Method for simulating grazing in alpine meadow pasture

Abstract

The application discloses a kind of alpine meadow pasture grazing simulation method, belong to the field of pasture grazing simulation, comprising: collecting alpine meadow initial topography and water distribution data, simulating cattle trampling distribution mode, obtain preliminary topography change distribution diagram;Calculate the depth and position caused by trampling Concave, simulate muddy ground deformation propagation, determine the fine topography reconstruction grid;If the depth of Concave exceeds threshold value, adjust slope parameter and simulate surface water flow direction, obtain updated water flow path diagram;Extract water accumulation area, if uneven distribution, then iteratively simulate the secondary influence of trampling, obtain cycle influence sequence;Integrate water resource data to predict long-term water change trend, determine water flow rule diagram under the condition of interference stable state;Verify and optimize parameters through historical grazing data, obtain refined topography water flow interaction model;Export key interference index and generate sustainable management recommendation distribution, obtain final ecological simulation output.

Description

Technical Field

[0001] This invention belongs to the field of pasture grazing simulation, and in particular relates to a method for simulating grazing in alpine meadow pastures. Background Technology

[0002] Alpine meadows, as key ecosystems in high-altitude regions, possess irreplaceable value in soil and water conservation, biodiversity maintenance, and livestock production. Current research on the impacts of grazing on meadow ecosystems mainly focuses on the direct damage to vegetation cover caused by livestock grazing, and the macro-level assessment of grassland degradation caused by overgrazing. In terms of simulation methods, existing techniques mostly employ statistical models or remote sensing-based analysis of vegetation index changes, attempting to establish an empirical relationship between grazing intensity and meadow productivity. Some studies have also begun to introduce hydrological models to assess the macro-level impacts of grazing on regional runoff and soil moisture balance, providing some data support for meadow management.

[0003] However, existing research and simulation methods generally neglect the dynamic coupling mechanisms between livestock activity and landforms and water resources at subtle scales. Particularly in humid environments, existing technologies lack effective means to capture and simulate the subtle landform alterations caused by livestock trampling on muddy surfaces, such as minute depressions and deformation propagation, and how these alterations change the flow and accumulation patterns of surface water. This makes it difficult to quantify the complex cyclical disturbance process of "trampling causing landform changes, landform changes altering water flow distribution, and uneven water flow distribution, in turn affecting subsequent livestock trampling." Traditional models typically treat grazing disturbance as an exogenous variable, failing to dynamically reflect the real-time feedback relationship between livestock behavior, micro-topographic evolution, and surface water distribution, thus limiting the ability to accurately predict the evolution of meadow eco-hydrological processes under grazing disturbance. Summary of the Invention

[0004] To solve the above-mentioned technical problems, the present invention provides a method for simulating grazing in alpine meadow pastures, comprising: Based on the initial geomorphological features and water distribution data of the alpine meadow, the distribution pattern of livestock trampling behavior on the muddy ground was simulated to obtain a preliminary geomorphological change distribution map. Based on the preliminary landform change distribution map, the depth and location of depressions caused by livestock trampling are obtained, and the fine landform modification grid is determined by simulating the deformation propagation of muddy ground under trampling. If the depression depth in the fine landform modification grid exceeds a preset threshold, the slope parameter within the grid is adjusted, and an updated water flow path map is obtained by simulating the flow direction of surface water on the modified landform. The water accumulation area is obtained based on the updated water flow path map. If the water accumulation area is unevenly distributed due to landform modification, the secondary impact of livestock trampling on the area is simulated iteratively to obtain the cyclic impact sequence. Based on the cyclical influence sequence, water resource distribution data are integrated, and the water flow pattern map under the disturbance steady state is determined by predicting the long-term trend of surface water accumulation. Based on the water flow pattern diagram, historical grazing data is obtained for verification. If the deviation between the simulation results and the actual water distribution is less than a preset threshold, the simulation parameters are optimized to obtain a refined geomorphological water flow interaction model. Key disturbance indicators are obtained based on the refined geomorphological and water flow interaction model, and a recommended distribution for sustainable management of alpine meadows is generated through data visualization. If the recommended distribution covers all cyclically affected areas, the final ecological simulation output is obtained.

[0005] Optionally, the step of simulating the distribution pattern of livestock trampling behavior on muddy ground based on the initial geomorphological features and water distribution data of the alpine meadow to obtain a preliminary geomorphological change distribution map includes: Initial geomorphological and moisture distribution data of alpine meadow areas were collected using satellite remote sensing data and ground sensors to construct a comprehensive dataset and obtain environmental information records. Based on the comprehensive dataset, gridded information of landform features and water distribution is extracted, and missing data is filled in to obtain complete environmental basic data. Based on the aforementioned environmental baseline data, combined with the activity range of livestock trampling behavior and the distribution characteristics of muddy ground, the distribution pattern of trampling behavior is simulated to obtain a preliminary distribution map of the impact of trampling. Based on the preliminary distribution map, key areas affected by trampling are extracted. If the moisture distribution value of the key area exceeds a preset threshold, the landform changes in the area are weighted to obtain high-risk deformation areas. Based on the data of the high-risk deformation area, combined with the initial geomorphological features, a spatial overlay analysis of the geomorphological change trend is performed to obtain the dynamic distribution results of the geomorphological change. Based on the dynamic distribution results, a prediction model for landform changes is constructed to determine the cumulative impact distribution of long-term trampling behavior on landforms.

[0006] Optionally, based on the preliminary landform change distribution map, the depth and location of depressions caused by livestock trampling are obtained, and a fine landform modification grid is determined by simulating the deformation propagation of muddy ground under trampling, including: Based on the preliminary landform change distribution map, initial landform elevation information and the distribution area of ​​livestock trampling are obtained, thus obtaining basic data on the trampling impact range and landform changes. Based on the trampling impact range, calculate the indentation depth at each trampling location and determine the spatial distribution characteristics of the indentation depth; Based on the spatial distribution characteristics of the depression depth and combined with the physical properties of the muddy ground, the deformation propagation path under trampling is simulated to obtain the dynamic results of deformation propagation. Based on the dynamic results of the deformation propagation, the deformation intensity of the muddy ground in different areas is extracted, and combined with the distribution map of landform changes, the grid units of the fine modification are divided, and the deformation state of each grid unit is determined. Based on the deformation state of the grid cells, the impact range of trampling on the minor modifications is mapped, and the priority ranking of the modified grid cells is obtained. Based on the priority sorting of the modified grid and combined with the depression depth data, a detailed landform modification implementation plan is generated. If the deformation intensity of the grid cell exceeds the preset threshold, it is processed first to obtain the final modified grid division plan. Based on the final transformation grid division scheme, the simulation results of landform changes and deformation propagation are integrated to construct a landform transformation data archive and determine a digital grid model suitable for subsequent processing.

[0007] Optionally, if the depression depth in the fine landform modification grid exceeds a preset threshold, the slope parameter within the grid is adjusted, and an updated water flow path map is obtained by simulating the flow direction of surface water on the modified landform, including: Scan the terrain data within the grid division to obtain the distribution of depression depth and determine whether it exceeds the preset threshold; If the depression depth exceeds the preset threshold, the slope parameter is adjusted locally to generate modified landform data. Based on the modified landform data, the flow direction of surface water is simulated to obtain a preliminary water flow trend map; Based on the preliminary water flow trend map, flow direction data is extracted, and the continuity distribution of the water flow path is determined by combining the boundary conditions of the grid division. Based on the continuous distribution of the water flow path, the location information of the path intersections and branching points is obtained, and a detailed water flow path map is generated. The detailed water flow path map is overlaid with data, and combined with the slope parameters after the landform modification, it is determined whether the path update conforms to the preset flow logic. If the path update conforms to the flow logic, the final water flow path map is output, completing the analysis and recording of the surface water flow direction.

[0008] Optionally, based on the updated water flow path map, water accumulation areas are obtained. If the water accumulation areas are unevenly distributed due to landform modification, the secondary impact of livestock trampling on the areas is iteratively simulated to obtain a cyclic impact sequence, including: Based on the updated water flow path map, the distribution information of water accumulation areas is obtained, and the areas are initially divided to obtain preliminary results of the distribution pattern. The uniformity of the regional distribution is assessed based on the preliminary results of the distribution pattern. If the distribution is uneven, the uneven areas are marked using a preset threshold to identify key areas that need further analysis. Based on the relevant data of landform modification extracted from the key areas, and combined with the changes in water flow paths, the effect of landform modification on water accumulation was analyzed, and the distribution characteristics of the modification impact were obtained. Based on the distribution characteristics of the impact of the modification, iterative simulation of livestock trampling behavior was conducted to obtain data on the secondary effects of trampling on the waterlogged area. Based on the secondary action data, analyze the dynamic changes of regional impact, construct a cyclic impact sequence, and determine the stability of the water accumulation area during the cyclic process; Based on the results of the cyclical influence sequence and the data from path analysis, the long-term changing trend of the waterlogged area is simulated to determine the final distribution adjustment scheme. Based on the final distribution adjustment scheme, key information is extracted, and a correlation database between livestock trampling and water accumulation is constructed to obtain complete business analysis results.

[0009] Optionally, based on the cyclical impact sequence, water resource distribution data are integrated, and a water flow pattern map under disturbed steady-state conditions is determined by predicting long-term surface water change trends, including: Acquire water resource distribution data and organize the cyclical impact sequence. Through time series segmentation processing, obtain the basic dataset of water resource distribution under cyclical impact. Based on the aforementioned basic dataset of water resource distribution, the changes in surface water accumulation are simulated and calculated to determine the dynamic distribution characteristics of surface water accumulation. Based on the dynamic distribution characteristics of the surface water, the long-term trend of change is analyzed. If the trend of change exceeds the preset threshold, the data is stratified to obtain the key influencing factors of long-term change. Based on the key influencing factors of the long-term changes, and combined with the data characteristics under the disturbance stable state, a distribution model of the water flow pattern is constructed, and the stability of the water flow distribution under the disturbance condition is judged. Based on the stability of the water flow distribution and the results of the fusion state analysis, a preliminary layer of the distribution pattern is generated to obtain a distribution map of the water flow pattern under disturbed stable conditions. Based on the water flow pattern distribution map, the predicted trend is corrected to determine the final long-term trend map of surface water accumulation.

[0010] Optionally, based on the water flow pattern diagram, historical grazing data is obtained for verification. If the deviation between the simulation results and the actual water distribution is less than a preset threshold, the simulation parameters are optimized to obtain a refined geomorphological water flow interaction model, including: Based on historical grazing data, record information related to water flow patterns was obtained, and a grazing distribution dataset under time series was compiled to determine the preliminary range of water flow influence. Based on the grazing distribution dataset and combined with geomorphic interaction features, an initial simulated water distribution framework was constructed, and preliminary results of the simulated water distribution were obtained. The preliminary results of the simulated water distribution are compared with the actual water distribution data, the deviation between the two is calculated, and it is determined whether the deviation is less than a preset threshold. If the deviation is less than the preset threshold, the current parameter settings are retained, the corresponding water flow interaction mode is obtained, and the association rules between water flow patterns and landform interaction are determined. If the deviation is greater than or equal to the preset threshold, the parameter configuration is adjusted, the simulated water distribution results are regenerated, and the updated deviation comparison data is obtained. Based on the updated deviation comparison data, the association rules between water flow interaction and landform interaction are iteratively optimized, a refined landform-water flow interaction model is constructed, and the final simulation framework is determined. Based on the refined geomorphological water flow interaction model and combined with historical grazing data, the model's adaptability in different time periods was verified, and stable water flow pattern simulation results were obtained.

[0011] Optionally, key disturbance indicators are obtained based on the refined geomorphological-water flow interaction model, and a recommended distribution for sustainable management of alpine meadows is generated through data visualization. If the recommended distribution covers all cyclically affected areas, the final ecological simulation output is obtained, including: Based on the refined geomorphological and water flow interaction model, core interference index data are obtained, and the index data is classified and organized to obtain a preliminary set of interference indicators. Based on the preliminary set of interference indicators, a recommended distribution map of alpine meadows is generated, and spatial overlay analysis is performed on the regional distribution characteristics in the map to determine the preliminary coverage of the distribution map. Based on the preliminary coverage area, extract the boundary data of the cyclic influence area, and perform vectorization processing on the boundary data to obtain the precise range dataset of the cyclic influence area; Spatial comparison analysis is performed on the precise range dataset and the recommended distribution map. If the recommended distribution map does not completely cover the cyclic influence area, data is added to the uncovered area to obtain a complete distribution coverage map. Based on the complete distribution cover map and combined with the sustainable management strategy database, a management recommendation plan for alpine meadows is generated, and management measures related to the cyclical impact area are selected to determine the final set of management strategies. Based on the final set of management strategies, combined with the ecological simulation framework, a preset ecological dynamic model is run to obtain simulated response data of alpine meadows under different management strategies, and to determine the long-term stability trend of the ecosystem. Based on the simulated response data, the final ecological simulation results are generated and formatted to obtain structured ecological management recommendation report data.

[0012] On the other hand, the present invention also provides an electronic device including a memory, a processor, and a computing program stored in the memory and executable on the processor, wherein the processor implements the method when executing the computing program.

[0013] On the other hand, the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method.

[0014] Compared with the prior art, the present invention has the following advantages and technical effects: The method for simulating the interaction between alpine meadow landforms and water resources provided by this invention addresses the technical problem of capturing subtle landform alterations and water flow distribution cycle disturbances in previous techniques, achieving the following beneficial effects: First, by fusing satellite remote sensing and ground sensor data, combined with agent modeling and cellular automata algorithms, it achieves for the first time accurate identification and dynamic simulation of muddy ground depressions, deformation propagation, and subtle landform alteration grids caused by livestock trampling. Second, by utilizing fluid dynamics simulation and iterative cyclic impact sequence analysis, it solves the problem that traditional methods cannot dynamically reflect the feedback relationship between landform alteration and water distribution, achieving a quantitative description of water runoff path updates under cyclic disturbances. Finally, through historical data verification and parameter optimization, it generates a sustainable management recommended distribution that covers all cyclic impact areas, thus providing a scientific and accurate decision-making basis for water resource regulation and grazing management in alpine meadows. Attached Figure Description

[0015] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a schematic diagram of the method flow according to an embodiment of the present invention. Detailed Implementation

[0016] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0017] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0018] Example 1 This embodiment provides a method for simulating grazing in alpine meadow pastures, including: Initial geomorphological features and water distribution data of alpine meadows were collected using satellite remote sensing data and ground sensors. Agent-based modeling was used to simulate the distribution pattern of livestock trampling behavior on muddy ground to obtain a preliminary geomorphological change distribution map. Based on the preliminary landform change distribution map, the depth and location of depressions caused by livestock trampling are calculated. The CellularAutomata algorithm is used to simulate the deformation propagation of muddy ground under trampling and to determine the fine landform modification grid. If the depression depth in the fine landform modification grid exceeds the preset threshold, the slope parameter in the grid is adjusted, and the flow direction of surface water on the modified landform is calculated through fluid dynamics simulation to obtain an updated water flow path map. Extract water accumulation areas from the updated water flow path map, determine whether these areas are unevenly distributed due to landform modification, and if unevenly distributed, iteratively simulate the secondary impact of livestock trampling on these areas to obtain a cyclic impact sequence. Based on the cyclical impact sequence, water resource distribution data are integrated, and the cellularautomata algorithm is used to predict the long-term trend of surface water accumulation and determine the water flow pattern under disturbed steady state. For the water flow pattern map, historical grazing data is obtained for verification. If the deviation between the simulation results and the actual water distribution is less than the preset threshold, the agent-based modeling parameters are optimized to obtain a refined geomorphological water flow interaction model. Key disturbance indicators are derived from a refined geomorphological and water flow interaction model. Recommended distributions for sustainable management of alpine meadows are generated through data visualization. It is then determined whether the recommended distributions cover all areas of cyclical impact, and the final ecological simulation output is obtained.

[0019] Optionally, the initial geomorphological features and water distribution data of the alpine meadow are collected through satellite remote sensing data and ground sensors, and agent-based modeling is used to simulate the distribution pattern of livestock trampling behavior on muddy ground to obtain a preliminary geomorphological change distribution map, including: Initial geomorphological and moisture distribution data of alpine meadow areas were collected by satellite remote sensing systems and ground sensor networks to construct a comprehensive dataset and obtain preliminary environmental information records. Based on the comprehensive dataset, gridded information on landform features and water distribution is extracted, and spatial interpolation methods are used to fill in the missing data to determine complete environmental baseline data. Based on environmental baseline data, and combined with the activity range of livestock trampling behavior and the distribution characteristics of muddy ground, an agent-based modeling method is used to simulate the distribution pattern of trampling behavior and obtain a preliminary distribution map of the impact of trampling. The key areas affected by trampling are extracted from the preliminary distribution map. If the moisture distribution value of the key area exceeds the preset threshold, the landform changes in the area are weighted to identify potential high-risk deformation areas. After obtaining data on high-risk deformation areas, the trend of landform change is spatially overlaid and analyzed in conjunction with the initial landform features to obtain the dynamic distribution results of landform change. Based on the dynamic distribution results, a predictive model for landform changes was constructed to address the impact of livestock trampling in alpine meadow areas, and the cumulative impact distribution of long-term trampling behavior on landforms was determined.

[0020] Optionally, the step of calculating the depth and location of depressions caused by livestock trampling based on the preliminary landform change distribution map, simulating the deformation propagation of muddy ground under trampling using the CellularAutomata algorithm, and determining the fine landform modification grid includes: By collecting data from the landform change distribution map, we can obtain initial landform elevation information and the distribution area of ​​livestock trampling. Using digital processing technology, we can obtain preliminary data on the trampling impact range and basic landform changes. Based on the preliminary impact range of the trampling, data was extracted from the specific locations of the livestock trampling, the depth of the indentation at each location was calculated, and statistical analysis tools were used to determine the spatial distribution characteristics of the indentation depth. Based on the spatial distribution characteristics of the depression depth and the physical properties of muddy ground, an initial model of deformation propagation is constructed. The cellular automata algorithm is used to simulate the deformation propagation path under trampling, and the dynamic results of deformation propagation are obtained. From the dynamic results of deformation propagation, the deformation intensity of muddy ground in different regions is extracted. Combined with the distribution map of landform changes, the grid units of fine modification are divided, and the deformation state of each grid unit is determined. Based on the deformation state of the grid cells, the interaction between livestock trampling and muddy ground is analyzed to map the impact range of trampling on minor modifications and obtain the priority ranking of the modification grids. By prioritizing the grid and combining it with the depression depth data at specific locations, a detailed landform modification implementation plan is generated. If the deformation intensity of a certain grid cell exceeds a preset threshold, it is prioritized for processing, thus obtaining the final grid division scheme. For the final transformation grid division scheme, the simulation results of landform changes and deformation propagation are integrated to construct a complete landform transformation data archive and determine a digital grid model suitable for subsequent processing.

[0021] Optionally, if the depression depth in the fine landform modification grid exceeds a preset threshold, the slope parameter within the grid is adjusted, and the flow direction of surface water on the modified landform is calculated through fluid dynamics simulation to obtain an updated water flow path map, including: By scanning the terrain data within the grid division, the distribution of depression depth is obtained, and it is determined whether it exceeds the preset threshold. If the depression depth exceeds the preset threshold, the slope parameters will be locally adjusted to generate modified landform data. Based on the modified landform data, the flow direction of surface water was calculated using fluid dynamics simulation methods to obtain a preliminary water flow trend map. Flow direction data is extracted from the preliminary flow trend map, and the continuity distribution of the flow path is determined by combining the boundary conditions of the grid division. Based on the continuous distribution of water flow paths, the location information of path intersections and branching points is obtained to generate a detailed water flow path map; By overlaying detailed water flow path maps and combining them with slope parameters after landform modification, it can be determined whether the path update conforms to the preset flow logic. If the path update conforms to the flow logic, the final water flow path map is output, completing the analysis and recording of the surface water flow direction.

[0022] Optionally, the step of extracting water accumulation areas from the updated water flow path map, determining whether these areas are unevenly distributed due to landform modification, and if unevenly distributed, iteratively simulating the secondary impact of livestock trampling on these areas to obtain a cyclic impact sequence, including: By processing the data from the water flow path map, we obtained the distribution information of water accumulation areas. We then used spatial analysis tools to preliminarily divide these areas and obtained preliminary results of the distribution pattern. Based on the preliminary results of the distribution pattern, the uniformity of the regional distribution is evaluated. If the distribution is uneven, the uneven areas are marked using a preset threshold to identify key areas that need further analysis. Relevant data on landform modification were extracted from the marked key areas. Combined with the changes in water flow paths, the effect of landform modification on water accumulation was analyzed, and the distribution characteristics of the modification impact were obtained. To address the distribution characteristics of the impact of the transformation, an iterative simulation model of livestock trampling was constructed. The random forest algorithm was used to predict trampling behavior and obtain data on the secondary effects of trampling on the waterlogged area. By analyzing the dynamic changes of regional impacts through secondary action data, a cyclic impact sequence is constructed to determine the stability of water accumulation areas during the cyclic process. By obtaining the results of the cyclical impact sequence and combining them with the path analysis data, the long-term trend of water accumulation area is simulated to determine the final distribution adjustment plan. Key information is extracted from the final distribution adjustment plan, data is stored to assess the sustainability of regional impacts, and a correlation database between livestock trampling and water accumulation is constructed to obtain complete business analysis results.

[0023] Optionally, the step of integrating water resource distribution data based on the cyclic influence sequence, using the cellularautomata algorithm to predict long-term surface water accumulation trends, and determining the water flow pattern map under disturbed steady-state conditions includes: We acquire water resource distribution data, perform preliminary processing on the cyclical impact sequence, and obtain a basic dataset of water resource distribution under the cyclical impact through time series segmentation. Based on the basic dataset of water resource distribution under the influence of cycles, the cellularautomata algorithm is used to simulate and calculate the changes in surface water accumulation, and to determine the dynamic distribution characteristics of surface water accumulation. By analyzing the dynamic distribution characteristics of surface water, the long-term trend of change is analyzed. If the trend of change exceeds the preset threshold, the data is stratified to obtain the key influencing factors of long-term change. Based on the key influencing factors of long-term changes and the data characteristics under the disturbance steady state, a distribution model of water flow pattern is constructed to determine the stability of water flow distribution under disturbance conditions. Based on the stability of water flow distribution and the results of the fusion state analysis, a preliminary layer of distribution patterns is generated to obtain a distribution map of water flow patterns under disturbed stable conditions. By using the water flow pattern distribution map and data fusion technology to correct the predicted trend, the final long-term trend map of surface water accumulation is determined.

[0024] Optionally, for the water flow pattern map, historical grazing data is obtained for verification. If the deviation between the simulation results and the actual water distribution is less than a preset threshold, the agent-based modeling parameters are optimized to obtain a refined geomorphological water flow interaction model, including: By using historical grazing data, we can obtain records related to water flow patterns, compile a time-series grazing distribution dataset, and determine the preliminary range of water flow influence. Based on the grazing distribution dataset and combined with geomorphic interaction features, an initial simulated water distribution framework was constructed. The agent-based modeling method was used to obtain preliminary results of the simulated water distribution. The preliminary results of the simulated water distribution are compared with the actual water distribution data, the deviation between the two is calculated, and it is determined whether the deviation is less than a preset threshold. If the deviation is less than the preset threshold, the current parameter settings are retained, the corresponding water flow interaction mode is obtained, and the association rules between water flow patterns and landform interaction are determined. If the deviation is greater than or equal to the preset threshold, the parameter configuration of agent-based modeling is adjusted, the simulated water distribution results are regenerated, and the updated deviation comparison data is obtained. By using the updated deviation comparison data, the association rules between water flow interaction and landform interaction are iteratively optimized, a refined landform-water flow interaction model is constructed, and the final simulation framework is determined. A refined geomorphological-water-flow interaction model was obtained, and combined with historical grazing data, the model's adaptability in different time periods was verified to obtain stable simulation results of water flow patterns.

[0025] Optionally, the step of deriving key disturbance indicators from the refined geomorphological-water-flow interaction model, generating a recommended distribution for sustainable management of alpine meadows through data visualization, determining whether the recommended distribution covers all cyclically impacted areas, and obtaining the final ecological simulation output includes: By refining the geomorphological and water flow interaction model, core interference index data were obtained. The index data were then classified and organized using a stratified sampling method to obtain a preliminary set of interference indicators. Based on the preliminary set of disturbance indicators, a recommended distribution map of alpine meadows was generated using data visualization tools. Spatial overlay analysis was performed on the regional distribution characteristics in the map to determine the preliminary coverage of the distribution map. Boundary data of the cyclic impact area are extracted from the initial coverage area, and the boundary data are vectorized using geographic information system tools to obtain a precise dataset of the cyclic impact area. Spatial comparative analysis is performed on the precise range dataset and the recommended distribution map. If the comparison results show that the recommended distribution map does not completely cover the cyclic influence area, the data of the uncovered area is supplemented by interpolation method to obtain a complete distribution coverage map. Based on the complete distribution cover map and combined with the sustainable management strategy database, management recommendations for alpine meadows are generated. Logical matching method is used to screen out management measures that are highly related to the cyclical impact area and determine the final set of management strategies. By combining the final set of management strategies with an ecological simulation framework, a pre-set ecological dynamic model is run to obtain simulated response data of alpine meadows under different management strategies, and to determine the long-term stability trend of the ecosystem. Based on the simulated response data, a data integration tool is used to generate the final ecological simulation results. The output results are then formatted to obtain structured ecological management recommendation report data.

[0026] Example 2 like Figure 1 As shown, this embodiment provides a method for simulating grazing in alpine meadow pastures, including: S101. Initial geomorphic features and water distribution data of alpine meadows were collected using satellite remote sensing data and ground sensors. Agent-based modeling was used to simulate the distribution pattern of livestock trampling behavior on muddy ground to obtain a preliminary geomorphic change distribution map.

[0027] Initial geomorphological and moisture distribution data for alpine meadow regions were collected using satellite remote sensing systems and ground sensor networks to construct a comprehensive dataset, obtaining preliminary environmental information records. Based on this comprehensive dataset, gridded information of geomorphological features and moisture distribution was extracted, and spatial interpolation methods were used to fill in missing data, determining complete basic environmental data. For this basic environmental data, combined with the activity range of livestock trampling behavior and the distribution characteristics of muddy ground, an agent-based modeling method was used to simulate the distribution patterns of trampling behavior, obtaining a preliminary distribution map of trampling impacts. Key areas affected by trampling were extracted from the preliminary distribution map. If the moisture distribution value of a key area exceeded a preset threshold, the geomorphological changes in that area were weighted to identify potentially high-risk deformation areas. After obtaining data on high-risk deformation areas, spatial overlay analysis was performed on the geomorphological change trends, combined with the initial geomorphological features, to obtain dynamic distribution results of geomorphological changes. Based on these dynamic distribution results, a predictive model for geomorphological changes was constructed for the impact of livestock trampling in alpine meadow regions, determining the cumulative distribution of long-term trampling behavior on the geomorphology.

[0028] Satellite remote sensing systems acquire surface reflectance of alpine meadows through multispectral imagery, while ground sensor networks monitor soil volumetric moisture content in real time.

[0029] In one embodiment, the Kriging interpolation algorithm is used to spatially fill in the missing soil moisture data, transforming the original discrete points into a gridded matrix with a resolution of 1 meter by 1 meter, thus ensuring the continuity and integrity of the environmental basic data.

[0030] Specifically, the agent-based modeling method treats each livestock as an independent agent, setting its step size to 0.5 meters and assigning movement probability weights based on meadow vegetation cover. When the agent enters a muddy area with a moisture content exceeding 0.35 cubic meters per cubic meter, its trampling behavior is simulated, generating a trampling frequency heatmap. This heatmap serves as a preliminary distribution map, visually reflecting the distribution of physical pressure exerted on the ground by livestock activity.

[0031] In one possible implementation, a landform deformation weighting coefficient is introduced for key areas if the moisture distribution value exceeds a threshold of 0.45.

[0032] For example, the soil shear strength in the area is reduced by 30%, and surface subsidence is calculated through spatial overlay analysis combined with initial slope data. If the calculated subsidence depth exceeds 5 centimeters, it is identified as a high-risk deformation area. This approach can effectively identify sensitive zones where soil structure is weakened due to water saturation.

[0033] For example, a long short-term memory neural network algorithm is used to construct a geomorphological change prediction model. The inputs are historical trampling frequency, soil moisture content, and geomorphological deformation, while the output is the cumulative geomorphological deformation trend over the next 12 months. This model can quantify the degree of damage to meadow micro-topography caused by long-term trampling. This method can not only accurately predict the evolution of high-risk areas but also provide a scientific basis for meadow rotational grazing management, effectively preventing meadow degradation and soil erosion caused by excessive trampling, and achieving a balance between ecological protection and livestock development.

[0034] S102. Based on the preliminary landform change distribution map, calculate the depth and location of depressions caused by livestock trampling, and use the cellularautomata algorithm to simulate the deformation propagation of muddy ground under trampling to determine the fine landform modification grid.

[0035] By collecting data from the landform change distribution map, initial landform elevation information and the distribution area of ​​livestock trampling were obtained. Digital processing technology was used to obtain preliminary data on the trampling impact range and basic landform changes. Based on the preliminary trampling impact range, data was extracted from specific locations of livestock trampling, and the depression depth at each location was calculated. Statistical analysis tools were used to determine the spatial distribution characteristics of the depression depth. Based on the spatial distribution characteristics of the depression depth and combined with the physical properties of the muddy ground, an initial model of deformation propagation was constructed. Cellular automata algorithms were used to simulate the deformation propagation path under trampling, obtaining dynamic results of deformation propagation. From the dynamic results of deformation propagation, the deformation intensity of the muddy ground in different areas was extracted. Combined with the landform change distribution map, grid cells for fine-tuning were divided, and the deformation state of each grid cell was determined. Based on the deformation state of the grid cells, the interaction between livestock trampling and the muddy ground was analyzed, mapping the impact range of trampling on fine-tuning and obtaining the priority ranking of the modification grids. By prioritizing the grid and combining it with the depression depth data at specific locations, a detailed landform modification implementation plan is generated. If the deformation intensity of a certain grid cell exceeds a preset threshold, it is prioritized for processing, resulting in the final modified grid division scheme. For the final modified grid division scheme, the simulation results of landform changes and deformation propagation are integrated to construct a complete landform modification data archive and determine a digital grid model suitable for subsequent processing.

[0036] In one embodiment, the digital processing of topographic elevation information and trampling distribution areas involves converting remote sensing images into elevation point cloud data and combining them with a vector layer of livestock activity trajectories to construct a basic grid with a resolution of 0.5 meters.

[0037] Specifically, spatial interpolation algorithms are used to transform discrete trampling points into continuous pressure distribution surfaces. Initial topographic elevation data are input, and a matrix containing surface undulation features is output.

[0038] For example, based on the spatial distribution characteristics of the depression depth, statistical analysis tools are used to calculate the elevation difference within the grid. By setting a depth range of 0.1 meters to 0.5 meters, the degree of depression is divided into three levels: mild, moderate, and severe.

[0039] In one possible implementation, a cellular automata algorithm is used to simulate deformation propagation. Each grid cell is defined as a state node, and the trampling pressure in the neighborhood is used as an input parameter. The dynamic vector field of deformation propagation is output by iteratively calculating the diffusion path of pressure in the muddy medium.

[0040] Specifically, for the deformation propagation model of the physical properties of muddy ground, soil moisture content is set as a weighting factor. When the moisture content exceeds 60%, the deformation propagation coefficient increases to 1.2 to simulate the fluidity of muddy ground. By overlaying the deformation intensity with the landform change map, finely modified grid cells are divided.

[0041] For example, when the deformation intensity of a certain grid cell exceeds the 0.3-meter threshold, it is marked as a high-priority modification area.

[0042] In one embodiment, the priority ranking of the modified grid is based on a weighted sum of depression depth and deformation intensity, with depth accounting for 0.6 and deformation intensity accounting for 0.4. This logic generates implementation plans for subtle landform modifications, outputting a digital archive containing coordinate locations, estimated backfill volume, and leveling priorities. This method, by quantifying deformation states, ensures accurate identification of high-risk areas and achieves deep integration of landform modification data and the digital grid model, providing precise geospatial support for subsequent ecological restoration.

[0043] S103. If the depression depth in the fine landform modification grid exceeds the preset threshold, adjust the slope parameters within the grid and calculate the flow direction of surface water on the modified landform through fluid dynamics simulation to obtain an updated water flow path map.

[0044] By scanning the terrain data within the grid, the distribution of depression depth is obtained, and it is determined whether it exceeds a preset threshold. If the depression depth exceeds the preset threshold, the slope parameters are locally adjusted to generate modified terrain data. Based on the modified terrain data, a fluid dynamics simulation method is used to calculate the flow direction of surface water, resulting in a preliminary water flow trend map. Flow direction data is extracted from the preliminary water flow trend map, and the continuity distribution of the water flow path is determined by combining it with the boundary conditions of the grid. For the continuity distribution of the water flow path, the location information of path intersections and branching points is obtained to generate a detailed water flow path map. By overlaying the detailed water flow path map with the modified slope parameters, it is determined whether the path update conforms to the preset flow logic. If the path update conforms to the flow logic, the final water flow path map is output, completing the analysis and recording of the surface water flow direction.

[0045] In one embodiment, 0.25-meter resolution grid elevation data acquired by a UAV LiDAR is input to scan the grid terrain around livestock gathering areas, such as drinking points. A depression depth threshold of 0.15 meters is set. If a grid is detected to have a depression depth of 0.2 meters due to long-term trampling by livestock, a local adjustment of the slope parameter is triggered.

[0046] Specifically, the inverse distance weighted interpolation algorithm is used to smoothly adjust the slope parameter of the grid and its surrounding neighborhood from the original 2 degrees to 5 degrees in order to fill in the trampling potholes and output the updated geomorphic elevation matrix.

[0047] In one embodiment, the updated topographic elevation matrix is ​​input, and a two-dimensional shallow water equation is used as the basis for the hydrodynamic simulation. The initial rainfall parameter is set to 20 mm per hour to simulate the evolution of surface runoff on muddy ground. A preliminary flow trend map is output by calculating the flow velocity and direction vector for each grid cell.

[0048] For example, in an area where the slope is adjusted to 5 degrees, the calculated water flow velocity vector is 0.3 meters per second, pointing towards the lower, non-trampled area.

[0049] Specifically, flow direction vectors are extracted from the preliminary flow trend map, and combined with the water collection conditions at the grid boundaries, an eight-directional unidirectional flow algorithm is used to track the flow trajectory. When the flow from multiple adjacent grids converges into the same grid, that grid is marked as a path intersection point. When the flow from a grid diverges into two adjacent grids due to micro-topographical uplift, it is marked as a branching point.

[0050] For example, at the low-lying confluence of livestock migration routes, three path intersections and one branching point were identified, generating a detailed water flow path map containing node coordinates.

[0051] In one embodiment, a detailed water flow path map is spatially overlaid with a modified slope parameter layer. The flow logic rule is set so that water flow must follow the downward slope direction and cannot form a closed loop in non-depression grids. If a path is detected to have reverse flow or form a stagnant water zone at a grid with a slope of 2 degrees, it is determined to be illogical and the local elevation needs to be fine-tuned. If all paths satisfy the gravity-driven downward flow logic, the final water flow path map is output, completing the recording of the drainage direction for water accumulation in the trampled area.

[0052] S104. Extract water accumulation areas from the updated water flow path map, determine whether these areas are unevenly distributed due to landform modification, and if they are unevenly distributed, iteratively simulate the secondary impact of livestock trampling on these areas to obtain a cyclic impact sequence.

[0053] By processing data from water flow path maps, the distribution information of water accumulation areas is obtained. Spatial analysis tools are used to preliminarily divide these areas, yielding preliminary results of the distribution pattern. Based on these preliminary results, the uniformity of regional distribution is assessed. If the distribution is uneven, non-uniform areas are marked using preset thresholds to identify key areas requiring further analysis. Relevant data on landform modification is extracted from these marked key areas. Combined with changes in water flow paths, the impact of landform modification on water accumulation is analyzed, revealing the distribution characteristics of the modification's influence. Based on these distribution characteristics, an iterative simulation model of livestock trampling is constructed. A random forest algorithm is used to predict trampling behavior, obtaining data on the secondary effects of trampling on water accumulation areas. Using this secondary effect data, the dynamic changes in regional impacts are analyzed, a cyclical impact sequence is constructed, and the stability of water accumulation areas during the cycle is assessed. The results of the cyclical impact sequence, combined with path analysis data, are used to simulate the long-term trend of water accumulation areas, determining the final distribution adjustment plan. Key information is extracted from the final distribution adjustment plan, and data on the persistence of regional impacts is stored to construct a correlation database between livestock trampling and water accumulation, yielding complete business analysis results.

[0054] S105. Based on the cyclical influence sequence, integrate water resource distribution data, use the cellularautomata algorithm to predict the long-term trend of surface water accumulation, and determine the water flow pattern map under the disturbance steady state.

[0055] Water resource distribution data is acquired, and preliminary processing of the cyclical impact sequence is performed. Through time series segmentation, a basic dataset of water resource distribution under cyclical impact is obtained. Based on this dataset, the CellularAutomata algorithm is used to simulate and calculate changes in surface water accumulation, determining its dynamic distribution characteristics. The long-term trend of surface water accumulation is analyzed based on these dynamic distribution characteristics. If the trend exceeds a preset threshold, the data is stratified to identify key influencing factors for long-term changes. For these key influencing factors, a distribution model of water flow patterns is constructed based on data characteristics under disturbed stable conditions to assess the stability of water flow distribution under disturbed conditions. Based on the stability of water flow distribution and the results of the fusion state analysis, a preliminary layer of distribution patterns is generated, obtaining a distribution map of water flow patterns under disturbed stable conditions. Using this distribution map, data fusion techniques are applied to correct the predicted trend, determining the final long-term trend map of surface water accumulation.

[0056] One possible implementation involves acquiring cyclical impact sequence data including livestock trampling frequency and water depth. Continuous monitoring data spanning up to 12 months is segmented into quarterly time series to extract water resource distribution characteristics during the spring snowmelt period and the autumn drought period, constructing a basic water resource distribution dataset including dimensions such as soil moisture and runoff.

[0057] For example, using the aforementioned basic dataset, a cellular automata algorithm is employed to simulate changes in surface water accumulation. The study area is divided into 5m x 5m grid cells, with each cell representing the water depth. Topographic slope and prior compaction are input as parameters for the transformation rule. When the water level difference between adjacent cells exceeds 0.15 meters, a water flow transfer rule is triggered, outputting the water depth values ​​for each grid cell at different time steps, thereby determining the dynamic distribution characteristics of surface water accumulation. This approach accurately captures the driving effect of micro-topographical changes on macro-level water flow accumulation.

[0058] It should be noted that when analyzing long-term trends, if the annual rate of change of the waterlogged area in a certain region exceeds a preset threshold of 15 percent, the data for that region will be stratified.

[0059] Specifically, principal component analysis was used to stratify the topographic relief, vegetation cover, and livestock activity intensity to extract key influencing factors that contribute more than 80% to changes in water accumulation, such as the density of trampling paths at specific elevations.

[0060] In one possible implementation, a distribution model of water flow patterns is constructed by combining data on the stable state of disturbance within 30 days after livestock stop trampling. By inputting the aforementioned key influencing factors and the soil infiltration rate during the stable period, a Markov chain model is used to calculate the transfer probability of water flow between different micro-topographical features, determine the stability of water flow distribution under continuous trampling disturbance, and generate a preliminary layer.

[0061] For example, after obtaining the distribution map of water flow patterns under disturbed steady-state conditions, Kalman filtering data fusion technology is used to weight and correct the rainfall data from the actual weather station with the preliminary predicted trend. The rainfall data weight is set to 0.4, and the historical water accumulation weight is set to 0.6, ultimately outputting a more accurate long-term trend map of surface water accumulation. This effectively eliminates observation errors from a single data source and improves the reliability of long-term trend prediction.

[0062] S106. Based on the water flow pattern map, obtain historical grazing data for verification. If the deviation between the simulation results and the actual water distribution is less than the preset threshold, optimize the agent-based modeling parameters to obtain a refined geomorphological water flow interaction model.

[0063] By utilizing historical grazing data, records related to water flow patterns are obtained, and a time-series grazing distribution dataset is compiled to determine the initial water flow influence range. Based on the grazing distribution dataset and geomorphic interaction features, an initial simulated water distribution framework is constructed. Agent-based modeling is used to obtain preliminary simulated water distribution results. These preliminary results are compared with actual water distribution data, and the deviation is calculated to determine if it is less than a preset threshold. If the deviation is less than the threshold, the current parameter settings are retained, and the corresponding water flow interaction pattern is obtained to determine the association rules between water flow patterns and geomorphic interaction. If the deviation is greater than or equal to the preset threshold, the agent-based modeling parameter configuration is adjusted, and the simulated water distribution results are regenerated, resulting in updated deviation comparison data. Using the updated deviation comparison data, the association rules between water flow interaction and geomorphic interaction are iteratively optimized to construct a refined geomorphic-water flow interaction model, determining the final simulation framework. The refined geomorphic-water flow interaction model is obtained and, combined with historical grazing data, its adaptability in different time periods is verified to obtain stable water flow pattern simulation results.

[0064] One possible implementation involves acquiring historical grazing data from the past 10 years and extracting livestock movement trajectories and watering point distribution coordinates. These coordinates are then divided into monthly time series to construct a grazing distribution dataset.

[0065] For example, the grid resolution is set to 50 meters by 50 meters, and the average monthly livestock stay time in each grid is counted. If the stay time exceeds 120 hours, it is marked as a high-frequency grazing area, thereby determining the preliminary range of water flow influence.

[0066] It should be noted that when constructing the simulation framework based on the interaction features of the terrain, the above-mentioned high-frequency grazing area data and digital elevation model data are input. An agent-based modeling algorithm is used, with water flow micro-clusters set as independent agents. These agents are given behavioral rules that allow them to accelerate flow on slopes greater than 15 degrees and accumulate in depressions. By simulating the movement paths of 10,000 water flow agents, preliminary results of the simulated water distribution are output, namely the simulated water depth values ​​within each grid.

[0067] In one possible implementation, the simulated water depth value is compared with the measured remote sensing water distribution data grid by grid, and the deviation value is calculated using the root mean square error algorithm.

[0068] For example, a preset threshold is set to 0.15 meters. If the calculated root mean square error is 0.22 meters, which is greater than the preset threshold, the parameter adjustment mechanism is triggered.

[0069] Specifically, the penetration parameter of the agent in areas with vegetation coverage greater than 60 percent was adjusted from 0.3 to 0.45, and the agent-based modeling algorithm was rerun.

[0070] For example, after 3 to 5 iterations, the root mean square error is reduced to 0.12 meters. At this point, parameters such as permeability and flow velocity are retained to generate a refined geomorphological-flow interaction model. This model is then applied to another 5 years of independent grazing datasets for cross-validation, outputting probability maps of water flow distribution under different seasons, thereby determining stable simulation results of water flow patterns.

[0071] S107. Extract key disturbance indicators from the refined geomorphological and water flow interaction model, generate recommended distribution for sustainable management of alpine meadows through data visualization, determine whether the recommended distribution covers all cyclically affected areas, and obtain the final ecological simulation output.

[0072] By refining the geomorphological-water-flow interaction model, core disturbance indicator data were obtained. A stratified sampling method was used to classify and organize the indicator data, resulting in a preliminary set of disturbance indicators. Based on this preliminary set, a recommended distribution map of the alpine meadow was generated using data visualization tools. Spatial overlay analysis was performed on the regional distribution characteristics of the map to determine its preliminary coverage. Boundary data of the cyclic impact area were extracted from the preliminary coverage. Geographic Information System (GIS) tools were used to vectorize the boundary data, obtaining a precise dataset of the cyclic impact area. Spatial comparison analysis was performed between the precise dataset and the recommended distribution map. If the comparison showed that the recommended distribution map did not fully cover the cyclic impact area, interpolation methods were used to supplement the data for the uncovered areas, resulting in a complete distribution coverage map. Based on the complete distribution coverage map and a sustainable management strategy database, recommended management schemes for the alpine meadow were generated. Logical matching was used to select management measures highly correlated with the cyclic impact area, determining the final set of management strategies. Using the final set of management strategies and an ecological simulation framework, a pre-set ecological dynamic model was run to obtain simulated response data of the alpine meadow under different management strategies, assessing the long-term stability trend of the ecosystem. Based on the simulated response data, a data integration tool is used to generate the final ecological simulation results. The output results are then formatted to obtain structured ecological management recommendation report data.

[0073] In one possible implementation, soil moisture change rate and vegetation trampling depth are extracted as core disturbance indicators from the parameters output by the refined geomorphological-water flow interaction model. A stratified sampling method is used, dividing the alpine meadow into two levels based on altitude: 3000-3500 meters and 3500-4000 meters. Indicator samples are randomly selected from each level to obtain a preliminary set of disturbance indicators.

[0074] Specifically, the preliminary set of interference indicators is input into a data visualization tool, and the kernel density estimation algorithm is used to process the sample point density to generate a recommended distribution map of alpine meadows. Based on the regional distribution characteristics in this map, it is spatially overlaid with a topographic slope layer, and areas with slopes greater than 15 degrees are designated as key areas of interest, thereby determining the preliminary coverage of the distribution map.

[0075] For example, boundary data of the cyclically affected area, influenced by alternating grazing and water flow, is extracted from the initial coverage area. Using the raster-to-polygon function in a Geographic Information System (GIS) tool, this boundary data is vectorized to obtain a dataset containing the precise extent of the cyclically affected area with specific coordinate information.

[0076] In one possible implementation, a spatial comparative analysis is performed between the precise range dataset and the recommended distribution map. If it is found that the recommended distribution map of a certain valley area does not completely cover the cyclic influence area, the Kriging interpolation algorithm is used to estimate the distribution probability of the uncovered area using the interference index values ​​of the surrounding known sampling points. For example, the distribution probability value of the area is calculated to be 0.65, and then the data is supplemented to obtain a complete distribution coverage map.

[0077] Specifically, based on a complete distribution cover map and a database of sustainable management strategies containing historical governance experience, recommended management plans for alpine meadows are generated. A logical matching method is used, with the matching rule set as follows: when soil moisture is below 15% and vegetation trampling depth is greater than 5 cm in a certain area, management measures such as a 3-month grazing rest and reseeding are selected to determine the final set of management strategies.

[0078] For example, the final set of management strategies is input into the ecological simulation framework, a state transition model is run, and simulated response data of alpine meadows under different management strategies are obtained. For instance, the output shows the trend data of vegetation cover recovering to 80% in the next 5 years, thereby determining the long-term stability trend of the ecosystem. Based on the simulated response data, a data integration tool is used to generate the final ecological simulation results output, which is then formatted to obtain structured ecological management recommendation report data containing regional identifiers, strategy content, and expected indicators.

[0079] On the other hand, this embodiment also provides an electronic device, including a memory, a processor, and a computing program stored in the memory and executable on the processor, wherein the processor implements the method when executing the computing program.

[0080] On the other hand, this embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method.

[0081] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for simulating grazing in alpine meadow pastures, characterized in that, include: Based on the initial geomorphological features and water distribution data of the alpine meadow, the distribution pattern of livestock trampling behavior on the muddy ground was simulated to obtain a preliminary geomorphological change distribution map. Based on the preliminary landform change distribution map, the depth and location of depressions caused by livestock trampling are obtained, and the fine landform modification grid is determined by simulating the deformation propagation of muddy ground under trampling. If the depression depth in the fine landform modification grid exceeds a preset threshold, the slope parameter within the grid is adjusted, and an updated water flow path map is obtained by simulating the flow direction of surface water on the modified landform. The water accumulation area is obtained based on the updated water flow path map. If the water accumulation area is unevenly distributed due to landform modification, the secondary impact of livestock trampling on the area is simulated iteratively to obtain the cyclic impact sequence. Based on the cyclical influence sequence, water resource distribution data are integrated, and the water flow pattern map under the disturbance steady state is determined by predicting the long-term trend of surface water accumulation. Based on the water flow pattern diagram, historical grazing data is obtained for verification. If the deviation between the simulation results and the actual water distribution is less than a preset threshold, the simulation parameters are optimized to obtain a refined geomorphological water flow interaction model. Key disturbance indicators are obtained based on the refined geomorphological and water flow interaction model, and a recommended distribution for sustainable management of alpine meadows is generated through data visualization. If the recommended distribution covers all cyclically affected areas, the final ecological simulation output is obtained.

2. The method according to claim 1, characterized in that, Based on the initial geomorphological features and water distribution data of the alpine meadow, the distribution pattern of livestock trampling behavior on muddy ground is simulated to obtain a preliminary geomorphological change distribution map, including: Initial geomorphological and moisture distribution data of alpine meadow areas were collected using satellite remote sensing data and ground sensors to construct a comprehensive dataset and obtain environmental information records. Based on the comprehensive dataset, gridded information of landform features and water distribution is extracted, and missing data is filled in to obtain complete environmental basic data. Based on the aforementioned environmental baseline data, combined with the activity range of livestock trampling behavior and the distribution characteristics of muddy ground, the distribution pattern of trampling behavior is simulated to obtain a preliminary distribution map of the impact of trampling. Based on the preliminary distribution map, key areas affected by trampling are extracted. If the moisture distribution value of the key area exceeds a preset threshold, the landform changes in the area are weighted to obtain high-risk deformation areas. Based on the data of the high-risk deformation area, combined with the initial geomorphological features, a spatial overlay analysis of the geomorphological change trend is performed to obtain the dynamic distribution results of the geomorphological change. Based on the dynamic distribution results, a prediction model for landform changes is constructed to determine the cumulative impact distribution of long-term trampling behavior on landforms.

3. The method according to claim 1, characterized in that, Based on the preliminary landform change distribution map, the depth and location of depressions caused by livestock trampling are obtained. By simulating the deformation propagation of muddy ground under trampling, a fine-grained landform modification grid is determined, including: Based on the preliminary landform change distribution map, initial landform elevation information and the distribution area of ​​livestock trampling are obtained, thus obtaining basic data on the trampling impact range and landform changes. Based on the trampling impact range, calculate the indentation depth at each trampling location and determine the spatial distribution characteristics of the indentation depth; Based on the spatial distribution characteristics of the depression depth and combined with the physical properties of the muddy ground, the deformation propagation path under trampling is simulated to obtain the dynamic results of deformation propagation. Based on the dynamic results of the deformation propagation, the deformation intensity of the muddy ground in different areas is extracted, and combined with the distribution map of landform changes, the grid units of the fine modification are divided, and the deformation state of each grid unit is determined. Based on the deformation state of the grid cells, the impact range of trampling on the minor modifications is mapped, and the priority ranking of the modified grid cells is obtained. Based on the priority sorting of the modified grid and combined with the depression depth data, a detailed landform modification implementation plan is generated. If the deformation intensity of the grid cell exceeds the preset threshold, it is processed first to obtain the final modified grid division plan. Based on the final transformation grid division scheme, the simulation results of landform changes and deformation propagation are integrated to construct a landform transformation data archive and determine a digital grid model suitable for subsequent processing.

4. The method according to claim 1, characterized in that, If the depression depth in the fine landform modification grid exceeds a preset threshold, the slope parameters within the grid are adjusted, and an updated water flow path map is obtained by simulating the flow direction of surface water on the modified landform, including: Scan the terrain data within the grid division to obtain the distribution of depression depth and determine whether it exceeds the preset threshold; If the depression depth exceeds the preset threshold, the slope parameter is adjusted locally to generate modified landform data. Based on the modified landform data, the flow direction of surface water is simulated to obtain a preliminary water flow trend map; Based on the preliminary water flow trend map, flow direction data is extracted, and the continuity distribution of the water flow path is determined by combining the boundary conditions of the grid division. Based on the continuous distribution of the water flow path, the location information of the path intersections and branching points is obtained, and a detailed water flow path map is generated. The detailed water flow path map is overlaid with data, and combined with the slope parameters after the landform modification, it is determined whether the path update conforms to the preset flow logic. If the path update conforms to the flow logic, the final water flow path map is output, completing the analysis and recording of the surface water flow direction.

5. The method according to claim 1, characterized in that, Based on the updated water flow path map, water accumulation areas are obtained. If the water accumulation areas are unevenly distributed due to landform modification, the secondary impact of livestock trampling on the areas is iteratively simulated to obtain a cyclic impact sequence, including: Based on the updated water flow path map, the distribution information of water accumulation areas is obtained, and the areas are initially divided to obtain preliminary results of the distribution pattern. The uniformity of the regional distribution is assessed based on the preliminary results of the distribution pattern. If the distribution is uneven, the uneven areas are marked using a preset threshold to identify key areas that need further analysis. Based on the relevant data of landform modification extracted from the key areas, and combined with the changes in water flow paths, the effect of landform modification on water accumulation was analyzed, and the distribution characteristics of the modification impact were obtained. Based on the distribution characteristics of the impact of the modification, iterative simulation of livestock trampling behavior was conducted to obtain data on the secondary effects of trampling on the waterlogged area. Based on the secondary action data, analyze the dynamic changes of regional impact, construct a cyclic impact sequence, and determine the stability of the water accumulation area during the cyclic process; Based on the results of the cyclical influence sequence and the data from path analysis, the long-term changing trend of the waterlogged area is simulated to determine the final distribution adjustment scheme. Based on the final distribution adjustment scheme, key information is extracted, and a correlation database between livestock trampling and water accumulation is constructed to obtain complete business analysis results.

6. The method according to claim 1, characterized in that, Based on the aforementioned cyclical impact sequence, water resource distribution data are integrated, and by predicting long-term surface water accumulation trends, a water flow pattern map under disturbed steady-state conditions is determined, including: Acquire water resource distribution data and organize the cyclical impact sequence. Through time series segmentation processing, obtain the basic dataset of water resource distribution under cyclical impact. Based on the aforementioned basic dataset of water resource distribution, the changes in surface water accumulation are simulated and calculated to determine the dynamic distribution characteristics of surface water accumulation. Based on the dynamic distribution characteristics of the surface water, the long-term trend of change is analyzed. If the trend of change exceeds the preset threshold, the data is stratified to obtain the key influencing factors of long-term change. Based on the key influencing factors of the long-term changes, and combined with the data characteristics under the disturbance stable state, a distribution model of the water flow pattern is constructed, and the stability of the water flow distribution under the disturbance condition is judged. Based on the stability of the water flow distribution and the results of the fusion state analysis, a preliminary layer of the distribution pattern is generated to obtain a distribution map of the water flow pattern under disturbed stable conditions. Based on the water flow pattern distribution map, the predicted trend is corrected to determine the final long-term trend map of surface water accumulation.

7. The method according to claim 1, characterized in that, Based on the aforementioned water flow pattern diagram, historical grazing data is obtained for verification. If the deviation between the simulation results and the actual water distribution is less than a preset threshold, the simulation parameters are optimized to obtain a refined geomorphological water flow interaction model, including: Based on historical grazing data, record information related to water flow patterns was obtained, and a grazing distribution dataset under time series was compiled to determine the preliminary range of water flow influence. Based on the grazing distribution dataset and combined with geomorphic interaction features, an initial simulated water distribution framework was constructed, and preliminary results of the simulated water distribution were obtained. The preliminary results of the simulated water distribution are compared with the actual water distribution data, the deviation between the two is calculated, and it is determined whether the deviation is less than a preset threshold. If the deviation is less than the preset threshold, the current parameter settings are retained, the corresponding water flow interaction mode is obtained, and the association rules between water flow patterns and landform interaction are determined. If the deviation is greater than or equal to the preset threshold, the parameter configuration is adjusted, the simulated water distribution results are regenerated, and the updated deviation comparison data is obtained. Based on the updated deviation comparison data, the association rules between water flow interaction and landform interaction are iteratively optimized, a refined landform-water flow interaction model is constructed, and the final simulation framework is determined. Based on the refined geomorphological water flow interaction model and combined with historical grazing data, the model's adaptability in different time periods was verified, and stable water flow pattern simulation results were obtained.

8. The method according to claim 1, characterized in that, Based on the refined geomorphological-water flow interaction model, key disturbance indicators are obtained, and a recommended distribution for sustainable management of alpine meadows is generated through data visualization. If the recommended distribution covers all cyclically affected areas, the final ecological simulation output is obtained, including: Based on the refined geomorphological and water flow interaction model, core interference index data are obtained, and the index data is classified and organized to obtain a preliminary set of interference indicators. Based on the preliminary set of interference indicators, a recommended distribution map of alpine meadows is generated, and spatial overlay analysis is performed on the regional distribution characteristics in the map to determine the preliminary coverage of the distribution map. Based on the preliminary coverage area, extract the boundary data of the cyclic influence area, and perform vectorization processing on the boundary data to obtain the precise range dataset of the cyclic influence area; Spatial comparison analysis is performed on the precise range dataset and the recommended distribution map. If the recommended distribution map does not completely cover the cyclic influence area, data is added to the uncovered area to obtain a complete distribution coverage map. Based on the complete distribution cover map and combined with the sustainable management strategy database, a management recommendation plan for alpine meadows is generated, and management measures related to the cyclical impact area are selected to determine the final set of management strategies. Based on the final set of management strategies, combined with the ecological simulation framework, a preset ecological dynamic model is run to obtain simulated response data of alpine meadows under different management strategies, and to determine the long-term stability trend of the ecosystem. Based on the simulated response data, the final ecological simulation results are generated and formatted to obtain structured ecological management recommendation report data.

9. An electronic device comprising a memory, a processor, and a computing program stored in the memory and executable on the processor, characterized in that, When the processor executes the computing program, it implements the method of any one of claims 1-8.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1-8.

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