Power grid water and soil conservation method and system based on three-dimensional simulation

Abstract

The invention provides a power grid water and soil conservation method and system based on three-dimensional simulation, and relates to the technical field of power grid water and soil, and the method comprises the steps: building a power grid project three-dimensional scene model based on terrain, soil, vegetation and hydro meteorological data, and simulating water and soil loss risk distribution. Then, a water and soil conservation facility layout scheme including intercepting and draining ditches, a grit chamber, a vegetation recovery zone, a retaining wall and the like is established; a multi-objective optimization algorithm is adopted, the minimum construction cost and the optimal water and soil conservation effect are taken as objectives, the position, specification and number of facilities are optimized, and a plurality of candidate schemes are obtained. And finally, simulating water and soil loss processes under different rainfall working conditions based on computational fluid mechanics, selecting a scheme with the highest comprehensive score as a final optimization scheme in combination with a fuzzy comprehensive evaluation method, and outputting a three-dimensional construction layout diagram and a technical parameter list. The water and soil loss risk in power grid engineering construction and operation can be effectively reduced, the engineering cost is saved, and the water and soil conservation effect is improved.

Description

Technical Field

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

[0002] In the traditional power grid construction process, the design and implementation of soil and water conservation plans often rely on experience and two-dimensional drawings, lacking in refined simulation and analysis of complex terrain, landforms and hydrological conditions. This leads to a lack of scientific basis for the design of soil and water conservation measures, making it difficult to effectively control soil erosion, and may even cause geological disasters, affecting the safety and stable operation of power grid projects. At the same time, traditional scheme 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 scheme design mainly relies on experience and two-dimensional drawings, which makes it difficult to accurately simulate the soil and water loss process under complex terrain conditions, resulting in inaccurate layout and parameter selection of soil and water conservation measures, making it difficult to achieve the best prevention and control effect. Most of the existing soil and water conservation scheme evaluation methods are based on simple indicator calculations or expert judgments, lacking comprehensive comparison and optimization of different schemes, making it difficult to select the best scheme. Traditional soil and water conservation schemes are difficult to intuitively display the layout and effects of soil and water conservation measures, which is not convenient for engineering construction and subsequent maintenance management. Summary of the invention

[0004] The embodiment of the present invention provides a three-dimensional simulation-based soil and water conservation method and system for a power grid, which can solve the problems in the prior art.

[0005] According to a first aspect of the embodiments of the present invention,

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

[0007] Soil parameters, vegetation coverage and hydrological and meteorological data are aligned and UV texture coordinate projection is used to generate a basic layer. The dynamic distribution of vegetation growth is predicted using soil organic matter content and water holding capacity. At the same time, the soil moisture migration 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 distribution of vegetation coverage are input into the prediction model to calculate the results of soil and water loss. Based on the results of soil and water loss, a risk assessment system is established and the 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 scene model of the power grid project containing soil and water loss risk distribution information is generated.

[0008] Based on the three-dimensional scene model of the power grid project, a layout plan of soil and water conservation facilities including intercepting drainage ditches, sedimentation ponds, vegetation restoration belts and retaining walls is established; a multi-objective optimization algorithm is used, with the minimum construction cost of soil and water conservation facilities and the optimal soil and water conservation effect as the objective function, and the location parameters, specification parameters and quantity parameters of the soil and water conservation facilities are multi-objective optimized and calculated to obtain multiple candidate soil and water conservation measures optimization plans;

[0009] The candidate optimization schemes 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 process under different rainfall conditions are simulated to calculate the amount of soil and water loss; a 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 the candidate optimization schemes for soil and water conservation measures are comprehensively scored; the candidate optimization scheme for soil and water conservation measures with the highest comprehensive score is selected as the final optimization scheme, and the three-dimensional construction layout diagram and technical parameter list of the final optimization scheme are output.

[0010] Soil parameters, vegetation coverage and hydrological and meteorological data are aligned and UV texture coordinate projection is used to generate a basic layer. The dynamic distribution of vegetation growth is predicted using soil organic matter content and water holding capacity. At the same time, the soil moisture migration 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 distribution of vegetation coverage are input into the prediction model to calculate the results of soil and water loss. Based on the results, a risk assessment system is established and the 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 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 converted 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;

[0012] Establishing a UV texture coordinate system on the surface of the three-dimensional terrain model, and using an equi-angle projection method to map the registered soil parameter data, the registered vegetation coverage data, and the registered hydrological and meteorological data to the UV texture coordinate system, respectively, to generate a soil parameter texture layer, a vegetation coverage texture layer, and a hydrological element texture layer;

[0013] Establishing a vegetation growth prediction model according to the soil organic matter content and water holding capacity in the soil parameter texture layer, associating the output result of the vegetation growth prediction model with the vegetation coverage texture layer, and generating a vegetation coverage dynamic distribution layer;

[0014] 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 moisture 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 coverage 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;

[0015] A soil and water loss risk assessment index system is established based on the soil and water loss prediction results, 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 by 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.

[0016] Based on the three-dimensional scene model of the power grid project, a layout plan of soil and water conservation facilities including intercepting drainage ditches, sedimentation basins, vegetation restoration belts and retaining walls is established; a multi-objective optimization algorithm is adopted, with the minimum construction cost of soil and water conservation facilities and the optimal soil and water conservation effect as the objective function, and the location parameters, specification parameters and quantity parameters of the soil and water conservation facilities are multi-objective optimized and calculated, and multiple candidate optimization plans for soil and water conservation measures are obtained, including:

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

[0018] The D8 algorithm is used to calculate the surface runoff path, and the initial layout position of the intercepting drainage ditch is set on the runoff path. The initial layout position of the intercepting drainage ditch includes the position of the trapezoidal cross-section intercepting ditch set in the area with a slope greater than 15 degrees and the position of the rectangular cross-section drainage ditch set in the area with a slope less than 15 degrees; the initial layout position of the sand settling tank is set at the outlet corresponding to the catchment area data; the initial layout position of the vegetation restoration belt and the initial layout position of the retaining wall are set in the area corresponding to the slope position data;

[0019] 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, and determine the height parameter constraints of the retaining wall based on the slope stability analysis;

[0020] A multi-objective optimization model for soil and water conservation facilities is constructed, and the location parameters, specification parameters and quantity parameters of the intercepting drainage ditch, the sedimentation tank, the vegetation restoration belt and the retaining wall are used as decision variables, and the minimum construction cost of the soil and water conservation facilities and the optimal soil and water conservation effect are used as 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;

[0021] A non-dominated sorting genetic algorithm is used to solve the multi-objective optimization model, with the population size set to 100, the evolutionary generations set to 200, the crossover probability set to 0.9, and the mutation probability set to 0.1; dominant individuals are selected through a tournament selection strategy, a simulated binary crossover operator and a polynomial mutation operator are used to generate new solutions, and an elite retention strategy is introduced to maintain the optimal solution;

[0022] According to the terrain characteristics of the three-dimensional scenario model of the power grid project, the soil and water loss sensitive areas are divided, and the value range of the decision variables is limited to the soil and water loss sensitive areas; the associated constraints between the facilities are established, including the connection constraints between the intercepting drainage ditch and the sedimentation tank, and the coordinated constraints between the vegetation restoration zone and the retaining wall, so as to obtain multiple candidate optimization schemes for soil and water conservation measures.

[0023] A multi-objective optimization model for soil and water conservation facilities is constructed, and the location parameters, specification parameters and quantity parameters of the intercepting drainage ditch, the sedimentation tank, the vegetation restoration belt and the retaining wall are used as decision variables. The objective function is to minimize the construction cost of soil and water conservation facilities and optimize the soil and water conservation effect. The model includes:

[0024] Establish a soil and water conservation facility optimization model, with the minimum construction cost of soil and water conservation facilities and the optimal soil and water conservation effect as the objective function, and take the starting and ending coordinates, cross-sectional shape, ditch depth, bottom width and top width of the intercepting drainage ditch, the center point coordinates, pool length, pool width and pool depth of the sedimentation tank, the four corners coordinates of the vegetation restoration zone, planting density and vegetation type, the starting and ending coordinates, wall height, top width and bottom width of the retaining wall and the number of various facilities as decision variables, and establish constraint conditions based on project site restrictions, design specification requirements, investment limits and protection standards;

[0025] 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 and the transportation distance, the concrete pouring cost is calculated according to the volume of the concrete structure, and the vegetation restoration cost is calculated according to the area of ​​the vegetation restoration area. The minimum sum of the sub-project costs of the earth 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;

[0026] Among them, the objective function corresponding to the construction cost of 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 cost coefficient of earthwork excavation, V is the earthwork excavation volume, and L is the average transportation distance;

[0031] ;

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

[0033] ;

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

[0035] The hydraulic model is used to calculate the runoff interception rate of the intercepting drainage ditch and the sedimentation tank under the designed rainfall conditions, the sediment interception rate of the sedimentation tank is calculated based on the law of sediment movement, the slope protection rate is calculated according to the vegetation coverage of the vegetation restoration zone and the slope stability of the retaining wall, and the weighted sum maximum value 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;

[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 indicator;

[0039] ;

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

[0041] ;

[0042] 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 slope;

[0043] ;

[0044] Among them, A1 is the inlet sediment concentration, and A2 is the outlet sediment concentration;

[0045] ;

[0046] Among them, T is the sediment particle settling velocity, ρs is the sediment density, ρ is the water density, g is the gravitational acceleration, 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, k1 and k2 are weight coefficients;

[0049] ;

[0050] Among them, 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.

[0051] The non-dominated sorting genetic algorithm is used to solve the multi-objective optimization model. The population size is set to 100, the evolutionary generation 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, and the simulated binary crossover operator and polynomial mutation operator are used to generate new solutions. The elite retention strategy is introduced to maintain the optimal solution, including:

[0052] A non-dominated sorting genetic algorithm is used to solve the multi-objective optimization model, and the population size is set to 100 individuals, each individual is composed of a geometric parameter vector and a position parameter vector of a soil and water conservation facility, and the geometric parameter vector and the position parameter vector constitute the decision variable space of the individual; 100 initial individuals are generated in the decision variable space by using the Latin hypercube sampling method to form an initial population, a constraint condition set is established according to the engineering site restrictions and design specification requirements, and the constraint condition set is used to perform a feasibility test on each individual of the initial population, and the individuals that do not meet the constraint conditions are adjusted to the feasible solution space through parameter repair operations, so as to obtain an initial feasible population that meets the constraint conditions;

[0053] The construction cost objective function value and the soil and water conservation effect objective function value of each individual in the initial feasible population are calculated by using a parallel computing framework and a response surface agent model, the initial feasible population is divided into non-dominated frontier surfaces of different levels by using a fast non-dominated sorting algorithm, and the crowding distance value of individuals of the same non-dominated level is calculated;

[0054] The crossover probability is set to 0.9, and a tournament selection strategy is adopted to select a dominant individual from the non-dominated frontier as a parent individual. The selection of the dominant individual is 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 individual to generate a child individual. The feasibility of the child individual is tested using the constraint condition set, and the child individual that does not meet the constraint condition is adjusted to a feasible solution space through a parameter repair operation;

[0055] 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, and the 100 best individuals are selected from the merged population based on the updated non-dominated level and crowding distance values ​​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.

[0056] The candidate water and soil conservation measures optimization schemes are respectively introduced into the three-dimensional scene 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, and the amount of water and soil loss is calculated, including:

[0057] A basic digital elevation scene is constructed by using an irregular triangulated network algorithm, and a three-dimensional geometric model including intercepting drainage ditches, sand settling ponds, vegetation restoration belts and retaining walls is established according to the layout information of various facilities in the candidate soil and water conservation measures optimization plan. The three-dimensional geometric model is merged with the basic digital elevation scene by using Boolean operations to generate multiple terrain scene models containing water conservation facilities;

[0058] For the terrain scene model, an unstructured grid division algorithm is used to discretize it, 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 calculation grid with local encryption characteristics; meteorological data of the project area are collected, and heavy rain data with multiple return periods are obtained. The rain type distribution method is used to convert the heavy rain data into a time-series rainfall process line, and the rainfall process line is applied to the top boundary of the discrete calculation grid to construct the boundary condition of time-varying rainfall intensity;

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

[0060] According to a second aspect of the embodiments of the present invention,

[0061] Provided is a power grid soil and water conservation system based on three-dimensional simulation, including:

[0062] The first unit is used to align soil parameters, vegetation coverage and hydrological and meteorological data and generate a basic layer through UV texture coordinate projection. The dynamic distribution of vegetation growth is predicted using soil organic matter content and water holding capacity. At the same time, the soil moisture migration 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 distribution of vegetation coverage are input into the prediction model to calculate the results of soil and water loss. Based on the results of soil and water loss, a risk assessment system is established and the weight is determined through hierarchical analysis. The risk level is calculated using fuzzy membership and mapped to the three-dimensional model surface 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;

[0063] The second unit is used to establish a layout plan of soil and water conservation facilities including intercepting drainage ditches, sand settling ponds, vegetation restoration belts 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 soil and water conservation facilities and the optimal soil and water conservation effect as the objective function, to perform multi-objective optimization calculations on the location parameters, specification parameters and quantity parameters of the soil and water conservation facilities, and obtain multiple candidate soil and water conservation measures optimization plans;

[0064] The third unit is used to import the candidate soil and water conservation measures optimization schemes 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 process under different rainfall conditions, and calculate the amount of soil and water loss; adopt a fuzzy comprehensive evaluation method, comprehensively consider the soil and water conservation rate, vegetation restoration rate, runoff reduction rate and project investment cost, and comprehensively score the candidate soil and water conservation measures optimization schemes; select the candidate soil and water conservation measures optimization scheme with the highest comprehensive score as the final optimization scheme, and output the three-dimensional construction layout diagram and technical parameter list of the final optimization scheme.

[0065] According to a third aspect of the embodiments of the present invention,

[0066] An electronic device is provided, comprising:

[0067] processor;

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

[0069] The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0070] According to a fourth aspect of the embodiments of the present invention,

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

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

[0073] 1. Visualization of soil and water loss risk: Through 3D simulation technology, the soil and water loss risk in the power grid project area is presented in a visual way, which facilitates an intuitive understanding of the risk distribution and provides a scientific basis for the formulation of soil and water conservation plans.

[0074] 2. Optimize soil and water conservation plans: Utilize multi-objective optimization algorithms, comprehensively consider construction costs and soil and water conservation effects, optimize the layout, specifications, and quantity of soil and water conservation facilities, and achieve a win-win situation in economic and ecological benefits.

[0075] 3. Improve the effectiveness of soil and water conservation measures: Use computational fluid dynamics to simulate the soil and water loss process under different rainfall conditions, and use fuzzy comprehensive evaluation methods to evaluate candidate solutions, and ultimately select the optimal solution to ensure the effectiveness and reliability of soil and water conservation measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 A schematic diagram of a flow chart of a method for soil and water conservation in a power grid based on three-dimensional simulation according to an embodiment of the present invention;

[0077] Figure 2 The present invention is a schematic structural diagram of a power grid soil and water conservation system based on three-dimensional simulation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0078] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

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

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

[0081] S11. Soil parameters, vegetation coverage and hydrological and meteorological data are aligned and a basic layer is generated through UV texture coordinate projection. The dynamic distribution of vegetation growth is predicted using soil organic matter content and water holding capacity. At the same time, the soil moisture migration process is calculated based on rainfall data and soil physical parameters to obtain the moisture content distribution. Then, the terrain slope, soil moisture content distribution and dynamic distribution of vegetation coverage are input into the prediction model to calculate the results of soil and water loss. A risk assessment system is established based on the results of soil and water loss, and the weight is determined through hierarchical analysis. The risk level is calculated using fuzzy membership and then mapped to the three-dimensional model surface 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;

[0082] S12. Based on the three-dimensional scene model of the power grid project, a layout plan of soil and water conservation facilities including intercepting drainage ditches, sedimentation basins, vegetation restoration belts and retaining walls is established; a multi-objective optimization algorithm is used, with the minimum construction cost of soil and water conservation facilities and the optimal soil and water conservation effect as the objective function, and the location parameters, specification parameters and quantity parameters of the soil and water conservation facilities are multi-objective optimized and calculated to obtain multiple candidate soil and water conservation measures optimization plans;

[0083] S13. Import the candidate optimization schemes for soil and water conservation measures into the three-dimensional scenario model of the power grid project respectively; based on the computational fluid dynamics method, simulate the surface runoff and soil erosion process under different rainfall conditions, and calculate the amount of soil and water loss; adopt a fuzzy comprehensive evaluation method, comprehensively consider the soil and water conservation rate, vegetation restoration rate, runoff reduction rate and project investment cost, and comprehensively score the candidate optimization schemes for soil and water conservation measures; select the candidate optimization scheme for soil and water conservation measures with the highest comprehensive score as the final optimization scheme, and output the three-dimensional construction layout diagram and technical parameter list of the final optimization scheme.

[0084] In an optional implementation, soil parameters, vegetation coverage and hydrological and meteorological data are aligned and a base layer is generated through UV texture coordinate projection. The dynamic distribution of vegetation growth is predicted using soil organic matter content and water holding capacity. At the same time, the soil moisture migration 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 distribution of vegetation coverage are input into the prediction model to calculate the soil and water loss results. Based on the results, 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 three-dimensional model surface 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:

[0085] The soil parameter data, vegetation cover data and hydrological and meteorological data are uniformly converted 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;

[0086] Establishing a UV texture coordinate system on the surface of the three-dimensional terrain model, and using an equi-angle projection method to map the registered soil parameter data, the registered vegetation coverage data, and the registered hydrological and meteorological data to the UV texture coordinate system, respectively, to generate a soil parameter texture layer, a vegetation coverage texture layer, and a hydrological element texture layer;

[0087] Establishing a vegetation growth prediction model according to the soil organic matter content and water holding capacity in the soil parameter texture layer, associating the output result of the vegetation growth prediction model with the vegetation coverage texture layer, and generating a vegetation coverage dynamic distribution layer;

[0088] 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 moisture 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 coverage 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;

[0089] A soil and water loss risk assessment index system is established based on the soil and water loss prediction results, 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 by 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.

[0090] A method for constructing a three-dimensional scenario model for soil and water loss risk assessment of power grid projects. This method integrates multi-source data such as soil, vegetation, hydrology and meteorology into a three-dimensional terrain model to achieve a visual assessment of soil and water loss risks.

[0091] First, obtain the 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 the DEM data of the area with a resolution of 10 meters. The soil data includes soil type, organic matter content, water holding capacity, etc., the vegetation data includes vegetation type, coverage, etc., and the hydrological and meteorological data includes daily rainfall, temperature, etc.

[0092] The acquired soil parameter data, vegetation cover data, and hydrological and meteorological data are uniformly converted to the same coordinate system and projection system as the DEM data. For example, all data are converted to the WGS84 coordinate system and UTM projection system. This step ensures that all data are spatially aligned for subsequent analysis and processing.

[0093] A UV texture coordinate system is established 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, and can map two-dimensional image information to the surface of a three-dimensional model. Using the equi-angle projection method, the registered soil parameter data, vegetation coverage data, and hydrological and meteorological data are respectively mapped to the UV texture coordinate system to generate corresponding texture layers. For example, the soil organic matter content data is converted into a grayscale image, where a higher grayscale value indicates a higher organic matter content, and the image is used as a soil organic matter content texture layer. Similarly, a vegetation coverage texture layer and a hydrological element texture layer are generated.

[0094] A vegetation growth prediction model is established 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. The output of the model is superimposed on the original vegetation coverage texture layer to generate a vegetation coverage dynamic distribution layer to simulate the impact of vegetation growth changes on soil erosion. For example, if the prediction model shows that the vegetation coverage of a certain area has increased, the value of the area in the vegetation coverage dynamic distribution layer is updated.

[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 moisture migration process. This model takes into account factors such as rainfall intensity, soil saturation, and soil hydraulic conductivity to simulate the infiltration and migration process of soil moisture. The calculation results generate a soil moisture distribution layer, which reflects the soil moisture content in different areas. For example, areas with heavy rainfall have higher soil moisture content.

[0096] The terrain slope data, soil moisture distribution layer and vegetation cover dynamic distribution layer are input into the distributed soil erosion prediction model to calculate 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 to calculate the soil erosion amount of each grid unit.

[0097] A soil and water loss risk assessment index system is established based on the soil and water loss prediction results. For example, the amount of soil and water loss is divided into three risk levels: low, medium and high. The weights of terrain factors, soil factors, vegetation factors and rainfall factors are determined by the analytic hierarchy process. For example, experts score to determine 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 accounts for 10% of the weight. The soil and water loss risk level distribution layer is calculated through the fuzzy membership function. For example, the amount of soil and water loss 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 and water loss risk level distribution layer onto the 3D terrain model surface, generating a 3D scene model of the power grid project with soil and water loss risk distribution information. For example, low-risk areas are rendered green, medium-risk areas are rendered yellow, and high-risk areas are rendered red.

[0099] The proposed solution can:

[0100] Improve assessment accuracy: By integrating multi-source data and refined modeling, the accuracy of soil and water loss risk assessment is improved, and high-risk areas can be identified more accurately. Enhance visualization effect: Mapping soil and water loss risk information into a three-dimensional scene model realizes the visualization of risk assessment results, making it easy to intuitively understand the risk distribution. Auxiliary decision support: Providing decision support for power grid project planning, design and construction, helping to optimize project layout, reduce soil and water loss risks, and protect the ecological environment.

[0101] In an optional implementation, based on the three-dimensional scene model of the power grid project, a layout plan of soil and water conservation facilities including intercepting drainage ditches, sand settling ponds, vegetation restoration belts and retaining walls is established; a multi-objective optimization algorithm is adopted, with the minimum construction cost of soil and water conservation facilities and the optimal soil and water conservation effect as the objective function, and the location parameters, specification parameters and quantity parameters of the soil and water conservation facilities are multi-objective optimized and calculated, and multiple candidate soil and water conservation measures optimization plans are obtained, including:

[0102] Determine the layout area of ​​intercepting drainage 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 chamber according to the catchment area data of the three-dimensional scene model of the power grid project, and determine the layout area of ​​the vegetation restoration belt and the layout area of ​​the retaining wall according to the slope position data of the three-dimensional scene model of the power grid project;

[0103] The D8 algorithm is used to calculate the surface runoff path, and the initial layout position of the intercepting drainage ditch is set on the runoff path. The initial layout position of the intercepting drainage ditch includes the position of the trapezoidal cross-section intercepting ditch set in the area with a slope greater than 15 degrees and the position of the rectangular cross-section drainage ditch set in the area with a slope less than 15 degrees; the initial layout position of the sand settling tank is set at the outlet corresponding to the catchment area data; the initial layout position of the vegetation restoration belt and the initial layout position of the retaining wall are set in the area corresponding to the slope position data;

[0104] 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, and determine the height parameter constraints of the retaining wall based on the slope stability analysis;

[0105] A multi-objective optimization model for soil and water conservation facilities is constructed, and the location parameters, specification parameters and quantity parameters of the intercepting drainage ditch, the sedimentation tank, the vegetation restoration belt and the retaining wall are used as decision variables, and the minimum construction cost of the soil and water conservation facilities and the optimal soil and water conservation effect are used as 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;

[0106] A non-dominated sorting genetic algorithm is used to solve the multi-objective optimization model, with the population size set to 100, the evolutionary generations set to 200, the crossover probability set to 0.9, and the mutation probability set to 0.1; dominant individuals are selected through a tournament selection strategy, a simulated binary crossover operator and a polynomial mutation operator are used to generate new solutions, and an elite retention strategy is introduced to maintain the optimal solution;

[0107] According to the terrain characteristics of the three-dimensional scenario model of the power grid project, the soil and water loss sensitive areas are divided, and the value range of the decision variables is limited to the soil and water loss sensitive areas; the associated constraints between the facilities are established, including the connection constraints between the intercepting drainage ditch and the sedimentation tank, and the coordinated constraints between the vegetation restoration zone and the retaining wall, so as to obtain multiple candidate optimization schemes for soil and water conservation measures.

[0108] Based on the 3D scene model, a method for optimizing the layout of soil and water conservation facilities for power grid projects is constructed, which includes the following steps:

[0109] First, obtain the three-dimensional scene model data of the power grid project. The model contains data such as terrain slope, catchment area, and slope location, providing basic information for the subsequent layout of soil and water conservation facilities. For example, high-precision terrain data can be obtained through drone aerial photography and lidar scanning to build a three-dimensional scene model of the power grid project.

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

[0111] Next, the D8 algorithm is used to calculate the surface runoff path and determine the initial layout position of the intercepting drainage ditch. The initial layout position of the grit chamber is set at the outlet corresponding to the catchment area. The initial layout position of the vegetation restoration belt and the retaining wall is set in the area corresponding to the slope position. For example, three runoff paths are calculated according to the D8 algorithm, and three intercepting drainage ditches are set on each path.

[0112] After that, the specification parameter constraints of the soil and water conservation facilities are established. The cross-sectional size range of the intercepting drainage ditch is determined according to the Manning formula. The volume range of the sedimentation tank is determined according to the 24-hour rainstorm sediment production. The width range of the vegetation restoration zone is determined according to the slope gradient. The height range of the retaining wall is determined according to the slope stability analysis. For example, the cross-sectional size range of the intercepting drainage ditch calculated according to the Manning formula is 0.5 meters to 1 meter in width and 0.5 meters to 1 meter in depth. The volume range of the sedimentation tank is 10 cubic meters to 20 cubic meters calculated according to the 24-hour rainstorm sediment production. The width range of the vegetation restoration zone is determined to be 2 meters to 5 meters according to the slope gradient. The height range of the retaining wall is determined to be 1 meter to 3 meters according to the slope stability analysis.

[0113] A multi-objective optimization model for soil and water conservation facilities was constructed. The location parameters, specification parameters, and quantity parameters of intercepting drainage ditches, sedimentation basins, vegetation restoration belts, and retaining walls were taken as decision variables. The minimum construction cost of soil and water conservation facilities and the optimal soil and water conservation effect were taken as objective functions. The construction cost included the sub-project costs of earthwork excavation, concrete pouring, and vegetation restoration. The soil and water conservation effect included runoff interception rate, sediment interception rate, and slope protection rate.

[0114] 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 evolution generations is set to 200, the crossover probability is set to 0.9, and the mutation probability is set to 0.1. The tournament selection strategy is used to select dominant individuals. The simulated binary crossover operator and the polynomial mutation operator are used to generate new solutions. The elite retention strategy is introduced to maintain the optimal solution.

[0115] According to the terrain characteristics of the three-dimensional scenario model of the power grid project, the sensitive areas of soil and water loss are divided, and the value range of the decision variables is limited to the sensitive areas of soil and water loss. The associated constraints between the facilities are established, including the connection constraints between the intercepting drainage ditch and the grit chamber, and the coordinated constraints between the vegetation restoration belt and the retaining wall. Finally, multiple candidate optimization schemes for soil and water conservation measures are obtained. For example, Scheme 1: Set up 5 intercepting drainage ditches, 2 grit chambers, the width of the vegetation restoration belt is 3 meters, and the height of the retaining wall is 2 meters; Scheme 2: Set up 7 intercepting drainage ditches, 3 grit chambers, the width of the vegetation restoration belt is 4 meters, and the height of the retaining wall is 2.5 meters.

[0116] The proposed solution can:

[0117] Reduce construction costs: Through the multi-objective optimization algorithm, the lowest construction cost scheme can be obtained, thus saving project investment. Improve soil and water conservation effect: By optimizing the layout and specification parameters of soil and water conservation facilities, the runoff interception rate, sediment interception rate and slope protection rate can be maximized to effectively control soil and water loss. Diversify the scheme: This method can generate multiple candidate schemes for decision makers to choose the most appropriate one according to the actual situation.

[0118] In an optional implementation, a multi-objective optimization model of soil and water conservation facilities is constructed, the location parameters, specification parameters and quantity parameters of the intercepting drainage ditch, the sand settling tank, the vegetation restoration zone and the retaining wall are used as decision variables, and the minimum construction cost of the soil and water conservation facilities and the optimal soil and water conservation effect are used as objective functions, including:

[0119] Establish a soil and water conservation facility optimization model, with the minimum construction cost of soil and water conservation facilities and the optimal soil and water conservation effect as the objective function, and take the starting and ending coordinates, cross-sectional shape, ditch depth, bottom width and top width of the intercepting drainage ditch, the center point coordinates, pool length, pool width and pool depth of the sedimentation tank, the four corners coordinates of the vegetation restoration zone, planting density and vegetation type, the starting and ending coordinates, wall height, top width and bottom width of the retaining wall and the number of various facilities as decision variables, and establish constraint conditions based on project site restrictions, 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 earth excavation cost is calculated by multiplying the earth excavation volume and the transportation distance, the concrete pouring cost is calculated according to the volume of the concrete structure, and the vegetation restoration cost is calculated according to the area of ​​the vegetation restoration area. The minimum sum of the sub-project costs of the earth 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 earth excavation cost, C2 is the concrete pouring cost, and C3 is the vegetation restoration cost;

[0124] ;

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

[0126] ;

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

[0128] ;

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

[0130] The hydraulic model is used to calculate the runoff interception rate of the intercepting drainage ditch and the sedimentation tank under the designed rainfall conditions, the sediment interception rate of the sedimentation tank is calculated based on the law of sediment movement, the slope protection rate is calculated according to the vegetation coverage of the vegetation restoration zone and the slope stability of the retaining wall, and the weighted sum maximum value 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, and w3 are the weight coefficients of each indicator;

[0134] ;

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

[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 slope;

[0138] ;

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

[0140] ;

[0141] Among them, T is the sediment particle settling velocity, ρs is the sediment density, ρ is the water density, g is the gravitational acceleration, 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, k1 and k2 are weight coefficients;

[0144] ;

[0145] Among them, 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.

[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 location, specifications, quantity and other parameters of four types of soil and water conservation facilities: intercepting drainage ditches, sedimentation basins, vegetation restoration belts and retaining walls.

[0147] First, determine the basic information of the project area, such as the boundary, topographic data, hydrological data, and soil type. For example, the coordinates of the project area boundary 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 rainfall over many years, the maximum rainfall in 24 hours, etc., and the soil type is sandy loam.

[0148] Next, based on the collected basic information and design specifications, the range of values ​​for various design parameters was determined. For example, the depth of the intercepting drainage ditch ranged from 0.5m to 1.5m, the depth of the sedimentation pond ranged from 1m to 2m, the planting density of the vegetation restoration zone ranged from 5 to 10 plants per square meter, and the height of the retaining wall ranged from 2m to 4m.

[0149] Then, multiple scheme combinations are generated according to the range of design parameter values. For example, 100 schemes are randomly generated, each of which contains specific values ​​of parameters such as the location, specifications and quantity of intercepting drainage ditches, sedimentation basins, vegetation restoration belts and retaining walls.

[0150] For each scheme, its construction cost and soil and water conservation effect are calculated respectively. The construction cost includes earth excavation cost, concrete pouring cost and vegetation restoration cost. The earth excavation cost is obtained by multiplying the excavated earthwork volume by the unit earth excavation cost and the average transportation distance. The concrete pouring cost is obtained by multiplying the concrete structure volume 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 expressed by the weighted sum of 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 interception ditch to the flow rate 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 sedimentation tank to the inlet sediment concentration. 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 scheme, the earth excavation volume is 1,000 cubic meters, the average transportation distance is 500 meters, and the unit earth excavation cost is 50 yuan per cubic meter, so the earth excavation cost is 250,000 yuan. The volume of the concrete structure of this scheme is 50 cubic meters, and the unit concrete pouring cost is 800 yuan per cubic meter, so the concrete pouring cost is 40,000 yuan. The vegetation restoration area of ​​this scheme is 2,000 square meters, and the unit vegetation restoration cost is 20 yuan per square meter, so the vegetation restoration cost is 40,000 yuan. Therefore, the total construction cost of this scheme is 330,000 yuan. Assuming that the runoff interception rate of this scheme 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, the comprehensive soil and water conservation effect of this scheme is 79%.

[0152] Finally, the scheme with the lowest construction cost and the best soil and water conservation effect is selected from all the schemes as the final scheme. For example, among 100 schemes, the scheme with the lowest construction cost and the highest soil and water conservation effect is selected as the final scheme.

[0153] The proposed solution can:

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

[0155] In an optional implementation, a non-dominated sorting genetic algorithm is used to solve the multi-objective optimization model, the population size is set to 100, the evolutionary generation 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 a tournament selection strategy, a simulated binary crossover operator and a polynomial mutation operator are used to generate new solutions, and an elite retention strategy is introduced to maintain the optimal solution, including:

[0156] A non-dominated sorting genetic algorithm is used to solve the multi-objective optimization model, and the population size is set to 100 individuals, each individual is composed of a geometric parameter vector and a position parameter vector of a soil and water conservation facility, and the geometric parameter vector and the position parameter vector constitute the decision variable space of the individual; 100 initial individuals are generated in the decision variable space by using the Latin hypercube sampling method to form an initial population, a constraint condition set is established according to the engineering site restrictions and design specification requirements, and the constraint condition set is used to perform a feasibility test on each individual of the initial population, and the individuals that do not meet the constraint conditions are adjusted to the feasible solution space through parameter repair operations, so as to obtain an initial feasible population that meets the constraint conditions;

[0157] The construction cost objective function value and the soil and water conservation effect objective function value of each individual in the initial feasible population are calculated by using a parallel computing framework and a response surface agent model, the initial feasible population is divided into non-dominated frontier surfaces of different levels by using a fast non-dominated sorting algorithm, and the crowding distance value of individuals of the same non-dominated level is calculated;

[0158] The crossover probability is set to 0.9, and a tournament selection strategy is adopted to select a dominant individual from the non-dominated frontier as a parent individual. The selection of the dominant individual is 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 individual to generate a child individual. The feasibility of the child individual is tested using the constraint condition set, and the child individual that does not meet the constraint condition is adjusted to a feasible solution space through a parameter repair operation;

[0159] 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, and the 100 best individuals are selected from the merged population based on the updated non-dominated level and crowding distance values ​​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.

[0160] The multi-objective optimization design method of soil and water conservation based on non-dominated sorting genetic algorithm is used to solve the engineering problem of optimizing construction cost and soil and water conservation effect at the same time. This method adopts the idea of ​​evolutionary computation, simulates natural selection and genetic mechanism, and searches for the best layout plan of soil and water conservation facilities under the premise of meeting engineering constraints.

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

[0162] Next, the feasibility of the initial population is tested. According to the constraints of the project site and the requirements of the design specifications, a set of constraints is established, such as facilities cannot exceed the site boundaries and facilities cannot overlap. Each individual is tested. If the constraints are not met, adjustments are made through parameter repair operations, such as moving facilities that exceed the boundaries back into the boundaries until all constraints are met. Finally, an initial feasible population that meets the constraints is obtained.

[0163] Then, the objective function value is calculated for each individual in the initial feasible population. The parallel computing framework and response surface agent model are used to calculate the construction cost and soil and water conservation effect of each individual. For example, the construction cost can consider the earthwork volume, material cost, etc., and the soil and water conservation effect can consider reducing soil erosion and reducing runoff. Assuming that the geometric parameter vector of an individual is [10,5,2] and the position parameter vector is [100,200], it is calculated that its construction cost is 1,000 yuan and the soil and water conservation effect is 90%.

[0164] Next, the population is non-dominated sorted. The fast non-dominated sorting algorithm is used to divide the population into non-dominated frontiers of different levels. A non-dominated solution refers to 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 is higher than that of B, then A dominates B. All non-dominated solutions are formed into the first non-dominated frontier, and then non-dominated solutions are continued to be found from the remaining solutions to form the second non-dominated frontier, and so on. At the same time, the crowding distance value of individuals of the same non-dominated level is calculated to measure the density of solutions around the individuals.

[0165] Then, the selection operation is performed. The crossover probability is set to 0.9, and the tournament selection strategy is used to select the dominant individual from the non-dominated frontier as the parent individual. The tournament selection strategy is to randomly select several individuals, compare their non-dominated levels and crowding distance values, and select the best individual. For example, three individuals A, B, and C are randomly selected. A belongs to the first non-dominated frontier, and B and C belong to the second non-dominated frontier. Then A is selected as the parent individual.

[0166] Next, the crossover operation is performed. The simulated binary crossover operator is used to perform a crossover operation on the selected parent 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 parent individual A is [10,5,2], and the geometric parameter vector of parent individual B is [12,6,3]. The geometric parameter vector of offspring individual C generated after crossover may be [11,5.5,2.5]. The feasibility of the generated offspring individuals is tested, and the parameter repair operation is performed on the offspring individuals that do not meet the constraints.

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

[0168] Merge the mutated offspring individuals with the parent individuals to form a merged population of 200. Repeat the fast non-dominated sorting and crowding distance calculation on the merged population. Based on the updated non-dominated rank and crowding distance values, select the 100 best individuals from the merged population to form a new generation population. Adopt the elite retention strategy to directly retain the individuals with the best non-dominated rank in the new generation population to the next generation population. Repeat the above steps until the preset number of evolutionary generations is reached, such as 200 generations.

[0169] The proposed solution can:

[0170] Improved 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. Achieved the coordinated optimization of soil and water conservation effects and construction costs: found the solution with the lowest construction cost under the premise of meeting the requirements of soil and water conservation effects, and achieved a win-win situation of economic and ecological benefits. Improved the scientificity and rationality of soil and water conservation facility design: By considering multiple factors and constraints, found the best solution that is more in line with the actual situation, and improved the reliability and effectiveness of the design.

[0171] In an optional implementation, the candidate soil and water conservation measures optimization schemes are respectively introduced into the three-dimensional scene 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, and the amount of soil and water loss is calculated, including:

[0172] A basic digital elevation scene is constructed by using an irregular triangulated network algorithm, and a three-dimensional geometric model including intercepting drainage ditches, sand settling ponds, vegetation restoration belts and retaining walls is established according to the layout information of various facilities in the candidate soil and water conservation measures optimization plan. The three-dimensional geometric model is merged with the basic digital elevation scene by using Boolean operations to generate multiple terrain scene models containing water conservation facilities;

[0173] For the terrain scene model, an unstructured grid division algorithm is used to discretize it, 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 calculation grid with local encryption characteristics; meteorological data of the project area are collected, and heavy rain data with multiple return periods are obtained. The rain type distribution method is used to convert the heavy rain data into a time-series rainfall process line, and the rainfall process line is applied to the top boundary of the discrete calculation grid to construct the boundary condition of time-varying rainfall intensity;

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

[0175] During the construction of power grid projects, excavation, landfill and other construction operations will disturb the surface vegetation and soil, which can easily cause soil erosion and have an adverse impact on the ecological environment. In order 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 based on computational fluid dynamics to simulate the amount of soil erosion under different rainfall conditions, thereby optimizing soil and water conservation measures.

[0176] First, obtain high-precision digital elevation model (DEM) data of the project area, such as DEM data with a resolution of 1 meter. Use the irregular triangulated network algorithm to construct a basic three-dimensional digital elevation scene. This scene accurately reflects the topographic undulations of the project area.

[0177] Then, according to the designed water and soil conservation measures, such as setting up intercepting drainage ditches and sedimentation tanks in specific slopes and areas, planting vegetation on exposed surfaces, and building 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 as a trapezoidal cross-section, and the width and depth are determined according to the design plan. The model of the sedimentation tank can be set as a rectangle or cylinder, and the size is determined according to the design plan. The vegetation can be represented by a simplified geometric shape or voxel model, and the height and density are set according to the actual situation. The retaining wall can be set as a rectangular plate structure. These 3D geometric models are integrated with the previously constructed basic digital elevation scene, and Boolean operations are used for superposition operations to generate multiple 3D terrain scene models containing different water conservation facility schemes.

[0178] Next, mesh each 3D terrain scene model containing water conservation facilities. Unstructured meshing algorithms, such as Delaunay triangulation or Voronoi polygon algorithms, are used to discretize the 3D terrain scene into a series of interconnected units. In order to more accurately simulate the movement of water flow and soil particles, meshes are encrypted in the area where the water conservation facilities are located and near the surface layer. For example, near the intercepting drain and the sedimentation tank, 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 the near-wall flow characteristics. In this way, a discrete computational grid with local encryption characteristics is formed.

[0179] Collect meteorological data in the project area and obtain rainstorm data with different return periods (e.g., 2 years, 5 years, 10 years, 20 years), including rainfall, rainfall duration, and rainfall intensity. Use rain type distribution methods, such as Chicago rain type or Huff rain type, to convert rainstorm data with different return periods into time-series rainfall process lines, such as rainfall intensity data per hour or per minute. Use these rainfall process lines as boundary conditions for time-varying rainfall intensity and apply them to the top boundary of the discrete computational grid to simulate different rainfall scenarios.

[0180] Based on the constructed discrete computational grid and time-varying rainfall intensity boundary conditions, computational fluid dynamics methods are used for numerical simulation. Surface runoff is simulated using a multiphase flow model, treating water and air as two different fluid phases to simulate the interaction between them. Soil erosion is simulated using the Euler-Lagrangian coupling method, in which the water flow is described by the Euler method and the soil particles are described by the Lagrangian method, simulating the process of water carrying and transporting soil particles. By setting the time step (for example, 0.1 seconds) and the convergence criterion (for example, the residual is less than 1e-5), the surface runoff model and the soil erosion model are coupled and solved. During the solution process, numerical methods such as the finite volume method or the finite element method can be used to discretize and solve the control equations. Finally, the spatiotemporal evolution data of the surface runoff velocity field, water depth distribution and soil erosion amount are obtained.

[0181] Finally, based on the simulated data, the characteristic parameters of soil and water loss for each terrain scenario model are calculated. For example, the surface runoff coefficient can be obtained by calculating the ratio of surface runoff to rainfall, the soil erosion modulus can be obtained by calculating the amount of soil loss per unit area, the facility interception efficiency can be obtained by calculating the ratio of the amount of sediment intercepted by the water conservation facility to the total amount of sediment, and the total amount of accumulated soil and water loss can be obtained by integrating the amount of soil loss during the entire simulation period. By comparing these characteristic parameters under different water conservation schemes, the effectiveness of different schemes can be evaluated and the optimal water and soil conservation measures can be selected.

[0182] The proposed solution can:

[0183] Accurate prediction: This method can simulate the soil erosion process under different rainfall scenarios based on high-precision terrain data and meteorological data, so as to accurately predict the effects of different soil and water conservation measures. Optimized design: By comparing the simulation results of different schemes, the design of soil and water conservation measures can be optimized, such as adjusting the location and size of intercepting drainage 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. Reduce costs: Through simulation analysis, different schemes can be evaluated before construction, avoiding unnecessary investment and reducing construction costs.

[0184] In an optional implementation, 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, and the candidate soil and water conservation measures optimization scheme is comprehensively scored; the candidate soil and water conservation measures optimization scheme with the highest comprehensive score is selected as the final optimization scheme, and the three-dimensional construction layout diagram and technical parameter list of the scheme are output, including:

[0185] The candidate optimization schemes for soil and water conservation measures are scored by using a fuzzy comprehensive evaluation method, and a comprehensive evaluation of the candidate optimization schemes for soil and water conservation measures is achieved by establishing a multi-dimensional evaluation index system, which includes a soil and water conservation rate index, a vegetation restoration rate index, a runoff reduction rate index, and an engineering investment cost index;

[0186] Automatic monitoring equipment is used to obtain 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; remote sensing image analysis method is used to obtain vegetation coverage data, and the vegetation recovery rate index is calculated. The vegetation recovery rate index is determined by the ratio of the restored vegetation area to the total area of ​​the restorable vegetation area; hydrological monitoring equipment is used to obtain runoff data, and the runoff reduction rate index is calculated. The runoff reduction rate index is determined by the ratio of the runoff reduction of soil and water conservation facilities to the total regional runoff; the bill of quantities method is used to calculate the project investment cost index, and the project investment cost index is determined by the product of the project volume of various soil and water conservation measures and the market unit price;

[0187] The indicators in the multidimensional evaluation index system are standardized, and the soil and water conservation rate indicator, the vegetation restoration rate indicator and the runoff reduction rate indicator are converted into positive membership values ​​by using the maximum value standardization method, and the engineering investment cost indicator is converted into a negative membership value by using the minimum value standardization method; a pairwise comparison judgment matrix of the multidimensional evaluation index system is constructed, and the eigenvalues ​​and eigenvectors of the judgment matrix are calculated based on the expert scoring results, and the eigenvectors are standardized to obtain weight coefficient vectors, and the rationality of the weight coefficient vectors is confirmed by consistency test;

[0188] The standardized membership values ​​of various indicators and the corresponding weight coefficients are weighted and summed to obtain the comprehensive scores of each candidate scheme, and the candidate schemes are ranked based on the comprehensive scores, and the candidate scheme with the highest comprehensive score is selected as the optimal soil and water conservation scheme; a construction layout model of the optimal soil and water conservation scheme is constructed using three-dimensional modeling software, and engineering construction drawings are generated based on the construction layout model, including the general layout of soil and water conservation facilities, partial construction drawings and node structure details; a list of technical parameters for the optimal soil and water conservation scheme is established.

[0189] The design and selection of soil and water conservation measures optimization scheme is a complex process that requires comprehensive consideration of multiple factors. Here we provide a soil and water conservation measures optimization scheme based on fuzzy comprehensive evaluation method, and give detailed technical details and implementation steps.

[0190] First, a multi-dimensional indicator system for evaluating candidate solutions needs to be determined, including soil and water conservation rate, vegetation restoration rate, runoff reduction rate, and project investment cost.

[0191] Next, it is necessary to obtain data for 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. Automatic monitoring equipment can be used to obtain real-time data. For example, if the actual soil erosion modulus is 200 tons / square kilometer / year and the regional background soil erosion modulus is 500 tons / square kilometer / year, the soil and water conservation rate is 40%. The vegetation recovery rate is determined by the ratio of the restored vegetation area to the total area of ​​the restorable vegetation 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 restorable vegetation area is 20 hectares, the vegetation recovery rate is 50%. The runoff reduction rate is determined by the ratio of the runoff reduction of soil and water conservation facilities to the total regional runoff. Hydrological monitoring equipment can be used to obtain runoff data. For example, if the runoff reduction is 50 cubic meters / second and the total regional runoff is 200 cubic meters / second, the runoff reduction rate is 25%. The project investment cost is determined by multiplying the engineering volume of various soil and water conservation measures by the market unit price, and is calculated using the bill of quantities method. For example, if the engineering volume of a certain measure is 1,000 cubic meters and the market unit price is 200 yuan / cubic meter, then the cost of the measure is 200,000 yuan.

[0192] Then, each indicator is standardized. The maximum value standardization method is used for the soil and water conservation rate, vegetation restoration rate and runoff reduction rate, that is, the actual value of the indicator is divided by the maximum value of the indicator in all schemes. The minimum value standardization method is used for the project investment cost, that is, the minimum value of the indicator is divided by the actual value of the indicator. Assuming that the soil and water conservation rates of the four candidate schemes are 40%, 50%, 60% and 70% respectively, the standardized values ​​are 0.57, 0.71, 0.86 and 1 respectively.

[0193] Next, a pairwise comparison judgment matrix of the indicator system is constructed, and the eigenvalues ​​and eigenvectors of the judgment matrix are calculated based on the expert scoring results. The weight coefficient vector is obtained after the eigenvector is standardized, and a consistency test is performed 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 soil and water conservation rate, vegetation restoration rate, runoff reduction rate and project investment cost are 30%, 20%, 40% and 10% respectively.

[0194] The standardized membership values ​​of each indicator and the corresponding weight coefficients are weighted and summed to obtain the comprehensive score of each candidate solution.

[0195] The candidate solutions are ranked based on the comprehensive scores, and the candidate solution with the highest comprehensive score is selected as the optimal solution.

[0196] Finally, a 3D modeling software was used to construct a construction layout model of the optimal solution, generate engineering construction drawings, including the overall layout of soil and water conservation facilities, partial construction drawings and node structure details, and establish a list of technical parameters for the optimal solution.

[0197] The proposed solution can:

[0198] Improve decision-making efficiency: This method provides a quantitative evaluation method, avoids subjective judgment, and improves 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: Detailed construction layout drawings and technical parameter lists provide guidance for project implementation and ensure smooth progress of the project.

[0199] Figure 2 FIG. 1 is a schematic diagram of a structure of a power grid soil and water conservation system based on three-dimensional simulation according to an embodiment of the present invention. Figure 2 As shown, the system comprises:

[0200] The first unit is used to align soil parameters, vegetation coverage and hydrological and meteorological data and generate a basic layer through UV texture coordinate projection. The dynamic distribution of vegetation growth is predicted using soil organic matter content and water holding capacity. At the same time, the soil moisture migration 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 distribution of vegetation coverage are input into the prediction model to calculate the results of soil and water loss. Based on the results of soil and water loss, a risk assessment system is established and the weight is determined through hierarchical analysis. The risk level is calculated using fuzzy membership and mapped to the three-dimensional model surface 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;

[0201] The second unit is used to establish a layout plan of soil and water conservation facilities including intercepting drainage ditches, sand settling ponds, vegetation restoration belts 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 soil and water conservation facilities and the optimal soil and water conservation effect as the objective function, to perform multi-objective optimization calculations on the location parameters, specification parameters and quantity parameters of the soil and water conservation facilities, and obtain multiple candidate soil and water conservation measures optimization plans;

[0202] The third unit is used to import the candidate soil and water conservation measures optimization schemes 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 process under different rainfall conditions, and calculate the amount of soil and water loss; adopt a fuzzy comprehensive evaluation method, comprehensively consider the soil and water conservation rate, vegetation restoration rate, runoff reduction rate and project investment cost, and comprehensively score the candidate soil and water conservation measures optimization schemes; select the candidate soil and water conservation measures optimization scheme with the highest comprehensive score as the final optimization scheme, and output the three-dimensional construction layout diagram and technical parameter list of the final optimization scheme.

[0203] According to a third aspect of the embodiments of the present invention,

[0204] An electronic device is provided, comprising:

[0205] processor;

[0206] a memory for storing processor-executable instructions;

[0207] The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0208] According to a fourth aspect of the embodiments of the present invention,

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

[0210] The present invention may be a method, an apparatus, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present invention.

[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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for soil and water conservation in power grid based on three-dimensional simulation, characterized in that: include: Soil parameters, vegetation coverage and hydrological and meteorological data are aligned and UV texture coordinate projection is used to generate a basic layer. The dynamic distribution of vegetation growth is predicted using soil organic matter content and water holding capacity. At the same time, the soil moisture migration 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 distribution of vegetation coverage are input into the prediction model to calculate the results of soil and water loss. Based on the results of soil and water loss, a risk assessment system is established and the 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 scene model of the power grid project containing soil and water loss risk distribution information is generated. Based on the three-dimensional scene model of the power grid project, a layout plan of soil and water conservation facilities including intercepting drainage ditches, sedimentation ponds, vegetation restoration belts and retaining walls is established; a multi-objective optimization algorithm is used, with the minimum construction cost of soil and water conservation facilities and the optimal soil and water conservation effect as the objective function, and the location parameters, specification parameters and quantity parameters of the soil and water conservation facilities are multi-objective optimized and calculated to obtain multiple candidate soil and water conservation measures optimization plans; The candidate optimization schemes for soil and water conservation measures are respectively introduced 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; 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 the candidate optimization schemes for soil and water conservation measures are comprehensively scored; The candidate soil and water conservation measures 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 coverage and hydrological and meteorological data are aligned and UV texture coordinate projection is used to generate a basic layer. The dynamic distribution of vegetation growth is predicted using soil organic matter content and water holding capacity. At the same time, the soil moisture migration 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 distribution of vegetation coverage are input into the prediction model to calculate the results of soil and water loss. Based on the results, a risk assessment system is established and the 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 scene model of the power grid project containing soil and water loss risk distribution information is generated, including: The soil parameter data, vegetation cover data and hydrological and meteorological data are uniformly converted 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 using an equi-angle projection method to map the registered soil parameter data, the registered vegetation coverage data, and the registered hydrological and meteorological data to the UV texture coordinate system, respectively, to generate a soil parameter texture layer, a vegetation coverage texture layer, and a hydrological element texture layer; Establishing a vegetation growth prediction model according to the soil organic matter content and water holding capacity in the soil parameter texture layer, associating the output result of the vegetation growth prediction model with the vegetation coverage texture layer, and generating a vegetation coverage 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 moisture 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 coverage 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; A soil and water loss risk assessment index system is established based on the soil and water loss prediction results, 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 by 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 scene model of the power grid project, a layout plan of soil and water conservation facilities including intercepting drainage ditches, sedimentation basins, vegetation restoration belts and retaining walls is established; a multi-objective optimization algorithm is adopted, with the minimum construction cost of soil and water conservation facilities and the optimal soil and water conservation effect as the objective function, and the location parameters, specification parameters and quantity parameters of the soil and water conservation facilities are multi-objective optimized and calculated, and multiple candidate optimization plans for soil and water conservation measures are obtained, including: Determine the layout area of ​​intercepting drainage 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 chamber according to the catchment area data of the three-dimensional scene model of the power grid project, and determine the layout area of ​​the vegetation restoration belt and the layout area of ​​the retaining wall according to 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 position of the intercepting drainage ditch is set on the runoff path. The initial layout position of the intercepting drainage ditch includes the position of the trapezoidal cross-section intercepting ditch set in the area with a slope greater than 15 degrees and the position of the rectangular cross-section drainage ditch set in the area with a slope less than 15 degrees; the initial layout position of the sand settling tank is set at the outlet corresponding to the catchment area data; the initial layout position of the vegetation restoration belt and the initial layout position 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, and determine the height parameter constraints of the retaining wall based on the slope stability analysis; A multi-objective optimization model for soil and water conservation facilities is constructed, and the location parameters, specification parameters and quantity parameters of the intercepting drainage ditch, the sedimentation tank, the vegetation restoration belt and the retaining wall are used as decision variables, and the minimum construction cost of the soil and water conservation facilities and the optimal soil and water conservation effect are used as 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; A non-dominated sorting genetic algorithm is used to solve the multi-objective optimization model, with the population size set to 100, the evolutionary generations set to 200, the crossover probability set to 0.9, and the mutation probability set to 0.1; dominant individuals are selected through a tournament selection strategy, a simulated binary crossover operator and a polynomial mutation operator are used to generate new solutions, and an elite retention strategy is introduced to maintain the optimal solution; According to the terrain characteristics of the three-dimensional scenario model of the power grid project, the soil and water loss sensitive areas are divided, and the value range of the decision variables is limited to the soil and water loss sensitive areas; the associated constraints between the facilities are established, including the connection constraints between the intercepting drainage ditch and the sedimentation tank, and the coordinated constraints between the vegetation restoration zone and the retaining wall, so as 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, and the location parameters, specification parameters and quantity parameters of the intercepting drainage ditch, the sedimentation tank, the vegetation restoration belt and the retaining wall are used as decision variables. The objective function is to minimize the construction cost of soil and water conservation facilities and optimize the soil and water conservation effect. The model includes: Establish a soil and water conservation facility optimization model, with the minimum construction cost of soil and water conservation facilities and the optimal soil and water conservation effect as the objective function, and take the starting and ending coordinates, cross-sectional shape, ditch depth, bottom width and top width of the intercepting drainage ditch, the center point coordinates, pool length, pool width and pool depth of the sedimentation tank, the four corners 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 number of various facilities as decision variables, and establish constraint conditions 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 and the transportation distance, the concrete pouring cost is calculated according to the volume of the concrete structure, and the vegetation restoration cost is calculated according to the area of ​​the vegetation restoration area. The minimum sum of the sub-project costs of the earth 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; 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, and VC 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; The hydraulic model is used to calculate the runoff interception rate of the intercepting drainage ditch and the sedimentation tank under the designed rainfall conditions, the sediment interception rate of the sedimentation tank is calculated based on the law of sediment movement, the slope protection rate is calculated according to the vegetation coverage of the vegetation restoration zone and the slope stability of the retaining wall, and the weighted sum maximum value 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 drainage ditch, and Q0 is the flow rate under the design rainfall conditions; ； 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 slope; ； Among them, A1 is the inlet sediment concentration, and A2 is the outlet sediment concentration; ； Among them, T is the sediment particle settling velocity, ρs is the sediment density, ρ is the water density, g is the gravitational acceleration, d is the sediment particle size, and μ is the dynamic viscosity of water; ； Among them, Cc is the vegetation coverage, Fs is the slope stability safety factor, k1 and k2 are weight coefficients; ； Among them, 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: The non-dominated sorting genetic algorithm is used to solve the multi-objective optimization model. The population size is set to 100, the evolutionary generation 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, and the simulated binary crossover operator and polynomial mutation operator are used to generate new solutions. The 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, and the population size is set to 100 individuals, each individual is composed of a geometric parameter vector and a position parameter vector of a soil and water conservation facility, and the geometric parameter vector and the position parameter vector constitute the decision variable space of the individual; 100 initial individuals are generated in the decision variable space by using the Latin hypercube sampling method to form an initial population, a constraint condition set is established according to the engineering site restrictions and design specification requirements, and the constraint condition set is used to perform a feasibility test on each individual of the initial population, and the individuals that do not meet the constraint conditions are adjusted to the feasible solution space through parameter repair operations, so as to obtain an initial feasible population that meets the constraint conditions; The construction cost objective function value and the soil and water conservation effect objective function value of each individual in the initial feasible population are calculated by using a parallel computing framework and a response surface agent model, the initial feasible population is divided into non-dominated frontier surfaces of different levels by using a fast non-dominated sorting algorithm, and the crowding distance value of individuals of the same non-dominated level is calculated; The crossover probability is set to 0.9, and a tournament selection strategy is adopted to select a dominant individual from the non-dominated frontier as a parent individual. The selection of the dominant individual is 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 individual to generate a child individual. The feasibility of the child individual is tested using the constraint condition set, and the child individual that does not meet the constraint condition is adjusted to a 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, and the 100 best individuals are selected from the merged population based on the updated non-dominated level and crowding distance values ​​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 by using an irregular triangulated network algorithm, and a three-dimensional geometric model including intercepting drainage ditches, sand settling ponds, vegetation restoration belts and retaining walls is established according to the layout information of various facilities in the candidate soil and water conservation measures optimization plan. The three-dimensional geometric model is merged with the basic digital elevation scene by using Boolean operations to generate multiple terrain scene models containing water conservation facilities; For the terrain scene model, an unstructured grid division algorithm is used to discretize it, 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 calculation grid with local encryption characteristics; meteorological data of the project area are collected, and heavy rain data with multiple return periods are obtained. The rain type distribution method is used to convert the heavy rain data into a time-series rainfall process line, and the rainfall process line is applied to the top boundary of the discrete calculation grid to construct the boundary condition of time-varying rainfall intensity; Based on the discrete computational grid and the time-varying rainfall intensity boundary condition, a surface runoff model is constructed using a multiphase flow model, and a soil erosion model is constructed using an Euler-Lagrange coupling method. The two models are coupled and solved by setting a time step and a convergence criterion to obtain the spatiotemporal evolution data of the surface runoff velocity field, the water depth distribution, and the amount of soil erosion. Based on the spatiotemporal evolution data, the characteristic parameters of soil and water loss of each terrain scenario model are calculated, including the surface runoff coefficient, the soil erosion modulus, the 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 described in any one of claims 1 to 6, characterized in that: include: The first unit is used to align soil parameters, vegetation coverage and hydrological and meteorological data and generate a basic layer through UV texture coordinate projection. The dynamic distribution of vegetation growth is predicted using soil organic matter content and water holding capacity. At the same time, the soil moisture migration 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 distribution of vegetation coverage are input into the prediction model to calculate the results of soil and water loss. Based on the results of soil and water loss, a risk assessment system is established and the weight is determined through hierarchical analysis. The risk level is calculated using fuzzy membership and mapped to the three-dimensional model surface 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; The second unit is used to establish a layout plan of soil and water conservation facilities including intercepting drainage ditches, sand settling ponds, vegetation restoration belts 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 soil and water conservation facilities and the optimal soil and water conservation effect as the objective function, to perform multi-objective optimization calculations on the location parameters, specification parameters and quantity parameters of the soil and water conservation facilities, and obtain multiple candidate soil and water conservation measures optimization plans; The third unit is used to import the candidate soil and water conservation measures optimization schemes 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 process under different rainfall conditions to calculate the amount of soil and water loss; adopt a fuzzy comprehensive evaluation method, comprehensively consider the soil and water conservation rate, vegetation restoration rate, runoff reduction rate and project investment cost, and comprehensively score the candidate soil and water conservation measures optimization schemes; The candidate soil and water conservation measures 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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