Preparation method of ultra-high strength cement-based grouting material for wind power generation

Abstract

The present invention relates to the technical field of cement-based materials, and specifically to a method for preparing an ultra-high-strength cement-based grouting material specifically for wind power generation. In the present invention, by quantifying the ratio of cement, aggregate and admixture, the discrete element method is used to simulate and analyze the stress and bonding conditions in the contact area between cement particles and aggregate particles, and the gap distribution between particles is adjusted in combination with a genetic algorithm. The application of the discrete element method also enables the mechanical behavior of the particles to be refined, avoiding the problem of local stress concentration that may be caused by the randomness of particle arrangement. The microscopic stress distribution data is continuously mapped by the radial basis function interpolation method, and a macroscopic stress distribution model is established by finite element analysis, which provides data support for the design of the material's crack resistance and bearing capacity. By optimizing the synergistic effect of microstructure and macroscopic stress, the comprehensive performance of the cement-based grouting material in terms of fatigue resistance, deformation resistance and environmental erosion resistance is improved, thereby ensuring mechanical stability and long-term durability.

Description

Technical Field

[0001] The present invention relates to the technical field of cement-based materials, and in particular to a method for preparing an ultra-high-strength cement-based grouting material specially used for wind power generation. Background Art

[0002] The field of cement-based material technology aims to research and develop composite materials based on cement. By scientifically designing formulas and optimizing preparation processes, the materials are endowed with specific properties and functions to meet the needs of various engineering and application scenarios.

[0003] The purpose of the preparation method of ultra-high strength cement-based grouting materials for wind power generation is to produce grouting materials that can meet the high strength, durability and stability requirements of wind power tower foundations by optimizing the material formula and preparation process. The material needs to have good mechanical properties under extreme load conditions and be able to resist fatigue, deformation and environmental erosion that may occur during long-term use to ensure the safety and service life of wind power equipment.

[0004] Existing technologies lack scientific modeling of the microstructure of particles. The arrangement of particles is usually randomly distributed, which cannot avoid local stress concentration and trigger the initiation and expansion of cracks. An effective analysis model for the correlation between micromechanical behavior and macroscopic performance has not yet been established, resulting in the design optimization of materials remaining at a single level and making it difficult to comprehensively improve their performance. Under extreme load conditions, the grouting material may experience performance fluctuations or local damage, manifested as insufficient crack resistance, increased fatigue damage and decreased durability, affecting the foundation stability of wind power equipment, shortening the equipment service life and increasing maintenance costs. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a method for preparing an ultra-high strength cement-based grouting material specially used for wind power generation.

[0006] In order to achieve the above-mentioned object, the present invention adopts the following technical solution: a method for preparing ultra-high strength cement-based grouting material for wind power generation, comprising the following steps:

[0007] Step 1: Based on the mix ratio requirements of cement-based grouting materials, select a specific water-cement ratio, admixture ratio, and aggregate grading, accurately quantify the cement-aggregate ratio, and generate a preliminary mix ratio result;

[0008] Step 2: Based on the initial mix ratio result, the stress and bonding conditions in the contact area between cement particles and aggregate particles are simulated by discrete element method, and the particle gaps are adjusted according to the stress and bonding conditions by genetic algorithm to obtain microstructure adjustment results;

[0009] Step 3: Based on the microstructure adjustment results, the microscopic contact force distribution is calculated by adjusting the contact model parameters, simulating the forces between particles under different pressures, and obtaining mechanical behavior prediction results;

[0010] Step 4: Based on the mechanical behavior prediction results, extract the microstress distribution data, use the radial basis function interpolation method to interpolate the microstress, map the discrete microstress data into a continuous distribution field, and use the finite element method to perform correlation analysis on the microstress and macrostress to generate a macrostress distribution model;

[0011] Step 5: Based on the macroscopic stress distribution model, by optimizing parameters including water-cement ratio, admixture ratio, and aggregate gradation, a formula with appropriate crack resistance and bearing capacity is designed for the specific environment of the wind farm, and an optimized formula result is obtained;

[0012] Step 6: Based on the optimized formula results, conduct a mixing test on the actual material, adjust the experimental formula according to the material performance indicators and through actual application tests, and generate experimental verification results;

[0013] Step 7: Based on the experimental verification results, the material performance is evaluated according to the strength and durability of the material to confirm whether it meets the specific requirements of wind power generation and obtain a performance evaluation result.

[0014] As a further solution of the present invention, the specific steps of generating the initial ratio result are:

[0015] According to the requirements of the proportion of cement-based grouting materials, cement, aggregates and admixtures are selected, and weights are measured using metering equipment. The water-cement ratio, aggregate gradation and admixture ratio are adjusted to ensure accuracy. The proportion parameters are recorded and the proportion parameter data is generated.

[0016] Based on the mix parameter data, cement, aggregate and admixture are sequentially added to the mixing equipment in proportion, mixed by mechanical stirring, the speed and time of the stirring equipment are adjusted, the mixing state is checked and the operation is adjusted until the materials are uniform, thereby generating a uniformly proportioned material;

[0017] Based on the uniformly proportioned material, a test mold is filled with the material, and the molding performance is tested using standard test tools. At the same time, the water-cement ratio and aggregate grading data are adjusted to optimize the operation until the grouting material ratio requirements are met, and the initial ratio result is output;

[0018] Among them, the initial proportioning results include the mass ratio of cement to aggregate, water-cement ratio and admixture ratio.

[0019] As a further solution of the present invention, the specific steps of generating the microstructure adjustment result are:

[0020] Based on the initial mix ratio results, cement particle and aggregate particle parameters are imported, particle shape, size, and distribution characteristics are measured using a scanning electron microscope, the particles are discretized using the discrete element method, a particle point distribution model is established, and particle contact areas and contact point properties are defined to generate a particle contact model;

[0021] Based on the particle contact model, a force analysis is performed between particles. A constant pressure load is used to apply pressure to the particles. The force transmission path between the particles is determined by measuring the force change at the contact point. The bonding strength between the particles is analyzed to generate particle force bonding data.

[0022] Based on the particle force bonding data, a genetic algorithm is used to optimize the arrangement and gaps between the particles. An initial population is generated using the genetic algorithm, with each individual representing a particle arrangement. The combined performance of particle porosity and bonding strength is evaluated, and particle arrangement schemes with excellent performance are selected for crossover and mutation operations. The particle arrangement and gap configuration are optimized generation by generation, and the position and contact point properties of the particles are adjusted to output the microstructure adjustment results.

[0023] The microstructure adjustment results include particle size distribution, particle gap distribution and porosity.

[0024] As a further solution of the present invention, the force transmission path between particles is determined by measuring the force change at the contact point, and the geometric parameters of the contact point are extracted according to the contact point between the particles, including the position, the normal direction of the contact surface and the contact radius, and the initial conditions of the normal force and the tangential force are defined for each contact point. The contact point generates a force response by applying an external force by loading and the force change data is recorded. A virtual force sensor is placed at the contact point position, and the external force is gradually loaded and the dynamic changes of the normal force and the tangential force are recorded. The force change curve is established by decomposing the normal component and the tangential component of the force, and the force path between the particle contact points is connected according to the force change, the force transmission order between the particles is extracted and the complete path is recorded, the stress distribution of the contact point is calculated, and the stress value is extracted through the relationship between the contact area and the normal force. The stress change of the contact point is recorded, and the force equilibrium state between the particles is verified in combination with the force path and the stress distribution.

[0025] As a further solution of the present invention, the specific steps of generating the mechanical behavior prediction result are:

[0026] Based on the microstructure adjustment results, the contact point positions, contact areas, and contact normal forces between the particles are extracted, the geometric relationships and mechanical parameters between the particles are analyzed point by point using standardized tools, the variation range of the normal forces and tangential forces of the particles is recorded, the contact parameter values in the model are adjusted, the external loading conditions and action range are set, and the contact model parameter data is generated;

[0027] Based on the contact model parameter data, a loading tool is used to apply different levels of pressure between the particles, and the external force is gradually increased to observe the force state of the contact point. By recording the dynamic response of the normal force and tangential force between the particles point by point, the change of the force distribution of the contact point is analyzed, and the microscopic contact force data between the particles is summarized according to the requirements of mechanical analysis to generate microscopic contact force distribution data;

[0028] Based on the microscopic contact force distribution data, the dynamic response of the inter-particle force is calculated under multiple pressure conditions by loading a simulator, the mechanical relationship of the contact points is gradually adjusted, the distribution pattern of the force pairs between the particles is established, the overall force trend of the particles is analyzed, and the future force state is predicted, and the mechanical behavior prediction results are output;

[0029] The mechanical behavior prediction results include particle contact force distribution, force uniformity and mechanical equilibrium state between particles.

[0030] As a further solution of the present invention, the specific steps of generating the macro stress distribution model are:

[0031] Based on the mechanical behavior prediction results, the point data of the particles are interpolated by radial basis function interpolation method, and the microscopic stress field of the particles is established by regional division. The force changes in the contact area between the particles are analyzed and regional stress integration is completed to generate microscopic stress distribution data;

[0032] Based on the microscopic stress distribution data, a mapping relationship from microscopic to macroscopic is established to merge the microscopic force change data of the particles, and the macroscopic stress input between the particles is distributed according to the coordinate area. The distribution trend of the macroscopic stress is analyzed to generate stress correlation data;

[0033] Based on the stress correlation data, the macro stress value is calculated by the finite element method for each coordinate region and the stress distribution field is constructed. At the same time, the macro mechanical change trend of the entire particle is recorded and the macro stress distribution model is output;

[0034] The macroscopic stress distribution model includes a macroscopic stress distribution diagram, a stress concentration area and a stress transfer path.

[0035] As a further solution of the present invention, the radial basis function interpolation method is according to the formula:

[0036]

[0037] Where: x is the coordinate of the target particle point, x i are the known coordinates of the contact points between particles, ||xx i || is the Euclidean distance between the target particle point and the contact point between known particles, λ i is the weight coefficient, φ(||xx i||) is the radial basis function, β is the smoothing factor, P(x) is the polynomial function, α is the weight adjustment coefficient, ψ(x,v) is the dynamic weight function, and v is the velocity vector of the target particle.

[0038] As a further solution of the present invention, the specific steps of generating the optimized formula result are:

[0039] Based on the macro-stress distribution model, extracting data on the influence of water-cement ratio, admixture ratio and aggregate gradation on crack resistance and bearing capacity, analyzing the correlation between parameters and macro-stress distribution item by item, summarizing the key parameters affecting crack resistance and bearing capacity, screening the initial range of water-cement ratio, admixture ratio and aggregate gradation, and generating parameter correlation data;

[0040] Based on the parameter correlation data, a multi-group mix test plan was adopted to gradually change the water-cement ratio, admixture ratio, and aggregate gradation. The crack resistance and bearing capacity of each mix were experimentally evaluated. The bearing performance was measured using a pressure test bench, and the crack resistance performance was recorded using a crack detection system. The performance of each mix parameter was summarized and the adjustment range was adjusted to generate optimized parameter data.

[0041] Based on the optimized parameter data and in combination with the specific environmental conditions of the wind farm, the water-cement ratio, admixture ratio, and aggregate gradation are adjusted according to the environmental load requirements and temperature and humidity characteristics. Simulation verification of crack resistance and bearing capacity is performed region by region. By screening the proportion data that meets the conditions, a proportion scheme that meets the design requirements in both crack resistance and bearing capacity is ultimately determined, and the optimized formula result is output;

[0042] The optimized formula results include the optimized water-cement ratio, the optimized admixture ratio and the optimized aggregate gradation.

[0043] As a further solution of the present invention, the specific steps of generating the experimental verification results are:

[0044] Based on the optimized formula results, cement, aggregates and admixtures are weighed in batches using precise weighing equipment. After verifying the weight of the materials using metering equipment, they are added to the mixer in sequence. The speed and time of the mixing equipment are set to mix the materials. The uniformity of the materials during the mixing process is observed and the mixing data is recorded to generate mixing test data.

[0045] Based on the mixing test data, the mixed material is tested for crack resistance, strength, and fluidity using a material performance tester, strength data is recorded using a pressure loading device, crack resistance performance is collected using a crack monitoring device, and deviation data is collated and analyzed. The formulation parameters are improved by adjusting the water-cement ratio and the admixture ratio to generate experimental adjustment formulation data;

[0046] Adjust the formulation data based on the experiment, repeatedly adjust the formulation, conduct mixing and performance testing, gradually verify the material properties through pressure loading and crack monitoring equipment, and finally determine the formulation parameters through comprehensive analysis, record the material's crack resistance and strength data, and output the experimental verification results;

[0047] The experimental verification results include crack resistance test results, bearing capacity test results and durability test results.

[0048] As a further solution of the present invention, the specific steps of generating the performance evaluation result are:

[0049] Based on the experimental verification results, the strength and durability test data of the material are extracted, and the test results are sorted item by item using data analysis tools. By comparing the crack resistance and load-bearing performance data, a material performance record table is established to generate performance evaluation data;

[0050] Based on the performance evaluation data, combined with the climate and load environment data of the wind farm, by matching material performance with environmental conditions and conducting comparative analysis item by item, the adaptation data is collated and the final conclusions are summarized using data recording tools to generate environmental adaptation data;

[0051] Based on the environmental adaptation data, the material's strength, crack resistance, and durability data are comprehensively considered, and performance indicators are verified one by one to confirm whether they meet the design requirements. The final performance indicator evaluation results are recorded and archived, and the performance evaluation results are output;

[0052] The performance evaluation results include the material's compressive strength, tensile strength and long-term durability indicators.

[0053] Compared with the prior art, the advantages and positive effects of the present invention are:

[0054] 1. In this invention, by quantifying the ratio of cement, aggregate and admixture, the discrete element method is used to simulate and analyze the stress and bonding conditions in the contact area between cement particles and aggregate particles. In combination with the genetic algorithm, the gap distribution between particles is adjusted to make the microstructure of the material more uniform and dense, thereby improving the stability of the internal structure.

[0055] 2. In this invention, the application of the discrete element method further refines the mechanical behavior of the particles, avoiding the problem of local stress concentration that may be caused by the randomness of particle arrangement. The radial basis function interpolation method is used to continuously map the microscopic stress distribution data, effectively bridging the discreteness of microscopic particle stress and forming an accurate correlation between the microscopic stress distribution and the macroscopic stress field.

[0056] 3. In the present invention, a macroscopic stress distribution model is established by using finite element analysis, which provides data support for the design of the material's crack resistance and bearing capacity. By optimizing the synergistic effect of microstructure and macroscopic stress, the comprehensive performance of cement-based grouting materials in terms of fatigue resistance, deformation resistance and environmental erosion resistance is improved, ensuring mechanical stability and long-term durability. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a schematic diagram of the main steps of the present invention;

[0058] Figure 2 This is a schematic diagram of the refinement of S1 of the present invention;

[0059] Figure 3 This is a schematic diagram of the refinement of S2 of the present invention;

[0060] Figure 4 This is a schematic diagram of the refinement of S3 of the present invention;

[0061] Figure 5 This is a schematic diagram of the refinement of S4 of the present invention;

[0062] Figure 6 This is a schematic diagram of the refinement of S5 of the present invention;

[0063] Figure 7 This is a schematic diagram of the refinement of S6 of the present invention;

[0064] Figure 8 This is a detailed schematic diagram of S7 of the present invention. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0066] See also Figure 1 The present invention provides a technical solution: a method for preparing an ultra-high strength cement-based grouting material for wind power generation, comprising the following steps:

[0067] S1: According to the requirements of the proportion of cement-based grouting materials, a specific water-cement ratio, admixture ratio and aggregate grading are selected to accurately quantify the ratio of cement to aggregate and generate the initial proportion result;

[0068] S2: Based on the initial mix ratio results, the discrete element method is used to simulate the stress and bonding conditions in the contact area between cement particles and aggregate particles. At the same time, a genetic algorithm is used to adjust the particle gap according to the stress and bonding conditions to obtain the microstructure adjustment results;

[0069] S3: Based on the microstructure adjustment results, the microscopic contact force distribution is calculated by adjusting the contact model parameters, simulating the forces between particles under different pressures, and obtaining the mechanical behavior prediction results;

[0070] S4: Based on the mechanical behavior prediction results, extract the micro-stress distribution data, use the radial basis function interpolation method to interpolate the micro-stress, map the discrete micro-stress data into a continuous distribution field, and use the finite element method to perform correlation analysis on the micro-stress and macro-stress to generate a macro-stress distribution model;

[0071] S5: Based on the macroscopic stress distribution model, by optimizing parameters including water-cement ratio, admixture ratio, and aggregate gradation, a formula with appropriate crack resistance and bearing capacity is designed for the specific environment of the wind farm, and the optimized formula is obtained;

[0072] S6: Based on the optimized formula results, conduct mixing tests on actual materials, adjust the experimental formula according to material performance indicators and through actual application tests, and generate experimental verification results;

[0073] S7: Based on the experimental verification results, the material performance is evaluated according to the strength and durability of the material to confirm whether it meets the specific needs of wind power generation and obtain the performance evaluation results.

[0074] See also Figure 2 , the specific steps to generate the initial ratio results are:

[0075] S101: Select cement, aggregate, and admixtures based on the mix ratio requirements of cement-based grouting materials, use metering equipment to measure weight, adjust the water-cement ratio, aggregate gradation, and admixture ratio to ensure accuracy, and record the ratio parameters to generate mix ratio parameter data;

[0076] S102: Based on the mix parameter data, cement, aggregate, and admixture are added to the mixing equipment in proportion and sequentially, and mechanical stirring is used to mix them. The speed and time of the mixing equipment are adjusted, the mixing state is checked, and the operation is adjusted until the materials are uniform, thereby generating a uniformly proportioned material;

[0077] S103: Based on uniformly proportioned materials, a test mold is filled with the materials and the molding performance is tested using standard test tools. At the same time, the water-cement ratio and aggregate gradation data are adjusted for optimization until the grouting material ratio requirements are met and the initial ratio result is output;

[0078] Based on the proportion requirements of cement-based grouting materials, metering equipment is used to measure the weight of cement, aggregates and admixtures. The metering equipment includes an electronic balance and an automatic weighing device. The accuracy of the electronic balance is 0.01g, and the weighing range of the automatic weighing device is 1g to 50kg. The measurement operation is performed according to the weight ratio of cement (40%-60%), aggregate (30%-50%), and admixture (5%-10%), and the actual weight value of each component is recorded. The water-cement ratio range is adjusted to 0.3-0.5, the aggregate particle size range is 5-20mm, and the admixture ratio parameter is 5%-10% to ensure the accuracy of the ratio data and generate the ratio parameter data;

[0079] Based on the proportion parameter data, cement, aggregate and admixture are added to the mixing equipment in proportion in sequence. The mixing equipment is a double-shaft mixer. The speed control range of the mixing equipment is 60-120 rpm. The order of addition is to add aggregate first, then cement, and finally admixture. Mechanical stirring is used for mixing. The speed of the mixing equipment is set to 90 rpm and the stirring time is set to 5 minutes. The operation state of the equipment is adjusted by setting the stirring interval time to 1 minute. The mixing state is monitored by a video monitoring system to check the degree of mixing uniformity. The operation is adjusted by adjusting the stirring time range to 3-7 minutes and the speed range to 60-120 rpm until the mixing is uniform and a uniform proportioned material is generated.

[0080] Based on uniformly proportioned materials, a test mold with a size of 40mm×40mm×160mm is used for material filling. The material is pressed into the mold layer by layer at a pressure of 20kPa using a pressure filling device to control the compactness of the filling material. Standard test tools such as an electronic fluidity tester are used to test the forming performance and measure the flow properties of the filled material. The measurement parameters include the flow extension value range of 200mm-250mm. The material ratio is optimized by adjusting the water-cement ratio range of 0.3-0.5 and the aggregate grading range of 5-20mm. During the optimization process, single parameters are gradually changed and the corresponding fluidity data are measured and tested repeatedly until the ratio requirements of the grouting material are met, generating the initial ratio result.

[0081] Among them, the initial proportioning results include the mass ratio of cement to aggregate, water-cement ratio and admixture ratio.

[0082] See also Figure 3 , the specific steps to generate the microstructure adjustment results are:

[0083] S201: Based on the initial mix ratio results, cement particle and aggregate particle parameters are imported. The particle shape, size, and distribution characteristics are measured using a scanning electron microscope. The particles are discretized using the discrete element method to establish a particle point distribution model. The particle contact area and contact point properties are defined to generate a particle contact model.

[0084] S202: Based on the particle contact model, perform inter-particle force analysis. Apply constant pressure to the particles using constant pressure loading. Determine the force transmission path between the particles by measuring the force change at the contact point. Analyze the bond strength between the particles to generate particle force and bond data.

[0085] S203: Based on the particle force and bonding data, the arrangement and gaps between particles are optimized using a genetic algorithm. The genetic algorithm is used to generate an initial population, with each individual representing a particle arrangement. The combined performance of particle porosity and bonding strength is evaluated, and particle arrangement schemes with excellent performance are selected for crossover and mutation operations. The particle arrangement and gap configuration are optimized generation by generation, and the particle position and contact point properties are adjusted to output the microstructure adjustment results.

[0086] Based on the initial ratio results, the parameters of cement particles and aggregate particles were imported, and the particles were measured using a scanning electron microscope. The measurement content included particle shape, particle size and particle distribution characteristics. The resolution of the scanning electron microscope was set to 1nm. The polygon fitting algorithm was used to fit and characterize the shape boundary of the particle shape. The particle size was measured by the minimum circumscribed circle method to measure its diameter range. The particle distribution characteristics were calculated using the uniformity distribution index. The particles were discretized using the discrete element method. A point distribution model was established based on the spatial coordinates of the particles. The particle points were described using a three-dimensional Cartesian coordinate system with a coordinate range of 0-100mm. The radius of the particle contact area was defined as 1mm. The contact point properties included contact stiffness and bond strength. The contact stiffness was set to 100N / mm and the initial bond strength was 10MPa. The particle contact model was generated.

[0087] Based on the particle contact model, the force between particles is analyzed. A constant pressure loading method is used to apply pressure to the particles. The loading pressure is set to 50 MPa. The loading process is achieved through simulated distributed loading technology. The loading range covers the particle contact area. The force transmission path between particles is determined by measuring the change data of the contact point force. The measurement data sampling frequency is set to 10 Hz. The force transmission path is identified by the maximum force point step-by-step tracking algorithm. The identification range includes all contact points between particles. The bonding strength between particles is analyzed. The bonding strength calculation is based on the pressure distribution of the contact points. The pressure distribution is completed by Gaussian distribution fitting. The fitting parameters include the pressure mean and standard deviation to generate the particle force bonding data.

[0088] Based on the particle force and adhesion data, a genetic algorithm is used to optimize the arrangement and gap between particles. The initial population is generated by the genetic algorithm, and the population size is set to 100. Each individual in the population represents a particle arrangement. The genetic code of each individual is binary coded. The combined performance of particle porosity and adhesion strength is evaluated. The porosity is calculated based on the spatial proportion of the particle arrangement, and the adhesion strength is calculated based on the average pressure of the particle contact points. The particle arrangement scheme with excellent performance is selected, and the selection ratio is 20% of the total population. The selected individuals are crossovered using a single-point crossover method with a crossover probability of 0.8. The individuals after crossover are mutated with a mutation probability of 0.05. The mutation operation includes adjusting the particle position and redefining the contact point properties. The particle arrangement and gap configuration are optimized generation by generation. The number of optimization generations is set to 50. Finally, the particle position coordinate range and contact point properties, including contact stiffness and adhesion strength parameters, are adjusted to generate the microstructure adjustment results.

[0089] Among them, the microstructure adjustment results include particle size distribution, particle gap distribution and porosity.

[0090] The force transmission path between particles is determined by measuring the force change at the contact point. Based on the contact points between particles, the geometric parameters of the contact points are extracted, including the position, normal direction of the contact surface and contact radius. The initial conditions of the normal force and tangential force are defined for each contact point. The contact point generates a force response by applying external force and the force change data is recorded. A virtual force sensor is placed at the contact point position, and the external force is gradually loaded and the dynamic changes of the normal force and tangential force are recorded. The force change curve is established by decomposing the normal component and tangential component of the force, and the force path between the particle contact points is connected according to the force change. The force transmission order between the particles is extracted and the complete path is recorded. The stress distribution of the contact point is calculated. At the same time, the stress value is extracted through the relationship between the contact area and the normal force. The stress change of the contact point is recorded, and the force equilibrium state between the particles is verified by combining the force path and stress distribution.

[0091] See also Figure 4 , the specific steps to generate the mechanical behavior prediction results are:

[0092] S301: Based on the microstructure adjustment results, the contact point position, contact area and contact normal force between the particles are extracted. The geometric relationship and mechanical parameters between the particles are analyzed point by point using standardized tools. The variation range of the normal force and tangential force of the particles is recorded. The contact parameter values in the model are adjusted, the external loading conditions and action range are set, and the contact model parameter data is generated.

[0093] S302: Based on the contact model parameter data, a loading tool is used to apply different levels of pressure between the particles. The external force is gradually increased to observe the force state of the contact points. The dynamic response of the normal force and tangential force between the particles is recorded point by point, and the changes in the force distribution of the contact points are analyzed. The microscopic contact force data between the particles are summarized according to the requirements of mechanical analysis, and the microscopic contact force distribution data is generated.

[0094] S303: Based on the microscopic contact force distribution data, the dynamic response of the inter-particle force is calculated under multiple pressure conditions through a loading simulator. The mechanical relationship of the contact points is gradually adjusted to establish the distribution pattern of the force pairs between the particles. The overall force trend of the particles is analyzed and the future force state is predicted. The mechanical behavior prediction results are output.

[0095] Based on the results of microstructure adjustment, the contact point position, contact area and contact normal force between particles are extracted, and the geometric relationship between particles is analyzed point by point using a geometric analysis tool. The analysis tool parameters include the particle coordinate position and the particle radius range is 0.1mm to 10mm. The contact area is calculated based on the overlapping area of the two particles. The contact point area is solved by three-dimensional coordinates using an analytical algorithm to analyze the mechanical parameters between particles. The normal force is calculated based on the distribution of inter-particle pressure in the normal direction of the contact point. The tangential force is calculated using a contact point friction model with a friction coefficient set to 0.3 to 0.5. The variation range of the normal force and tangential force is recorded point by point. The variation range of the normal force is 0 to 100N, and the variation range of the tangential force is 0 to 50N. The contact parameter values in the model are adjusted, including the contact point stiffness range set to 10 to 100N / mm and the bond strength range set to 1 to 10MPa. The external loading conditions and action range are set. The loading condition is constant pressure, and the loading range covers all contact points of the particle distribution model to generate contact model parameter data.

[0096] Based on the contact model parameter data, a loading tool is used to apply different levels of pressure between the particles. The pressure level setting range is 10MPa to 50MPa. The loading tool realizes the loading process through a finite loading plate. The loading plate movement speed is set to 1mm / s. The external force is gradually increased to the target pressure value. The dynamic response of the normal force and tangential force between the particles is recorded point by point through a pressure sensor. The sampling frequency of the pressure sensor is set to 100Hz. The recorded data includes the time, position, normal force and tangential force values of the contact point. The force distribution of the contact point is calculated. The normal force distribution is analyzed using a pressure distribution diagram. The tangential force is linearly fitted through a friction force model. The microscopic contact force data between the particles are summarized. The summarized data content includes the time series of the particle force, the contact point distribution area and the mechanical response trend, and the microscopic contact force distribution data is generated.

[0097] Based on the microscopic contact force distribution data, the dynamic response of the inter-particle force is calculated under multiple pressure conditions through a loading simulator. The loading simulator sets the pressure range to 5MPa to 50MPa, the loading increment is 5MPa, and the duration of each pressure calculation is 60 seconds. The mechanical relationship of the contact points is gradually adjusted. The adjustment content includes the contact point position coordinate range and the distribution of pressure values. The position coordinate range is adjusted to 0 to 50mm. The pressure value distribution is generated by the Gaussian fitting method to generate a continuous pressure field. The distribution pattern of the force pairs between the particles is established. The force pairs include the paired distribution of normal force and tangential force. The paired relationship is constructed through the maximum pressure and friction force distribution point. The overall force trend of the particles is analyzed, and the force field of the particles in the overall model is vector-synthesized to predict the future force state and generate mechanical behavior prediction results.

[0098] Among them, the mechanical behavior prediction results include particle contact force distribution, force uniformity and mechanical equilibrium state between particles.

[0099] See also Figure 5 , the specific steps to generate the macro stress distribution model are:

[0100] S401: Based on the mechanical behavior prediction results, the radial basis function interpolation method is used to interpolate the particle point data, and the particle micro-stress field is established through regional division. The force changes in the contact area between particles are analyzed and regional stress integration is completed to generate micro-stress distribution data;

[0101] S402: Based on the microscopic stress distribution data, a mapping relationship from microscopic to macroscopic is established to merge the microscopic force variation data of the particles. At the same time, the macroscopic stress input between the particles is distributed according to the coordinate area, and the distribution trend of the macroscopic stress is analyzed to generate stress correlation data.

[0102] S403: Based on the stress correlation data, the macro stress value is calculated for each coordinate region by the finite element method and the stress distribution field is constructed. At the same time, the macro mechanical change trend of the entire particle is recorded and the macro stress distribution model is output;

[0103] Based on the mechanical behavior prediction results, the radial basis function interpolation method is used to interpolate the particle point data. The radial basis function interpolation method uses the Gaussian function. The basis function parameters include the shape parameter 0.5 and the influence radius 10mm. The coordinate range of the interpolation point is 0 to 50mm. The coordinates of each contact point and the corresponding force value are used for interpolation. During the interpolation process, the stress value of the target point is obtained by point-by-point calculation. The interpolation results are divided into regions, and the quadtree partitioning algorithm is used to divide the particle region into multiple sub-regions. The minimum regional unit size is 1mm×1mm. The force changes in the contact area between the particles are analyzed point by point, and the gradient algorithm is used to calculate the stress change trend of the contact area. The regional stress integration is completed and summarized into the total force data of each region to generate micro stress distribution data.

[0104] Based on the microscopic stress distribution data, the data is merged by establishing a mapping relationship from micro to macro. The linear weighted method is used to merge the microscopic force change data of the particles. The weighting factor is calculated based on the area ratio of the sub-region where the particle is located, and the weighting range is 0.1 to 1. The macroscopic stress input between the particles is distributed according to the coordinate region. The stress value of the regional center point is used as the initial value of the input point and is extended to the entire region through an interpolation algorithm. The distribution trend of the macroscopic stress is analyzed, and the principal component analysis method is used to extract the main features of the stress changes in different regions. The number of features is set to 2 to 3. The overall change trend of the macroscopic stress is summarized to generate stress correlation data.

[0105] Based on the stress correlation data, the macro stress value is calculated by the finite element method for each coordinate region. The three-dimensional finite element method is used to calculate the stress in the region. The eight-node three-dimensional isoparametric element is selected as the element type. The size of each element is 5mm×5mm×5mm. The input stress parameters include the normal stress range of 0 to 50MPa and the tangential stress range of 0 to 20MPa. The point-by-point iterative solution method is used in the calculation process. The number of iterations is set to 100 times and the convergence error range is set to 0.001. The stress distribution field is constructed and the distribution field is visualized using a three-dimensional grid diagram. At the same time, the overall macroscopic mechanical change trend of the particle is recorded. The recorded content includes the particle's force time series, stress change rate and the directional characteristics of the macroscopic force distribution, and a macroscopic stress distribution model is generated.

[0106] Among them, the macroscopic stress distribution model includes the macroscopic stress distribution diagram, stress concentration area and stress transfer path.

[0107] Radial basis function interpolation method, according to the formula:

[0108]

[0109] Where: x is the coordinate of the target particle point, x iare the known coordinates of the contact points between particles, ||xx i || is the Euclidean distance between the target particle point and the contact point between known particles, λ i is the weight coefficient, φ(||xx i ||) is the radial basis function, β is the smoothing factor, P(x) is the polynomial function, α is the weight adjustment coefficient, ψ(x,v) is the dynamic weight function, and v is the velocity vector of the target particle;

[0110] Execution process: First, according to the distribution characteristics of the particles in the grouting material, determine the coordinates x of the target particle point and the coordinates x of the contact point between the known particles. i , calculate the distance between particles||xx i ||, and through the radial basis function φ(||xx i ||), solve the interpolation function value between the target point and the known contact point, then, through the least squares fitting method, establish the polynomial function P(x) based on the overall microscopic stress data of the grouting material to supplement the global trend of the stress distribution between the particles, then introduce the velocity vector v of the target particle to dynamically correct the microscopic stress deviation caused by the fluidity of the grouting, where γ is the velocity field diffusion factor, determined in combination with the fluidity experimental data of the grouting material, c is the center point of the velocity field, indicating the center of the main flow direction, then, calculate the weight adjustment coefficient α according to the stress change amplitude of the contact point between the particles, obtained by fitting the particle contact test, and finally the calculated weight coefficient λ i 、Interpolation function value φ(||xx i ||), the polynomial function P(x), and the dynamic correction function ψ(x,v) are substituted into the formula to generate the microstress distribution value of the target particle point, which is used to comprehensively describe the micromechanical properties of the grouting material.

[0111] See also Figure 6 , the specific steps to generate the optimized formula results are:

[0112] S501: Based on the macro-stress distribution model, extract the data on the influence of water-cement ratio, admixture ratio and aggregate gradation on crack resistance and bearing capacity. By analyzing the correlation between parameters and macro-stress distribution item by item, summarize the key parameters affecting crack resistance and bearing capacity, select the initial range of water-cement ratio, admixture ratio and aggregate gradation, and generate parameter correlation data;

[0113] S502: Based on the parameter correlation data, a multi-mix test plan is used to gradually change the water-cement ratio, admixture ratio, and aggregate gradation. Experimental evaluations of crack resistance and bearing capacity are conducted for each mix. The bearing capacity is measured using a pressure test bench, and the crack resistance performance is recorded using a crack detection system. The performance of each mix parameter is summarized and adjusted within a range to generate optimized parameter data.

[0114] S503: Based on the optimized parameter data and the specific environmental conditions of the wind farm, the water-cement ratio, admixture ratio, and aggregate gradation are adjusted according to the environmental load requirements and temperature and humidity characteristics. Simulation verification of crack resistance and bearing capacity is performed region by region. By screening the mix ratio data that meets the requirements, a mix ratio scheme that meets both the crack resistance and bearing capacity requirements is ultimately determined, and the optimized formula is output.

[0115] Based on the macro-stress distribution model, the influence data of water-cement ratio, admixture ratio and aggregate gradation on crack resistance and bearing capacity were extracted. The correlation between water-cement ratio, admixture ratio and aggregate gradation and macro-stress distribution was analyzed item by item using stepwise regression analysis. The model equation of stepwise regression analysis was in the form of linear polynomial. The input parameters included water-cement ratio range of 0.3-0.5, admixture ratio range of 5%-10% and aggregate gradation range of 5-20mm. The target parameters were stress distribution changes of crack resistance and bearing capacity. The key parameters affecting crack resistance and bearing capacity were screened by the size and significance test of partial regression coefficient. The statistical significance of partial regression coefficient was tested by P value, and P value less than 0.05 was set as the screening standard. The initial range of water-cement ratio, admixture ratio and aggregate gradation was adjusted according to the screening results. The specific values of each parameter within the range were recorded to generate parameter association data.

[0116] Based on parameter correlation data, a multi-group mix test scheme was adopted to gradually change the water-cement ratio, admixture ratio and aggregate gradation, and the crack resistance and bearing capacity of each mix were experimentally evaluated. The experiment was completed jointly using a pressure test bench and a crack detection system. The loading range of the pressure test bench was set to 0-50MPa, and the loading method was step-by-step loading with an increment of 5MPa per level and a loading time of 10 seconds per level. The bearing performance data including the bearing capacity limit and the corresponding loading pressure were recorded. The crack detection system used a high-definition camera to monitor the time, location and width of crack generation. The camera resolution was set to 0.1mm, and the crack width range was set to 0.01-5mm. By summarizing the crack resistance and bearing performance of each mix parameter, the parameter values within the range were adjusted using a step-by-step screening method. The parameter combination with the best performance was retained during the adjustment process to generate optimized parameter data.

[0117] Based on the optimized parameter data, the water-cement ratio, admixture ratio, and aggregate gradation are adjusted in combination with the specific environmental conditions of the wind farm. The environmental conditions include environmental load requirements and temperature and humidity characteristics. The environmental load range is set to 10-50MPa, the temperature range is -20℃ to 40℃, and the humidity range is 10%-90%. The water-cement ratio, admixture ratio, and aggregate gradation are gradually adjusted by region. During the adjustment process, simulation calculations are used to verify the crack resistance and bearing capacity. The simulation calculations are completed through finite element simulations. The input parameters include the specific values within the adjusted ratio range and the environmental conditions characteristics of the region. The ratio data that meets the crack resistance and bearing capacity requirements are screened, and the specific parameter values after the final screening are recorded to determine the ratio scheme that meets the environmental requirements of the wind farm and generate the optimized formula results.

[0118] Among them, the optimized formula results include the optimized water-cement ratio, the optimized admixture ratio and the optimized aggregate gradation.

[0119] See also Figure 7 ,The specific steps to generate experimental verification results are:

[0120] S601: Based on the optimized formula results, cement, aggregates, and admixtures are weighed in batches using precision weighing equipment. After verifying the weight of the materials using metering equipment, they are added to the mixer in sequence. The speed and time of the mixing equipment are set to mix the materials. The uniformity of the materials during the mixing process is observed and the mixing data is recorded to generate mixing test data.

[0121] S602: Based on the mixing test data, the mixed material is tested for crack resistance, strength, and fluidity using a material performance tester. Strength data is recorded using a pressure loading device, and crack resistance performance is collected using a crack monitoring device. Deviation data is collated and analyzed, and formulation parameters are improved by adjusting the water-cement ratio and admixture ratio to generate experimental adjustment formulation data.

[0122] S603: Adjust the formula data based on the experiment. After repeated adjustments, conduct mixing and performance testing. Use pressure loading and crack monitoring equipment to gradually verify the material properties. Finally, determine the formula parameters through comprehensive analysis, record the material's crack resistance and strength data, and output the experimental verification results.

[0123] Based on the optimized formula results, cement, aggregates and admixtures were weighed in batches using precision weighing equipment. The weighing equipment included a high-precision electronic balance and an automatic weighing system. The accuracy of the electronic balance was set to 0.01g, and the maximum weighing range of the automatic weighing system was 50kg. The weight of each batch of materials was verified one by one, and the weighing range of cement, aggregates, and admixtures was recorded using metering equipment. The weighing range was 10-20kg, 20-30kg, and 1-5kg, respectively. The weighed materials were added to the mixer in sequence. The mixer speed was set to 60-120rpm, and the mixing time was set to 5 minutes. The mixing equipment was operated in an intermittent operation mode with an intermittent time of 1 minute. The uniformity of the materials during the mixing process was observed in real time using a mixing state monitoring device. Mixing data including speed, time, and uniformity level were recorded to generate mixing test data.

[0124] Based on the mixed test data, a material performance tester was used to test the crack resistance, strength and fluidity of the mixed material. The crack resistance test used a crack monitoring system. The system collected crack extension data through a high-definition camera with a resolution of 0.1mm and a crack width measurement range of 0.01-5mm. The strength test was completed using a pressure loading device with a loading range of 0-50MPa and a loading mode of step-by-step loading with an increment of 5MPa per level and a loading time of 10 seconds per level. The fluidity test was completed using an extension value measuring instrument with an extension value measurement range of 200mm-300mm. The collected crack resistance and strength data were analyzed one by one, and the deviation values in the material properties were sorted out using data processing tools. The single-factor variance analysis method was used to identify the main ratio parameters affecting the performance. The water-cement ratio was adjusted to 0.3-0.5 and the admixture ratio was adjusted to 5%-8% to generate experimental adjustment formula data.

[0125] Adjust the formula data based on the experiment, repeatedly adjust the formula, and then conduct mixing and performance testing. Use pressure loading equipment and crack monitoring systems to record the bearing capacity data and crack resistance performance of each test. The bearing capacity data includes the maximum bearing capacity and the corresponding deformation. The crack resistance performance data includes the crack initiation time and crack width change. By summarizing the performance of the mix ratio of each test, a weighted average method is used to comprehensively calculate the crack resistance and strength data of different mix ratios. The calculation weight is determined based on the influence coefficient of crack width and strength limit. The mix ratio parameter range that meets the requirements is gradually screened. Finally, the specific values of the water-cement ratio, admixture ratio and aggregate gradation of the mix ratio are determined. The crack resistance and strength data of the final material are recorded to generate experimental verification results.

[0126] Among them, the experimental verification results include crack resistance test results, bearing capacity test results and durability test results.

[0127] See also Figure 8,The specific steps to generate performance evaluation results are:

[0128] S701: Based on the experimental verification results, extract the strength and durability test data of the material, use data analysis tools to organize the test results item by item, establish a material performance record table by comparing the crack resistance and load-bearing performance data, and generate performance evaluation data;

[0129] S702: Based on the performance evaluation data, combined with the wind farm's climate and load environment data, a comparative analysis is performed item by item by matching material properties with environmental conditions. The adaptation data is collated and the final conclusions are summarized using data recording tools to generate environmental adaptation data.

[0130] S703: Based on the environmental adaptation data, the material's strength, crack resistance, and durability data are integrated, and performance indicators are verified one by one to confirm whether they meet the design requirements. The final performance indicator evaluation results are recorded and archived, and the performance evaluation results are output;

[0131] Based on the experimental verification results, the strength and durability test data of the material were extracted, and the test results were sorted item by item using data analysis tools. The strength test data included the compressive strength range of 0-50MPa and the tensile strength range of 0-10MPa. The durability test data included the fatigue resistance times and erosion resistance level. The Python data analysis library Pandas was used to tabulate the test data. The crack resistance performance data were summarized into the same column according to the crack width and crack generation time, and the load-bearing performance data were summarized into another column according to the loading pressure and corresponding deformation. Duplicates and invalid data were deleted by defining data cleaning rules. When comparing the crack resistance and load-bearing performance data, Matplotlib was used to draw a line chart to visualize the performance trend according to the material load condition and crack generation time. By comparing the mean and standard deviation of each data set, test results with a data deviation range of less than 10% were selected. A material performance record table was established, which recorded the compressive strength, tensile strength, crack generation time and corresponding load values to generate performance evaluation data.

[0132] Based on the performance evaluation data, combined with the climate and load environment data of the wind farm, the climate data includes the temperature range of -20℃ to 40℃ and the humidity range of 10%-90%, and the load data includes the foundation pressure range of 10MPa to 50MPa. By matching the material performance and environmental conditions, a comparative analysis is conducted item by item, and the regression analysis method is used to verify the correlation between the material performance and environmental parameters. The regression model is in the form of a quadratic polynomial, with the input variables being temperature, humidity and load data, and the output variables being crack resistance and bearing performance indicators. The Scikit-learn tool is used for model training. The training data set includes material strength data and crack extension data under different climate conditions. The regression model parameters are set to a maximum number of iterations of 500 and a learning rate of 0.01. The results of each set of regression analysis are classified and sorted, and the adaptation of each material ratio under different environmental conditions is recorded. The adaptation table is generated based on the sorted results, and the data of the adapted materials are recorded in the table, and environmental adaptation data is generated.

[0133] Based on environmental adaptation data, the material's strength, crack resistance, and durability data are integrated to verify performance indicators one by one. Finite element analysis tools are used to simulate material performance according to regional environmental conditions. The input parameters of the finite element analysis model include the material compressive strength range of 0-50MPa, the tensile strength range of 0-10MPa, and the crack extension width range of 0.01-5mm. The model is loaded one by one for simulation verification under the temperature, humidity conditions, and load range of each region. Each set of data is dynamically recorded during the simulation process. After verification of material performance indicators, the final crack resistance, strength, and durability indicators are recorded. The performance simulation and experimental results of all regions are archived to generate performance evaluation results.

[0134] Among them, the performance evaluation results include the material's compressive strength, tensile strength and long-term durability indicators.

[0135] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A method for preparing ultra-high strength cement-based grouting material for wind power generation, characterized in that: The following steps are involved: Step 1: Based on the mix ratio requirements of cement-based grouting materials, select a specific water-cement ratio, admixture ratio, and aggregate grading, accurately quantify the cement-aggregate ratio, and generate a preliminary mix ratio result; Step 2: Based on the initial mix ratio, the stress and bonding conditions in the contact area between cement particles and aggregate particles are simulated by discrete element method, and the particle gaps are adjusted according to the stress and bonding conditions by genetic algorithm to obtain microstructure adjustment results; Step 3: Based on the microstructure adjustment results, the microscopic contact force distribution is calculated by adjusting the contact model parameters, simulating the forces between particles under different pressures, and obtaining mechanical behavior prediction results; Step 4: Based on the mechanical behavior prediction results, extract the microstress distribution data, use the radial basis function interpolation method to interpolate the microstress, map the discrete microstress data into a continuous distribution field, and use the finite element method to perform correlation analysis on the microstress and macrostress to generate a macrostress distribution model; Step 5: Based on the macroscopic stress distribution model, by optimizing parameters including water-cement ratio, admixture ratio, and aggregate gradation, a formula with appropriate crack resistance and bearing capacity is designed for the specific environment of the wind farm, and an optimized formula result is obtained; Step 6: Based on the optimized formula results, conduct a mixing test on the actual material, adjust the experimental formula according to the material performance indicators and through actual application tests, and generate experimental verification results; Step 7: Based on the experimental verification results, the material performance is evaluated according to the strength and durability of the material to confirm whether it meets the specific requirements of wind power generation and obtain a performance evaluation result.

2. The method for preparing ultra-high strength cement-based grouting material for wind power generation according to claim 1, characterized in that: The specific steps for generating the initial ratio result are as follows: According to the requirements of the proportion of cement-based grouting materials, cement, aggregates and admixtures are selected, and weights are measured using metering equipment. The water-cement ratio, aggregate gradation and admixture ratio are adjusted to ensure accuracy. The proportion parameters are recorded and the proportion parameter data is generated. Based on the mix parameter data, cement, aggregate and admixture are sequentially added to the mixing equipment in proportion, mixed by mechanical stirring, the speed and time of the stirring equipment are adjusted, the mixing state is checked and the operation is adjusted until the materials are uniform, thereby generating a uniformly proportioned material; Based on the uniformly proportioned material, a test mold is filled with the material, and the molding performance is tested using standard test tools. At the same time, the water-cement ratio and aggregate grading data are adjusted to optimize the operation until the grouting material ratio requirements are met, and the initial ratio result is output; The initial proportioning results include the mass ratio of cement to aggregate, water-cement ratio and admixture ratio.

3. The method for preparing ultra-high strength cement-based grouting material for wind power generation according to claim 1, characterized in that: The specific steps for generating the microstructure adjustment result are: Based on the initial mix ratio results, cement particle and aggregate particle parameters are imported, particle shape, size, and distribution characteristics are measured using a scanning electron microscope, the particles are discretized using the discrete element method, a particle point distribution model is established, and particle contact areas and contact point properties are defined to generate a particle contact model; Based on the particle contact model, a force analysis is performed between particles. A constant pressure load is used to apply pressure to the particles. The force transmission path between the particles is determined by measuring the force change at the contact point. The bonding strength between the particles is analyzed to generate particle force bonding data. Based on the particle force bonding data, a genetic algorithm is used to optimize the arrangement and gaps between the particles. An initial population is generated using the genetic algorithm, with each individual representing a particle arrangement. The combined performance of particle porosity and bonding strength is evaluated, and particle arrangement schemes with excellent performance are selected for crossover and mutation operations. The particle arrangement and gap configuration are optimized generation by generation, and the position and contact point properties of the particles are adjusted to output the microstructure adjustment results. The microstructure adjustment results include particle size distribution, particle gap distribution and porosity.

4. The method for preparing ultra-high strength cement-based grouting material for wind power generation according to claim 3, characterized in that: The method determines the force transmission path between particles by measuring the force change at the contact point, extracts the geometric parameters of the contact point according to the contact point between the particles, including the position, the normal direction of the contact surface and the contact radius, defines the initial conditions of the normal force and the tangential force for each contact point, applies an external force by loading so that the contact point generates a force response and records the force change data, places a virtual force sensor at the contact point position, gradually loads the external force and records the dynamic changes of the normal force and the tangential force, establishes a force change curve by decomposing the normal component and the tangential component of the force, connects the force path between the particle contact points according to the force change, extracts the force transmission order between the particles and records the complete path, calculates the stress distribution of the contact point, and extracts the stress value according to the relationship between the contact area and the normal force, records the stress change of the contact point, and verifies the force equilibrium state between the particles in combination with the force path and the stress distribution.

5. The method for preparing ultra-high strength cement-based grouting material for wind power generation according to claim 1, characterized in that: The specific steps for generating the mechanical behavior prediction results are: Based on the microstructure adjustment results, the contact point positions, contact areas, and contact normal forces between the particles are extracted, the geometric relationships and mechanical parameters between the particles are analyzed point by point using standardized tools, the variation range of the normal forces and tangential forces of the particles is recorded, the contact parameter values in the model are adjusted, the external loading conditions and action range are set, and the contact model parameter data is generated; Based on the contact model parameter data, a loading tool is used to apply different levels of pressure between the particles, and the external force is gradually increased to observe the force state of the contact point. By recording the dynamic response of the normal force and tangential force between the particles point by point, the change of the force distribution of the contact point is analyzed, and the microscopic contact force data between the particles is summarized according to the requirements of mechanical analysis to generate microscopic contact force distribution data; Based on the microscopic contact force distribution data, the dynamic response of the inter-particle force is calculated under multiple pressure conditions by loading a simulator, the mechanical relationship of the contact points is gradually adjusted, the distribution pattern of the force pairs between the particles is established, the overall force trend of the particles is analyzed, and the future force state is predicted, and the mechanical behavior prediction results are output; The mechanical behavior prediction results include particle contact force distribution, force uniformity and mechanical equilibrium state between particles.

6. The method for preparing ultra-high strength cement-based grouting material for wind power generation according to claim 1, characterized in that: The specific steps for generating the macro stress distribution model are: Based on the mechanical behavior prediction results, the point data of the particles are interpolated by radial basis function interpolation method, and the microscopic stress field of the particles is established by regional division. The force changes in the contact area between the particles are analyzed and regional stress integration is completed to generate microscopic stress distribution data; Based on the microscopic stress distribution data, a mapping relationship from microscopic to macroscopic is established to merge the microscopic force change data of the particles, and the macroscopic stress input between the particles is distributed according to the coordinate area. The distribution trend of the macroscopic stress is analyzed to generate stress correlation data; Based on the stress correlation data, the macro stress value is calculated by the finite element method for each coordinate region and the stress distribution field is constructed. At the same time, the macro mechanical change trend of the entire particle is recorded and the macro stress distribution model is output; The macroscopic stress distribution model includes a macroscopic stress distribution diagram, a stress concentration area and a stress transfer path.

7. The method for preparing ultra-high strength cement-based grouting material for wind power generation according to claim 1, characterized in that: The radial basis function interpolation method is based on the formula: Where: x is the coordinate of the target particle point, x i are the known coordinates of the contact points between particles, ||xx i || is the Euclidean distance between the target particle point and the contact point between known particles, λ i is the weight coefficient, φ(||xx i ||) is the radial basis function, P(x) is the polynomial function, α is the weight adjustment coefficient, ψ(x,v) is the dynamic weight function, and v is the velocity vector of the target particle.

8. The method for preparing ultra-high strength cement-based grouting material for wind power generation according to claim 1, characterized in that: The specific steps for generating the optimized formula result are: Based on the macro-stress distribution model, extracting data on the influence of water-cement ratio, admixture ratio and aggregate gradation on crack resistance and bearing capacity, analyzing the correlation between parameters and macro-stress distribution item by item, summarizing the key parameters affecting crack resistance and bearing capacity, screening the initial range of water-cement ratio, admixture ratio and aggregate gradation, and generating parameter correlation data; Based on the parameter correlation data, a multi-group mix test plan was adopted to gradually change the water-cement ratio, admixture ratio, and aggregate gradation. The crack resistance and bearing capacity of each mix were experimentally evaluated. The bearing performance was measured using a pressure test bench, and the crack resistance performance was recorded using a crack detection system. The performance of each mix parameter was summarized and the adjustment range was adjusted to generate optimized parameter data. Based on the optimized parameter data and in combination with the specific environmental conditions of the wind farm, the water-cement ratio, admixture ratio, and aggregate gradation are adjusted according to the environmental load requirements and temperature and humidity characteristics. Simulation verification of crack resistance and bearing capacity is performed region by region. By screening the proportion data that meets the conditions, a proportion scheme that meets the design requirements in both crack resistance and bearing capacity is ultimately determined, and the optimized formula result is output; The optimized formula results include the optimized water-cement ratio, the optimized admixture ratio and the optimized aggregate gradation.

9. The method for preparing ultra-high strength cement-based grouting material for wind power generation according to claim 1, characterized in that: The specific steps for generating the experimental verification results are: Based on the optimized formula results, cement, aggregates and admixtures are weighed in batches using precise weighing equipment. After verifying the weight of the materials using metering equipment, they are added to the mixer in sequence. The speed and time of the mixing equipment are set to mix the materials. The uniformity of the materials during the mixing process is observed and the mixing data is recorded to generate mixing test data. Based on the mixing test data, the mixed material is tested for crack resistance, strength, and fluidity using a material performance tester, strength data is recorded using a pressure loading device, crack resistance performance is collected using a crack monitoring device, and deviation data is collated and analyzed. The formulation parameters are improved by adjusting the water-cement ratio and the admixture ratio to generate experimental adjustment formulation data; Adjust the formulation data based on the experiment, repeatedly adjust the formulation, conduct mixing and performance testing, gradually verify the material properties through pressure loading and crack monitoring equipment, and finally determine the formulation parameters through comprehensive analysis, record the material's crack resistance and strength data, and output the experimental verification results; The experimental verification results include crack resistance test results, bearing capacity test results and durability test results.

10. The method for preparing ultra-high strength cement-based grouting material for wind power generation according to claim 1, characterized in that: The specific steps for generating the performance evaluation results are: Based on the experimental verification results, the strength and durability test data of the material are extracted, and the test results are sorted item by item using data analysis tools. By comparing the crack resistance and load-bearing performance data, a material performance record table is established to generate performance evaluation data; Based on the performance evaluation data, combined with the climate and load environment data of the wind farm, by matching material performance with environmental conditions and conducting comparative analysis item by item, the adaptation data is collated and the final conclusions are summarized using data recording tools to generate environmental adaptation data; Based on the environmental adaptation data, the material's strength, crack resistance, and durability data are comprehensively considered, and performance indicators are verified one by one to confirm whether they meet the design requirements. The final performance indicator evaluation results are recorded and archived, and the performance evaluation results are output; The performance evaluation results include the material's compressive strength, tensile strength and long-term durability indicators.

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