ensuring the adaptability and effectiveness of the repair strategy through the dynamic adjustment mechanism.

[0067] Based on the same technical concept, an embodiment of the present application further provides a computer device. Refer to Figure 3 As shown, it is a schematic structural diagram of a computer device 300 provided by an embodiment of the present application, including a processor 301, a memory 302, and a bus 303. Among them, the memory 302 is used to store execution instructions, including an internal memory 3021 and an external memory 3022; here, the internal memory 3021 is also called the main memory, which is used to temporarily store the operation data in the processor 301 and the data exchanged with the external memory 3022 such as a hard disk. The processor 301 exchanges data with the external memory 3022 through the internal memory 3021. When the computer device 300 runs, the processor 301 communicates with the memory 302 through the bus 303.

[0068] The present application further provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are run on a computer, the computer is caused to execute the steps of the method for analyzing the rapid restoration strategy of alpine grasslands.

[0069] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described system, system and units can refer to the corresponding processes in the foregoing method embodiments and will not be described herein again.

[0070] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0071] As described above, the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims

1. A method for analyzing rapid restoration strategies for alpine grasslands, characterized in that: The alpine grassland rapid restoration strategy analysis method includes: The collected alpine grassland multispectral images were scored for quality and image restoration, and a standardized image dataset was obtained through terrain correction. Extracting spectral and texture features according to the standardized image data set, classifying the features into degradation types, and obtaining a degradation type distribution feature set and a grassland quality score table; Based on the degradation type distribution feature set and grassland quality score table, a multi-dimensional assessment is conducted in combination with remote sensing indicators, terrain indicators and soil sampling data to generate a degradation degree spatial distribution matrix and a grassland status index set; Based on the spatial distribution matrix of the degree of degradation and the grassland status index set, a regional restoration program library and a list of technical parameters are formed through historical restoration case analysis and parameter optimization; Based on the regional restoration solution library and technical parameter list, a spatiotemporal response analysis is conducted in combination with meteorological data and terrain data to obtain a restoration expected indicator matrix and effect evaluation table; Based on the restoration expected indicator matrix and effect evaluation table, a restoration strategy optimization scheme and a dynamically adjusted parameter set are obtained through multi-objective optimization and online monitoring data analysis.

2. The method for analyzing the rapid restoration strategy of alpine grassland according to claim 1, characterized in that: The quality scoring and image restoration processing of the collected alpine grassland multispectral images are performed, and a standardized image data set is obtained through terrain correction, including: The alpine grassland multispectral image is divided into blocks to obtain an image block sequence; Calculate the clarity coefficient and signal-to-noise ratio parameter of each image block according to the image block sequence to generate a quality score result; Compare the quality score result with the quality threshold, and mark the area below the threshold as the area to be repaired; By means of band combination analysis, the cloud layer and shadow of the area to be repaired are identified to obtain an interference area map; Perform image reconstruction according to the interference area map and the spectral information of the adjacent area to generate a repaired image; registering the repaired image with terrain elevation data to extract terrain relief coefficient; The restored image is subjected to radiation correction based on the terrain relief coefficient and the incident angle parameter to generate a standardized image data set.

3. The method for analyzing the rapid restoration strategy of alpine grassland according to claim 1, characterized in that: The method extracts spectral and texture features according to the standardized image data set, classifies the features into degradation types, and obtains a degradation type distribution feature set and a grassland quality score table, including: Separating the standardized image data set by bands and calculating the reflectance value of each band; Normalize the separated band data to generate a band feature matrix; Calculating vegetation index and moisture index based on the band characteristic matrix to form a spectral characteristic vector; Calculating gray-level co-occurrence matrix parameters and local image gradient values ​​for the standardized image data set to obtain a texture parameter set; The spectral feature vector is merged with the texture parameter set to construct a degradation feature table; Analyzing the degradation feature table through classification and discrimination to generate a degradation type distribution feature set; The degradation type distribution feature set is scored and calculated in combination with historical sample data to obtain a grassland quality score table.

4. The method for analyzing the rapid restoration strategy of alpine grassland according to claim 1, characterized in that: The method is based on the degradation type distribution feature set and grassland quality score table, combined with remote sensing indicators, terrain indicators and soil sampling data to conduct multi-dimensional evaluation, generate a degradation degree spatial distribution matrix and grassland status index set, including: Extracting spatial location information from the degradation type distribution feature set and establishing an evaluation unit grid; Mapping the score values ​​in the grassland quality score table to the evaluation unit grid to form a basic score matrix; Performing spatial interpolation on the vegetation index and coverage data in the remote sensing indicators to obtain a vegetation state matrix; Calculating the slope and aspect combination values ​​from the terrain indicators and constructing a terrain influence matrix; Calculating organic matter content and nutrient index based on the soil sampling data to generate a soil property matrix; The basic scoring matrix, vegetation status matrix, terrain influence matrix and soil property matrix are weighted and superimposed to generate a degradation degree spatial distribution matrix; The degradation degree spatial distribution matrix is ​​subjected to regional statistics and classification to obtain a grassland state index set.

5. The method for analyzing the rapid restoration strategy of alpine grassland according to claim 1, characterized in that: According to the spatial distribution matrix of the degree of degradation and the grassland status index set, through historical restoration case analysis and parameter optimization, a regional restoration program library and a list of technical parameters are formed, including: Performing spatial clustering on the degradation degree spatial distribution matrix to divide the restoration area units; Classifying the restoration area units according to the grassland state index set to obtain a restoration priority ranking table; Extract restoration measure parameters and effect records from historical restoration case data to establish a restoration experience database; Matching the parameters in the restoration experience database with the unit characteristics of the restoration area to generate a preliminary restoration measure set; Numerical optimization is performed on the fence height, overseeding density, and fertilizer application parameters in the preliminary restoration measures to form a regional restoration plan library; The parameters of the restoration measures in the regional restoration solution library are summarized and sorted to generate a technical parameter list.

6. The method for analyzing the rapid restoration strategy of alpine grassland according to claim 1, characterized in that: The above-mentioned regional restoration solution library and technical parameter list are combined with meteorological data and terrain data to perform spatiotemporal response analysis to obtain restoration expected indicator matrix and effect evaluation table, including: Extracting spatial distribution information of restoration measures from the regional restoration solution library and constructing a spatiotemporal response analysis grid; Mapping the parameter values ​​in the technical parameter list to the spatiotemporal response analysis grid to obtain a parameter space distribution diagram; Performing time series decomposition on the precipitation and temperature data in the meteorological data to generate a meteorological change trend graph; Calculating the moisture accumulation index and radiation amount from the terrain data to form an environmental condition matrix; Perform spatial overlay analysis on the parameter spatial distribution map and the environmental condition matrix to obtain a restoration expected index matrix; Perform spatiotemporal statistics and indicator calculation on the restoration expected indicator matrix to generate an effect evaluation table.

7. The method for analyzing the rapid restoration strategy of alpine grassland according to claim 1, characterized in that: Based on the restoration expected indicator matrix and effect evaluation table, through multi-objective optimization and online monitoring data analysis, a restoration strategy optimization scheme and a dynamic adjustment parameter set are obtained, including: Extracting the restoration target value from the restoration expected index matrix and constructing the target parameter space; Performing weight calculation on the evaluation indicators in the effect evaluation table to generate an indicator weight vector; The vegetation coverage and soil moisture data in the online monitoring data are compared in time series to obtain a monitoring deviation table; Based on the monitoring deviation table, the repair parameters are adjusted in intervals to form a parameter constraint condition set; Performing repair effect analysis based on the target parameter space and the indicator weight vector to generate a repair strategy optimization plan; The repair strategy is parameter-adjusted in combination with the parameter constraint condition set, and a dynamically adjusted parameter set is output.

8. A system for analyzing a rapid restoration strategy for alpine grassland, used to implement the method for analyzing a rapid restoration strategy for alpine grassland according to any one of claims 1 to 7, characterized in that: The alpine grassland rapid restoration strategy analysis system comprises: The acquisition module is used to perform quality scoring and image restoration on the collected alpine grassland multispectral images, and obtain a standardized image dataset through terrain correction; A classification module is used to extract spectral and texture features according to the standardized image data set, classify the features into degradation types, and obtain a degradation type distribution feature set and a grassland quality score table; An evaluation module is used to perform multi-dimensional evaluation based on the degradation type distribution feature set and grassland quality score table, combined with remote sensing indicators, terrain indicators and soil sampling data, to generate a degradation degree spatial distribution matrix and a grassland status index set; An optimization module is used to form a regional restoration program library and a technical parameter list based on the degradation degree spatial distribution matrix and grassland state index set through historical restoration case analysis and parameter optimization; An analysis module is used to perform spatiotemporal response analysis based on the regional restoration solution library and technical parameter list in combination with meteorological data and terrain data to obtain a restoration expected indicator matrix and an effect evaluation table; The monitoring module is used to obtain a repair strategy optimization scheme and a dynamically adjusted parameter set based on the repair expected indicator matrix and effect evaluation table through multi-objective optimization and online monitoring data analysis.

9. A computer device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the computer device is running, the processor and the memory communicate via the bus, and when the machine-readable instructions are executed by the processor, the steps of the method for analyzing a rapid restoration strategy for alpine grassland as described in any one of claims 1 to 7 are performed.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the method for analyzing the rapid restoration strategy of the alpine grassland according to any one of claims 1 to 7 is implemented.

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