VR simulation-based bare rock slope ecological restoration scheme demonstration method

Abstract

The invention discloses a bare rock slope ecological restoration scheme demonstration method based on VR simulation, and the method comprises the steps: obtaining the multi-source data of a bare rock slope, and carrying out the fusion processing, and generating a three-dimensional digital model and an ecological parameter distribution diagram. Key nodes in the slope are identified, a connection network between the key nodes is constructed, and a preliminary resource allocation scheme and a network topology structure are formed; water resource flowing and vegetation growth processes are simulated by means of a VR simulation technology, and a resource allocation scheme and a water flow distribution prediction map are optimized. And obtaining an optimal solution of resource allocation in combination with a vegetation coverage target and a matrix material characteristic. According to the distribution table of the key nodes, a visual model of the slope restoration process is generated through three-dimensional rendering and dynamic simulation, ecological response data is analyzed to optimize a restoration scheme, and finally a complete slope ecological restoration scheme is formed. According to the method, intelligent generation and optimization of the bare rock slope ecological restoration scheme are realized, and the restoration efficiency and accuracy are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of slope ecological restoration, and in particular relates to a method for demonstrating a bare rock slope ecological restoration solution based on VR simulation. Background Art

[0002] As an important field of ecological engineering and environmental governance, ecological restoration of bare rock slopes is directly related to the sustainable use of land resources and the stability of the ecosystem. Its research and practice are of irreplaceable key significance for preventing soil erosion and improving regional ecological functions. With the intensification of global environmental problems, especially the increasingly serious problem of large-scale bare rock slopes caused by mining and engineering construction, the research of restoration technology has become an urgent need. However, traditional restoration methods mostly rely on field experiments and empirical designs, and generally have the limitations of low efficiency, uneven resource allocation, and unpredictable restoration effects. Field operations are not only time-consuming and costly, but also difficult to achieve systematic planning in large areas of complex terrain, resulting in often unsatisfactory restoration results.

[0003] In this context, the shortcomings of existing methods have gradually been exposed, especially when faced with large-scale bare rock slopes, which lack the ability to comprehensively consider and dynamically optimize the overall ecological network. The core challenges focus on three major technical factors: how to achieve the rational allocation of restoration resources, accurate identification of key areas, and visual verification of restoration plans through technical means. Because these factors have not yet been effectively resolved, problems such as uneven distribution of water resources, difficulty in sustaining vegetation coverage, and lack of targeted matrix material configuration often occur during the restoration process, which in turn leads to insufficient systematicity and adaptability in large-scale slope restoration. Especially for bare rock slopes with complex terrain and changeable ecological conditions, traditional methods find it difficult to intuitively present the potential effects of restoration plans during the design stage, and cannot flexibly adjust strategies according to regional characteristics, which further exacerbates the uniqueness of the technical difficulties.

[0004] In response to the above problems, it is urgent to propose a demonstration method for ecological restoration of bare rock slopes based on VR simulation. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention proposes a demonstration method of bare rock slope ecological restoration scheme based on VR simulation to solve the problems existing in the above-mentioned prior art.

[0006] To achieve the above objectives, the present invention provides a method for demonstrating a bare rock slope ecological restoration solution based on VR simulation, comprising:

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

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

[0009] A connection network between key nodes is generated through graph theory algorithms. Combined with the water resource distribution characteristics and matrix material requirements, a preliminary resource allocation plan and network topology structure for the slope area are obtained.

[0010] Based on the preliminary resource allocation plan and network topology, VR simulation technology was used to simulate the water resource flow and vegetation growth process to obtain the optimized resource allocation plan and water flow distribution prediction map;

[0011] Based on the optimized resource allocation plan and water flow distribution prediction map, combined with vegetation coverage goals and substrate material characteristics, a genetic algorithm is used to obtain the optimal solution for resource allocation, and then a distribution table of vegetation type, substrate material usage, and water resource supply for each key node is obtained;

[0012] Based on the allocation table of key nodes, a visual model of the slope restoration process was generated using three-dimensional rendering technology and dynamic simulation. The ecological response data was analyzed to optimize the restoration plan and obtain the final slope ecological restoration plan.

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

[0014] A data fusion algorithm is used to process terrain data and ecological condition data to generate an initial three-dimensional model of the slope area;

[0015] Extract slope data and rock mass distribution characteristics based on the initial 3D model to determine the spatial structure of the slope area;

[0016] By analyzing the relationship between moisture content data and ecological parameters, the changing trend of ecological parameters is determined. If the changing trend of ecological parameters exceeds the preset threshold, the support vector machine algorithm is used to classify the abnormal areas and obtain the classification results.

[0017] integrating the classification results with the spatial structure to generate a final three-dimensional digital model of the slope area;

[0018] The final three-dimensional digital model and ecological parameters were superimposed using raster processing technology to obtain the ecological parameter distribution map.

[0019] Optionally, generating a connection network between key nodes through a graph theory algorithm, combining water resource distribution characteristics and matrix material requirements to obtain a preliminary resource allocation plan and network topology structure for the slope area, includes:

[0020] The geographical coordinates and priority ranking of key nodes are processed through graph theory algorithms to generate a connection network between key nodes;

[0021] Using overlay technology to fuse the connection network and water resource distribution characteristics to obtain a preliminary resource allocation plan;

[0022] The matrix material requirements and the preliminary resource allocation plan are integrated to obtain the network topology of the slope area.

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

[0024] Use VR simulation technology to obtain simulated data on water resource flow and vegetation growth to identify areas with uneven distribution;

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

[0026] According to the adjusted connection weight distribution, the optimized resource allocation plan is obtained to determine the changing trend of water flow distribution;

[0027] Analyze the matching degree of water resource flow through the changing trend of water flow distribution, determine whether it exceeds the preset threshold, and 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 plan and network topology structure are integrated through superposition technology to obtain the updated connection weight;

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

[0030] Optionally, the optimized resource allocation scheme and water flow distribution prediction map are combined with the vegetation coverage target and the substrate material characteristics to obtain the optimal solution for resource allocation through a genetic algorithm, thereby obtaining a distribution table of vegetation type, substrate material usage and water resource supply at each key node, including:

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

[0032] Adjust the relationship between vegetation cover and substrate materials according to the preliminary allocation plan and determine the distribution ratio of vegetation types and material usage;

[0033] Clustering method is used to process the correspondence between key nodes and total supply to obtain the optimized allocation table;

[0034] Analyze the changing trend of water flow distribution through the optimized distribution table and determine the adaptive distribution of vegetation types;

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

[0036] The vegetation coverage and water flow distribution are integrated according to the updated allocation scheme to obtain the final key node allocation table.

[0037] Optionally, the allocation table based on key nodes utilizes three-dimensional rendering technology and dynamic simulation to generate a visual model of the slope restoration process, analyzes ecological response data to optimize the restoration plan, and obtains a final slope ecological restoration plan, including:

[0038] Based on the allocation table of key nodes, a visual model of the restoration plan is generated. The dynamic changes in vegetation coverage, water resource distribution, and matrix material layout are presented through 3D rendering technology. Simulation videos of the slope restoration process and ecological response data of key nodes are output.

[0039] Based on the simulation video and ecological response data of 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 node is lower than the expected value, the matrix material ratio and water resource supply are adjusted to obtain an updated restoration plan parameter table;

[0040] By rerunning the dynamic simulation technology using the updated restoration plan parameter table, the adaptability of the restoration plan to complex terrain was verified, and the final slope ecological restoration plan, including a vegetation distribution map, a water resource management map, and a matrix material configuration map, was output.

[0041] Optionally, a visualization model of the restoration plan is generated based on the allocation table of key nodes, and the dynamic change trends of vegetation coverage, water resource distribution, and matrix material layout are presented through three-dimensional rendering technology. A simulation video of the slope restoration process and ecological response data of key nodes are output, including:

[0042] Obtain initial data for the repair plan through the allocation table of key nodes, generate a 3D model using 3D rendering technology, and output preliminary dynamic change trends;

[0043] Extract the corresponding relationship between vegetation cover and water resource distribution from the preliminary dynamic change trend, determine the layout distribution of matrix materials, and obtain the adjusted three-dimensional model;

[0044] Analyze the response characteristics of key nodes based on the adjusted three-dimensional model, obtain the distribution data of ecological responses, and determine the integrity of dynamic changes;

[0045] If the completeness of the dynamic changes does not reach the preset threshold, the ratio of vegetation cover to matrix material is adjusted through iterative techniques to obtain an updated three-dimensional model;

[0046] Based on the updated 3D model, water resource distribution and ecological response data are integrated to generate a simulation video of slope restoration and output a video frame sequence.

[0047] Analyze ecological response data of key nodes through video frame sequences.

[0048] Optionally, the simulation video and the ecological response data of key nodes are used to analyze the correlation between vegetation growth and soil erosion using a machine learning algorithm. If the vegetation coverage rate of the key node is lower than the expected value, the matrix material ratio and water resource supply are adjusted to obtain an updated restoration plan parameter table, including:

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

[0050] Analyze the correlation between vegetation growth and soil erosion based on the initial distribution data and determine the changing trend;

[0051] If the variation trend shows that the coverage is lower than the preset threshold, the ratio of the matrix materials is adjusted through iterative techniques to obtain an updated layout distribution;

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

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

[0054] The ecological restoration characteristics of key nodes are analyzed based on the dynamic response data to obtain an updated parameter table of the restoration plan.

[0055] Optionally, the dynamic simulation technology is re-run using the updated restoration plan parameter table to verify the adaptability of the restoration plan in complex terrain, and a final slope ecological restoration plan is output, including a vegetation distribution map, a water resources management map, and a matrix material configuration map, including:

[0056] Through the updated restoration plan parameter table, dynamic simulation technology is used to process complex terrain data to obtain terrain adaptability distribution;

[0057] According to the terrain adaptability distribution, the vegetation distribution map is determined by integrating the ecological characteristics of the slope;

[0058] Based on the vegetation distribution map and combined with water resources data, a water resources management map is obtained;

[0059] Through the water resource management diagram, the matrix material ratio is adjusted to obtain the matrix material configuration diagram;

[0060] The random forest algorithm is used to determine the matching degree between vegetation distribution and water resources based on the matrix material configuration map. If the matching degree is lower than the preset threshold, the matrix material ratio is readjusted.

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

[0062] Optionally, it also includes uploading the final slope ecological restoration plan, the vegetation distribution map, water resource management map and matrix material configuration map to the cloud database through the data interface, and using blockchain technology to record the plan generation and optimization process to obtain a traceable digital archive of the restoration plan.

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

[0064] The present invention discloses a method for demonstrating an ecological restoration solution for bare rock slopes based on VR simulation. The method constructs a three-dimensional digital model of the slope through multi-source data fusion, identifies key nodes, and establishes an intelligent networking system. Dynamic simulation technology is used to simulate water resource flow and vegetation growth, and genetic algorithms are combined to optimize resource allocation solutions. The system also uses machine learning to analyze the correlation between vegetation growth and soil erosion, and dynamically adjusts restoration parameters. Ultimately, a visual restoration solution is generated that includes vegetation distribution, water resource management, and matrix material configuration. The present invention realizes the intelligent generation and optimization of ecological restoration solutions for bare rock slopes, improves restoration efficiency and accuracy, and provides a solution for slope ecological restoration under complex terrain conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of this application. The exemplary embodiments and descriptions of this application are intended to explain this application and do not constitute an improper limitation on this application. In the accompanying drawings:

[0066] Figure 1 The figure is a flowchart of a repair solution demonstration method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0067] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

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

[0069] Example 1

[0070] like Figure 1 As shown, this embodiment provides a method for demonstrating a bare rock slope ecological restoration solution based on VR simulation, including the following steps:

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

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

[0073] A connection network between key nodes is generated through graph theory algorithms. Combined with the water resource distribution characteristics and matrix material requirements, a preliminary resource allocation plan and network topology structure for the slope area are obtained.

[0074] Based on the preliminary resource allocation plan and network topology, VR simulation technology was used to simulate the water resource flow and vegetation growth process to obtain the optimized resource allocation plan and water flow distribution prediction map;

[0075] Based on the optimized resource allocation plan and water flow distribution prediction map, combined with vegetation coverage goals and substrate material characteristics, a genetic algorithm is used to obtain the optimal solution for resource allocation, and then a distribution table of vegetation type, substrate material usage, and water resource supply for each key node is obtained;

[0076] Based on the allocation table of key nodes, a visual model of the slope restoration process was generated using three-dimensional rendering technology and dynamic simulation. The ecological response data was analyzed to optimize the restoration plan and obtain the final slope ecological restoration plan.

[0077] The method of obtaining the topographic data and ecological condition data of the bare rock slope and fusing them to obtain a three-dimensional digital model of the slope area and a distribution map of ecological parameters can be implemented, including:

[0078] A data fusion algorithm is used to process terrain data and ecological condition data to generate an initial three-dimensional model of the slope area. Slope data and rock mass distribution characteristics are extracted based on the initial three-dimensional model to determine the spatial structure of the slope area. The relationship between moisture content data and ecological parameters is analyzed to determine the trend of ecological parameter changes. If the trend of ecological parameter changes exceeds a preset threshold, a support vector machine algorithm is used to classify the abnormal areas to obtain classification results. The classification results and spatial structure are integrated to generate a final three-dimensional digital model of the slope area. Rasterization processing technology is used to overlay the final three-dimensional digital model with the ecological parameters to obtain an ecological parameter distribution map.

[0079] As a specific implementation, collecting terrain data through remote sensing imagery and ground sensors is a common method for slope analysis. For example, remote sensing imagery can acquire high-resolution terrain elevation data with a resolution of up to 0.5 meters via satellite. Ground sensors are deployed at key slope locations to collect elevation, slope, and moisture content in real time. For example, on a certain mountain slope, remote sensing data revealed an average elevation of 800 meters, a slope ranging from 15° to 45°, and sensors recorded a moisture content of 20% to 35%. This multi-source data provides the foundation for subsequent fusion.

[0080] In one possible implementation, data fusion technology can use a weighted average method to process multi-source datasets. Elevation data is assigned a weight of 0.6, and slope data is assigned a weight of 0.4. After fusion, an initial three-dimensional model of the slope area is generated. Specifically, the fusion process can be implemented using GIS software to generate point cloud data with a density of 10 points per square meter, which preliminarily reflects the slope profile. This method can effectively reduce the error of a single data point and improve model accuracy. When extracting slope and rock mass distribution characteristics based on the initial version of the three-dimensional model, it is understandable that slope data is obtained through gradient analysis, while rock mass distribution is determined by combining the spectral characteristics of remote sensing imagery. For example, areas with slopes greater than 30° are mostly exposed rock, accounting for approximately 40%, while areas with gentler slopes are covered with soil and vegetation. This spatial structure analysis helps identify potential landslide risk areas.

[0081] It's important to note that the relationship between moisture content and ecological parameters can be analyzed using statistical regression methods. Assuming that every 5% increase in moisture content leads to a 10% increase in vegetation coverage, when the moisture content falls below 15%, coverage drops below 30%. If the trend of an ecological parameter exceeds a threshold, for example, a fluctuation in vegetation coverage exceeding 20%, it indicates a threat to ecological stability. This type of analysis can intuitively reflect the impact of moisture on the ecosystem.

[0082] In one embodiment, a support vector machine algorithm is used to classify abnormal areas. Input features include slope, moisture content, and vegetation cover, and the classification results are divided into stable and abnormal areas. For example, an area with a slope of 40°, a moisture content of 10%, and a vegetation cover of 25% is classified as an abnormal area. This classification method is computationally efficient and can quickly locate problem areas. Specifically, the classification results are integrated with the spatial structure to generate the final version of the 3D digital model.

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

[0084] It is feasible to identify key nodes based on the three-dimensional digital model and ecological parameter distribution map using spatial analysis algorithms. By calculating the slope change rate, water flow convergence index and vegetation suitability factor, the water convergence area and vegetation growth potential area in the slope can be determined, and the geographic coordinates and priority ranking of the key nodes can be output.

[0085] As a specific implementation method, the three-dimensional model is processed by a spatial analysis algorithm, the slope change rate and the water flow convergence index are calculated, and the preliminary zoning data of the slope area are obtained. The preliminary zoning data and the ecological parameters are superimposed by rasterization technology to determine the spatial range of the water convergence area. The distribution characteristics of the water convergence area are analyzed by the vegetation suitability factor to determine the boundary of the vegetation growth potential area. If the boundary of the vegetation growth potential area does not meet the preset threshold, the support vector machine algorithm is used to classify the abnormal area to obtain the classification result. According to the integration of the classification result and the slope change rate, the distribution position of the key nodes is identified and the geographic coordinate data is output. The 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. The priority ranking is integrated with the ecological parameter distribution map through superposition processing technology to obtain the distribution characteristics of the key nodes in the slope area.

[0086] The method can be implemented by generating a connection network between key nodes through a graph theory algorithm, combining water resource distribution characteristics and matrix material requirements to obtain a preliminary resource allocation plan and network topology structure for the slope area, including:

[0087] The geographic coordinates and priority rankings of key nodes are processed through graph theory algorithms to generate a connection network between key nodes. Superposition technology is used to integrate the connection network with water resource distribution characteristics to obtain a preliminary resource allocation plan. The demand for matrix materials and the preliminary resource allocation plan are integrated to obtain the network topology structure of the slope area.

[0088] As a specific implementation method, when processing geographic coordinates and priority sorting through graph theory algorithms, 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 geographic coordinates of 10 key nodes are known, and the priority is sorted from high to low. The graph theory algorithm generates a weighted undirected graph by calculating the distance and priority difference between the nodes. The edge length between adjacent nodes is determined by the actual distance, and the weight is linked to the priority. For example, the node with coordinates 110.5° east longitude and 25.3° north latitude is about 150 meters away from the node with coordinates 110.6° east longitude and 25.4° north latitude, and the priorities are 1 and 2 respectively. The edge weight can be set to the inverse of the priority difference. This method intuitively reflects the spatial and importance relationship between nodes.

[0089] In one possible implementation, overlay technology integrates the connected network with water resource distribution characteristics, using water quantity data as a network attribute layer. For example, a water resource distribution map for a region shows that nodes near low-lying areas receive 50 cubic meters of water per day, while higher areas receive only 10 cubic meters per day. This overlay assigns higher weights to edges in the connected network near water-rich areas, and initial resource allocation plans tend to favor these nodes. Specifically, a node with abundant water resources could see its connected edges receive an increase in resource allocation from 20% to 35%, providing a basis for subsequent adjustments.

[0090] It's important to note that when adjusting resource allocation based on matrix material demand, the distribution and quantity of matrix material required are key variables. For example, in a certain slope area, matrix material demand is concentrated at the central node, with a demand of 200 tons, while supply points are primarily located in the north. After this adjustment, the resource allocation plan prioritizes matrix material from the northern node to the central region, optimizing the network topology and reducing transportation redundancy.

[0091] If isolated nodes exist in the network topology, clustering algorithms can group them based on spatial distance and attribute similarity. For example, consider an isolated node located at 110.7° East longitude and 25.5° North latitude, with a water flow of only 5 cubic meters per day. Clustering groups these nodes with similar water flow rates, and then determines their connectivity. After grouping, isolated nodes are reconnected to the network through new edges, improving overall connectivity.

[0092] For the grouped connected network, the degree of match between water resource distribution and substrate material can be determined by comparing the water volume at each node with its substrate demand. For example, if the total water volume of three nodes in a group is 80 cubic meters per day and the substrate demand is 150 tons, the match is high, and the optimized resource allocation plan will prioritize meeting the needs of this group. This matching optimizes resource utilization efficiency.

[0093] In one embodiment, overlay processing integrates the optimized resource allocation plan with the network topology to generate a final connected network. For example, if the resource allocation ratio of a node in the middle of a slope is adjusted to 40%, the network topology shows that its connection edges to surrounding nodes increase to three, reflecting the concentration of resources. This integration provides a clear spatial framework for management.

[0094] When adjusting the priority ranking of key nodes based on the final connected network, nodes with sufficient water volume and strong connectivity are prioritized. For example, if the water volume at a node increases from 20 cubic meters per day to 60 cubic meters per day, the number of connected edges increases from 1 to 3, and the priority rises from 5 to 2, the output coordinates will be adjusted to 110.6° East longitude and 25.4° North latitude. This adjustment improves resource allocation.

[0095] The method is feasible to simulate the water resource flow and vegetation growth process based on the preliminary resource allocation plan and network topology structure using VR simulation technology to obtain the optimized resource allocation plan and water flow distribution prediction map, including:

[0096] VR simulation technology is used to obtain simulated data on water resource flow and vegetation growth to determine areas of uneven distribution. For areas of uneven distribution, a clustering algorithm is used to group nodes in the network topology to obtain an adjusted connection weight distribution. Based on the adjusted connection weight distribution, an optimized resource allocation plan is obtained to determine the changing trend of water flow distribution. The matching degree of water resource flow is analyzed through the changing trend of water flow distribution to determine whether it exceeds the preset threshold and obtain a preliminary water flow distribution prediction map. If there is still uneven water flow distribution in the preliminary water flow distribution prediction map, the optimized resource allocation plan and the network topology are integrated through superposition technology to obtain updated connection weights. Based on the updated connection weights, a linear regression algorithm is used to adjust the resource allocation ratio to obtain the final water flow distribution prediction map.

[0097] As a specific implementation, dynamic simulation technology is used to obtain simulated data on water flow and vegetation growth. This method simulates the flow path of water resources in slope areas and the response of vegetation to water. For example, in one slope area, the simulation showed that water flow was concentrated in the south, with a daily flow of 100 cubic meters, while the north had only 20 cubic meters. The vegetation growth simulation showed that the coverage rate in the south was 80%, while the coverage rate in the north was only 30%. This data clearly reveals the uneven distribution characteristics and provides a basis for subsequent analysis.

[0098] To address unevenly distributed areas, a clustering algorithm is used to group nodes in the network topology, allowing for both water flow and spatial distance. In one possible implementation, five nodes in the south, with high water flow and close proximity, are grouped together, averaging 70 cubic meters per day. Three nodes in the north, with low water flow, are grouped together, averaging only 15 cubic meters per day. The adjusted connection weight distribution is then reassigned based on the grouping results. For example, the weight between nodes in the southern group is increased to 0.8, reflecting the concentration of water resources.

[0099] When optimizing resource allocation based on the adjusted connection weight distribution, the changing trend in water flow distribution is reflected in the weight differences. The southern group, due to its higher weight, saw its resource allocation ratio increased from 30% to 50%, while the northern group saw its ratio decreased to 10%. This adjustment brought water flow trends closer to equilibrium. Trend analysis revealed that peak water flow in the south decreased by 20 cubic meters per day, while in the north it increased slightly by 5 cubic meters per day, significantly improving the matching.

[0100] In one embodiment, when determining whether the degree of water resource flow matching exceeds a preset threshold based on changing trends, the threshold can be set at a water volume deviation of no more than 30 cubic meters per day. For example, the preliminary forecast shows that the water volume difference between the southern and northern regions still reaches 55 cubic meters per day, exceeding the threshold and indicating that the uneven distribution still needs to be optimized. This step provides a clear direction for subsequent adjustments.

[0101] If the predicted water flow distribution remains uneven, the resource allocation plan and network topology are integrated through overlay technology. Updated connection weights will take into account the relationship between water volume and nodes. For example, if a node in the south has excess water, its connection weight with the northern node will be increased from 0.5 to 0.7, causing water flow to be diverted northward. This updated weight distribution is more closely aligned with actual needs.

[0102] When the resource allocation ratio is adjusted using a linear regression algorithm based on the updated connection weights, the future distribution can be predicted using historical water flow data. In one possible implementation, the resource ratio of the southern node is reduced from 50% to 40%, and the northern node is increased to 20%. The final prediction map shows that the water volume difference is reduced to 25 cubic meters per day. This adjustment makes the water flow distribution more even and improves the conditions for vegetation growth. In one embodiment, when judging the completion status of the optimization adjustment, if the difference in coverage between the north and the south is less than 20%, it is considered that the adjustment is in place. This result shows that the matching degree between water resources and vegetation needs has been improved, providing reliable support for slope ecological management.

[0103] The optimized resource allocation scheme and water flow distribution prediction map are implemented, combined with the vegetation coverage target and the substrate material characteristics, to obtain the optimal solution for resource allocation through a genetic algorithm, thereby obtaining a distribution table of vegetation type, substrate material usage and water resource supply at each key node, including:

[0104] The matching relationship between the optimized resource allocation plan and the water flow distribution prediction map is calculated through a genetic algorithm to obtain a preliminary allocation plan for key nodes; the relationship between vegetation coverage and substrate material is adjusted according to the preliminary allocation plan to determine the distribution ratio of vegetation type and material usage; a clustering method is used to process the correspondence between key nodes and the total supply to obtain an optimized allocation table; the changing trend of water flow distribution is analyzed through the optimized allocation table to determine the adaptive distribution of vegetation types; if the adaptive distribution does not reach the preset threshold, the substrate material usage and the total supply are adjusted through iterative technology to obtain an updated allocation plan; according to the updated allocation plan, the vegetation coverage and water flow distribution are integrated to obtain the final key node allocation table.

[0105] As a specific implementation, a genetic algorithm is used to calculate the matching relationship between resource allocation and water flow distribution. This method simulates the process of natural selection to find a preliminary allocation plan for key nodes. For example, in a certain slope area with a total water resource of 200 cubic meters per day, the genetic algorithm, through multiple iterations, screened 10 key nodes, allocating 120 cubic meters per day to five nodes in the south and 80 cubic meters per day to five nodes in the north. This allocation, based on a fitness assessment of water flow paths and node requirements, provides a preliminary reflection of the matching between resources and demand.

[0106] The relationship between vegetation cover and substrate material is adjusted according to the preliminary distribution plan. The vegetation type and material usage will be adjusted according to changes in water availability. For example, the south has sufficient water, which is suitable for planting deep-rooted, moisture-tolerant plants. The coverage rate is set at 70%, and the substrate material is mainly permeable sandy soil, with a usage of about 2 tons per node. The north has less water, so shallow-rooted, drought-tolerant plants are selected. The coverage rate is set at 40%, and the substrate material tends to be water-retaining clay, with a usage of about 1.5 tons per node. This distribution ratio is reasonably divided based on water availability and vegetation habits.

[0107] When using clustering to map key nodes to total supply, clustering can be done based on water volume and inter-node distance. In one possible implementation, five nodes in the south, with high water volume and close proximity, are grouped together, with a total supply adjusted to 130 cubic meters per day. Five nodes in the north, with low water volume, are grouped together, with a total supply of 70 cubic meters per day. This generates an optimized allocation table that clearly demonstrates the matching of supply and nodes.

[0108] By analyzing the changing trends in water flow distribution through the optimized distribution table, the dynamic changes after water volume adjustments can be observed. For example, the water flow in the south changes from a concentrated trend to a dispersed one, decreasing by 10 cubic meters per day, while it increases slightly by 5 cubic meters in the north. This reveals the adaptability of vegetation types, resulting in stable growth of deep-rooted plants in the south and improved survival rates of drought-tolerant plants in the north. If the adaptive distribution does not reach the preset threshold, for example, the coverage difference must be less than 20%, the material usage and total supply are adjusted through iterative techniques. In one embodiment, detection found that the coverage difference between the north and the south was 25%, so the substrate usage in the south was reduced to 1.8 tons, the total supply was reduced to 125 cubic meters / day, and the usage in the north was increased to 1.7 tons, and the supply was increased to 75 cubic meters / day. The updated distribution plan makes the water flow distribution more balanced.

[0109] It is understandable that the key node allocation table will integrate the relationship between the two. For example, the water volume at a node in the south is reduced from 25 cubic meters / day to 20 cubic meters / day, and the coverage rate is stabilized at 65%; the water volume at a node in the north is increased to 15 cubic meters / day, and the coverage rate rises to 45%. The final allocation table is thus determined, and the node resource distribution is more in line with reality. When a detailed distribution map of vegetation types and matrix materials is generated through the final key node allocation table, the characteristics of each region can be intuitively displayed. In one possible implementation method, the south is marked as a deep-rooted vegetation area, with a sandy soil dosage of 2 tons per hectare and a coverage rate of 65%; the north is a drought-resistant vegetation area, with a clay dosage of 1.7 tons per hectare and a coverage rate of 45%. This distribution map provides a clear basis for slope management.

[0110] The allocation table based on key nodes can be implemented by using 3D rendering technology and dynamic simulation to generate a visual model of the slope restoration process, analyze ecological response data to optimize the restoration plan, and obtain the final slope ecological restoration plan, including:

[0111] Based on the allocation table of key nodes, a visualization model of the restoration plan is generated. The dynamic changing trends of vegetation coverage, water resource distribution and matrix material layout are presented through 3D rendering technology, and a simulation video of the slope restoration process and the ecological response data of key nodes are output. Based on the simulation video and the ecological response data of 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 node is lower than the expected value, the matrix material ratio and water resource supply are adjusted to obtain an updated restoration plan parameter table. Using the updated restoration plan parameter table, the dynamic simulation technology is re-run to verify the adaptability of the restoration plan in complex terrain, and the final slope ecological restoration plan is output, including a vegetation distribution map, a water resource management map and a matrix material configuration map.

[0112] Furthermore, based on the allocation table of key nodes, a visualization model of the restoration plan is generated, and the dynamic change trends of vegetation coverage, water resource distribution and matrix material layout are presented through 3D rendering technology. Simulation videos of the slope restoration process and ecological response data of key nodes are output, including:

[0113] Initial data for the restoration plan is obtained through a distribution table of key nodes, and a three-dimensional model is generated using three-dimensional rendering technology to output a preliminary dynamic change trend. The correspondence between vegetation coverage and water resource distribution is extracted from the preliminary dynamic change trend, the layout distribution of the matrix material is determined, and an adjusted three-dimensional model is obtained. The response characteristics of the key nodes are analyzed based on the adjusted three-dimensional model, the distribution data of the ecological response is obtained, and the integrity of the dynamic change is judged. If the dynamic change integrity does not reach the preset threshold, the ratio of vegetation coverage to matrix material is adjusted through iterative technology to obtain an updated three-dimensional model. Based on the updated three-dimensional model, the water resource distribution and ecological response data are integrated to generate a simulation video of the slope restoration and output a video frame sequence. The ecological response data of the key nodes are analyzed through the video frame sequence.

[0114] As a specific implementation method, initial data for the restoration plan is obtained through a detailed allocation table. This step transforms the water volume, vegetation type, and substrate material usage in the table into a workable data base. For example, if the total water resource for a slope area is 150 cubic meters per day, the allocation table shows that the water volume at the southern node is 90 cubic meters per day and the water volume at the northern node is 60 cubic meters per day, with vegetation coverage rates of 60% and 35%, respectively. This data provides the basis for subsequent modeling.

[0115] When using 3D rendering technology to generate a 3D model, the allocation table data can be imported into the rendering software to generate a topographical profile of the slope and a view of the resource distribution. For example, the southern part of the slope is shown as a high-water area, with the terrain rendered as a green vegetation belt, while the northern part is a light-colored drought-tolerant area with low water volume. This preliminary model intuitively reflects the resource layout.

[0116] Extracting the correlation between vegetation cover and water resource distribution from preliminary dynamic trends involves analyzing the impact of water volume fluctuations on vegetation. In one possible implementation, if water volume in the south decreases from 90 cubic meters per day to 85 cubic meters per day, the vegetation cover rate will drop slightly to 58%, while water volume in the north increases to 65 cubic meters per day, and the vegetation cover rate rises to 38%. This correlation provides a basis for the layout of substrate materials, such as a preference for permeable sand in the south and water-retaining clay in the north. This results in an adjusted 3D model with sand concentrated in the south at 1.5 tons per node and clay at 1.2 tons per node in the north.

[0117] When analyzing the response characteristics of key nodes within the adjusted 3D model, we can observe response data on vegetation growth and water flow changes. For example, at the southern node, due to sufficient water, vegetation height increased by 10 centimeters, while at the northern node, survival rates increased by 15% due to enhanced water retention. These ecological response data reflect the integrity of dynamic changes. If integrity is not met, such as when coverage fluctuates by more than 10%, the proportions are adjusted through iterative techniques.

[0118] In one embodiment, the coverage rate in the south is reduced to 55%, the sand is reduced to 1.3 tons, the coverage rate in the north is increased to 40%, the clay is increased to 1.4 tons, and the updated three-dimensional model is more stable. When the water resource distribution and ecological response data are integrated according to the updated three-dimensional model, multi-dimensional information is integrated to generate a simulation video. The video frame sequence shows that the water flow in the south is gradually dispersed, the vegetation grows steadily, the water volume in the north increases slowly, and the distribution of drought-resistant plants is more even. For example, the water volume at a certain node in the south is stabilized at 20 cubic meters / day, the vegetation coverage rate is maintained at 55%, the water volume at the northern node increases to 15 cubic meters / day, and the coverage rate reaches 42%. The final plan is thus determined.

[0119] In one possible implementation, the southern region is designated as a medium-dense vegetation zone, requiring 1.3 tons of sandy soil per hectare, while the northern region is designated as a sparse, drought-tolerant zone, requiring 1.4 tons of clay per hectare. The resulting ecological response distribution table for key nodes clearly demonstrates the characteristics of each region. For example, the southern node achieves an 85% water resource utilization rate, while the northern node's survival rate rises to 90%, providing a reliable basis for slope restoration. This approach ensures the coordination of resources and ecology.

[0120] It is feasible to use a machine learning algorithm to analyze the correlation between vegetation growth and soil erosion based on the simulation video and the ecological response data of key nodes. If the vegetation coverage rate of the key node is lower than the expected value, the matrix material ratio and water resource supply are adjusted to obtain an updated restoration plan parameter table, including:

[0121] The random forest algorithm is used to extract the ecological response characteristics of key nodes from the simulation video to obtain initial distribution data; the correlation between vegetation growth and soil erosion is 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 the matrix material is adjusted through iterative technology to obtain an updated layout distribution; the water resource supply is integrated according to the updated layout distribution to generate an adjusted parameter table; the simulation video frame sequence is updated with the adjusted parameter table to obtain dynamic response data; the ecological restoration characteristics of key nodes are analyzed based on the dynamic response data to obtain an updated restoration plan parameter table.

[0122] As a specific implementation, the random forest algorithm extracts ecological response characteristics of key nodes from simulated videos. This method utilizes the multidimensional data in the video frame sequence. For example, a simulated video of a slope area shows that the vegetation height at the southern node is 20 cm and the soil erosion rate is 0.5 tons / day, while the vegetation height at the northern node is 15 cm and the soil erosion rate is 0.8 tons / day. By analyzing these characteristics, the random forest algorithm generates initial distribution data reflecting the ecological status of each node.

[0123] When analyzing the correlation between vegetation growth and soil erosion using the initial distribution data, we observed an inverse relationship between soil erosion and vegetation coverage. For example, soil erosion was lower in the southern region with a 60% coverage rate, while it was higher in the northern region with a 35% coverage rate. This correlation was confirmed through feature importance analysis using random forests, providing a basis for subsequent trend analysis.

[0124] Furthermore, through iterative techniques, the amount of permeable sand in the south can be increased from 1 ton per node to 1.2 tons per node, while the amount of water-retaining clay in the north can be reduced from 1.2 tons per node to 1 ton per node. This layout distribution can then be updated to improve stability. Water resource supply can be integrated based on the updated layout distribution. For example, the water supply in the south can be adjusted to 80 cubic meters per day, while that in the north can be increased to 70 cubic meters per day. This generates an adjusted parameter table. This adjustment takes into account the dynamic balance of water resources and ensures the growth needs of vegetation.

[0125] In one embodiment, a parameter table shows that the southern region has 60% sand and 40% clay, while the reverse is true in the northern region, visually reflecting the shift in resource allocation. The simulated video frame sequence is updated using the adjusted parameter table. As can be seen, the frame sequence shows that vegetation in the south gradually recovers to a height of 18 centimeters, while loss in the north decreases to 0.6 tons per day. Dynamic response data is thus generated, clearly demonstrating the adjusted ecological trends.

[0126] Dynamic response data was used to analyze ecological restoration characteristics at key nodes. In one example, coverage at the southern node stabilized at 58%, with water loss reduced to 0.4 tons per day. Coverage in the northern node increased to 40%, with water loss at 0.5 tons per day. This resulted in a final parameter table, reflecting the optimized ecological restoration results.

[0127] The stable distribution of vegetation growth and soil erosion is extracted from the final parameter table. For example, the southern part is marked as a medium-dense vegetation area, the amount of sand is fixed at 1.2 tons / hectare, and the loss is controlled at 0.3 tons / day. The northern part is a sparse and drought-resistant area, the amount of clay is 1 ton / hectare, and the loss is stable at 0.4 tons / day. The complete program data is thus formed to ensure the coordination of soil and water conservation and ecological restoration. For example, this method gradually improves the plan through multi-faceted 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 erosion, providing reliable support for slope restoration.

[0128] It is feasible to re-run the dynamic simulation technology through the updated restoration plan parameter table to verify the adaptability of the restoration plan in complex terrain and output the final slope ecological restoration plan, including vegetation distribution map, water resource management map and matrix material configuration map, including:

[0129] Using the updated restoration plan parameter table, dynamic simulation technology is used to process complex terrain data to obtain the terrain adaptability distribution. Based on the terrain adaptability distribution, the ecological characteristics of the slope are integrated to determine the vegetation distribution map. Based on the vegetation distribution map and combined with water resource data, a water resource management map is obtained. Through the water resource management map, the matrix material ratio is adjusted to obtain a matrix material configuration map. Using the random forest algorithm, the matching degree between vegetation distribution and water resources is determined based on the matrix material configuration map. If the matching degree is lower than the preset threshold, the matrix material ratio is readjusted. The final slope ecological restoration plan is output through the adjusted matrix material configuration map.

[0130] As a specific implementation method, dynamic simulation technology is used to process complex terrain data using parameter tables in the restoration plan. This method can convert terrain characteristics such as ups and downs and slope changes into quantifiable distribution data. For example, if a slope area has a slope of 20 degrees in the south and 30 degrees in the north, dynamic simulation technology can simulate the terrain changes and generate an adaptive distribution map, marking the southern area as a flat area and the northern area as a steep area. It should be noted that this distribution map provides a topographic basis for subsequent vegetation layout.

[0131] Integrating the ecological characteristics of the slopes with a distribution that is adaptive to the terrain, a preliminary layout can be determined based on soil type and vegetation adaptability. In one possible implementation, the southern, flatter areas, with their loose soil, are suitable for herbaceous plants with well-developed root systems, while the northern, steeper slopes are favored for drought-tolerant shrubs. For example, 2,000 herbaceous plants per hectare could be planted in the south, while 1,000 shrubs per hectare could be planted in the north. This layout takes into account the compatibility of topography and ecology.

[0132] Based on the preliminary layout and combined with water resource data, the initial structure of the management map was generated. Water allocation can be used to reflect vegetation demand. For example, if the southern region receives 60 cubic meters of water per day and the northern region receives 40 cubic meters per day, the initial structure shows the southern region as a high-water-demand area and the northern region as a low-water-demand area. This structure intuitively reflects the initial logic of water resource allocation.

[0133] An updated version of the configuration diagram is obtained by adjusting the proportion of matrix materials through the initial structure. In one embodiment, the permeable sand in the south is increased to 1.5 tons per hectare, and the water-retaining clay in the north is increased to 1.3 tons per hectare. It can be understood that this adjustment optimizes the soil's ability to retain water and provides better matrix conditions for vegetation growth. The random forest algorithm is used to determine the matching degree between vegetation distribution and water resources. For example, the analysis shows that the vegetation coverage rate in the south reaches 65%, the water resource utilization rate is 80%, the coverage rate 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 revealed the problem of insufficient water resources in the north through feature analysis. In response to this situation, the clay ratio in the north was readjusted to 1.5 tons per hectare, the water volume was increased to 50 cubic meters per day, and the matching degree was improved to 78%.

[0134] The adjusted configuration diagram generated a complete output plan. Specifically, the complete data included a southern section with 55% sand and 45% clay, and a water volume of 60 cubic meters per day; and a northern section with 40% sand and 60% clay, and a water volume of 50 cubic meters per day. This data provided a comprehensive resource allocation basis for slope restoration.

[0135] The dynamic response of the slope ecology is verified based on the complete data. In one embodiment, the vegetation height in the south is stabilized at 25 cm, and gradually recovers to 18 cm in the north. This dynamic response reflects the positive impact of the program on the ecosystem and ensures the coordinated development of terrain and vegetation. The results are extracted from the final program distribution. For example, the south is defined as a stable ecological zone with 1.5 tons of sand 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 feasible options also include uploading the final slope ecological restoration plan, including vegetation distribution maps, water resource management maps, and matrix material configuration maps, to the cloud database through the data interface, and using blockchain technology to record the plan generation and optimization process to obtain a traceable digital archive of the restoration plan.

[0137] Specifically, the vegetation distribution, water resource management, and matrix material data in the restoration plan are obtained through the data interface and uploaded to the cloud data storage structure to obtain the initial storage record. Blockchain technology is used to record the generation process of the initial storage record, generating a first blockchain data unit. The optimization process data is integrated with the first blockchain data unit and updated to a second blockchain data unit. The second blockchain data unit is used to determine the matching degree between the vegetation distribution and the water resource management. If the matching degree is lower than a preset threshold, the matrix material data is adjusted to generate a third blockchain data unit. Based on the third blockchain data unit, a traceability identifier is obtained to generate the complete structure of the digital archive. A random forest algorithm is used to determine the integrity of the uploaded plan based on the complete structure of the digital archive, resulting in the final digital archive. The final digital archive is used to determine the persistent storage status of the cloud data and generate a distributed archive of the plan.

[0138] As a specific implementation method, data on vegetation distribution, water management, and substrate materials used in the restoration plan can be obtained through a data interface. This data can be extracted from the slope monitoring system. The vegetation coverage rate in the south is 2,000 herbaceous plants per hectare, and in the north it is 1,000 shrubs. The water allocation is 60 cubic meters per day in the south and 40 cubic meters in the north. The substrate materials are 1.5 tons of sand in the south and 1.3 tons of clay in the north. This data is uploaded to the cloud through a standardized interface, forming the initial storage record. This approach ensures structured data storage, facilitating subsequent access.

[0139] When using blockchain technology to record the generation process of the initial storage record, the data upload time, source device ID, and data hash value can be encapsulated into a block to generate the first blockchain data unit. Specifically, the first blockchain data unit records the data integrity for both the southern and northern regions. For example, the hash value indicates that the data has not been tampered with. This approach ensures data credibility. When integrating optimization process data into the first blockchain data unit, adjusted water resource data can be added—for example, if the water volume in the north increases to 50 cubic meters—to update the second blockchain data unit. It should be noted that the second unit not only retains the original record but also reflects the optimization traces. For example, an adjustment log is added to the block to record the basis for the water volume change, thereby enhancing the transparency of the plan. When using the second blockchain data unit to determine the compatibility between vegetation distribution and water resource management, a matching threshold of 75% can be set. For example, the matching degree in the south is 80%, while in the north, due to insufficient water, it is only 70%. If the matching degree falls below the threshold, the matrix material is adjusted, such as increasing the clay content in the north to 1.5 tons, to generate the third blockchain data unit. As you can understand, this adjustment ensures the traceability of each change through the continuity of the data chain. A traceability identifier is obtained from the third blockchain data unit, generating a unique digital signature, such as "SlopeEco_20250318_V3," and embedding it into the complete digital archive structure. Specifically, the archive includes the entire data chain of vegetation, water resources, and substrate materials. In one example, the archive shows that the clay content in the northern part increased from 40% to 60%, and the water volume increased from 40 to 50 cubic meters. This structure provides a complete historical basis for the plan.

[0140] A random forest algorithm is used to assess the integrity of digital archives, analyzing the missing rate and consistency of data fields within the archives. For example, data integrity in the southern region was 98% and in the northern region was 95%, achieving an overall standard of over 90%. This analysis, through multi-dimensional feature verification, ensures the reliability of uploaded data and lays the foundation for cloud-based archiving.

[0141] When determining the persistent storage status of cloud data through the final digital archive, the archive can be distributed across multiple nodes. For example, Node A can store vegetation data, while Node B can store water resource data. It should be noted that this distributed archiving approach improves the long-term availability of the solution through data redundancy. For example, if a node fails, other nodes can still provide the complete archive. This approach provides technical support for the persistent management of slope restoration solutions.

[0142] The above are merely preferred embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for demonstrating ecological restoration of bare rock slopes based on VR simulation, characterized in that: include: Obtain topographic data and ecological condition data of the bare rock slope, and after fusion processing, obtain a three-dimensional digital model of the slope area and a distribution map of ecological parameters; Based on the three-dimensional digital model and ecological parameter distribution map, a spatial analysis algorithm was used to identify key nodes, including water collection areas and vegetation growth potential areas in the slope. A connection network between key nodes is generated through graph theory algorithms. Combined with the water resource distribution characteristics and matrix material requirements, a preliminary resource allocation plan and network topology structure for the slope area are obtained. Based on the preliminary resource allocation plan and network topology, VR simulation technology was used to simulate the water resource flow and vegetation growth process to obtain the optimized resource allocation plan and water flow distribution prediction map; Based on the optimized resource allocation plan and water flow distribution prediction map, combined with vegetation coverage goals and substrate material characteristics, a genetic algorithm is used to obtain the optimal solution for resource allocation, and then a distribution table of vegetation type, substrate material usage, and water resource supply for each key node is obtained; Based on the allocation table of key nodes, a visual model of the slope restoration process was generated using three-dimensional rendering technology and dynamic simulation. The ecological response data was analyzed to optimize the restoration plan and obtain the final slope ecological restoration plan.

2. The method according to claim 1, characterized in that The acquisition of topographic data and ecological condition data of the bare rock slope, and the fusion processing to obtain a three-dimensional digital model of the slope area and an ecological parameter distribution map, include: A data fusion algorithm is used to process terrain data and ecological condition data to generate an initial three-dimensional model of the slope area; Extract slope data and rock mass distribution characteristics based on the initial 3D model to determine the spatial structure of the slope area; By analyzing the relationship between moisture content data and ecological parameters, the changing trend of ecological parameters is determined. If the changing trend of ecological parameters exceeds the preset threshold, the support vector machine algorithm is used to classify the abnormal areas and obtain the classification results. integrating the classification results with the spatial structure to generate a final three-dimensional digital model of the slope area; The final three-dimensional digital model and ecological parameters were superimposed using raster processing technology to obtain the ecological parameter distribution map.

3. The method according to claim 1, characterized in that The connection network between key nodes is generated by graph theory algorithm, and the preliminary resource allocation plan and network topology structure of the slope area are obtained by combining the water resource distribution characteristics and matrix material requirements, including: The geographical coordinates and priority ranking of key nodes are processed through graph theory algorithms to generate a connection network between key nodes; Using overlay technology to fuse the connection network and water resource distribution characteristics to obtain a preliminary resource allocation plan; The matrix material requirements and the preliminary resource allocation plan are integrated to obtain the network topology of the slope area.

4. The method according to claim 1, wherein Based on the preliminary resource allocation plan and network topology, VR simulation technology is used to simulate the water resource flow and vegetation growth process to obtain the optimized resource allocation plan and water flow distribution prediction map, including: Use VR simulation technology to obtain simulated data on water resource flow and vegetation growth to identify areas with uneven distribution; For unevenly distributed areas, a clustering algorithm is used to group nodes in the network topology to obtain the adjusted connection weight distribution; According to the adjusted connection weight distribution, the optimized resource allocation plan is obtained to determine the changing trend of water flow distribution; Analyze the matching degree of water resource flow through the changing trend of water flow distribution, determine whether it exceeds the preset threshold, and 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 plan and network topology structure are integrated through superposition technology to obtain the updated connection weight; According to the updated connection weights, the linear regression algorithm is used to adjust the resource allocation ratio to obtain the final water flow distribution prediction map.

5. The method according to claim 1, wherein Based on the optimized resource allocation plan and water flow distribution prediction map, combined with the vegetation coverage target and substrate material characteristics, the optimal solution for resource allocation is obtained through a genetic algorithm, and then a distribution table of vegetation type, substrate material usage and water resource supply for each key node is obtained, including: The matching relationship between the optimized resource allocation plan and the water flow distribution prediction map is calculated by genetic algorithm to obtain the preliminary allocation plan of key nodes; Adjust the relationship between vegetation cover and substrate materials according to the preliminary allocation plan and determine the distribution ratio of vegetation types and material usage; Clustering method is used to process the correspondence between key nodes and total supply to obtain the optimized allocation table; Analyze the changing trend of water flow distribution through the optimized distribution table and determine the adaptive distribution of vegetation types; If the adaptive distribution does not reach the preset threshold, the amount of matrix material used and the total supply are adjusted through iterative techniques to obtain an updated distribution plan; The vegetation coverage and water flow distribution are integrated according to the updated allocation scheme to obtain the final key node allocation table.

6. The method according to claim 1, characterized in that The allocation table based on key nodes utilizes 3D rendering technology and dynamic simulation to generate a visual model of the slope restoration process, analyzes ecological response data to optimize the restoration plan, and obtains the final slope ecological restoration plan, including: Based on the allocation table of key nodes, a visual model of the restoration plan is generated. The dynamic changes in vegetation coverage, water resource distribution, and matrix material layout are presented through 3D rendering technology. Simulation videos of the slope restoration process and ecological response data of key nodes are output. Based on the simulation video and the ecological response data of 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 node is lower than the expected value, the matrix material ratio and water resource supply are adjusted to obtain an updated restoration plan parameter table; By rerunning the dynamic simulation technology using the updated restoration plan parameter table, the adaptability of the restoration plan to complex terrain was verified, and the final slope ecological restoration plan, including a vegetation distribution map, a water resource management map, and a matrix material configuration map, was output.

7. The method according to claim 6, characterized in that Based on the allocation table of key nodes, a visualization model of the restoration plan is generated. The dynamic change trends of vegetation coverage, water resource distribution and matrix material layout are presented through 3D rendering technology. Simulation videos of the slope restoration process and ecological response data of key nodes are output, including: Obtain initial data for the repair plan through the allocation table of key nodes, generate a 3D model using 3D rendering technology, and output preliminary dynamic change trends; Extract the corresponding relationship between vegetation cover and water resource distribution from the preliminary dynamic change trend, determine the layout distribution of matrix materials, and obtain the adjusted three-dimensional model; Analyze the response characteristics of key nodes based on the adjusted three-dimensional model, obtain the distribution data of ecological responses, and determine the integrity of dynamic changes; If the completeness of the dynamic changes does not reach the preset threshold, the ratio of vegetation cover to matrix material is adjusted through iterative techniques to obtain an updated three-dimensional model; Based on the updated 3D model, water resource distribution and ecological response data are integrated to generate a simulation video of slope restoration and output a video frame sequence. Analyze ecological response data of key nodes through video frame sequences.

8. The method according to claim 7, characterized in that The simulation video and the ecological response data of key nodes are used to analyze the correlation between vegetation growth and soil erosion using a machine learning algorithm. If the vegetation coverage rate of the key node is lower than the expected value, the matrix material ratio and water resource supply are adjusted to obtain an updated restoration plan parameter table, including: The ecological response characteristics of key nodes are extracted from the simulation video using the random forest algorithm to obtain the initial distribution data; Analyze the correlation between vegetation growth and soil erosion based on the initial distribution data and determine the changing trend; If the variation trend shows that the coverage is lower than the preset threshold, the ratio of the matrix materials is adjusted through iterative techniques to obtain an updated layout distribution; The water resource supply is integrated according to the updated layout distribution to generate an adjusted parameter table; Update the simulation video frame sequence through the adjusted parameter table to obtain dynamic response data; The ecological restoration characteristics of key nodes are analyzed based on the dynamic response data to obtain an updated parameter table of the restoration plan.

9. The method according to claim 7, characterized in that The dynamic simulation technology is re-run through the updated restoration plan parameter table to verify the adaptability of the restoration plan in complex terrain, and the final slope ecological restoration plan is output, including vegetation distribution map, water resource management map and matrix material configuration map, including: Through the updated restoration plan parameter table, dynamic simulation technology is used to process complex terrain data to obtain terrain adaptability distribution; According to the terrain adaptability distribution, the vegetation distribution map is determined by integrating the ecological characteristics of the slope; Based on the vegetation distribution map and combined with water resources data, a water resources management map is obtained; Through the water resource management diagram, the matrix material ratio is adjusted to obtain the matrix material configuration diagram; The random forest algorithm is used to determine the matching degree between vegetation distribution and water resources based on the matrix material configuration map. If the matching degree is lower than the preset threshold, the matrix material ratio is readjusted. The final slope ecological restoration plan is output through the adjusted matrix material configuration diagram.

10. The method according to claim 1, characterized in that It also includes uploading the final slope ecological restoration plan, vegetation distribution map, water resource management map and matrix material configuration map to the cloud database through the data interface, and using blockchain technology to record the plan generation and optimization process to obtain a traceable digital archive of the restoration plan.

Images