A bare rock slope ecological restoration scheme demonstration method based on VR simulation

Abstract

The application discloses a kind of bare rock slope ecological restoration scheme demonstration method based on VR simulation, comprising: obtaining the multi-source data of bare rock slope, fusion processing generates three-dimensional digital model and ecological parameter distribution chart.Key nodes in slope are identified, and the connection network between key nodes is constructed, forming preliminary resource allocation scheme and network topology structure.With the aid of VR simulation technology, the water resource flow and vegetation growth process are simulated, and the resource allocation scheme and water flow distribution prediction map are optimized.Combining the vegetation coverage target and the characteristics of matrix material, the optimal solution of resource allocation is obtained.According to the distribution table of key nodes, a visual model of the slope restoration process is generated using three-dimensional rendering and dynamic simulation, and the ecological response data is analyzed to optimize the restoration scheme, and finally a complete slope ecological restoration scheme is formed.The application realizes the intelligent generation and optimization of bare rock slope ecological restoration scheme, and improves the restoration efficiency and accuracy.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of slope ecological restoration, and particularly relates to a bare rock slope ecological restoration scheme demonstration method based on VR simulation. BACKGROUND

[0002] As an important field of ecological engineering and environmental governance, bare rock slope ecological restoration is directly related to the sustainable use of land resources and the stability of the ecological system. Its research and practice have irreplaceable key significance for preventing soil erosion and improving regional ecological function. With the intensification of global environmental problems, especially the large-area bare rock slope problems caused by mining and engineering construction, the research of restoration technology has become an urgent need. However, traditional restoration methods mostly rely on field tests and experience-based design, and generally have the limitations of low efficiency, uneven resource allocation, and difficulty in predicting restoration effects. Field operations not only consume a lot of time and cost, but also are difficult to achieve systematic planning in large-area complex terrain, resulting in unsatisfactory restoration results.

[0003] Under this background, the shortcomings of existing methods are gradually exposed, especially when facing large-area bare rock slopes, there is a lack of overall ecological network planning and dynamic optimization capability. The core challenges focus on how to achieve reasonable allocation of restoration resources, accurate identification of key areas, and visualization verification of restoration schemes through technical means. Due to the fact that these factors have not been effectively solved, problems such as uneven distribution of water resources, difficulty in maintaining vegetation coverage, and lack of targeted substrate material allocation often occur during the restoration process, which further leads to insufficient system and adaptability of large-area slope restoration. Especially for bare rock slopes with complex terrain and variable ecological conditions, traditional methods are difficult to intuitively present the potential effects of restoration schemes in the design stage, and cannot flexibly adjust strategies according to regional characteristics, which further aggravates the uniqueness of technical problems.

[0004] In view of the above problems, it is urgent to propose a bare rock slope ecological restoration scheme demonstration method based on VR simulation. SUMMARY

[0005] To solve the above technical problems, the application proposes a bare rock slope ecological restoration scheme demonstration method based on VR simulation to solve the problems existing in the prior art.

[0006] To achieve the above purpose, the application provides a bare rock slope ecological restoration scheme demonstration method based on VR simulation, which comprises:

[0007] Obtain topographic data and ecological condition data of the bare rock slope, and obtain a three-dimensional digital model of the slope area and an ecological parameter distribution map after fusion processing;

[0008] Based on the three-dimensional digital model and the ecological parameter distribution map, a spatial analysis algorithm is used to identify key nodes, including water collection areas and vegetation growth potential areas in the slope;

[0009] A connection network between the key nodes is generated by a graph theory algorithm, and a preliminary resource allocation scheme and network topology structure of the slope area are obtained by combining water resource distribution characteristics and substrate material requirements;

[0010] Based on the preliminary resource allocation scheme and network topology structure, a VR simulation technology is used to simulate the water resource flow and vegetation growth process, and an optimized resource allocation scheme and water flow distribution prediction map are obtained;

[0011] Based on the optimized resource allocation scheme and water flow distribution prediction map, combined with vegetation coverage targets and substrate material characteristics, an optimal solution of resource allocation is obtained by a genetic algorithm, and then a distribution table of vegetation types, substrate material usage and water resource supply of each key node is obtained;

[0012] Based on the distribution table of the key nodes, a three-dimensional rendering technology and dynamic simulation are used to generate a visual model of the slope repair process, analyze ecological response data to optimize the repair scheme, and obtain the final slope ecological repair scheme.

[0013] Optionally, the topographic data and ecological condition data of the bare rock slope are obtained, and after fusion processing, a three-dimensional digital model and an ecological parameter distribution map of the slope area are obtained, including:

[0014] The topographic data and ecological condition data are processed by a data fusion algorithm to generate an initial three-dimensional model of the slope area;

[0015] The slope data and rock mass distribution characteristics are extracted according to the initial three-dimensional model to determine the spatial structure of the slope area;

[0016] Through water content data and ecological parameter relationship analysis, the trend of ecological parameter change is determined, and if the trend of ecological parameter change exceeds a preset threshold, a support vector machine algorithm is used to classify the abnormal area to obtain a classification result;

[0017] The classification result and the spatial structure are integrated to generate a final three-dimensional digital model of the slope area;

[0018] The final three-dimensional digital model and the ecological parameters are superimposed by using a rasterization processing technology to obtain an ecological parameter distribution map.

[0019] Optionally, the connection network between the key nodes is generated by a graph theory algorithm, and a preliminary resource allocation scheme and network topology structure of the slope area are obtained by combining water resource distribution characteristics and substrate material requirements, including:

[0020] The geographical coordinates and priority ranking of the key nodes are processed by a graph algorithm to generate a connection network between the key nodes;

[0021] The connection network and water resource distribution characteristics are fused by superposition technology to obtain a preliminary resource allocation scheme;

[0022] The demand for matrix material and the preliminary resource allocation scheme are integrated to obtain a network topology structure of the slope area.

[0023] Optionally, based on the preliminary resource allocation scheme and the network topology structure, a VR simulation technology is used to simulate the water resource flow and vegetation growth process to obtain an optimized resource allocation scheme and a water flow distribution prediction map, including:

[0024] The simulation data of water resource flow and vegetation growth are obtained by VR simulation technology to determine the unevenly distributed areas;

[0025] For the unevenly distributed areas, a clustering algorithm is used to group the nodes in the network topology structure to obtain an adjusted connection weight distribution;

[0026] According to the adjusted connection weight distribution, an optimized resource allocation scheme is obtained, and the change trend of water flow distribution is determined;

[0027] The matching degree of water resource flow is analyzed through the change trend of water flow distribution to determine whether it exceeds a preset threshold to obtain a preliminary water flow distribution prediction map;

[0028] If the water flow distribution in the preliminary water flow distribution prediction map is still uneven, the optimized resource allocation scheme and the network topology structure are fused by superposition technology to obtain updated connection weights;

[0029] According to the updated connection weights, a linear regression algorithm is used to adjust the resource allocation proportion to obtain a final water flow distribution prediction map.

[0030] Optionally, based on the optimized resource allocation scheme and the water flow distribution prediction map, the vegetation coverage target and the matrix material characteristics are combined, and a genetic algorithm is used to obtain the optimal solution of resource allocation to further obtain a distribution table of the vegetation type, matrix material usage, and water resource supply amount of each key node, including:

[0031] The matching relationship between the optimized resource allocation scheme and the water flow distribution prediction map is calculated by a genetic algorithm to obtain a preliminary allocation scheme of the key nodes;

[0032] The relationship between vegetation coverage and matrix material is adjusted according to the preliminary allocation scheme to determine the distribution proportion of vegetation type and material usage;

[0033] The correspondence between the key nodes and the total supply amount is processed by a clustering method to obtain an optimized distribution table;

[0034] By analyzing the change trend of water flow distribution through the optimized allocation table, the adaptive distribution of vegetation types is determined;

[0035] If the adaptive distribution does not reach the preset threshold, the amount of matrix material and the total amount of supply are adjusted through an iterative technique to obtain an updated allocation scheme;

[0036] According to the updated allocation scheme, the vegetation coverage and water flow distribution are fused to obtain a final key node allocation table.

[0037] Optionally, the key node-based allocation table generates a visual model of the slope repair process using three-dimensional rendering technology and dynamic simulation, analyzes ecological response data to optimize the repair scheme, and obtains a final slope ecological repair scheme, including:

[0038] According to the key node allocation table, a visual model of the repair scheme is generated, and the dynamic change trend of vegetation coverage, water resource distribution, and matrix material layout is presented through three-dimensional rendering technology, and a simulation video of the slope repair process and ecological response data of the key node are output;

[0039] For the simulation video and the ecological response data of the key node, a machine learning algorithm is used to analyze the correlation between vegetation growth and soil erosion. If the vegetation coverage rate of the key node is lower than the expected value, the matrix material ratio and water resource supply amount are adjusted to obtain an updated repair scheme parameter table;

[0040] By running the dynamic simulation technology again through the updated repair scheme parameter table, the adaptability of the repair scheme in complex terrain is verified, and a final slope ecological repair scheme is output, including a vegetation distribution map, a water resource management map, and a matrix material configuration map.

[0041] Optionally, according to the key node allocation table, a visual model of the repair scheme is generated, and the dynamic change trend of vegetation coverage, water resource distribution, and matrix material layout is presented through three-dimensional rendering technology, and a simulation video of the slope repair process and ecological response data of the key node are output, including:

[0042] The initial data of the repair scheme is obtained through the key node allocation table, a three-dimensional model is generated using three-dimensional rendering technology, and a preliminary dynamic change trend is output;

[0043] The corresponding relationship between vegetation coverage and water resource distribution is extracted from the preliminary dynamic change trend, the layout distribution of matrix material is determined, and an adjusted three-dimensional model is obtained;

[0044] The response characteristics of the key node are analyzed for the adjusted three-dimensional model, the distribution data of ecological response are obtained, and the integrity of the dynamic change is determined;

[0045] If the dynamic change integrity does not reach the preset threshold, the proportion of vegetation coverage and matrix material is adjusted through an iterative technique to obtain an updated three-dimensional model;

[0046] According to the updated three-dimensional model, water resource distribution and ecological response data are fused to generate a simulation video of slope repair, and a video frame sequence is output;

[0047] The ecological response data of the key nodes are analyzed through the video frame sequence.

[0048] Optionally, the simulation video and the ecological response data of the key nodes are analyzed for the correlation between vegetation growth and soil erosion by using a machine learning algorithm, and if the vegetation coverage of the key nodes is lower than an expected value, the proportion of matrix material and the water resource supply are adjusted to obtain an updated repair scheme parameter table, including:

[0049] The ecological response features of the key nodes are extracted from the simulation video by using a random forest algorithm to obtain initial distribution data;

[0050] The correlation between vegetation growth and soil erosion is analyzed for the initial distribution data to determine a change trend;

[0051] If the change trend shows that the coverage is lower than a preset threshold, the proportion of matrix material is adjusted through an iterative technique to obtain an updated layout distribution;

[0052] The water resource supply is fused according to the updated layout distribution to generate an adjusted parameter table;

[0053] The simulation video frame sequence is updated through the adjusted parameter table to obtain dynamic response data;

[0054] The ecological restoration features of the key nodes are analyzed for the dynamic response data to obtain an updated repair scheme parameter table.

[0055] Optionally, the dynamic simulation technique is re-run through the updated repair scheme parameter table to verify the adaptability of the repair scheme in complex terrain, and a final slope ecological repair scheme is output, including a vegetation distribution map, a water resource management map, and a matrix material configuration map, including:

[0056] The complex terrain data are processed through the updated repair scheme parameter table by using a dynamic simulation technique to obtain a terrain adaptability distribution;

[0057] The vegetation distribution map is determined by fusing the slope ecological features for the terrain adaptability distribution;

[0058] The water resource management map is obtained according to the vegetation distribution map in combination with water resource data;

[0059] The matrix material configuration map is obtained by adjusting the proportion of matrix material through the water resource management map.

[0060] The random forest algorithm is used to determine the matching degree of the vegetation distribution and the water resources according to the matrix material configuration graph, and if the matching degree is lower than a preset threshold, the proportion of the matrix material is adjusted again;

[0061] The final slope ecological restoration scheme is output through the adjusted matrix material configuration graph.

[0062] Optionally, the final slope ecological restoration scheme is uploaded to a cloud database through a data interface, and the vegetation distribution graph, the water resource management graph and the matrix material configuration graph are uploaded to the cloud database through the data interface, and the generation and optimization process of the scheme is recorded by using a blockchain technology to obtain a traceable restoration scheme digital archive.

[0063] Compared with the prior art, the present application has the following advantages and technical effects:

[0064] The present application discloses a kind of bare rock slope ecological restoration scheme demonstration method based on VR simulation, which constructs the three-dimensional digital model of slope by multi-source data fusion, identifies key node and establishes intelligent networking system.The water resource flow and vegetation growth are simulated using dynamic simulation technology, and the resource allocation scheme is optimized in combination with genetic algorithm.The system also uses machine learning to analyze the correlation between vegetation growth and soil erosion, and dynamically adjusts the repair parameters.Finally, the visual restoration scheme containing vegetation distribution, water resource management and matrix material configuration is generated.The present application realizes the intelligent generation and optimization of bare rock slope ecological restoration scheme, improves the repair efficiency and accuracy, and provides a solution for slope ecological restoration under complex terrain conditions. BRIEF DESCRIPTION OF DRAWINGS

[0065] The drawings constituting a part of the present application are used to provide further understanding of the present application, and the illustrative embodiments of the present application and their descriptions are used to explain the present application, and do not constitute undue limitation on the present application.In the drawings:

[0066] Figure 1 It is the repair scheme demonstration method flowchart of the embodiment of the present application. DETAILED DESCRIPTION

[0067] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.The present application will be described in detail below with reference to the drawings and in combination with embodiments.

[0068] It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system, such as a group of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in different order from here.

[0069] Embodiment one

[0070] As Figure 1 shown in the embodiment, a bare rock slope ecological restoration scheme demonstration method based on VR simulation is provided, including the following steps:

[0071] Obtain topographic data and ecological condition data of the bare rock slope, and obtain a three-dimensional digital model and an ecological parameter distribution map of the slope area after fusion processing;

[0072] Based on the three-dimensional digital model and the ecological parameter distribution map, a spatial analysis algorithm is used to identify key nodes, including water collection areas and vegetation growth potential areas in the slope;

[0073] A connection network between the key nodes is generated through a graph theory algorithm, and a preliminary resource allocation scheme and a network topology structure of the slope area are obtained in combination with water resource distribution characteristics and matrix material demand;

[0074] Based on the preliminary resource allocation scheme and the network topology structure, a VR simulation technology is used to simulate the water resource flow and the vegetation growth process, and an optimized resource allocation scheme and a water flow distribution prediction map are obtained;

[0075] Based on the optimized resource allocation scheme and the water flow distribution prediction map, in combination with vegetation coverage targets and matrix material characteristics, an optimal solution of resource allocation is obtained through a genetic algorithm, and then a distribution table of vegetation types, matrix material usage, and water resource supply amount of each key node is obtained;

[0076] Based on the distribution table of the key nodes, a three-dimensional rendering technology and dynamic simulation are used to generate a visual model of the slope restoration process, analyze ecological response data to optimize the restoration scheme, and obtain the final slope ecological restoration scheme.

[0077] The topographic data and ecological condition data of the bare rock slope are obtained, and the three-dimensional digital model and the ecological parameter distribution map of the slope area are obtained after fusion processing, including:

[0078] The terrain data and ecological condition data are processed by a data fusion algorithm to generate an initial three-dimensional model of the slope area; the slope data and rock mass distribution characteristics are extracted from the initial three-dimensional model to determine the spatial structure of the slope area; the ecological parameter change trend is determined through water content data and ecological parameter relationship analysis, and if the ecological parameter change trend exceeds a preset threshold, a support vector machine algorithm is used to classify the abnormal area to obtain a classification result; the classification result and the spatial structure are integrated to generate the final three-dimensional digital model of the slope area; the final three-dimensional digital model and the ecological parameters are superimposed by using a rasterization processing technology to obtain an ecological parameter distribution map.

[0079] As a specific implementation, collecting terrain data through remote sensing images and ground sensors is a common method for slope analysis. For example, high-resolution terrain elevation data can be obtained through satellite remote sensing images, with a resolution of 0.5 meters. Ground sensors are placed at key points on the slope to collect real-time elevation, slope, and moisture content. For example, in a certain mountain slope, remote sensing shows an average elevation of 800 meters, a slope range of 15° to 45°, and a sensor records a moisture content of 20% to 35%. These multi-source data provide a basis for subsequent fusion.

[0080] In one possible implementation, the data fusion technology can use a weighted average method to process multi-source data sets. Elevation data is given a weight of 0.6, and slope data is given a weight of 0.4. The fusion generates an initial version of the three-dimensional model of the slope area. Specifically, the fusion process can be implemented through GIS software to generate point cloud data with a density of 10 points per square meter, which preliminarily reflects the slope contour. This method can effectively reduce the error of single data and improve the accuracy of the model. When extracting the slope and rock mass distribution characteristics from the initial version of the three-dimensional model, it can be understood that the slope data is obtained through gradient analysis, and the rock mass distribution is determined in combination with the spectral characteristics of the remote sensing image. For example, areas with a slope greater than 30° are mostly bare rock, accounting for about 40%, while areas with a gentle slope are covered with soil and vegetation. This spatial structure analysis helps to identify potential landslide risk areas.

[0081] It should be noted that the relationship analysis between moisture content and ecological parameters can be achieved through statistical regression methods. Assuming that the moisture content increases by 5%, the vegetation coverage increases by 10%, and when the moisture content is less than 15%, the coverage rate decreases to less than 30%. If the trend of ecological parameters exceeds the threshold, for example, the vegetation coverage fluctuates more than 20%, it indicates that the ecological stability is threatened. This analysis can intuitively reflect the impact of moisture on ecology.

[0082] In one embodiment, a support vector machine algorithm is used for anomaly area classification. The input features include slope, moisture content, and vegetation coverage, and the classification results are divided into stable areas and abnormal areas. For example, a certain area with a slope of 40°, a moisture content of 10%, and a coverage rate of 25% is classified as an abnormal area. This classification method is efficient and can quickly locate problem areas. Specifically, the final version of the three-dimensional digital model is generated by integrating the classification results and the spatial structure.

[0083] In an embodiment, the integration process is completed by three-dimensional modeling software, and the model resolution is improved to 0.2 meters, and the details are clearer. This step combines abnormal areas and spatial features, significantly improving the application value of the model. For example, the raster processing technology can superimpose the final model with ecological parameters to generate a distribution map. In a case, the raster unit is set to 1 meter x 1 meter, and the vegetation coverage is represented by color depth, and the area with a coverage rate of less than 30% is marked in red. This distribution map intuitively shows the spatial variation of ecological parameters, facilitating monitoring and management. It can be understood that the above method improves the accuracy and efficiency of slope area analysis as a whole.

[0084] According to the three-dimensional digital model and the ecological parameter distribution map, a spatial analysis algorithm can be implemented to identify key nodes, determine the water collection area and vegetation growth potential area in the slope by calculating the slope change rate, water flow convergence index and vegetation suitability factor, and output the geographic coordinates and priority ranking of the key nodes.

[0085] As a specific implementation, a spatial analysis algorithm is used to process the three-dimensional model to calculate the slope change rate and water flow convergence index to obtain preliminary zoning data of the slope area. A rasterization technique is used to superimpose the preliminary zoning data and ecological parameters to determine the spatial range of the water collection area. The vegetation suitability factor is used to analyze the distribution characteristics of the water collection area to determine the boundary of the vegetation growth potential area. If the boundary of the vegetation growth potential area does not match the preset threshold, a support vector machine algorithm is used to classify the abnormal area to obtain a classification result. According to the classification result and the slope change rate, the distribution position of the key nodes is identified, and geographic coordinate data is output. A sorting algorithm is used to process the geographic coordinate data and the water flow convergence index to determine the priority ranking of the key nodes. A superposition processing technique is used to fuse the priority ranking and the ecological parameter distribution map to obtain the distribution characteristics of the key nodes in the slope area.

[0086] According to the three-dimensional digital model and the ecological parameter distribution map, a spatial analysis algorithm can be implemented to identify key nodes, determine the water collection area and vegetation growth potential area in the slope by calculating the slope change rate, water flow convergence index and vegetation suitability factor, and output the geographic coordinates and priority ranking of the key nodes.

[0087] The geographic coordinates and priority ranking of the key nodes are processed by a graph theory algorithm to generate a connection network between the key nodes. A superposition technique is used to fuse the connection network and the water resource distribution characteristics to obtain a preliminary resource allocation scheme. The demand for matrix materials is integrated with the preliminary resource allocation scheme to obtain the network topology structure of the slope area.

[0088] As a specific implementation, when processing geographical coordinates and priority ranking by graph theory algorithm, the core of graph theory is to regard key nodes as vertices in the graph, and the connection relationship between nodes constitutes an edge, forming a network structure. For example, in a certain slope area, the geographical coordinates of 10 key nodes are known, and the priority is ranked from high to low. The graph theory algorithm generates a weighted undirected graph by calculating the distance between nodes and the priority difference. The edge length between adjacent nodes is determined by the actual distance, and the weight is related to the priority. For example, the node with coordinates of east longitude 110.5° and north latitude 25.3° is about 150 meters away from the node with coordinates of east longitude 110.6° and north latitude 25.4°, and the priority is 1 and 2 respectively. The edge weight can be set as the inverse of the priority difference. This method intuitively reflects the correlation between the space and importance of the nodes.

[0089] In a possible implementation, when the superposition technology integrates the connection network and the water resource distribution characteristics, the water quantity data is taken as the attribute layer of the network. For example, a water resource distribution map of a certain area shows that the water quantity of the node near the low-lying area is 50 cubic meters per day, and the water quantity of the node on the high place is only 10 cubic meters per day. After superposition, the edges in the connection network near the area with abundant water quantity are given higher weights, and the preliminary resource allocation scheme tends to tilt the resources to these nodes. Specifically, because the node has sufficient water quantity, the resource allocation proportion of the connected edge is increased from 20% to 35%, which provides a basis for subsequent adjustment.

[0090] It should be noted that when adjusting the resource allocation scheme according to the demand of the matrix material, the distribution and demand of the matrix material are key variables. For example, the demand of the matrix material in a certain slope area is concentrated in the middle node, and the demand is 200 tons, while the supply points are mainly distributed in the north. After adjustment, the resource allocation scheme preferentially transports the matrix material of the northern node to the middle part, and the network topology structure is optimized, reducing the transportation redundancy.

[0091] If there is an isolated node in the network topology structure, the clustering algorithm can group them by spatial distance and attribute similarity. For example, a certain isolated node has coordinates of east longitude 110.7° and north latitude 25.5°, and the water quantity is only 5 cubic meters per day. After clustering, nodes with similar water quantity are grouped together to determine their connection state. After grouping, the isolated node is reconnected to the network through a new edge, improving the overall connectivity.

[0092] For the connection network after grouping, the matching degree of water resource distribution and matrix material can be obtained by comparing the water quantity and matrix demand of the nodes. For example, the total water quantity of three nodes in a group is 80 cubic meters per day, and the matrix demand is 150 tons, so the matching degree is high, and the optimized resource allocation scheme will preferentially meet the demand of the group. This matching optimizes the resource utilization efficiency.

[0093] In an embodiment, the superimposition processing technology integrates the optimized resource allocation scheme and the network topology to generate a final connection network. For example, the resource allocation proportion of a certain middle node in a slope is adjusted to 40%, and the network topology shows that the connection edges with the surrounding nodes increase to 3, reflecting the resource concentration characteristics. This integration provides a clear spatial framework for management.

[0094] According to the final connection network, the priority of the key node is adjusted. The node with sufficient water and strong connectivity has a higher priority. For example, the water quantity of a certain node increases from 20 cubic meters per day to 60 cubic meters per day, the connection edge increases from 1 to 3, the priority increases from 5 to 2, and the output of the adjusted coordinate distribution is east longitude 110.6°, north latitude 25.4°. This adjustment makes the resource allocation more reasonable.

[0095] The method can be implemented, and the preliminary resource allocation scheme and the network topology are used to simulate the water resource flow and the vegetation growth process by using the VR simulation technology to obtain the optimized resource allocation scheme and the water flow distribution prediction map, which includes:

[0096] The simulation data of water resource flow and vegetation growth are obtained by using the VR simulation technology to determine the unevenly distributed area. For the unevenly distributed area, the clustering algorithm is used to group the nodes in the network topology to obtain the adjusted connection weight distribution. According to the adjusted connection weight distribution, the optimized resource allocation scheme is obtained, and the change trend of the water flow distribution is determined. The matching degree of the water resource flow is analyzed through the change trend of the water flow distribution to determine whether it exceeds the preset threshold to obtain the preliminary water flow distribution prediction map. If the water flow distribution in the preliminary water flow distribution prediction map is still uneven, the superimposition technology is used to integrate the optimized resource allocation scheme and the network topology to obtain the updated connection weight. According to the updated connection weight, the linear regression algorithm is used to adjust the resource allocation proportion to obtain the final water flow distribution prediction map.

[0097] As a specific implementation, the simulation data of water resource flow and vegetation growth are obtained by using the dynamic simulation technology. This method simulates the flow path of water resources in the slope area and the response of vegetation to water. For example, in a certain slope area, the simulation shows that the water flow in the south is concentrated, with a daily flow of 100 cubic meters, while the water flow in the north is only 20 cubic meters. The vegetation growth simulation reflects that the coverage rate in the south is 80%, while the coverage rate in the north is only 30%. This data directly reveals the uneven distribution characteristics and provides a basis for subsequent analysis.

[0098] For unevenly distributed areas, the clustering algorithm is used to group the nodes in the network topology, which can be grouped according to water flow and spatial distance. In one possible implementation, the 5 nodes in the south are divided into a group because of the high water volume and the short distance, and the average water volume is 70 cubic meters per day, while the 3 nodes in the north have low water volume and are divided into another group, with an average of only 15 cubic meters per day. The adjusted connection weight distribution is revalued according to the grouping result, for example, the weight between the nodes in the southern group is increased to 0.8, which reflects the concentration of water resources.

[0099] When obtaining the optimized resource allocation scheme according to the adjusted connection weight distribution, the change trend of water flow distribution is reflected through the weight difference. Preferably, the southern group has a high weight, and the resource allocation ratio is increased from 30% to 50%, while the northern group is decreased to 10%. This adjustment makes the water flow trend closer to balance, and through trend analysis, it is found that the water flow peak period in the south is reduced by 20 cubic meters per day, and the water flow in the north is increased by 5 cubic meters per day, and the matching degree is significantly improved.

[0100] In one embodiment, when judging whether the matching degree of water resources flow exceeds the preset threshold value through the change trend, the threshold value can be set as the water volume deviation not exceeding 30 cubic meters per day. Exemplarily, the preliminary prediction graph shows that the water volume difference between the south and the north still reaches 55 cubic meters per day, exceeding the threshold value, indicating that the uneven distribution still needs to be optimized. This step provides a clear direction for subsequent adjustment.

[0101] If the water flow distribution in the prediction graph is still uneven, when the resource allocation scheme and the network topology are fused through the superposition technology, the updated connection weight will comprehensively consider the water volume and the relationship between the nodes. For example, because a node in the south has excess water volume, the connection weight between it and the node in the north is increased from 0.5 to 0.7, which promotes the water flow to the north. The updated weight distribution is closer to the actual demand.

[0102] According to the updated connection weight, the resource allocation ratio is adjusted by using the linear regression algorithm, which can predict the future distribution through historical water flow data. In one possible implementation, the resource ratio of the southern node is reduced from 50% to 40%, and the resource ratio of the northern node is increased to 20%, and the final prediction graph shows that the water volume difference is reduced to 25 cubic meters per day. This adjustment makes the water flow distribution more uniform, and the vegetation growth conditions are improved. In one embodiment, when judging the completion state of the optimization adjustment, if the difference between the north and the south coverage rate is less than 20%, it is considered that the adjustment is in place. This result shows that the matching degree of water resources and vegetation demand is improved, which provides reliable support for slope ecological management.

[0103] It can be implemented that, based on the optimized resource allocation scheme and the water flow distribution prediction graph, the optimal solution of resource allocation is obtained through the genetic algorithm combined with the vegetation coverage target and the substrate material characteristics, and then a distribution table of the vegetation type, the substrate material amount and the water resource supply amount of each key node is obtained, including:

[0104] The matching relationship between the optimized resource allocation scheme and the water flow distribution prediction map is calculated by a genetic algorithm to obtain a preliminary allocation scheme of the key nodes; the relationship between the vegetation coverage and the substrate material is adjusted according to the preliminary allocation scheme to determine the distribution proportion of the vegetation type and the material amount; a clustering method is used to process the corresponding relationship between the key nodes and the total supply to obtain an optimized allocation table; the change trend of the water flow distribution is analyzed through the optimized allocation table to determine the adaptive distribution of the vegetation type; if the adaptive distribution does not reach a preset threshold, the amount of the substrate material and the total supply are adjusted through an iteration technique to obtain an updated allocation scheme; and the vegetation coverage and the water flow distribution are fused according to the updated allocation scheme to obtain a final key node allocation table.

[0105] As a specific implementation, the matching relationship between the resource allocation and the water flow distribution is calculated by a genetic algorithm, which simulates the process of natural selection to find a preliminary allocation scheme of the key nodes. For example, in a certain slope area, the total amount of water resources is 200 cubic meters per day, and the genetic algorithm selects 10 key nodes through multiple iterations, with 5 nodes in the south being allocated to 120 cubic meters per day and 5 nodes in the north being allocated to 80 cubic meters per day. This allocation is based on the fitness evaluation of the water flow path and node demand and preliminarily reflects the matching between resources and demand.

[0106] According to the preliminary allocation scheme, the relationship between the vegetation coverage and the substrate material is adjusted, and the vegetation type and the material amount are adjusted with the change in water amount. Exemplarily, the water amount in the south is sufficient, and deep-rooted and moisture-tolerant plants are suitable for planting, the coverage rate is set to 70%, and the substrate material mainly uses sandy soil with good water permeability, with an amount of about 2 tons per node. The water amount in the north is less, and shallow-rooted and drought-tolerant plants are selected, the coverage rate is set to 40%, and the substrate material is biased towards water-retaining clay, with an amount of about 1.5 tons per node. This distribution proportion is reasonably divided according to the water amount and the vegetation habit.

[0107] When the clustering method is used to process the corresponding relationship between the key nodes and the total supply, the nodes can be grouped according to the water amount and the distance between the nodes. In a possible implementation, the 5 nodes in the south are grouped into one group because of high water amount and close distance, and the total supply is adjusted to 130 cubic meters per day; the 5 nodes in the north are grouped into another group because of low water amount, and the total supply is 70 cubic meters per day. The optimized allocation table is thus generated, clearly reflecting the matching between the supply and the nodes.

[0108] When analyzing the trend of water flow distribution by the optimized allocation table, the dynamic changes of water quantity adjustment can be observed. For example, the water flow in the south changes from a concentrated trend to a dispersed trend, with a daily average decrease of 10 cubic meters, and the water flow in the north increases slightly by 5 cubic meters. The adaptability of vegetation types is thus revealed, with deep-rooted plants growing stably in the south and drought-tolerant plants having an increased survival rate in the north. If the adaptability distribution does not reach the preset threshold, for example, the coverage difference is less than 20%, the amount of material and the total amount of supply are adjusted through an iterative technique. In one embodiment, it is found that the coverage difference in the north and south is 25%, so the amount of substrate in the south is reduced to 1.8 tons, the total amount of supply is reduced to 125 cubic meters per day, the amount of substrate in the north is increased to 1.7 tons, and the supply is increased to 75 cubic meters per day. The updated allocation scheme makes the water flow distribution more balanced.

[0109] It can be understood that the key node allocation table integrates both relationships. For example, the water quantity of a node in the south decreases from 25 cubic meters per day to 20 cubic meters per day, and the coverage rate stabilizes at 65%; the water quantity of a node in the north increases to 15 cubic meters per day, and the coverage rate increases to 45%. The final allocation table thus determines that the node resource distribution is more in line with the actual situation. When generating a detailed distribution map of vegetation types and substrate materials by the final key node allocation table, the characteristics of each region can be intuitively displayed. In one possible implementation, the south is marked as a deep-rooted vegetation area, with a sand soil amount of 2 tons per hectare and a coverage rate of 65%; the north is a drought-tolerant vegetation area, with a clay amount of 1.7 tons per hectare and a coverage rate of 45%. Such a distribution map provides a clear basis for slope management.

[0110] In an implementable manner, the key node-based allocation table generates a visual model of the slope repair process using three-dimensional rendering technology and dynamic simulation, analyzes ecological response data to optimize the repair scheme, and obtains a final slope ecological repair scheme, including:

[0111] According to the key node allocation table, a visual model of the repair scheme is generated, and the dynamic change trend of vegetation coverage, water resource distribution, and substrate material layout is presented by three-dimensional rendering technology. The simulation video of the slope repair process and the ecological response data of the key nodes are output. For the simulation video and the ecological response data of the key nodes, a machine learning algorithm is used to analyze the correlation between vegetation growth and water and soil loss. If the vegetation coverage rate of the key nodes is lower than the expected value, the proportion of substrate materials and the amount of water supply are adjusted, and an updated repair scheme parameter table is obtained. By using the updated repair scheme parameter table, the dynamic simulation technology is re-run to verify the adaptability of the repair scheme in complex terrain, and a final slope ecological repair scheme is output, including a vegetation distribution map, a water resource management map, and a substrate material configuration map.

[0112] Further, the visual model of the repair scheme is generated according to the allocation table of the key nodes, and the dynamic change trend of the vegetation coverage, water resource distribution and substrate material layout is presented through three-dimensional rendering technology, and the simulation video of the slope repair process and the ecological response data of the key nodes are output, including:

[0113] The initial data of the repair scheme is obtained through the allocation table of the key nodes, a three-dimensional model is generated by using three-dimensional rendering technology, and the preliminary dynamic change trend is output; the corresponding relationship between the vegetation coverage and the water resource distribution is extracted from the preliminary dynamic change trend, the layout distribution of the substrate material is determined, and the adjusted three-dimensional model is obtained; the response characteristics of the key nodes are analyzed for the adjusted three-dimensional model, the distribution data of the ecological response are obtained, and the integrity of the dynamic change is judged; if the integrity of the dynamic change does not reach a preset threshold, the proportion of the vegetation coverage and the substrate material is adjusted through an iteration technique, and the updated three-dimensional model is obtained; the simulation video of the slope repair is generated according to the updated three-dimensional model, the water resource distribution and the ecological response data are fused, and the video frame sequence is output; the ecological response data of the key nodes are analyzed through the video frame sequence.

[0114] As a specific implementation, the initial data of the repair scheme is obtained through a detailed allocation table, and this step is to convert the water amount, vegetation type and substrate material amount in the table into an operable data basis. For example, the total water resource of a certain slope area is 150 cubic meters per day, the allocation table shows that the water amount of the southern node is 90 cubic meters per day, and the water amount of the northern node is 60 cubic meters per day, and the vegetation coverage rates are 60% and 35% respectively. These data provide a basis for subsequent modeling.

[0115] When the three-dimensional model is generated by using the three-dimensional rendering technology, the allocation table data can be imported into the rendering software to generate the terrain contour and resource distribution view of the slope. Illustratively, the southern part is displayed as a high water amount area, the terrain rendering is a green vegetation belt, and the northern part is a light color drought-resistant area under low water amount. The preliminary model intuitively reflects the resource layout.

[0116] When the corresponding relationship between the vegetation coverage and the water resource distribution is extracted from the preliminary dynamic change trend, the influence of the water amount change on the vegetation is analyzed. In a possible implementation, the water amount of the southern part decreases from 90 cubic meters per day to 85 cubic meters per day, the coverage rate decreases slightly to 58%, the water amount of the northern part increases to 65 cubic meters per day, and the coverage rate increases to 38%. This corresponding relationship provides a basis for the layout of the substrate material, for example, the southern part is inclined to the water-permeable sand, and the northern part is inclined to the water-retaining clay. The adjusted three-dimensional model is thus generated, and the sand is distributed at 1.5 tons per node in the southern part, and the clay is distributed at 1.2 tons per node in the northern part.

[0117] The response characteristics of the key nodes are analyzed for the adjusted three-dimensional model, and the response data of the vegetation growth and water flow changes can be observed. For example, the vegetation height in the southern node increases by 10 cm due to sufficient water, and the survival rate in the northern node increases by 15% due to enhanced water retention. These ecological response data reflect the integrity of dynamic changes. If the integrity does not meet the standard, such as a coverage fluctuation of more than 10%, the proportion is adjusted through an iterative technique.

[0118] In an embodiment, the southern coverage decreases to 55%, the sand decreases to 1.3 tons, the northern coverage increases to 40%, the clay increases to 1.4 tons, and the updated three-dimensional model is more stable. According to the fusion of water resource distribution and ecological response data based on the updated three-dimensional model, a simulation video is generated by integrating multi-dimensional information. The video frame sequence shows that the water flow in the south gradually disperses, the vegetation grows stably, the water flow in the north slowly increases, and the drought-resistant plant distribution is more uniform. Exemplarily, the water flow in the southern node is stabilized at 20 cubic meters per day, and the vegetation coverage is maintained at 55%. The water flow in the northern node increases to 15 cubic meters per day, and the coverage reaches 42%. Therefore, the final solution is determined.

[0119] In a possible implementation, the southern part is marked as a medium-density vegetation area, and the sand usage is 1.3 tons per hectare. The northern part is a sparse drought-resistant area, and the clay usage is 1.4 tons per hectare. The output key node ecological response distribution table clearly shows the characteristics of each region, for example, the water resource utilization rate in the southern node reaches 85%, and the survival rate in the northern node increases to 90%, providing a reliable basis for slope repair. This method ensures the coordination of resources and ecology.

[0120] Implementable, the simulation video and the ecological response data of the key nodes are analyzed using a machine learning algorithm to analyze the correlation between vegetation growth and soil erosion. If the vegetation coverage of the key node is lower than the expected value, adjust the matrix material ratio and water resource supply, and obtain an updated repair scheme parameter table, including:

[0121] The ecological response characteristics of the key nodes are extracted from the simulation video by a random forest algorithm to obtain initial distribution data. The correlation between vegetation growth and soil erosion is analyzed for the initial distribution data to determine the trend of change. If the coverage rate is lower than the preset threshold, the ratio of matrix material is adjusted through an iterative technique to obtain an updated layout distribution. The water resource supply is fused according to the updated layout distribution to generate an adjusted parameter table. The simulation video frame sequence is updated by the adjusted parameter table to obtain dynamic response data. The ecological restoration characteristics of the key nodes are analyzed for the dynamic response data to obtain an updated repair scheme parameter table.

[0122] As a specific implementation, when extracting the ecological response features of key nodes from the simulation video by the random forest algorithm, this method utilizes the multi-dimensional data in the video frame sequence. For example, a simulation video of a certain slope area shows that the vegetation height of the southern node is 20 cm, and the amount of soil erosion is 0.5 tons / day, while the vegetation height of the northern node is 15 cm, and the amount of soil erosion is 0.8 tons / day. Through the analysis of these features, the random forest generates initial distribution data reflecting the ecological state of each node.

[0123] When analyzing the correlation between vegetation growth and soil erosion based on the initial distribution data, the inverse relationship between soil erosion amount and vegetation coverage can be observed. For example, the southern node has a coverage of 60% and a lower erosion amount, while the northern node has a coverage of 35% and a higher erosion amount. This correlation is obtained through the feature importance analysis of the random forest, providing a basis for subsequent trend judgment.

[0124] Further, the amount of water-permeable sand in the southern part can be increased from 1 ton / node to 1.2 tons / node, and the amount of water-retaining clay in the northern part can be decreased from 1.2 tons / node to 1 ton / node through iterative techniques, and the layout distribution is updated to improve stability. According to the updated layout distribution, the water resource supply amount is integrated. For example, the water amount in the southern part is adjusted to 80 cubic meters / day, and the water amount in the northern part is increased to 70 cubic meters / day, generating an adjusted parameter table. This adjustment considers the dynamic balance of water resources to ensure the growth needs of vegetation.

[0125] In one embodiment, the parameter table shows that the southern sand soil accounts for 60%, and the clay accounts for 40%, and the northern part is the opposite, directly reflecting the changes in resource allocation. The simulation video frame sequence is updated based on the adjusted parameter table. It can be understood that the frame sequence shows that the vegetation in the southern part gradually recovers to a height of 18 cm, and the erosion amount in the northern part decreases to 0.6 tons / day. Dynamic response data is thus generated, clearly showing the adjusted ecological change trend.

[0126] The ecological restoration features of key nodes are analyzed based on the dynamic response data. In one embodiment, the coverage of the southern node stabilizes at 58%, and the erosion amount decreases to 0.4 tons / day, while the coverage of the northern node increases to 40%, and the erosion amount is 0.5 tons / day. The final parameter table is thus determined, embodying the optimization results of ecological restoration.

[0127] The stable distribution of vegetation growth and soil erosion is extracted from the final parameter table. For example, the south is marked as a medium- density vegetation area, the sand soil usage is fixed at 1.2 tons per hectare, and the erosion amount is controlled at 0.3 tons per day, and the north is a sparse drought- tolerant area, the clay usage is 1 ton per hectare, and the erosion amount is stable at 0.4 tons per day. The complete scheme data is thus formed, ensuring the coordination of soil and water conservation and ecological restoration. Exemplarily, this method gradually improves the scheme through multi- aspect analysis from initial feature extraction to final layout optimization. The adjusted resource distribution not only improves the vegetation survival rate but also significantly reduces the risk of soil and water loss, providing reliable support for slope repair.

[0128] The dynamic simulation technology can be implemented by updating the repair scheme parameter table to verify the adaptability of the repair scheme in complex terrain, and output the final slope ecological repair scheme, including a vegetation distribution map, a water resource management map, and a substrate material configuration map, including:

[0129] By updating the repair scheme parameter table, the dynamic simulation technology is used to process complex terrain data to obtain terrain adaptability distribution; based on the terrain adaptability distribution, the slope ecological characteristics are fused to determine the vegetation distribution map; based on the vegetation distribution map, the water resource data is combined to obtain the water resource management map; through the water resource management map, the proportion of substrate material is adjusted to obtain the substrate material configuration map; the random forest algorithm is used to judge the matching degree of vegetation distribution and water resources based on the substrate material configuration map, and if the matching degree is lower than a preset threshold, the proportion of substrate material is adjusted again; and the final slope ecological repair scheme is output through the adjusted substrate material configuration map.

[0130] As a specific implementation, the dynamic simulation technology is used to process complex terrain data through the parameter table in the repair scheme, which can convert the features such as the ups and downs of the terrain and the changes in the slope into quantifiable distribution data. For example, the south of a certain slope area has a slope of 20 degrees, and the north has a slope of 30 degrees. The dynamic simulation technology generates an adaptability distribution map by simulating the terrain changes, with the south marked as a gentle area and the north as a steep area. It should be noted that this distribution map provides a terrain basis for subsequent vegetation layout.

[0131] Based on the terrain adaptability distribution, the slope ecological characteristics are fused to determine the vegetation distribution map. For example, the south gentle area is suitable for planting herbaceous plants with developed root systems due to loose soil, and the north steep area selects drought- tolerant shrubs. For example, 2000 herbaceous plants per hectare are arranged in the south, and 1000 shrubs per hectare are arranged in the north. This layout considers the matching of terrain and ecology.

[0132] According to the preliminary layout combined with water resource data, the initial structure of the management map is obtained, and the vegetation demand can be reflected through water allocation. For example, the water supply in the south is 60 cubic meters per day, and the water supply in the north is 40 cubic meters per day. The initial structure shows that the south is a high water demand area, and the north is a low water demand area. This structure intuitively reflects the preliminary allocation logic of water resources.

[0133] The configuration map is updated by adjusting the proportion of matrix materials based on the initial structure. In one embodiment, the south increases the permeable sandy soil to 1.5 tons per hectare, and the north increases the water-retaining clay to 1.3 tons per hectare. It can be understood that this adjustment optimizes the soil's ability to retain water, providing better matrix conditions for vegetation growth. The random forest algorithm is used to judge the matching degree of vegetation distribution and water resources. For example, the analysis shows that the vegetation coverage in the south is 65%, and the water resource utilization rate is 80%, while the coverage in the north is 40%, and the utilization rate is 70%. If the preset matching threshold is 75%, the north does not meet the standard. It should be noted that the random forest reveals the problem of insufficient water resources in the north through feature analysis. In response to this situation, the proportion of clay in the north is adjusted to 1.5 tons per hectare, and the water quantity is increased to 50 cubic meters per day, and the matching degree is increased to 78%.

[0134] The complete data of the output scheme is generated based on the adjusted configuration map. Specifically, the complete data includes 55% sandy soil, 45% clay in the south, 60 cubic meters of water per day, 40% sandy soil, 60% clay in the north, and 50 cubic meters of water per day. This data provides a comprehensive basis for resource allocation for slope restoration.

[0135] The dynamic response of the slope ecology is verified according to the complete data. In one embodiment, the vegetation height in the south is stable at 25 centimeters, and the vegetation height in the north gradually recovers to 18 centimeters. This dynamic response reflects the positive impact of the scheme on the ecological system, ensuring the coordinated development of the terrain and vegetation. The results are extracted from the final scheme distribution. For example, the south is defined as a stable ecological zone with 1.5 tons of sandy soil per hectare and 60 cubic meters of water per day, and the north is a transitional recovery zone with 1.5 tons of clay per hectare and 50 cubic meters of water per day. It can be understood that this distribution not only adapts to the complex terrain, but also improves the long-term stability of the slope.

[0136] The final slope ecological restoration scheme can also be implemented by uploading the vegetation distribution map, water resource management map, and matrix material configuration map to the cloud database through the data interface, recording the generation and optimization process of the scheme using blockchain technology, and obtaining a traceable digital archive of the restoration scheme.

[0137] Specifically, the vegetation distribution, water resource management and substrate material data in the repair scheme are obtained through the data interface, uploaded to the cloud data storage structure, and the initial storage record is obtained. The generation process of the initial storage record is recorded by using the block chain technology to generate the first block chain data unit. For the first block chain data unit, the optimization process data is fused to update it to the second block chain data unit. Through the second block chain data unit, the matching degree of the vegetation distribution and the water resource management is judged, and if the matching degree is lower than the preset threshold, the substrate material data is adjusted to generate the third block chain data unit. According to the third block chain data unit, the traceability identifier is obtained, and the complete structure of the digital archive is generated. The random forest algorithm is used to judge the integrity of the scheme upload according to the complete structure of the digital archive, and the final digital archive is obtained. Through the final digital archive, the persistent storage state of the cloud data is determined, and the distributed archive of the scheme is generated.

[0138] As a specific implementation, the vegetation distribution, water resource management and substrate material data in the repair scheme are obtained through the data interface, which can be extracted from the slope monitoring system. The vegetation coverage in the south is 2000 herbaceous plants per hectare, and the vegetation coverage in the north is 1000 shrubs. The water resource allocation is 60 cubic meters per day in the south and 40 cubic meters per day in the north. The substrate material in the south is 1.5 tons of sandy soil, and the substrate material in the north is 1.3 tons of clay. These data are uploaded to the cloud through a standardized interface to form an initial storage record. It can be understood that this way ensures the structured storage of data, which is convenient for subsequent calling.

[0139] When the blockchain technology is used to record the generation process of the initial storage record, the data upload time, the source device ID and the data hash value can be encapsulated as a block to generate a first blockchain data unit. Specifically, the first blockchain data unit records the data integrity of the south and the north. For example, the hash value shows that the data has not been tampered with. This method provides a guarantee for the credibility of the data. When the first blockchain data unit is fused and optimized process data, the adjusted water resource data can be added, such as the north water volume increasing to 50 cubic meters, and the second blockchain data unit is updated. It should be noted that the second unit not only retains the original record, but also reflects the optimization trace. For example, the adjustment log is added in the block, which records the basis for the change of water volume, thereby improving the transparency of the scheme. When the matching degree of the second blockchain data unit is used to judge the matching degree of the vegetation distribution and the water resource management, the matching degree threshold can be set to 75%. Exemplarily, the matching degree of the south is 80%, and the matching degree of the north is only 70% due to insufficient water volume. If it is lower than the threshold, the substrate material is adjusted, such as the clay in the north increasing to 1.5 tons, to generate a third blockchain data unit. It can be understood that this adjustment ensures the traceability of each change through the continuity of the data chain. According to the traceability identifier obtained from the third blockchain data unit, a unique digital signature such as "SlopeEco_20250318_V3" can be generated and embedded in the complete structure of the digital archive. Specifically, the archive includes the whole data chain of vegetation, water resources and substrate materials. In one embodiment, the archive shows that the proportion of clay in the north increases from 40% to 60%, and the water volume increases from 40 to 50 cubic meters. This structure provides a complete historical basis for the scheme.

[0140] When the random forest algorithm is used to judge the integrity of the digital archive, the missing rate and consistency of the data fields in the archive can be analyzed. Exemplarily, the data integrity of the south is 98%, the data integrity of the north is 95%, and the overall data integrity reaches more than 90% of the preset standard. This analysis ensures the reliability of the uploaded data through multi-dimensional feature verification, laying a foundation for cloud storage.

[0141] When the final digital archive is used to determine the persistent storage state of the cloud data, the archive can be distributed and stored in multiple nodes. For example, node A stores vegetation data, and node B stores water resource data. It should be noted that this distributed storage improves the long-term availability of the scheme through data redundancy. For example, when a node fails, other nodes can still provide a complete archive. This way provides technical support for the persistent management of the slope repair scheme.

[0142] The above is only a preferred specific embodiment of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A bare rock slope ecological restoration scheme demonstration method based on VR simulation, characterized in that, The method comprises the following steps: Obtain topographic data and ecological condition data of the bare rock slope, and obtain a three-dimensional digital model and an ecological parameter distribution map of the slope region after fusion processing; Based on the three-dimensional digital model and the ecological parameter distribution map, a spatial analysis algorithm is used to identify key nodes, including water collection areas and vegetation growth potential areas in the slope; A connection network between the key nodes is generated by a graph theory algorithm, and a preliminary resource allocation scheme and network topology structure of the slope region are obtained by combining water resource distribution characteristics and substrate material demand; Based on the preliminary resource allocation scheme and network topology structure, a VR simulation technology is used to simulate the water resource flow and vegetation growth process, and an optimized resource allocation scheme and water flow distribution prediction map are obtained; Based on the optimized resource allocation scheme and water flow distribution prediction map, and combined with vegetation coverage targets and substrate material characteristics, an optimal solution of resource allocation is obtained by a genetic algorithm, and then a distribution table of vegetation types, substrate material usage and water resource supply of each key node is obtained; Based on the distribution table of the key nodes, a three-dimensional rendering technology and dynamic simulation are used to generate a visual model of the slope restoration process, ecological response data is analyzed to optimize the restoration scheme, and a final slope ecological restoration scheme is obtained; The connection network between the key nodes is generated by a graph theory algorithm, and a preliminary resource allocation scheme and network topology structure of the slope region are obtained by combining water resource distribution characteristics and substrate material demand, which comprises: The geographical coordinates and priority order of the key nodes are processed by a graph theory algorithm to generate a connection network between the key nodes; A superposition technology is used to fuse the connection network and the water resource distribution characteristics to obtain a preliminary resource allocation scheme; The demand for substrate material and the preliminary resource allocation scheme are integrated to obtain the network topology structure of the slope region; Based on the distribution table of the key nodes, a three-dimensional rendering technology and dynamic simulation are used to generate a visual model of the slope restoration process, ecological response data is analyzed to optimize the restoration scheme, and a final slope ecological restoration scheme is obtained, which comprises: According to the distribution table of the key nodes, a visual model of the restoration scheme is generated, the dynamic change trend of vegetation coverage, water resource distribution and substrate material layout is presented by a three-dimensional rendering technology, and a simulation video of the slope restoration process and ecological response data of the key nodes are outputted; For the simulation video and the ecological response data of the key nodes, a machine learning algorithm is used to analyze the correlation between vegetation growth and soil erosion, if the vegetation coverage rate of the key nodes is lower than the expected value, the substrate material ratio and water resource supply are adjusted, and an updated restoration scheme parameter table is obtained; Through the updated restoration scheme parameter table, the dynamic simulation technology is re-run to verify the adaptability of the restoration scheme in complex terrain, and a final slope ecological restoration scheme is outputted, including a vegetation distribution map, a water resource management map and a substrate material configuration map; According to the distribution table of the key nodes, a three-dimensional rendering technology and dynamic simulation are used to generate a visual model of the slope restoration process, and a simulation video of the slope restoration process and ecological response data of the key nodes are outputted, which comprises: Obtain initial data of the repair scheme through the allocation table of the key nodes, generate a three-dimensional model by using a three-dimensional rendering technology, and output a preliminary dynamic change trend; Extract the corresponding relationship between vegetation coverage and water resource distribution from the preliminary dynamic change trend, determine the layout distribution of the matrix material, and obtain an adjusted three-dimensional model; Analyze the response characteristics of the key nodes for the adjusted three-dimensional model, obtain distribution data of ecological response, and judge the integrity of the dynamic change; If the integrity of the dynamic change does not reach a preset threshold, adjust the proportion of the vegetation coverage and the matrix material through an iteration technology, and obtain an updated three-dimensional model; Fuse the water resource distribution and the ecological response data according to the updated three-dimensional model, generate a simulation video of the slope repair, and output a video frame sequence; Analyze the ecological response data of the key nodes through the video frame sequence.

2. The method of claim 1, wherein, The terrain data and the ecological condition data of the bare rock slope are obtained, and after fusion processing, a three-dimensional digital model and an ecological parameter distribution map of the slope region are obtained, including: The terrain data and the ecological condition data are processed by using a data fusion algorithm to generate an initial three-dimensional model of the slope region; Slope data and rock mass distribution characteristics are extracted from the initial three-dimensional model to determine the spatial structure of the slope region; Through water content data and ecological parameter relationship analysis, the ecological parameter change trend is judged, and if the ecological parameter change trend exceeds a preset threshold, a support vector machine algorithm is used to classify the abnormal region to obtain a classification result; Integrate the classification result and the spatial structure to generate a final three-dimensional digital model of the slope region; The final three-dimensional digital model and the ecological parameters are superimposed by using a raster processing technology to obtain an ecological parameter distribution map.

3. The method of claim 1, wherein, Based on the preliminary resource allocation scheme and the network topology structure, a VR simulation technology is used to simulate the water resource flow and the vegetation growth process to obtain an optimized resource allocation scheme and a water flow distribution prediction map, including: Obtain simulation data of water resource flow and vegetation growth through a VR simulation technology to judge unevenly distributed areas; For the unevenly distributed areas, a clustering algorithm is used to group the nodes in the network topology structure to obtain an adjusted connection weight distribution; According to the adjusted connection weight distribution, an optimized resource allocation scheme is obtained to determine the change trend of the water flow distribution; Through the change trend of the water flow distribution, the matching degree of the water resource flow is analyzed to determine whether it exceeds a preset threshold to obtain a preliminary water flow distribution prediction map; If the water flow distribution in the preliminary water flow distribution prediction map is still uneven, the optimized resource allocation scheme and the network topology structure are fused by using superposition technology to obtain an updated connection weight; According to the updated connection weight, a linear regression algorithm is used to adjust the resource allocation proportion to obtain a final water flow distribution prediction map.

4. The method of claim 1, wherein, Based on the optimized resource allocation scheme and the water flow distribution prediction map, the vegetation coverage target and the matrix material characteristics are combined, and the optimal solution of resource allocation is obtained by using a genetic algorithm, and then a distribution table of the vegetation type, the matrix material amount, and the water resource supply amount of each key node is obtained, including: The matching relationship between the optimized resource allocation scheme and the water flow distribution prediction map is calculated by using a genetic algorithm to obtain a preliminary allocation scheme of the key nodes; According to the preliminary allocation scheme, the relationship between vegetation coverage and matrix material is adjusted to determine the distribution ratio of vegetation types and the amount of matrix material; The correspondence between the key nodes and the total supply is processed using clustering method to obtain an optimized allocation table; Through the optimized allocation table, the change trend of water flow distribution is analyzed to determine the adaptive distribution of vegetation types; If the adaptive distribution does not reach the preset threshold, the amount of matrix material and the total supply are adjusted through iterative technique to obtain an updated allocation scheme; According to the updated allocation scheme, the vegetation coverage and water flow distribution are fused to obtain a final key node allocation table.

5. The method of claim 1, wherein, The ecological response data of the simulation video and the key nodes are analyzed using machine learning algorithm to analyze the correlation between vegetation growth and soil erosion. If the vegetation coverage rate of the key nodes is lower than the expected value, the matrix material ratio and water resource supply are adjusted to obtain an updated repair scheme parameter table, including: The ecological response characteristics of the key nodes are analyzed based on the initial distribution data to determine the change trend; If the change trend shows that the coverage rate is lower than the preset threshold, the ratio of matrix material is adjusted through iterative technique to obtain an updated layout distribution; The water resource supply is fused according to the updated layout distribution to generate an adjusted parameter table; The simulation video frame sequence is updated through the adjusted parameter table to obtain dynamic response data; The ecological restoration characteristics of the key nodes are analyzed based on the dynamic response data to obtain an updated repair scheme parameter table. The dynamic simulation technology is re-run based on the updated repair scheme parameter table to verify the adaptability of the repair scheme in complex terrain, and the final slope ecological restoration scheme is output, including vegetation distribution map, water resource management map and matrix material configuration map, including:

6. The method of claim 1, wherein, The complex terrain data is processed using dynamic simulation technology based on the updated repair scheme parameter table to obtain terrain adaptability distribution; Based on the terrain adaptability distribution, the vegetation distribution map is determined by fusing the slope ecological characteristics; Based on the vegetation distribution map, the water resource management map is obtained by combining water resource data; The matrix material ratio is adjusted through the water resource management map to obtain the matrix material configuration map; The matching degree of vegetation distribution and water resource is judged based on the matrix material configuration map using random forest algorithm. If the matching degree is lower than the preset threshold, the matrix material ratio is adjusted again. The final slope ecological restoration scheme is output through the adjusted matrix material configuration map.

7. The method of claim 1, further comprising uploading the final slope ecological restoration scheme to a cloud database through a data interface, including the vegetation distribution map, the water resource management map and the matrix material configuration map, recording the generation and optimization process of the scheme using blockchain technology, and obtaining a traceable repair scheme digital archive. ​ ​

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