A Grid Soil and Water Conservation Method and System Based on 3D Simulation

Abstract

The present invention provides a power grid soil and water conservation method and system based on 3D simulation, which relates to the technical field of power grid soil and water technology. It includes constructing a 3D scene model of the power grid project based on terrain, soil, vegetation, and hydrometeorological data, and simulating the distribution of soil erosion risks. Then, a layout plan of soil and water conservation facilities including intercepting and draining ditches, grit chambers, vegetation restoration zones, and retaining walls is established. The multi-objective optimization algorithm is used to optimize the location, specifications, and quantity of the facilities with the goals of minimizing construction costs and maximizing soil and water conservation effects, obtaining multiple candidate solutions. Finally, based on computational fluid dynamics, the soil erosion process under different rainfall conditions is simulated, and combined with the fuzzy comprehensive evaluation method, the solution with the highest comprehensive score is selected as the final optimized solution, and a 3D construction layout drawing and a list of technical parameters are output. The present invention can effectively reduce the soil erosion risk during the construction and operation of power grid projects, save project costs, and improve the soil and water conservation effect.

Description

Technical Field

[0001] The present invention relates to grid soil and water technology, and particularly to a grid soil and water conservation method and system based on three-dimensional simulation. Background Art

[0002] In the process of traditional grid project construction, the design and implementation of soil and water conservation plans often rely on experience and two-dimensional drawings, lacking refined simulation and analysis of complex terrain, landforms, and hydrological conditions. This results in the design of soil and water conservation measures lacking a scientific basis, making it difficult to effectively control soil erosion, and may even trigger geological disasters, affecting the safe and stable operation of grid projects. At the same time, traditional plan evaluation methods are also relatively rough, making it difficult to quantitatively compare and optimize the effects of different soil and water conservation measures.

[0003] Traditional soil and water conservation plan design mainly relies on experience and two-dimensional drawings, making it difficult to accurately simulate the soil erosion process under complex terrain conditions, resulting in inaccurate layout and parameter selection of soil and water conservation measures, and it is difficult to achieve the best prevention and control effects. Most existing soil and water conservation plan evaluation methods are based on simple index calculations or expert judgments, lacking comprehensive comparison and optimization of different plans, and it is difficult to select the optimal plan. Traditional soil and water conservation plans are difficult to intuitively display the layout and effects of soil and water conservation measures, which is not convenient for engineering construction and later maintenance management. Summary of the Invention

[0004] Embodiments of the present invention provide a grid soil and water conservation method and system based on three-dimensional simulation, which can solve the problems in the prior art.

[0005] In the first aspect of the embodiments of the present invention,

[0006] A grid soil and water conservation method based on three-dimensional simulation is provided, including:

[0007] Register soil parameters, vegetation cover, and hydrometeorological data and generate a base layer through UV texture coordinate projection. Predict the dynamic distribution of vegetation growth using soil organic matter content and water holding capacity. At the same time, calculate the soil water migration process based on rainfall data and soil physical parameters to obtain the water content distribution. Then, input the terrain slope, soil water content distribution, and dynamic vegetation cover distribution into a prediction model to calculate the soil erosion result. Establish a risk assessment system based on the soil erosion result and determine the weights through analytic hierarchy process. Calculate the risk level using fuzzy membership degree and map it to the surface of the three-dimensional model through multi-scale rendering. Finally, generate a three-dimensional scene model of the grid project containing soil erosion risk distribution information;

[0008] Based on the three-dimensional scene model of the power grid project, a layout plan of soil and water conservation facilities including intercepting and draining ditches, grit chambers, vegetation restoration zones, and retaining walls is established; a multi-objective optimization algorithm is used to take the minimum construction cost of soil and water conservation facilities and the optimal soil and water conservation effect as the objective functions, and multi-objective optimization calculations are carried out on the position parameters, specification parameters, and quantity parameters of the soil and water conservation facilities to obtain multiple candidate optimized plans for soil and water conservation measures;

[0009] The candidate optimized plans for soil and water conservation measures are respectively imported into the three-dimensional scene model of the power grid project; based on the computational fluid dynamics method, the surface runoff and soil erosion processes under different rainfall conditions are simulated, and the soil and water loss amount is calculated; the fuzzy comprehensive evaluation method is used to comprehensively consider the soil and water conservation rate, vegetation restoration rate, runoff reduction rate, and project investment cost, and a comprehensive score is given to the candidate optimized plans for soil and water conservation measures; the candidate optimized plan for soil and water conservation measures with the highest comprehensive score is selected as the final optimized plan, and the three-dimensional construction layout drawing and technical parameter list of the final optimized plan are output.

[0010] The soil parameters, vegetation cover, and hydrological and meteorological data are registered and a base layer is generated through UV texture coordinate projection. The dynamic distribution of vegetation growth is predicted using the soil organic matter content and water holding capacity. At the same time, the soil water movement process is calculated based on rainfall data and soil physical parameters to obtain the water content distribution. Then, the terrain slope, soil water content distribution, and dynamic distribution of vegetation cover are input into the prediction model to calculate the soil and water loss results. Based on this result, a risk assessment system is established and the weights are determined through the analytic hierarchy process. After calculating the risk level using fuzzy membership, it is mapped to the surface of the three-dimensional model through multi-scale rendering. Finally, a three-dimensional scene model of the power grid project containing soil and water loss risk distribution information is generated, including:

[0011] The soil parameter data, vegetation cover data, and hydrological and meteorological data are uniformly transformed into the coordinate system of the terrain three-dimensional model to generate registered soil parameter data, registered vegetation cover data, and registered hydrological and meteorological data;

[0012] A UV texture coordinate system is established on the surface of the terrain three-dimensional model, and the registered soil parameter data, the registered vegetation cover data, and the registered hydrological and meteorological data are respectively mapped to the UV texture coordinate system using the equal-angle projection method to generate a soil parameter texture layer, a vegetation cover texture layer, and a hydrological element texture layer;

[0013] A vegetation growth prediction model is established based on the soil organic matter content and water holding capacity in the soil parameter texture layer, and the output result of the vegetation growth prediction model is associated with the vegetation cover texture layer to generate a dynamic distribution layer of vegetation cover;

[0014] Based on the rainfall data in the hydrological element texture layer and the soil physical parameters in the soil parameter texture layer, use the Green-Ampt infiltration model to calculate the soil moisture migration process and generate a soil water content distribution layer; input the slope data of the three-dimensional terrain model, the soil water content distribution layer and the vegetation cover dynamic distribution layer into the distributed soil erosion prediction model to calculate and obtain the soil erosion prediction result;

[0015] Based on the soil erosion prediction result, establish a soil erosion risk assessment index system, use the analytic hierarchy process to determine the weights of the terrain factor, soil factor, vegetation factor and rainfall factor, and calculate the soil erosion risk level distribution layer through the fuzzy membership function; use the multi-scale rendering technology to map the soil erosion risk level distribution layer to the surface of the three-dimensional terrain model to generate a three-dimensional scene model of the power grid project with soil erosion risk distribution information.

[0016] Based on the three-dimensional scene model of the power grid project, establish a layout plan for soil and water conservation facilities including intercepting drainage ditches, grit chambers, vegetation restoration zones and retaining walls; use the multi-objective optimization algorithm, with the minimum construction cost of soil and water conservation facilities and the best soil and water conservation effect as the objective function, perform multi-objective optimization calculations on the position parameters, specification parameters and quantity parameters of the soil and water conservation facilities to obtain multiple candidate optimized soil and water conservation measure plans, including:

[0017] Determine the layout area of the intercepting drainage ditch according to the terrain slope data of the three-dimensional scene model of the power grid project, determine the layout area of the grit chamber according to the catchment area data of the three-dimensional scene model of the power grid project, and determine the layout areas of the vegetation restoration zone and the retaining wall according to the slope position data of the three-dimensional scene model of the power grid project;

[0018] Use the D8 algorithm to calculate the surface runoff path, and set the initial layout positions of the intercepting drainage ditches on the runoff path. The initial layout positions of the intercepting drainage ditches include the positions of trapezoidal-section intercepting drainage ditches set in areas with a slope greater than 15 degrees and the positions of rectangular-section drainage ditches set in areas with a slope less than 15 degrees; set the initial layout position of the grit chamber at the outlet corresponding to the catchment area data; set the initial layout positions of the vegetation restoration zone and the retaining wall in the area corresponding to the slope position data;

[0019] Establish the constraint conditions for the specification parameters of the soil and water conservation facilities, determine the constraint of the cross-sectional dimension parameters of the intercepting drainage ditch based on the Manning formula, determine the constraint of the volume parameters of the grit chamber based on the sediment yield of 24-hour heavy rain, determine the constraint of the width parameters of the vegetation restoration zone based on the slope gradient, and determine the constraint of the height parameters of the retaining wall based on the slope stability analysis;

[0020] Build a multi-objective optimization model for soil and water conservation facilities. Take the position parameters, specification parameters, and quantity parameters of the intercepting and draining ditch, the grit chamber, the vegetation restoration zone, and the retaining wall as decision variables, and take the minimum construction cost of soil and water conservation facilities and the optimal soil and water conservation effect as the objective function. The construction cost includes the sub-project cost of earth excavation, concrete pouring, and vegetation restoration. The soil and water conservation effect includes the runoff interception rate, sediment interception rate, and slope protection rate.

[0021] Use the non-dominated sorting genetic algorithm to solve the multi-objective optimization model. Set the population size to 100, the number of generations of evolution to 200 generations, the crossover probability to 0.9, and the mutation probability to 0.1. Select superior individuals through the tournament selection strategy, use the simulated binary crossover operator and polynomial mutation operator to generate new solutions, and introduce the elite retention strategy to maintain the optimal solution.

[0022] Divide the soil and water loss sensitive areas according to the terrain characteristics of the three-dimensional scene model of the power grid project, and limit the value range of the decision variables within the soil and water loss sensitive areas. Establish the associated constraint conditions between facilities, including the connection constraint between the intercepting and draining ditch and the grit chamber, and the collaborative constraint between the vegetation restoration zone and the retaining wall, to obtain multiple candidate optimized soil and water conservation measure plans.

[0023] Build a multi-objective optimization model for soil and water conservation facilities. Take the position parameters, specification parameters, and quantity parameters of the intercepting and draining ditch, the grit chamber, the vegetation restoration zone, and the retaining wall as decision variables. The objective function of minimizing the construction cost of soil and water conservation facilities and optimizing the soil and water conservation effect includes:

[0024] Establish an optimization model for soil and water conservation facilities. Take the minimum construction cost of soil and water conservation facilities and the optimal soil and water conservation effect as the objective function. Take the starting and ending coordinates, cross-sectional shape, ditch depth, bottom width, and top width of the intercepting and draining ditch, the center point coordinates, pool length, pool width, and pool depth of the grit chamber, the four corner coordinates, planting density, and vegetation type of the vegetation restoration zone, the starting and ending coordinates, wall height, top width, and bottom width of the retaining wall, and the quantity of various facilities as decision variables. Establish constraint conditions according to the engineering site limitations, design specification requirements, investment limits, and protection standards.

[0025] Based on the decision variables and the construction cost of soil and water conservation facilities, calculate the earth excavation cost by multiplying the earth excavation volume by the transportation distance, calculate the concrete pouring cost according to the volume of the concrete structure, calculate the vegetation restoration cost based on the area of the vegetation restoration area, and take the minimum total sub-project cost of the earth excavation cost, the concrete pouring cost, and the vegetation restoration cost as the construction cost objective function, and at the same time combine the decision variables and the soil and water conservation effect.

[0026] Among them, the objective function corresponding to the construction cost of the soil and water conservation facilities is as follows:

[0027] ;

[0028] Among them, C is the total construction cost, C1 is the earth excavation cost, C2 is the concrete pouring cost, and C3 is the vegetation restoration cost;

[0029] ;

[0030] Among them, α1 is the unit construction cost coefficient for earth excavation, V is the earth excavation volume, and L is the average hauling distance;

[0031] ;

[0032] Among them, α2 is the unit construction cost coefficient for concrete pouring, and VC is the volume of the concrete structure;

[0033] ;

[0034] Among them, α3 is the unit construction cost coefficient for vegetation restoration, and A is the vegetation restoration area;

[0035] The runoff interception rate of the intercepting drainage ditch and the grit chamber under the design rainfall conditions is calculated by using a hydraulic model, the sediment interception rate of the grit chamber is calculated based on the sediment movement law, and the slope protection rate is calculated according to the vegetation coverage of the vegetation restoration zone and the slope stability of the retaining wall. The maximum weighted sum of the runoff interception rate, the sediment interception rate, and the slope protection rate is used as the objective function of the soil and water conservation effect;

[0036] Among them, the objective function corresponding to the soil and water conservation effect is as follows:

[0037] ;

[0038] Among them, η is the comprehensive soil and water conservation effect, η1 is the runoff interception rate, η2 is the sediment interception rate, η3 is the slope protection rate, and w1, w2, and w3 are the weight coefficients of each index;

[0039] ;

[0040] Among them, Q is the flow capacity of the intercepting drainage ditch, and Q0 is the runoff yield under the design rainfall conditions;

[0041] ;

[0042] Among them, n is the Manning roughness coefficient, B is the cross-sectional area of the flow, R is the hydraulic radius, and I is the hydraulic gradient;

[0043] ;

[0044] Among them, A1 is the imported sediment concentration and A2 is the exported sediment concentration;

[0045] ;

[0046] Among them, T is the sediment particle settlement velocity, ρs is the sediment density, ρ is the water density, g is the acceleration due to gravity, d is the sediment particle size, and μ is the dynamic viscosity of water;

[0047] ;

[0048] Among them, Cc is the vegetation coverage, Fs is the slope stability safety factor, and k1 and k2 are weight coefficients;

[0049] ;

[0050] Among them, c' is the effective cohesion, W is the weight of the sliding soil mass, α is the inclination angle of the sliding surface, φ' is the effective internal friction angle, and U is the length of the sliding surface.

[0051] The non-dominated sorting genetic algorithm is used to solve the multi-objective optimization model. The population size is set to 100, the number of generations of evolution is set to 200 generations, the crossover probability is set to 0.9, and the mutation probability is set to 0.1; the dominant individuals are selected through the tournament selection strategy, the simulated binary crossover operator and the polynomial mutation operator are used to generate new solutions, and the elite retention strategy is introduced to maintain the optimal solutions, including:

[0052] The non-dominated sorting genetic algorithm is used to solve the multi-objective optimization model. The population size is set to 100 individuals, and each individual consists of a geometric parameter vector and a position parameter vector of the soil and water conservation facilities. The geometric parameter vector and the position parameter vector constitute the decision variable space of the individual; within the decision variable space, the Latin hypercube sampling method is used to generate 100 initial individuals to form an initial population. According to the engineering site limit conditions and design specification requirements, a constraint condition set is established, and the constraint condition set is used to conduct a feasibility test on each individual of the initial population. The individuals that do not meet the constraint conditions are adjusted to the feasible solution space through parameter repair operations to obtain an initial feasible population that meets the constraint conditions;

[0053] The parallel computing framework and the response surface surrogate model are used to calculate the construction cost objective function value and the soil and water conservation effect objective function value of each individual in the initial feasible population. The fast non-dominated sorting algorithm is used to divide the initial feasible population into non-dominated frontiers of different levels, and the crowding distance value of individuals in the same non-dominated level is calculated;

[0054] Set the crossover probability to 0.9, and use the tournament selection strategy to select dominant individuals from the non-dominated front as parental individuals. The selection of the dominant individuals is based on the comparison results of the non-dominated rank and the crowding distance value. Perform crossover operations on the selected parental individuals using the simulated binary crossover operator to generate offspring individuals. Use the set of constraint conditions to conduct a feasibility test on the offspring individuals, and adjust the offspring individuals that do not meet the constraint conditions into the feasible solution space through parameter repair operations;

[0055] Set the mutation probability to 0.1, and use the polynomial mutation operator to perform mutation operations on the offspring individuals that have passed the feasibility test. Combine the mutated offspring individuals with the parental individuals to form a combined population of size 200. Repeat the fast non-dominated sorting and crowding distance calculation for the combined population, and select 100 optimal individuals from the combined population based on the updated non-dominated rank and crowding distance value to form a new generation population. Adopt the elitist retention strategy to directly retain the individual with the optimal non-dominated rank in the new generation population to the next generation population.

[0056] Import the candidate optimized soil and water conservation measure plans into the 3D scene model of the power grid project respectively; Based on the computational fluid dynamics method, simulate the surface runoff and soil erosion processes under different rainfall conditions, and calculate the soil and water loss amount including:

[0057] Construct a basic digital elevation scene through the triangulated irregular network algorithm. According to the layout information of various facilities in the candidate optimized soil and water conservation measure plans, establish a 3D geometric model including intercepting and draining ditches, grit chambers, vegetation restoration zones, and retaining walls. Use Boolean operations to fuse the 3D geometric model with the basic digital elevation scene to generate multiple terrain scene models with soil and water conservation facilities;

[0058] For the terrain scene model, use the unstructured grid division algorithm to discretize it. Set fine grids in the areas corresponding to the soil and water conservation facilities, and set boundary layer grids in the surface layer to capture the near-wall flow characteristics, forming a discretized computational grid with local refinement characteristics; Collect the meteorological data of the project area, obtain the rainstorm data of multiple return periods, and use the rain type distribution method to convert the rainstorm data into a time-series rainfall process line. Apply the rainfall process line to the top boundary of the discretized computational grid to construct a boundary condition with time-varying rainfall intensity;

[0059] Based on the discrete computational grid and the time-varying rainfall intensity boundary conditions, a surface runoff model is constructed using a multiphase flow model, and a soil erosion model is constructed using an Euler-Lagrange coupling method. By setting the time step and convergence criterion, the two models are coupled and solved to obtain the spatio-temporal evolution data of the surface runoff velocity field, water depth distribution, and soil erosion amount. Based on the spatio-temporal evolution data, the soil and water loss characteristic parameters of each terrain scenario model are calculated, including the surface runoff coefficient, soil erosion modulus, facility retention efficiency, and total cumulative soil and water loss.

[0060] In the second aspect of the embodiments of the present invention,

[0061] A power grid soil and water conservation system based on 3D simulation is provided, including:

[0062] The first unit is used to register soil parameters, vegetation cover, and hydrological and meteorological data and generate a base layer through UV texture coordinate projection. It predicts the dynamic distribution of vegetation growth using the soil organic matter content and water holding capacity. At the same time, it calculates the soil water movement process based on rainfall data and soil physical parameters to obtain the water content distribution. Then, it inputs the terrain slope, soil water content distribution, and dynamic vegetation cover distribution into a prediction model to calculate the soil and water loss results. Based on the soil and water loss results, a risk assessment system is established, and the weights are determined through hierarchical analysis. After calculating the risk level using fuzzy membership, it is mapped to the surface of the 3D model through multi-scale rendering, and finally, a 3D scene model of the power grid project containing soil and water loss risk distribution information is generated;

[0063] The second unit is used to establish a soil and water conservation facility layout plan including intercepting and draining ditches, grit chambers, vegetation restoration zones, and retaining walls based on the 3D scene model of the power grid project. Using a multi-objective optimization algorithm, with the minimum construction cost of soil and water conservation facilities and the best soil and water conservation effect as the objective functions, multi-objective optimization calculations are performed on the position parameters, specification parameters, and quantity parameters of the soil and water conservation facilities to obtain multiple candidate optimized soil and water conservation measure plans;

[0064] The third unit is used to import the candidate optimized soil and water conservation measure plans into the 3D scene model of the power grid project respectively. Based on the computational fluid dynamics method, it simulates the surface runoff and soil erosion processes under different rainfall conditions and calculates the soil and water loss amount. Using the fuzzy comprehensive evaluation method, considering the soil and water conservation rate, vegetation restoration rate, runoff reduction rate, and project investment cost comprehensively, a comprehensive score is given to the candidate optimized soil and water conservation measure plans. The candidate optimized soil and water conservation measure plan with the highest comprehensive score is selected as the final optimized plan, and the 3D construction layout drawing and technical parameter list of the final optimized plan are output.

[0065] In the third aspect of the embodiments of the present invention,

[0066] An electronic device is provided, including:

[0067] Processor;

[0068] A memory for storing processor-executable instructions;

[0069] Wherein, the processor is configured to call the instructions stored in the memory to execute the method described above.

[0070] In the fourth aspect of the embodiments of the present invention,

[0071] A computer-readable storage medium is provided, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the method described above is implemented.

[0072] The beneficial effects of this application are as follows:

[0073] 1. Visualize the risk of soil and water loss: Through three-dimensional simulation technology, the risk of soil and water loss in the power grid project area is presented in a visual way, which is convenient for intuitively understanding the risk distribution and provides a scientific basis for formulating soil and water conservation plans.

[0074] 2. Optimize the soil and water conservation plan: Using a multi-objective optimization algorithm, considering both construction costs and soil and water conservation effects, optimize the layout, specifications, and quantity of soil and water conservation facilities to achieve a win-win situation for economic and ecological benefits.

[0075] 3. Improve the effectiveness of soil and water conservation measures: Simulate the soil and water loss process under different rainfall conditions based on computational fluid dynamics methods, and evaluate candidate solutions using a fuzzy comprehensive evaluation method, and finally select the optimal solution to ensure the effectiveness and reliability of soil and water conservation measures. Description of the Drawings

[0076] Figure 1 It is a schematic flowchart of a power grid soil and water conservation method based on three-dimensional simulation in an embodiment of the present invention;

[0077] Figure 2 It is a schematic structural diagram of a power grid soil and water conservation system based on three-dimensional simulation in an embodiment of the present invention. Detailed Embodiments

[0078] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0079] The technical solution of the present invention will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

[0080] Figure 1 This is a schematic flow chart of a power grid soil and water conservation method based on three-dimensional simulation according to an embodiment of the present invention. As Figure 1 shown, the method includes:

[0081] S11. Register soil parameters, vegetation cover, and hydrometeorological data, and generate a base layer through UV texture coordinate projection. Predict the dynamic distribution of vegetation growth using soil organic matter content and water holding capacity. At the same time, calculate the soil water movement process based on rainfall data and soil physical parameters to obtain the water content distribution. Then, input the terrain slope, soil water content distribution, and dynamic distribution of vegetation cover into a prediction model to calculate the soil erosion result. Establish a risk assessment system based on the soil erosion result and determine the weight through hierarchical analysis. Calculate the risk level using fuzzy membership degree and map it to the surface of the three-dimensional model through multi-scale rendering. Finally, generate a three-dimensional scene model of the power grid project containing soil erosion risk distribution information;

[0082] S12. Based on the three-dimensional scene model of the power grid project, establish a layout plan for soil and water conservation facilities including intercepting and draining ditches, grit chambers, vegetation restoration zones, and retaining walls. Use a multi-objective optimization algorithm with the minimum construction cost of soil and water conservation facilities and the best soil and water conservation effect as the objective function to perform multi-objective optimization calculations on the position parameters, specification parameters, and quantity parameters of the soil and water conservation facilities, and obtain multiple candidate optimized solutions for soil and water conservation measures;

[0083] S13. Import the candidate optimized solutions for soil and water conservation measures into the three-dimensional scene model of the power grid project respectively. Based on the computational fluid dynamics method, simulate the surface runoff and soil erosion processes under different rainfall conditions and calculate the soil erosion amount. Use the fuzzy comprehensive evaluation method to comprehensively consider the soil and water conservation rate, vegetation restoration rate, runoff reduction rate, and project investment cost to comprehensively evaluate the candidate optimized solutions for soil and water conservation measures. Select the candidate optimized solution for soil and water conservation measures with the highest comprehensive score as the final optimized solution, and output the three-dimensional construction layout drawing and technical parameter list of the final optimized solution.

[0084] In an optional implementation, the soil parameters, vegetation cover, and hydrometeorological data are registered and a base layer is generated through UV texture coordinate projection. The dynamic distribution of vegetation growth is predicted using the soil organic matter content and water holding capacity. Meanwhile, the soil water movement process is calculated based on rainfall data and soil physical parameters to obtain the water content distribution. Then, the terrain slope, soil water content distribution, and dynamic distribution of vegetation cover are input into a prediction model to calculate the soil erosion result. Based on this result, a risk assessment system is established and the weights are determined through analytic hierarchy process. After calculating the risk level using fuzzy membership degree, it is mapped to the surface of the 3D model through multi-scale rendering, and finally a 3D scene model of the power grid project containing soil erosion risk distribution information is generated, including:

[0085] Unify the conversion of the soil parameter data, vegetation cover data, and hydrometeorological data to the coordinate system of the terrain 3D model to generate registered soil parameter data, registered vegetation cover data, and registered hydrometeorological data;

[0086] Establish a UV texture coordinate system on the surface of the terrain 3D model, and use the equiangular projection method to map the registered soil parameter data, the registered vegetation cover data, and the registered hydrometeorological data to the UV texture coordinate system respectively to generate a soil parameter texture layer, a vegetation cover texture layer, and a hydrological element texture layer;

[0087] Establish a vegetation growth prediction model based on the soil organic matter content and water holding capacity in the soil parameter texture layer, and associate the output result of the vegetation growth prediction model with the vegetation cover texture layer to generate a dynamic distribution layer of vegetation cover;

[0088] Based on the rainfall data in the hydrological element texture layer and the soil physical parameters in the soil parameter texture layer, use the Green-Ampt infiltration model to calculate the soil water movement process to generate a soil water content distribution layer; input the slope data of the terrain 3D model, the soil water content distribution layer, and the dynamic distribution layer of vegetation cover into a distributed soil erosion prediction model to calculate the soil erosion prediction result;

[0089] Establish a soil erosion risk assessment index system based on the soil erosion prediction result, use the analytic hierarchy process to determine the weights of terrain factors, soil factors, vegetation factors, and rainfall factors, and calculate the soil erosion risk level distribution layer through a fuzzy membership function; use multi-scale rendering technology to map the soil erosion risk level distribution layer to the surface of the terrain 3D model to generate a 3D scene model of the power grid project with soil erosion risk distribution information.

[0090] Three-dimensional Scenario Model Construction Method for Soil and Water Loss Risk Assessment in Power Grid Projects. This method integrates multi-source data such as soil, vegetation, hydrology and meteorology into a three-dimensional terrain model to achieve visual assessment of soil and water loss risks.

[0091] First, obtain DEM data, soil parameter data (such as soil type, organic matter content, water holding capacity, etc.), vegetation cover data (such as vegetation type, coverage, etc.) and hydrological and meteorological data (such as rainfall, temperature, etc.) of the study area. Taking a certain power grid project area as an example, obtain DEM data with a resolution of 10 meters in this area. The soil data includes soil type, organic matter content, water holding capacity, etc. The vegetation data includes vegetation type, coverage, etc. The hydrological and meteorological data includes daily rainfall, temperature, etc.

[0092] Unify the obtained soil parameter data, vegetation cover data and hydrological and meteorological data to the same coordinate system and projection system as the DEM data. For example, convert all data to the WGS84 coordinate system and UTM projection system. This step ensures the spatial alignment of all data for subsequent analysis and processing.

[0093] Establish a UV texture coordinate system on the surface of the three-dimensional terrain model. The UV texture coordinate system is similar to a two-dimensional plane unfolded on the surface of a three-dimensional model, which can map two-dimensional image information onto the surface of the three-dimensional model. Using the equiangular projection method, map the registered soil parameter data, vegetation cover data and hydrological and meteorological data onto the UV texture coordinate system respectively to generate corresponding texture layers. For example, convert the soil organic matter content data into a grayscale image, where a higher grayscale value represents a higher organic matter content, and use this image as the soil organic matter content texture layer. Similarly, generate the vegetation cover texture layer and the hydrological element texture layer.

[0094] Establish a vegetation growth prediction model based on the soil organic matter content and water holding capacity in the soil parameter texture layer. For example, assuming that vegetation growth is positively correlated with soil organic matter content and water holding capacity, a simple linear model can be constructed to predict vegetation coverage. Overlay the output result of this model with the original vegetation cover texture layer to generate a vegetation cover dynamic distribution layer, simulating the impact of vegetation growth changes on soil and water loss. For example, if the prediction model shows that the vegetation coverage in a certain area increases, update the value of this area in the vegetation cover dynamic distribution layer.

[0095] Based on the rainfall data in the hydrological element texture layer and the soil physical parameters in the soil parameter texture layer, the Green-Ampt infiltration model is used to calculate the soil water migration process. This model takes into account factors such as rainfall intensity, soil saturation, and soil hydraulic conductivity, and simulates the infiltration and migration process of soil water. The calculation results generate a soil water content distribution layer, which reflects the soil water content in different regions. For example, regions with larger rainfall amounts have higher soil water contents.

[0096] The terrain slope data, the soil water content distribution layer, and the vegetation cover dynamic distribution layer are input into the distributed soil erosion prediction model to obtain the soil erosion prediction results. For example, the revised Universal Soil Loss Equation (RUSLE) model is used, which takes into account factors such as terrain slope, soil erodibility, vegetation coverage, and rainfall amount, and calculates the soil erosion amount for each grid cell.

[0097] Based on the soil erosion prediction results, a soil erosion risk assessment index system is established. For example, the soil erosion amount is divided into three risk levels: low, medium, and high. The Analytic Hierarchy Process is used to determine the weights of the terrain factor, soil factor, vegetation factor, and rainfall factor. For example, expert scoring determines that the terrain slope accounts for 40% of the weight, the soil erodibility accounts for 30% of the weight, the vegetation coverage accounts for 20% of the weight, and the rainfall amount accounts for 10% of the weight. The soil erosion risk level distribution layer is calculated through the fuzzy membership function. For example, the soil erosion amount is converted into a membership value between 0 and 1, and the higher the value, the higher the risk.

[0098] The multi-scale rendering technology is used to map the soil erosion risk level distribution layer onto the surface of the three-dimensional terrain model to generate a three-dimensional scene model of the power grid project with soil erosion risk distribution information. For example, the low-risk areas are rendered green, the medium-risk areas are rendered yellow, and the high-risk areas are rendered red.

[0099] The solution of this application can:

[0100] Improve the evaluation accuracy: By integrating multi-source data and refined modeling, the accuracy of soil erosion risk assessment is improved, and high-risk areas can be identified more accurately. Enhance the visualization effect: Map the soil erosion risk information into the three-dimensional scene model, realizing the visual expression of the risk assessment results, and facilitating the intuitive understanding of the risk distribution. Assist decision-making support: Provide decision-making support for the planning, design, and construction of power grid projects, contribute to optimizing the project layout, reducing soil erosion risks, and protecting the ecological environment.

[0101] In an optional implementation manner, based on the three-dimensional scene model of the power grid project, a layout plan of soil and water conservation facilities including intercepting and draining ditches, grit chambers, vegetation restoration zones, and retaining walls is established; a multi-objective optimization algorithm is used, with the minimum construction cost of the soil and water conservation facilities and the optimal soil and water conservation effect as the objective functions, to perform multi-objective optimization calculations on the position parameters, specification parameters, and quantity parameters of the soil and water conservation facilities, and obtain multiple candidate optimized soil and water conservation measure plans, including:

[0102] Determine the layout area of the intercepting and draining ditches according to the terrain slope data of the three-dimensional scene model of the power grid project, determine the layout area of the grit chambers according to the catchment area data of the three-dimensional scene model of the power grid project, and determine the layout areas of the vegetation restoration zones and retaining walls according to the slope position data of the three-dimensional scene model of the power grid project;

[0103] Use the D8 algorithm to calculate the surface runoff path, and set the initial layout positions of the intercepting and draining ditches on the runoff path. The initial layout positions of the intercepting and draining ditches include the positions of trapezoidal-section intercepting and draining ditches set in areas with a slope greater than 15 degrees and the positions of rectangular-section drainage ditches set in areas with a slope less than 15 degrees; set the initial layout position of the grit chamber at the outlet corresponding to the catchment area data; set the initial layout positions of the vegetation restoration zone and the retaining wall in the area corresponding to the slope position data;

[0104] Establish the constraint conditions for the specification parameters of the soil and water conservation facilities. Based on the Manning formula, determine the constraint for the cross-sectional dimension parameters of the intercepting and draining ditches, determine the constraint for the volume parameters of the grit chambers based on the sediment yield of 24-hour heavy rain, determine the constraint for the width parameters of the vegetation restoration zones based on the slope gradient, and determine the constraint for the height parameters of the retaining walls based on the slope stability analysis;

[0105] Construct a multi-objective optimization model for the soil and water conservation facilities. Take the position parameters, specification parameters, and quantity parameters of the intercepting and draining ditches, the grit chambers, the vegetation restoration zones, and the retaining walls as decision variables, and take the minimum construction cost of the soil and water conservation facilities and the optimal soil and water conservation effect as the objective functions; the construction cost includes the sub-project engineering costs of earthwork excavation, concrete pouring, and vegetation restoration; the soil and water conservation effect includes the runoff interception rate, sediment interception rate, and slope protection rate;

[0106] Use the non-dominated sorting genetic algorithm to solve the multi-objective optimization model. Set the population size to 100, the number of evolutionary generations to 200 generations, the crossover probability to 0.9, and the mutation probability to 0.1; select dominant individuals through the tournament selection strategy, use the simulated binary crossover operator and the polynomial mutation operator to generate new solutions, and introduce the elite retention strategy to maintain the optimal solutions;

[0107] Divide the soil and water loss sensitive areas according to the topographic features of the 3D scene model of the power grid project, and limit the value range of the decision variables within the soil and water loss sensitive areas; establish the associated constraint conditions between facilities, including the connection constraint between the intercepting and draining ditch and the grit chamber, and the collaborative constraint between the vegetation restoration zone and the retaining wall, to obtain multiple candidate optimized soil and water conservation measure plans.

[0108] Based on the 3D scene model, construct an optimized layout method for soil and water conservation facilities of the power grid project, including the following steps:

[0109] First, obtain the 3D scene model data of the power grid project. This model includes data such as terrain slope, catchment area, and slope position, providing basic information for the subsequent layout of soil and water conservation facilities. For example, obtain high-precision terrain data through UAV aerial photography and lidar scanning to construct the 3D scene model of the power grid project.

[0110] Then, determine the initial layout areas of soil and water conservation facilities. According to the terrain slope data, divide the areas with a slope greater than 15 degrees into intercepting and draining ditch layout areas, and set trapezoidal cross-section intercepting and draining ditches; also divide the areas with a slope less than 15 degrees into intercepting and draining ditch layout areas, and set rectangular cross-section drainage ditches. According to the catchment area data, determine the grit chamber layout area, and set a grit chamber at the catchment outlet. According to the slope position data, determine the layout areas of the vegetation restoration zone and the retaining wall. For example, set a trapezoidal intercepting and draining ditch in the area with a slope of 20 degrees, set a rectangular drainage ditch in the area with a slope of 10 degrees, set a grit chamber at the outlet with a catchment area of 100 square meters, and set the vegetation restoration zone and the retaining wall at the top and bottom of the slope.

[0111] Next, use the D8 algorithm to calculate the surface runoff path, and thereby determine the initial layout positions of the intercepting and draining ditches. Set the initial layout position of the grit chamber at the outlet corresponding to the catchment area. Set the initial layout positions of the vegetation restoration zone and the retaining wall in the area corresponding to the slope position. For example, according to the D8 algorithm, three runoff paths are calculated, and three intercepting and draining ditches are set on the paths respectively.

[0112] After that, establish the specification parameter constraint conditions of soil and water conservation facilities. Determine the cross-section size range of the intercepting and draining ditch according to the Manning formula. Determine the volume range of the grit chamber according to the sediment yield of the 24-hour rainstorm. Determine the width range of the vegetation restoration zone according to the slope gradient. Determine the height range of the retaining wall according to the slope stability analysis. For example, according to the Manning formula, the calculated cross-section size range of the intercepting and draining ditch is a width of 0.5 meters to 1 meter and a depth of 0.5 meters to 1 meter. According to the sediment yield of the 24-hour rainstorm, the calculated volume range of the grit chamber is 10 cubic meters to 20 cubic meters. Determine the width range of the vegetation restoration zone to be 2 meters to 5 meters according to the slope gradient. Determine the height range of the retaining wall to be 1 meter to 3 meters according to the slope stability analysis.

[0113] Build a multi-objective optimization model for soil and water conservation facilities. Take the location parameters, specification parameters and quantity parameters of intercepting and draining ditches, grit chambers, vegetation restoration zones and retaining walls as decision variables. Take the minimum construction cost of soil and water conservation facilities and the optimal soil and water conservation effect as the objective functions. The construction cost includes the sub-project costs of earth excavation, concrete pouring and vegetation restoration. The soil and water conservation effect includes runoff interception rate, sediment interception rate and slope protection rate.

[0114] Use the non-dominated sorting genetic algorithm to solve the multi-objective optimization model. Set the population size to 100, the number of evolutionary generations to 200 generations, the crossover probability to 0.9, and the mutation probability to 0.1. Adopt the tournament selection strategy to select superior individuals. Use the simulated binary crossover operator and polynomial mutation operator to generate new solutions. Introduce the elite retention strategy to maintain the optimal solution.

[0115] Divide the soil and water loss sensitive areas according to the terrain characteristics of the 3D scene model of the power grid project, and limit the value range of the decision variables within the soil and water loss sensitive areas. Establish the associated constraint conditions between facilities, including the connection constraint between the intercepting and draining ditches and the grit chambers, and the coordination constraint between the vegetation restoration zones and the retaining walls. Finally, obtain multiple candidate optimized solutions for soil and water conservation measures. For example, Solution 1: Set 5 intercepting and draining ditches, 2 grit chambers, the width of the vegetation restoration zone is 3 meters, and the height of the retaining wall is 2 meters; Solution 2: Set 7 intercepting and draining ditches,  3 grit chambers, the width of the vegetation restoration zone is 4 meters, and the height of the retaining wall is 2.5 meters.

[0116] The solution of this application can:

[0117] Reduce the construction cost: Through the multi-objective optimization algorithm, the solution with the lowest construction cost can be obtained, thus saving the project investment. Improve the soil and water conservation effect: By optimizing the layout and specification parameters of the soil and water conservation facilities, the runoff interception rate, sediment interception rate and slope protection rate can be maximized, and soil and water loss can be effectively controlled. Achieve diversified solutions: This method can generate multiple candidate solutions for decision-makers to select the most suitable solution according to the actual situation.

[0118] In an optional implementation manner, building a multi-objective optimization model for soil and water conservation facilities, taking the location parameters, specification parameters and quantity parameters of the intercepting and draining ditches, the grit chambers, the vegetation restoration zones and the retaining walls as decision variables, and taking the minimum construction cost of the soil and water conservation facilities and the optimal soil and water conservation effect as the objective functions includes:

[0119] An optimization model for soil and water conservation facilities is established, with the minimum construction cost of soil and water conservation facilities and the optimal soil and water conservation effect as the objective function. The starting and ending coordinates, cross-sectional shape, ditch depth, bottom width and top width of the intercepting and drainage ditches, the central point coordinates, pool length, pool width and pool depth of the grit chambers, the four corner coordinates, planting density and vegetation type of the vegetation restoration zones, the starting and ending coordinates, wall height, top width and bottom width of the retaining walls, and the quantity of various facilities are used as decision variables. Constraint conditions are established based on engineering site limitations, design specification requirements, investment limits and protection standards;

[0120] Based on the decision variables and the construction cost of soil and water conservation facilities, the earthwork excavation cost is calculated by multiplying the earthwork excavation volume by the transportation distance, the concrete pouring cost is calculated according to the volume of the concrete structures, the vegetation restoration cost is calculated based on the area of the vegetation restoration area, and the minimum value of the sum of the sub-project construction costs of the earthwork excavation cost, the concrete pouring cost and the vegetation restoration cost is used as the construction cost objective function, while combining the decision variables and the soil and water conservation effect;

[0121] Among them, the objective function corresponding to the construction cost of soil and water conservation facilities is as follows:

[0122] ;

[0123] Among them, C is the total construction cost, C1 is the earthwork excavation cost, C2 is the concrete pouring cost, and C3 is the vegetation restoration cost;

[0124] ;

[0125] Among them, α1 is the unit construction cost coefficient for earthwork excavation, V is the earthwork excavation volume, and L is the average transportation distance;

[0126] ;

[0127] Among them, α2 is the unit construction cost coefficient for concrete pouring, and VC is the volume of the concrete structure;

[0128] ;

[0129] Among them, α3 is the unit construction cost coefficient for vegetation restoration, and A is the vegetation restoration area;

[0130] The runoff interception rate of the intercepting and drainage ditches and the grit chambers under the design rainfall conditions is calculated using a hydraulic model, the sediment interception rate of the grit chambers is calculated based on the sediment movement law, and the slope protection rate is calculated according to the vegetation coverage of the vegetation restoration zones and the slope stability of the retaining walls. The maximum weighted sum of the runoff interception rate, the sediment interception rate and the slope protection rate is used as the soil and water conservation effect objective function;

[0131] Among them, the objective function corresponding to the soil and water conservation effect is as follows:

[0132] ;

[0133] Among them, η is the comprehensive soil and water conservation effect, η1 is the runoff interception rate, η2 is the sediment interception rate, η3 is the slope protection rate, and w1, w2, w3 are the weight coefficients of each index;

[0134] ;

[0135] Among them, Q is the flow capacity of the intercepting and drainage ditch, and Q0 is the runoff yield under the design rainfall condition;

[0136] ;

[0137] Among them, n is the Manning roughness coefficient, B is the cross-sectional area of the water flow, R is the hydraulic radius, and I is the hydraulic gradient;

[0138] ;

[0139] Among them, A1 is the sediment concentration at the inlet and A2 is the sediment concentration at the outlet;

[0140] ;

[0141] Among them, T is the sediment particle settling velocity, ρs is the sediment density, ρ is the water density, g is the acceleration due to gravity, d is the sediment particle size, and μ is the dynamic viscosity of water;

[0142] ;

[0143] Among them, Cc is the vegetation coverage, Fs is the slope stability safety factor, and k1, k2 are the weight coefficients;

[0144] ;

[0145] Among them, c' is the effective cohesion, W is the weight of the sliding soil mass, α is the inclination angle of the sliding surface, φ' is the effective internal friction angle, and U is the length of the sliding surface.

[0146] The multi-objective optimization method for soil and water conservation aims to achieve the best soil and water conservation effect at the lowest construction cost. This method optimizes the parameters such as the location, specifications, and quantity of four types of soil and water conservation facilities, namely intercepting and drainage ditches, grit chambers, vegetation restoration zones, and retaining walls.

[0147] First, determine the basic information such as the boundary of the project area, topographic data, hydrological data, and soil type. For example, the boundary coordinates of the project area are (X1, Y1), (X2, Y2), (X3, Y3), (X4, Y4), the topographic data uses DEM data with a resolution of 1 meter, the hydrological data includes the average annual rainfall, the maximum 24-hour rainfall, etc., and the soil type is sandy loam.

[0148] Next, according to the collected basic information and design specifications, determine the value ranges of various design parameters. For example, the depth range of the intercepting and drainage ditch is from 0.5 meters to 1.5 meters, the depth range of the grit chamber is from 1 meter to 2 meters, the planting density range of the vegetation restoration zone is from 5 plants per square meter to 10 plants per square meter, and the height range of the retaining wall is from 2 meters to 4 meters.

[0149] Then, according to the value ranges of the design parameters, generate multiple plan combinations. For example, randomly generate 100 plans, and each plan contains the specific values of parameters such as the positions, specifications, and quantities of the intercepting and drainage ditch, grit chamber, vegetation restoration zone, and retaining wall.

[0150] For each plan, calculate its construction cost and soil and water conservation effect respectively. The construction cost includes the earthwork excavation cost, concrete pouring cost, and vegetation restoration cost. The earthwork excavation cost is obtained by multiplying the excavated earthwork volume by the unit earthwork excavation cost and the average transportation distance. The concrete pouring cost is obtained by multiplying the volume of the concrete structure by the unit concrete pouring cost. The vegetation restoration cost is obtained by multiplying the vegetation restoration area by the unit vegetation restoration cost. The soil and water conservation effect is represented by the weighted sum of the runoff interception rate, sediment interception rate, and slope protection rate. The runoff interception rate is calculated by the ratio of the flow capacity of the intercepting and drainage ditch to the runoff generation volume under the design rainfall conditions. The sediment interception rate is calculated by the ratio of the difference between the sediment concentration at the inlet and outlet of the grit chamber to the sediment concentration at the inlet. The slope protection rate is calculated by the weighted sum of the vegetation coverage and the slope stability safety factor of the retaining wall.

[0151] For example, for a certain plan, its earthwork excavation volume is 1000 cubic meters, the average transportation distance is 500 meters, and the unit earthwork excavation cost is 50 yuan per cubic meter, then the earthwork excavation cost is 250,000 yuan. The volume of the concrete structure of this plan is 50 cubic meters, and the unit concrete pouring cost is 800 yuan per cubic meter, then the concrete pouring cost is 40,000 yuan. The vegetation restoration area of this plan is 2000 square meters, and the unit vegetation restoration cost is 20 yuan per square meter, then the vegetation restoration cost is 40,000 yuan. Therefore, the total construction cost of this plan is 330,000 yuan. Assume that the runoff interception rate of this plan is 80%, the sediment interception rate is 90%, and the slope protection rate is 70%, and the weight coefficients are 0.4, 0.3, and 0.3 respectively, then the comprehensive soil and water conservation effect of this plan is 79%.

[0152] Finally, select the solution with the minimum construction cost and the best soil and water conservation effect from all the solutions as the final solution. For example, among 100 solutions, select the solution with the lowest construction cost and the highest soil and water conservation effect as the final solution.

[0153] The solutions of this application can:

[0154] Economic benefits: By optimizing the design parameters of soil and water conservation facilities, the construction cost can be effectively reduced and the project investment can be saved. Ecological benefits: By improving the soil and water conservation effect, soil and water loss can be effectively reduced and the ecological environment can be protected. Social benefits: By optimizing the layout of soil and water conservation facilities, the land use efficiency can be improved and the regional economic development can be promoted.

[0155] In an optional implementation manner, the non-dominated sorting genetic algorithm is used to solve the multi-objective optimization model. The population size is set to 100, the number of evolutionary generations is set to 200 generations, the crossover probability is set to 0.9, and the mutation probability is set to 0.1; the superior individuals are selected through the tournament selection strategy, the simulated binary crossover operator and the polynomial mutation operator are used to generate new solutions, and the elite retention strategy is introduced to maintain the optimal solutions, including:

[0156] The non-dominated sorting genetic algorithm is used to solve the multi-objective optimization model. The population size is set to 100 individuals, and each individual consists of a geometric parameter vector and a position parameter vector of the soil and water conservation facilities. The geometric parameter vector and the position parameter vector constitute the decision variable space of the individual; within the decision variable space, the Latin hypercube sampling method is used to generate 100 initial individuals to form an initial population. According to the engineering site limit conditions and design specification requirements, a constraint condition set is established, and the feasibility test is performed on each individual of the initial population using the constraint condition set. The individuals that do not meet the constraint conditions are adjusted to the feasible solution space through parameter repair operations to obtain an initial feasible population that meets the constraint conditions.

[0157] The parallel computing framework and the response surface surrogate model are used to calculate the construction cost objective function value and the soil and water conservation effect objective function value of each individual in the initial feasible population. The fast non-dominated sorting algorithm is used to divide the initial feasible population into non-dominated frontiers of different levels, and the crowding distance value of individuals in the same non-dominated level is calculated.

[0158] Set the crossover probability to 0.9, and use the tournament selection strategy to select dominant individuals from the non-dominated front as parental individuals. The selection of the dominant individuals is based on the comparison results of non-dominated ranks and crowding distance values. Perform crossover operations on the selected parental individuals using the simulated binary crossover operator to generate offspring individuals. Use the set of constraint conditions to perform feasibility tests on the offspring individuals, and adjust the offspring individuals that do not meet the constraint conditions into the feasible solution space through parameter repair operations;

[0159] Set the mutation probability to 0.1, and use the polynomial mutation operator to perform mutation operations on the offspring individuals that have passed the feasibility test. Combine the mutated offspring individuals with the parental individuals to form a combined population of size 200. Repeat the fast non-dominated sorting and crowding distance calculation for the combined population, and select 100 optimal individuals from the combined population based on the updated non-dominated ranks and crowding distance values to form a new generation population. Use the elitist retention strategy to directly retain the individual with the best non-dominated rank in the new generation population to the next generation population.

[0160] A multi-objective optimization design method for soil and water conservation based on non-dominated sorting genetic algorithm is used to solve the engineering problem of simultaneously optimizing construction costs and soil and water conservation effects. This method adopts the idea of evolutionary computation, simulates natural selection and genetic mechanisms, and searches for the best layout plan of soil and water conservation facilities on the premise of meeting engineering constraint conditions.

[0161] First, determine the geometric parameters of soil and water conservation facilities, such as length, width, height, etc., and position parameters, such as coordinates, etc. These parameters together constitute the decision variable space of individuals. Use the Latin hypercube sampling method to generate 100 initial individuals in the decision variable space to form an initial population. Each individual represents a possible layout plan of soil and water conservation facilities.

[0162] Next, perform feasibility tests on the initial population. According to the engineering site limit conditions and design specification requirements, establish a set of constraint conditions, such as facilities cannot exceed the site boundary, facilities cannot overlap with each other, etc. Check each individual, and if it does not meet the constraint conditions, adjust it through parameter repair operations, such as moving the facilities that exceed the boundary back inside the boundary until all constraint conditions are met. Finally, obtain an initial feasible population that meets the constraint conditions.

[0163] Then, calculate the objective function value for each individual in the initial feasible population. Using a parallel computing framework and a response surface surrogate model, calculate the construction cost and soil and water conservation effect of each individual respectively. For example, the construction cost can consider the volume of earthwork, material cost, etc., and the soil and water conservation effect can consider the reduction of soil erosion amount, the reduction of runoff, etc. Suppose the geometric parameter vector of an individual is [10, 5, 2], and the position parameter vector is [100, 200], and its calculated construction cost is 1000 yuan and the soil and water conservation effect is 90%.

[0164] Next, perform non-dominated sorting on the population. Using the fast non-dominated sorting algorithm, divide the population into non-dominated fronts at different levels. A non-dominated solution is a solution that is not inferior to other solutions in all objectives. For example, there are two solutions A and B. If the construction cost of A is lower than that of B and the soil and water conservation effect of A is higher than that of B, then A dominates B. All non-dominated solutions form the first non-dominated front, and then continue to find non-dominated solutions from the remaining solutions to form the second non-dominated front, and so on. At the same time, calculate the crowding distance value of individuals at the same non-dominated level, which is used to measure the density of solutions around an individual.

[0165] Then perform the selection operation. Set the crossover probability to 0.9, and use the tournament selection strategy to select dominant individuals from the non-dominated fronts as parental individuals. The tournament selection strategy means randomly selecting several individuals, comparing their non-dominated levels and crowding distance values, and selecting the optimal individual. For example, randomly select three individuals A, B, and C. A belongs to the first non-dominated front, and B and C belong to the second non-dominated front, then select A as the parental individual.

[0166] Next, perform the crossover operation. Use the simulated binary crossover operator to perform the crossover operation on the selected parental individuals to generate offspring individuals. The simulated binary crossover operator is an operator that simulates biological gene crossover and can generate new solutions. For example, the geometric parameter vector of parental individual A is [10, 5, 2], and the geometric parameter vector of parental individual B is [12, 6, 3]. The geometric parameter vector of the offspring individual C generated after crossover may be [11, 5.5, 2.5]. Conduct a feasibility test on the generated offspring individuals, and perform parameter repair operations on the offspring individuals that do not meet the constraint conditions. [[ID=SI]]

[0167] Then perform the mutation operation. Set the mutation probability to 0.1, and use the polynomial mutation operator to perform the mutation operation on the offspring individuals that have passed the feasibility test. The polynomial mutation operator is an operator that randomly changes the parameters of an individual and can increase the diversity of the population. For example, the geometric parameter vector of offspring individual C is [11, 5.5, 2.5], and after mutation, it may become [11.2, 5.4, 2.6].

[0168] The mutated offspring individuals and the parent individuals are combined to form a combined population of size 200. The fast non-dominated sorting and crowding distance calculation are repeatedly performed on the combined population. Based on the updated non-dominated ranks and crowding distance values, 100 optimal individuals are selected from the combined population to form a new generation population. The elitist retention strategy is adopted, and the individuals with the best non-dominated ranks in the new generation population are directly retained in the next generation population. The above steps are repeated until the preset number of evolutionary generations, such as 200 generations, is reached.

[0169] The solution of this application can:

[0170] Improve the design efficiency of soil and water conservation facilities: Through the automated optimization algorithm, the workload of manual design is reduced, and the design cycle is shortened. Achieve the collaborative optimization of soil and water conservation effects and construction costs: The solution with the lowest construction cost under the premise of meeting the requirements of soil and water conservation effects is found, achieving a win-win situation of economic and ecological benefits. Improve the scientificity and rationality of soil and water conservation facility design: By considering various factors and constraints, the best solution more in line with the actual situation is found, improving the reliability and effectiveness of the design.

[0171] In an optional implementation manner, the candidate optimized solutions for soil and water conservation measures are respectively imported into the 3D scene model of the power grid project; based on the computational fluid dynamics method, the surface runoff and soil erosion processes under different rainfall conditions are simulated, and the calculation of soil and water loss amount includes:

[0172] The basic digital elevation scene is constructed by the triangulated irregular network algorithm. According to the layout information of various facilities in the candidate optimized solutions for soil and water conservation measures, a 3D geometric model including intercepting and draining ditches, grit chambers, vegetation restoration zones and retaining walls is established. The 3D geometric model is fused with the basic digital elevation scene by Boolean operation to generate multiple terrain scene models containing soil and water conservation facilities;

[0173] For the terrain scene model, it is discretized by the unstructured grid division algorithm. Fine grids are set in the areas corresponding to the soil and water conservation facilities, and boundary layer grids are set on the surface layer to capture the near-wall flow characteristics, forming a discretized computational grid with local refinement characteristics; the meteorological data of the project area are collected, the rainstorm data of multiple return periods are obtained, and the rainstorm data are converted into a time-series rainfall process line by the rain type distribution method, and the rainfall process line is applied to the top boundary of the discretized computational grid to construct a boundary condition with a time-varying rainfall intensity;

[0174] Based on the discrete computational grid and the time-varying rainfall intensity boundary conditions, a surface runoff model is constructed using a multiphase flow model, and a soil erosion model is constructed using an Euler-Lagrange coupling method. By setting a time step and convergence criteria, the two models are coupled and solved to obtain the spatiotemporal evolution data of the surface runoff velocity field, water depth distribution, and soil erosion amount. Based on the spatiotemporal evolution data, the characteristic parameters of soil and water loss for each terrain scenario model are calculated, including the surface runoff coefficient, soil erosion modulus, facility interception efficiency, and the total amount of accumulated soil and water loss.

[0175] During power grid construction, excavation, landfilling, and other construction activities disturb surface vegetation and soil, easily causing soil erosion and adversely impacting the ecological environment. To effectively control soil erosion, it is necessary to design and optimize soil and water conservation measures based on the topography and climatic conditions along the power grid project. The following is a technical solution using computational fluid dynamics to simulate soil erosion under different rainfall conditions, thereby optimizing soil and water conservation measures.

[0176] First, obtain high-precision digital elevation model (DEM) data for the project area, such as one-meter resolution. Use a triangulated irregular network (TIN) algorithm to construct a basic three-dimensional digital elevation scene. This scene accurately reflects the topography of the project area.

[0177] Then, based on the designed soil and water conservation measures, such as installing intercepting drainage ditches and grit chambers in specific slopes and areas, planting vegetation on exposed surfaces, and constructing retaining walls in areas prone to landslides, 3D modeling software is used to construct 3D geometric models of these water conservation facilities. For example, the model of the intercepting drainage ditch can be set to a trapezoidal cross-section, with the width and depth determined according to the design plan. The model of the grit chamber can be set to a rectangular or cylindrical shape with the dimensions determined according to the design plan. Vegetation can be represented using simplified geometric shapes or voxel models, with the height and density set according to actual conditions. Retaining walls can be set to rectangular plate structures. These 3D geometric models are merged with the previously constructed basic digital elevation scene and overlaid using Boolean operations to generate multiple 3D terrain scene models containing different water conservation facility solutions.

[0178] Next, each 3D terrain scene model containing water conservation facilities is meshed. Using unstructured meshing algorithms, such as the Delaunay triangulation algorithm or the Voronoi polygon algorithm, the 3D terrain scene is discretized into a series of interconnected units. To more accurately simulate the movement of water flow and soil particles, meshes are refined in areas where water conservation facilities are located and near the surface layer. For example, near intercepting drains and sedimentation basins, the mesh size can be set to 0.1 meters, and a boundary layer mesh with a thickness of 0.01 meters is set in the surface layer to capture near-wall flow characteristics. This forms a discrete computational grid with localized refinement characteristics.

[0179] Collect meteorological data in the project area to obtain rainstorm data with different return periods (such as 2-year, 5-year, 10-year, 20-year), including rainfall amount, rainfall duration, and rainfall intensity, etc. Adopt a rain pattern distribution method, such as the Chicago rain pattern or Huff rain pattern, to convert the rainstorm data with different return periods into a time-series rainfall process line, such as rainfall intensity data per hour or per minute. Use these rainfall process lines as the boundary conditions of time-varying rainfall intensity and apply them to the top boundary of the discrete calculation grid to simulate different rainfall scenarios.

[0180] Based on the constructed discrete calculation grid and time-varying rainfall intensity boundary conditions, use the computational fluid dynamics method for numerical simulation. The surface runoff is simulated using a multiphase flow model, considering water and air as two different fluid phases and simulating their interactions. Soil erosion is simulated using the Euler-Lagrange coupling method, where the water flow is described by the Euler method and the soil particles are described by the Lagrange method to simulate the process of water flow carrying and transporting soil particles. By setting the time step (such as 0.1 seconds) and convergence criterion (such as the residual being less than 1e-5), the surface runoff model and soil erosion model are coupled and solved. In the solving process, numerical methods such as the finite volume method or finite element method can be used to discretize and solve the governing equations. Finally, the spatio-temporal evolution data of the surface runoff velocity field, water depth distribution, and soil erosion amount are obtained.

[0181] Finally, based on the simulated data, calculate the soil and water loss characteristic parameters for each terrain scenario model. For example, the surface runoff coefficient can be obtained by calculating the ratio of the surface runoff volume to the rainfall amount, the soil erosion modulus can be obtained by calculating the soil loss amount per unit area, the facility retention efficiency can be obtained by calculating the ratio of the sediment retained by the soil and water conservation facilities to the total sediment amount, and the cumulative total soil and water loss can be obtained by integrating the soil loss amount over the entire simulation period. By comparing these characteristic parameters under different soil and water conservation schemes, the effectiveness of different schemes can be evaluated, and the optimal soil and water conservation measure scheme can be selected.

[0182] The solution of this application can:

[0183] Precise prediction: This method can simulate the soil and water loss process under different rainfall scenarios based on high-precision terrain data and meteorological data, so as to precisely predict the effects of different soil and water conservation measure schemes. Optimization design: By comparing the simulation results of different schemes, the design of soil and water conservation measures can be optimized, such as adjusting the position and size of intercepting and draining ditches, the volume of sedimentation tanks, the type and density of vegetation, etc., so as to improve the effectiveness of soil and water conservation measures. Cost reduction: Through simulation analysis, different schemes can be evaluated before project construction, avoiding unnecessary investments and reducing project construction costs.

[0184] In an optional embodiment, the fuzzy comprehensive evaluation method is adopted to comprehensively consider the soil and water conservation rate, vegetation restoration rate, runoff reduction rate and project investment cost, and comprehensively score the candidate optimized soil and water conservation measure plans; the candidate optimized soil and water conservation measure plan with the highest comprehensive score is selected as the final optimized plan, and the three-dimensional construction layout diagram and technical parameter list of this plan are output, including:

[0185] The fuzzy comprehensive evaluation method is used to score the candidate optimized soil and water conservation measure plans, and the comprehensive evaluation of the candidate optimized soil and water conservation measure plans is realized by establishing a multi-dimensional evaluation index system. The multi-dimensional evaluation index system includes a soil and water conservation rate index, a vegetation restoration rate index, a runoff reduction rate index and a project investment cost index;

[0186] Automatic monitoring equipment is used to obtain the real-time data of each candidate plan, and the soil and water conservation rate index is calculated. The soil and water conservation rate index is determined by the ratio of the actual soil erosion modulus in the monitoring area to the regional background soil erosion modulus; the remote sensing image analysis method is used to obtain the vegetation coverage data, and the vegetation restoration rate index is calculated. The vegetation restoration rate index is determined by the ratio of the restored vegetation area to the total area of the vegetation recoverable area; hydrological monitoring equipment is used to obtain the runoff data, and the runoff reduction rate index is calculated. The runoff reduction rate index is determined by the ratio of the runoff reduction volume of the soil and water conservation facilities to the total regional runoff; the engineering quantity list method is used to calculate the project investment cost index. The project investment cost index is determined by the product of the engineering quantity of various soil and water conservation measures and the market unit price;

[0187] The indexes in the multi-dimensional evaluation index system are standardized. The soil and water conservation rate index, the vegetation restoration rate index and the runoff reduction rate index are converted into positive membership degree values by the maximum value standardization method, and the project investment cost index is converted into a negative membership degree value by the minimum value standardization method; a pairwise comparison judgment matrix of the multi-dimensional evaluation index system is constructed, and the eigenvalue and eigenvector of the judgment matrix are calculated based on the expert scoring results. The eigenvector is standardized to obtain the weight coefficient vector, and the rationality of the weight coefficient vector is confirmed through the consistency test; [[ID=X]] [[ID=Y]]

[0188] Perform a weighted summation operation on the membership degree values of the standardized indicators and the corresponding weight coefficients to obtain the comprehensive scores of each candidate solution. Sort the candidate solutions based on the comprehensive scores, and select the candidate solution with the highest comprehensive score as the optimal soil and water conservation solution; use 3D modeling software to construct the construction layout model of the optimal soil and water conservation solution, and generate engineering construction drawings based on the construction layout model, including the overall layout plan, sectional construction drawings, and node detail drawings of the soil and water conservation facilities; establish the technical parameter list of the optimal soil and water conservation solution.

[0189] The design and selection of the optimized soil and water conservation measures plan is a complex process that requires comprehensive consideration of multiple factors. Here, an optimized soil and water conservation measures plan based on the fuzzy comprehensive evaluation method is provided, along with detailed technical details and implementation steps.

[0190] First, it is necessary to determine the multi-dimensional indicator system for evaluating candidate solutions. This indicator system includes the soil and water conservation rate, vegetation restoration rate, runoff reduction rate, and project investment cost.

[0191] Next, it is necessary to obtain the data of each indicator. The soil and water conservation rate is determined by the ratio of the actual soil erosion modulus in the monitoring area to the regional background soil erosion modulus. Real-time data can be obtained using automatic monitoring equipment. For example, if the actual soil erosion modulus is 200 tons / km² / year and the regional background soil erosion modulus is 500 tons / km² / year, the soil and water conservation rate is 40%. The vegetation restoration rate is determined by the ratio of the restored vegetation area to the total area of the vegetation restoration area. Remote sensing image analysis methods can be used to obtain vegetation coverage data. For example, if the restored vegetation area is 10 hectares and the total area of the vegetation restoration area is 20 hectares, the vegetation restoration rate is 50%. The runoff reduction rate is determined by the ratio of the runoff reduction volume of the soil and water conservation facilities to the total runoff volume of the region. Runoff data can be obtained using hydrological monitoring equipment. For example, if the runoff reduction volume is 50 m³ / s and the total runoff volume of the region is 200 m³ / s, the runoff reduction rate is 25%. The project investment cost is determined by the product of the quantities of various soil and water conservation measures and the market unit price, and is calculated using the bill of quantities method. For example, if the quantity of a certain measure is 1000 m³ and the market unit price is 200 yuan / m³, the cost of this measure is 200,000 yuan.

[0192] Then, standardize each indicator. The soil and water conservation rate, vegetation restoration rate, and runoff reduction rate use the maximum value standardization method, that is, divide the actual value of the indicator by the maximum value of the indicator in all solutions. The project investment cost uses the minimum value standardization method, that is, divide the minimum value of the indicator by the actual value of the indicator. Assume that the soil and water conservation rates of four candidate solutions are 40%, 50%, 60%, and 70% respectively, then the standardized values are 0.57, 0.71, 0.86, and 1 respectively.

[0193] Next, construct a pairwise comparison judgment matrix for the index system, and calculate the eigenvalues and eigenvectors of the judgment matrix based on the expert scoring results. After standardizing the eigenvectors, obtain the weight coefficient vector and conduct a consistency test to confirm the rationality of the weight coefficient vector. Assume that the weight coefficient vector obtained after expert scoring is (0.3, 0.2, 0.4, 0.1), indicating that the weights of the soil and water conservation rate, vegetation restoration rate, runoff reduction rate, and engineering investment cost are 30%, 20%, 40%, and 10% respectively.

[0194] Perform a weighted summation operation on the membership degree values of each standardized index and the corresponding weight coefficients to obtain the comprehensive scores of each candidate solution.

[0195] Rank the candidate solutions based on the comprehensive scores, and select the candidate solution with the highest comprehensive score as the optimal solution.

[0196] Finally, use 3D modeling software to construct a construction layout model of the optimal solution, generate engineering construction drawings, including the overall layout plan, sectional construction drawings, and node detail drawings of the soil and water conservation facilities, and establish a technical parameter list of the optimal solution.

[0197] The solution of this application can:

[0198] Improve decision-making efficiency: This method provides a quantitative evaluation method, avoiding subjective judgment and improving decision-making efficiency. Optimize solution selection: By comprehensively considering multiple indicators, a better soil and water conservation solution can be selected to achieve the optimal allocation of resources. Facilitate project implementation: The detailed construction layout drawings and technical parameter list provide guidance for project implementation to ensure the smooth progress of the project.

[0199] Figure 2 This is a schematic structural diagram of a power grid soil and water conservation system based on 3D simulation according to an embodiment of the present invention, as Figure 2 shown, the system includes:

[0200] The first unit is used to register soil parameters, vegetation cover, and hydrological and meteorological data and generate a base layer through UV texture coordinate projection, predict the dynamic distribution of vegetation growth using soil organic matter content and water holding capacity, calculate the soil water migration process based on rainfall data and soil physical parameters to obtain the water content distribution, then input the terrain slope, soil water content distribution, and vegetation cover dynamic distribution into a prediction model to calculate the soil and water loss results, establish a risk assessment system based on the soil and water loss results and determine the weights through analytic hierarchy process, calculate the risk level using fuzzy membership degree, and map it to the surface of the 3D model through multi-scale rendering, and finally generate a 3D scene model of the power grid project containing soil and water loss risk distribution information;

[0201] A second unit, configured to establish a layout plan of soil and water conservation facilities including intercepting and draining ditches, grit chambers, vegetation restoration zones and retaining walls based on the three-dimensional scene model of the power grid project; adopt a multi-objective optimization algorithm, with the minimum construction cost of the soil and water conservation facilities and the optimal soil and water conservation effect as the objective functions, perform multi-objective optimization calculations on the position parameters, specification parameters and quantity parameters of the soil and water conservation facilities, and obtain multiple candidate optimized soil and water conservation measure plans;

[0202] A third unit, configured to import the candidate optimized soil and water conservation measure plans into the three-dimensional scene model of the power grid project respectively; based on the computational fluid dynamics method, simulate the surface runoff and soil erosion processes under different rainfall conditions, and calculate the soil and water loss amount; adopt the fuzzy comprehensive evaluation method, comprehensively consider the soil and water conservation rate, vegetation restoration rate, runoff reduction rate and project investment cost, and conduct a comprehensive score on the candidate optimized soil and water conservation measure plans; select the candidate optimized soil and water conservation measure plan with the highest comprehensive score as the final optimized plan, and output the three-dimensional construction layout drawing and technical parameter list of the final optimized plan.

[0203] In the third aspect of the embodiments of the present invention,

[0204] A kind of electronic device is provided, including:

[0205] A processor;

[0206] A memory for storing instructions executable by the processor;

[0207] Wherein, the processor is configured to call the instructions stored in the memory to execute the method described above.

[0208] In the fourth aspect of the embodiments of the present invention,

[0209] A computer-readable storage medium is provided, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the method described above is implemented.

[0210] The present invention can be a method, a device, a system and / or a computer program product. The computer program product can include a computer-readable storage medium, on which computer-readable program instructions for executing various aspects of the present invention are carried.

[0211] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A three-dimensional simulation-based method for soil and water conservation in power grids, characterized in that: include: Soil parameters, vegetation cover, and hydrometeorological data were aligned and UV texture coordinate projection was used to generate a base layer. Soil organic matter content and water holding capacity were used to predict the dynamic distribution of vegetation growth. Soil moisture migration was calculated based on rainfall data and soil physical parameters to obtain the moisture distribution. The terrain slope, soil moisture distribution, and dynamic vegetation cover distribution were then input into a prediction model to calculate soil and water loss results. A risk assessment system was established based on the soil and water loss results, and weights were determined through hierarchical analysis. The risk level was calculated using fuzzy membership and then mapped to the 3D model surface using multi-scale rendering. Ultimately, a 3D scenario model of the power grid project was generated, containing soil and water loss risk distribution information. Based on the three-dimensional scenario model of the power grid project, a layout plan for soil and water conservation facilities including intercepting drainage ditches, grit chambers, vegetation restoration belts, and retaining walls is established. A multi-objective optimization algorithm is used to perform multi-objective optimization calculations on the location parameters, specification parameters, and quantity parameters of the soil and water conservation facilities, with the objective functions of minimizing the construction cost of the soil and water conservation facilities and optimizing the soil and water conservation effect, to obtain multiple candidate optimization plans for soil and water conservation measures. The candidate soil and water conservation optimization schemes are respectively imported into the three-dimensional scenario model of the power grid project; based on the computational fluid dynamics method, the surface runoff and soil erosion process under different rainfall conditions are simulated to calculate the amount of soil and water loss; and a fuzzy comprehensive evaluation method is used to comprehensively consider the soil and water conservation rate, vegetation restoration rate, runoff reduction rate and project investment cost to comprehensively score the candidate soil and water conservation optimization schemes; The candidate soil and water conservation measure optimization plan with the highest comprehensive score is selected as the final optimization plan, and the three-dimensional construction layout drawing and technical parameter list of the final optimization plan are output.

2. The method according to claim 1, characterized in that Soil parameters, vegetation cover, and hydrometeorological data are aligned and UV texture coordinate projection is used to generate a base layer. Soil organic matter content and water holding capacity are used to predict the dynamic distribution of vegetation growth. Meanwhile, the soil moisture movement process is calculated based on rainfall data and soil physical parameters to obtain the moisture distribution. Then, the terrain slope, soil moisture distribution, and dynamic vegetation cover distribution are input into the prediction model to calculate soil and water loss results. Based on this result, a risk assessment system is established and the weights are determined through hierarchical analysis. The risk level is calculated using fuzzy membership and then mapped to the 3D model surface through multi-scale rendering. Finally, a 3D scenario model of the power grid project containing soil and water loss risk distribution information is generated, including: Converting the soil parameter data, vegetation cover data, and hydrological and meteorological data into a coordinate system of a three-dimensional terrain model to generate registered soil parameter data, registered vegetation cover data, and registered hydrological and meteorological data; Establishing a UV texture coordinate system on the surface of the three-dimensional terrain model, and mapping the registered soil parameter data, the registered vegetation cover data, and the registered hydrological and meteorological data to the UV texture coordinate system using an equi-angle projection method, thereby generating a soil parameter texture layer, a vegetation cover texture layer, and a hydrological element texture layer; Establishing a vegetation growth prediction model based on the soil organic matter content and water holding capacity in the soil parameter texture layer, and associating the output result of the vegetation growth prediction model with the vegetation cover texture layer to generate a vegetation cover dynamic distribution layer; Based on the rainfall data in the hydrological element texture layer and the soil physical parameters in the soil parameter texture layer, the Green-Ampt infiltration model is used to calculate the soil water migration process and generate a soil moisture distribution layer; the slope data of the three-dimensional terrain model, the soil moisture distribution layer, and the vegetation cover dynamic distribution layer are input into a distributed soil and water loss prediction model to calculate and obtain a soil and water loss prediction result; Based on the soil and water loss prediction results, a soil and water loss risk assessment index system is established, the weights of terrain factors, soil factors, vegetation factors and rainfall factors are determined by the hierarchical analysis method, and the soil and water loss risk level distribution layer is calculated by the fuzzy membership function; the soil and water loss risk level distribution layer is mapped to the surface of the three-dimensional terrain model using multi-scale rendering technology, and a three-dimensional scene model of the power grid project with soil and water loss risk distribution information is generated.

3. The method according to claim 1, characterized in that Based on the three-dimensional scenario model of the power grid project, a layout plan for soil and water conservation facilities, including intercepting drainage ditches, sedimentation basins, vegetation restoration belts, and retaining walls, was established. A multi-objective optimization algorithm was used to perform multi-objective optimization calculations on the location parameters, specification parameters, and quantity parameters of the soil and water conservation facilities, with the objective functions of minimizing the construction cost of the soil and water conservation facilities and optimizing the soil and water conservation effect. Multiple candidate optimization plans for soil and water conservation measures were obtained, including: Determine the layout area of intercepting drainage ditches based on the terrain slope data of the three-dimensional scene model of the power grid project, determine the layout area of grit chambers based on the catchment area data of the three-dimensional scene model of the power grid project, and determine the layout area of vegetation restoration belts and retaining walls based on the slope position data of the three-dimensional scene model of the power grid project; The D8 algorithm is used to calculate the surface runoff path, and the initial layout positions of intercepting and draining ditches are set on the runoff path. The initial layout positions of the intercepting and draining ditches include the positions of trapezoidal cross-section intercepting ditches set in areas with slopes greater than 15 degrees and the positions of rectangular cross-section draining ditches set in areas with slopes less than 15 degrees. The initial layout position of the grit chamber is set at the outlet corresponding to the catchment area data; the initial layout positions of the vegetation restoration belt and the initial layout positions of the retaining wall are set in the area corresponding to the slope position data. Establish specification parameter constraints for soil and water conservation facilities. Determine the cross-sectional dimension parameter constraints of the intercepting drainage ditch based on the Manning formula. Determine the volume parameter constraints of the sedimentation basin based on the 24-hour rainstorm sediment yield. Determine the width parameter constraints of the vegetation restoration zone based on the slope gradient. Determine the height parameter constraints of the retaining wall based on slope stability analysis. A multi-objective optimization model for soil and water conservation facilities is constructed, with the location parameters, specification parameters, and quantity parameters of the intercepting drainage ditch, the sedimentation basin, the vegetation restoration zone, and the retaining wall as decision variables, and the objective function being to minimize the construction cost of the soil and water conservation facilities and optimize the soil and water conservation effect; the construction cost includes the sub-project costs of earth excavation, concrete pouring, and vegetation restoration; and the soil and water conservation effect includes the runoff interception rate, sediment interception rate, and slope protection rate. A non-dominated sorting genetic algorithm was used to solve the multi-objective optimization model, with the population size set to 100, the number of evolutionary generations set to 200, the crossover probability set to 0.9, and the mutation probability set to 0.

1. A tournament selection strategy was used to select dominant individuals, and a simulated binary crossover operator and a polynomial mutation operator were used to generate new solutions. An elite retention strategy was introduced to maintain the optimal solution. Soil and water loss sensitive areas are divided according to the terrain characteristics of the three-dimensional scenario model of the power grid project, and the value range of the decision variable is limited to the soil and water loss sensitive areas; associated constraints are established between facilities, including the connection constraints between the intercepting drainage ditch and the sand settling tank, and the coordination constraints between the vegetation restoration zone and the retaining wall, to obtain multiple candidate optimization schemes for soil and water conservation measures.

4. The method according to claim 3, characterized in that A multi-objective optimization model for soil and water conservation facilities is constructed, with the location parameters, specification parameters, and quantity parameters of the intercepting drainage ditch, the grit chamber, the vegetation restoration zone, and the retaining wall as decision variables, and the minimum construction cost of the soil and water conservation facilities and the optimal soil and water conservation effect as objective functions, including: Establish a soil and water conservation facility optimization model, with minimizing the construction cost of soil and water conservation facilities and optimizing soil and water conservation effects as the objective function. The decision variables include the starting and ending coordinates, cross-sectional shape, ditch depth, bottom and top widths of the intercepting drainage ditch; the center coordinates, length, width, and depth of the sedimentation basin; the four corner coordinates, planting density, and vegetation type of the vegetation restoration zone; the starting and ending coordinates, wall height, top and bottom widths of the retaining wall; and the number of various facilities. Constraints are established based on project site restrictions, design specification requirements, investment limits, and protection standards. Based on the decision variables and the construction cost of soil and water conservation facilities, the earth excavation cost is calculated by multiplying the earth excavation volume by the transportation distance, the concrete pouring cost is calculated based on the volume of the concrete structure, and the vegetation restoration cost is calculated based on the area of the vegetation restoration area. The minimum sum of the sub-item construction costs of the earth excavation cost, the concrete pouring cost, and the vegetation restoration cost is used as the construction cost objective function, while taking into account the decision variables and the soil and water conservation effect; Among them, the objective function corresponding to the construction cost of soil and water conservation facilities is as follows: ； Among them, C is the total construction cost, C1 is the earth excavation cost, C2 is the concrete pouring cost, and C3 is the vegetation restoration cost; ； Among them, α1 is the unit cost coefficient of earthwork excavation, V is the earthwork excavation volume, and L is the average transportation distance; ； Among them, α2 is the unit cost coefficient of concrete pouring, V c is the volume of concrete structure; ； Among them, α3 is the unit cost coefficient of vegetation restoration, and A is the area of vegetation restoration; A hydraulic model is used to calculate the runoff retention rate of the intercepting drainage ditch and the grit chamber under the designed rainfall conditions. The sediment interception rate of the grit chamber is calculated based on the law of sediment movement. The slope protection rate is calculated based on the vegetation coverage of the vegetation restoration zone and the slope stability of the retaining wall. The maximum weighted sum of the runoff interception rate, the sediment interception rate, and the slope protection rate is used as the soil and water conservation effect objective function. Among them, the objective function corresponding to the soil and water conservation effect is as follows: ； Among them, η is the comprehensive soil and water conservation effect, η1 is the runoff interception rate, η2 is the sediment interception rate, η3 is the slope protection rate, and w1, w2, and w3 are the weight coefficients of each indicator; ； Among them, Q is the flow capacity of the intercepting ditch, and Q0 is the flow rate under the design rainfall conditions; ； Where n is the Manning roughness coefficient, B is the cross-sectional area of the water flow, R is the hydraulic radius, and I is the hydraulic slope; ； Among them, A1 is the inlet sediment concentration, and A2 is the outlet sediment concentration; ； Where T is the sediment particle settling velocity, ρ s is the density of sediment, ρ is the density of water, g is the acceleration of gravity, d is the particle size of sediment, and μ is the dynamic viscosity of water; ； Among them, C c is the vegetation coverage, F s is the slope stability safety factor, k1 and k2 are weight coefficients; ； Where c' is the effective cohesion, W is the weight of the sliding soil, α is the inclination angle of the sliding surface, φ' is the effective internal friction angle, and U is the length of the sliding surface.

5. The method according to claim 3, characterized in that A non-dominated sorting genetic algorithm is used to solve the multi-objective optimization model. The population size is set to 100, the number of evolution generations is set to 200, the crossover probability is set to 0.9, and the mutation probability is set to 0.

1. The dominant individuals are selected through a tournament selection strategy, and new solutions are generated using a simulated binary crossover operator and a polynomial mutation operator. An elite retention strategy is introduced to maintain the optimal solution, including: A non-dominated sorting genetic algorithm is used to solve the multi-objective optimization model, with the population size set to 100 individuals. Each individual is composed of a geometric parameter vector and a location parameter vector of a soil and water conservation facility, and the geometric parameter vector and the location parameter vector constitute the individual's decision variable space. A Latin hypercube sampling method is used to generate 100 initial individuals in the decision variable space to form an initial population. A constraint condition set is established based on the project site restrictions and design specification requirements. The constraint condition set is used to perform a feasibility test on each individual in the initial population. Individuals that do not meet the constraints are adjusted to within the feasible solution space through parameter repair operations, thereby obtaining an initial feasible population that meets the constraints. A parallel computing framework and a response surface agent model are used to calculate the construction cost objective function value and the soil and water conservation effect objective function value of each individual in the initial feasible population. A fast non-dominated sorting algorithm is used to divide the initial feasible population into non-dominated frontier surfaces of different levels, and the crowding distance value of individuals in the same non-dominated level is calculated. The crossover probability is set to 0.9, and a tournament selection strategy is used to select dominant individuals from the non-dominated frontier as parent individuals. The dominant individuals are selected based on the comparison result of the non-dominated level and the crowding distance value. A simulated binary crossover operator is used to perform a crossover operation on the selected parent individuals to generate offspring individuals. The feasibility of the offspring individuals is tested using the constraint condition set, and the offspring individuals that do not meet the constraint conditions are adjusted to the feasible solution space through a parameter repair operation. The mutation probability is set to 0.1, and a polynomial mutation operator is used to perform a mutation operation on the offspring individuals that have passed the feasibility test. The mutated offspring individuals are merged with the parent individuals to form a merged population with a size of 200. The fast non-dominated sorting and crowding distance calculation are repeatedly performed on the merged population. Based on the updated non-dominated level and crowding distance values, the 100 best individuals are selected from the merged population to form a new generation population. The elite retention strategy is used to directly retain the individuals with the best non-dominated level in the new generation population to the next generation population.

6. The method according to claim 1, characterized in that Importing the candidate soil and water conservation measure optimization schemes into the three-dimensional scene model of the power grid project respectively; Based on computational fluid dynamics methods, surface runoff and soil erosion processes under different rainfall conditions are simulated, and the amount of soil and water loss is calculated, including: A basic digital elevation scene is constructed using an irregular triangulated network algorithm. Based on the layout information of various facilities in the candidate soil and water conservation measure optimization plan, a three-dimensional geometric model including intercepting drainage ditches, grit chambers, vegetation restoration belts, and retaining walls is established. The three-dimensional geometric model is then integrated with the basic digital elevation scene using Boolean operations to generate multiple terrain scene models containing water conservation facilities. The terrain scenario model is discretized using an unstructured gridding algorithm. A fine grid is set in the area corresponding to the soil and water conservation facilities, and a boundary layer grid is set in the surface layer to capture the near-wall flow characteristics, forming a discrete computational grid with local encryption characteristics. Meteorological data is collected from the project area, and rainstorm data with multiple return periods is obtained. The rainstorm data is converted into a time-series rainfall process line using a rain pattern distribution method. The rainfall process line is applied to the top boundary of the discrete computational grid to construct a boundary condition for time-varying rainfall intensity. Based on the discrete computational grid and the time-varying rainfall intensity boundary conditions, a surface runoff model is constructed using a multiphase flow model, and a soil erosion model is constructed using an Euler-Lagrange coupling method. By setting a time step and convergence criteria, the two models are coupled and solved to obtain the spatiotemporal evolution data of the surface runoff velocity field, water depth distribution, and soil erosion amount. Based on the spatiotemporal evolution data, the characteristic parameters of soil and water loss for each terrain scenario model are calculated, including the surface runoff coefficient, soil erosion modulus, facility interception efficiency, and the total amount of accumulated soil and water loss.

7. A three-dimensional simulation-based power grid soil and water conservation system, used to implement the method according to any one of claims 1 to 6, characterized in that: include: The first unit is used to align soil parameters, vegetation cover, and hydrometeorological data and generate a base layer through UV texture coordinate projection. The dynamic distribution of vegetation growth is predicted using soil organic matter content and water holding capacity. The soil moisture movement process is calculated based on rainfall data and soil physical parameters to obtain the moisture content distribution. The terrain slope, soil moisture content distribution, and dynamic distribution of vegetation cover are then input into the prediction model to calculate soil and water loss results. Based on the soil and water loss results, a risk assessment system is established and weights are determined through hierarchical analysis. The risk level is calculated using fuzzy membership and mapped to the 3D model surface through multi-scale rendering. Finally, a 3D scenario model of the power grid project is generated that includes soil and water loss risk distribution information. The second unit is used to establish a layout plan for soil and water conservation facilities including intercepting drainage ditches, grit chambers, vegetation restoration belts, and retaining walls based on the three-dimensional scenario model of the power grid project; a multi-objective optimization algorithm is used to perform multi-objective optimization calculations on the location parameters, specification parameters, and quantity parameters of the soil and water conservation facilities, with the objective functions of minimizing the construction cost of the soil and water conservation facilities and optimizing the soil and water conservation effect, to obtain multiple candidate optimization plans for soil and water conservation measures; The third unit is used to import the candidate soil and water conservation optimization schemes into the three-dimensional scenario model of the power grid project; based on the computational fluid dynamics method, simulate the surface runoff and soil erosion process under different rainfall conditions to calculate the amount of soil and water loss; and use a fuzzy comprehensive evaluation method to comprehensively consider the soil and water conservation rate, vegetation restoration rate, runoff reduction rate and project investment cost to comprehensively score the candidate soil and water conservation optimization schemes; The candidate soil and water conservation measure optimization plan with the highest comprehensive score is selected as the final optimization plan, and the three-dimensional construction layout drawing and technical parameter list of the final optimization plan are output.

8. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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