Preparation method of special ultrahigh-strength cement-based grouting material for wind power generation

Abstract

The invention relates to the technical field of cement-based materials, in particular to a preparation method of a special ultra-high-strength cement-based grouting material for wind power generation, which comprises the following steps of: performing simulation analysis on stress and bonding conditions in a contact area of cement particles and aggregate particles by quantifying the proportion of cement, aggregate and an additive and adopting a discrete element method; gap distribution among particles is adjusted in combination with a genetic algorithm, mechanical behaviors of the particles are refined through application of a discrete element method, the problem of local stress concentration possibly caused by particle arrangement randomness is avoided, microcosmic stress distribution data are continuously mapped through a radial basis function interpolation method, and the stability of the microcosmic stress distribution data is improved. A macroscopic stress distribution model is established by adopting finite element analysis, data support is provided for the crack resistance and bearing capacity design of the material, the comprehensive performance of the cement-based grouting material in the aspects of fatigue resistance, deformation resistance, environmental erosion resistance and the like is improved by optimizing the synergistic effect of a microstructure and macroscopic stress, and the comprehensive performance of the cement-based grouting material in the aspects of fatigue resistance, deformation resistance, environmental erosion resistance and the like is improved. The mechanical stability and the long-term durability are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of cement-based materials, and particularly to a preparation method of a super-high-strength cement-based grouting material for wind power generation. Background Art

[0002] The technical field of cement-based materials aims to research and develop composite materials based on cement. By scientifically designing the formula and optimizing the preparation process, the materials are made to possess specific properties and functions to meet the requirements of various engineering and application scenarios.

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

[0004] The existing technology lacks scientific modeling of the particle microstructure. The particle arrangement usually shows a random distribution, which cannot avoid the phenomenon of local stress concentration, leading to the initiation and propagation of cracks. The correlation analysis model between the microscopic mechanical behavior and the macroscopic properties has not been effectively established, resulting in the design optimization of the material only staying at a single level, making it difficult to comprehensively improve its performance. Under extreme load conditions, the grouting material may exhibit performance fluctuations or local damage, manifested as insufficient crack resistance, increased fatigue damage, and decreased durability, affecting the foundation stability of the wind power generation equipment, shortening the service life of the equipment, and increasing the maintenance cost. Summary of the Invention

[0005] The purpose of the present invention is to solve the drawbacks existing in the prior art, and to propose a preparation method of a super-high-strength cement-based grouting material for wind power generation.

[0006] To achieve the above purpose, the present invention adopts the following technical solutions: A preparation method of a super-high-strength cement-based grouting material for wind power generation, comprising the following steps: Step 1: According to the ratio requirements of the cement-based grouting material, select a specific water-cement ratio, admixture ratio, and aggregate gradation, accurately quantify the ratio of cement to aggregate, and generate an initial ratio result; Step 2: Based on the initial ratio result, simulate the force and bonding conditions in the contact area between cement particles and aggregate particles by the discrete element method, and at the same time use the genetic algorithm to adjust the particle gap according to the force and bonding conditions to obtain a microscopic structure adjustment result; Step 3: According to the microscopic structure adjustment result, calculate the microscopic contact force distribution by adjusting the contact model parameters, simulate the forces between particles under different pressures, and obtain a mechanical behavior prediction result; Step 4: Based on the predicted results of the mechanical behavior, extract the microscopic stress distribution data, use the radial basis function interpolation method to interpolate the microscopic stress, map the discrete microscopic stress data into a continuous distribution field, and perform a correlation analysis on the microscopic stress and the macroscopic stress through the finite element method to generate a macroscopic stress distribution model; Step 5: Based on the macroscopic stress distribution model, by optimizing parameters including the water-cement ratio, admixture ratio, and aggregate gradation, design a formula suitable for crack resistance and bearing capacity for the specific environment of the wind farm to obtain the optimized formula result; Step 6: Based on the optimized formula result, conduct a mixing test on the actual materials, according to the material performance indicators, and adjust the experimental formula through actual application tests to generate the experimental verification result; Step 7: According to the experimental verification result, evaluate the material performance according to the strength and durability of the material, and confirm whether it meets the specific requirements of wind power generation to obtain the performance evaluation result.

[0007] As a further solution of the present invention, the specific steps for generating the initial mixing ratio result are as follows: According to the mixing ratio requirements of the cement-based grouting material, select cement, aggregate, and admixture, use metering equipment for weight measurement, adjust the water-cement ratio, aggregate gradation, and admixture ratio to ensure accuracy and record the ratio parameters at the same time to generate the mixing ratio parameter data; Based on the mixing ratio parameter data, sequentially add cement, aggregate, and admixture into the mixing equipment according to the ratio, use mechanical stirring for mixing, adjust the speed and time of the stirring equipment, check the mixing state and adjust the operation until the materials are uniform to generate the uniformly mixed materials; Based on the uniformly mixed materials, fill the materials into the test mold, test the forming performance through standard test tools, and at the same time perform optimization operations by adjusting the water-cement ratio and aggregate gradation data until the mixing ratio requirements of the grouting material are met, and output the initial mixing ratio result; Among them, the initial mixing ratio result includes the mass ratio of cement to aggregate, the water-cement ratio, and the admixture ratio.

[0008] As a further solution of the present invention, the specific steps for generating the microscopic structure adjustment result are as follows: Based on the initial mixing ratio result, import the parameters of cement particles and aggregate particles, use a scanning electron microscope to measure the particle shape, size, and distribution characteristics, perform discrete processing on the particles through the discrete element method, establish a particle point position distribution model, and define the particle contact area and contact point attributes to generate a particle contact model; Based on the particle contact model, conduct a force analysis between particles, apply pressure to the particles by applying a constant pressure load, determine the force transfer path between particles by measuring the force change at the contact points, and analyze the bond strength between particles to generate the particle force bond data; Based on the particle force-bonding data, optimize the arrangement and gaps between particles through a genetic algorithm. Generate an initial population using the genetic algorithm, where each individual represents a particle arrangement pattern. At the same time, evaluate the combined performance of particle porosity and bonding strength. Select particle arrangement schemes with excellent performance for crossover and mutation operations, optimize the particle arrangement and gap configuration generation by generation, and adjust the positions and contact point attributes of the particles, and output the microstructural adjustment results; Among them, the microstructural adjustment results include the size distribution of the particles, the distribution of particle gaps, and the porosity.

[0009] As a further solution of the present invention, determine the force transfer path between particles by measuring the force change at the contact points. According to the contact points between particles, extract the geometric parameters of the contact points, including position, normal direction of the contact surface, and contact radius. Define the initial conditions of normal force and tangential force for each contact point. Apply an external force through loading to make the contact points generate a force response and record the force change data. Place virtual force sensors at the contact point positions, gradually apply an external force and record the dynamic changes of the normal force and tangential force. Establish a force change curve by decomposing the normal component and tangential component of the force, and connect the force paths between particle contact points according to the force change, extract the transmission order of the force between particles and record the complete path, calculate the stress distribution at the contact points, and at the same time extract the stress value through the relationship between the contact area and the normal force, record the stress change at the contact points, and verify the force balance state between particles by combining the force path and the stress distribution.

[0010] As a further solution of the present invention, the specific steps for generating the mechanical behavior prediction results are as follows: Based on the microstructural adjustment results, extract the contact point positions, contact areas, and contact normal forces between particles. Use a standardization tool to perform point-by-point analysis of the geometric relationships and mechanical parameters between particles, record the change ranges of the normal force and tangential force of the particles, adjust the contact parameter values in the model, set the external loading conditions and action ranges, and generate contact model parameter data; Based on the contact model parameter data, use a loading tool to apply different levels of pressure between particles, gradually increase the external force and observe the force states of the contact points. By recording the dynamic responses of the normal force and tangential force between particles point by point, analyze the changes in the force distribution at the contact points, and summarize the micro-contact force data between particles according to the requirements of mechanical analysis to generate micro-contact force distribution data; Based on the micro-contact force distribution data, calculate the dynamic responses of the forces between particles under multiple pressure conditions through a loading simulator, gradually adjust the mechanical relationships at the contact points, establish the distribution pattern of the force pairs between particles, analyze the overall force trend of the particles and predict the future force states, and output the mechanical behavior prediction results; Among them, the predicted results of the mechanical behavior include the particle contact force distribution, the force uniformity, and the mechanical equilibrium state between particles.

[0011] As a further solution of the present invention, the specific steps for generating the macroscopic stress distribution model are as follows: According to the predicted results of the mechanical behavior, interpolation calculation is performed on the point position data of the particles by the radial basis function interpolation method, and a particle microscopic stress field is established through region division. Analyze the force changes in the contact area between particles and complete the regional stress integration to generate microscopic stress distribution data; Based on the microscopic stress distribution data, by establishing a mapping relationship from microscopic to macroscopic, merge the microscopic force change data of the particles, and at the same time allocate the macroscopic stress input between the particles according to the coordinate region, and analyze the distribution trend of the macroscopic stress to generate stress correlation data; Based on the stress correlation data, calculate the macroscopic stress values for each coordinate region by the finite element method and complete the construction of the stress distribution field. At the same time, record the macroscopic mechanical change trend of the whole particles and output the macroscopic stress distribution model; Among them, the macroscopic stress distribution model includes a macroscopic stress distribution diagram, a stress concentration area, and a stress transmission path.

[0012] As a further solution of the present invention, for the radial basis function interpolation method, according to the formula: Where: is the coordinate of the target particle point, is the known coordinate of the contact point between particles, is the Euclidean distance between the target particle point and the known contact point between particles, is the weight coefficient, is the radial basis function, is the smoothing factor, is the polynomial function, is the weight adjustment coefficient, is the dynamic weight function, is the velocity vector of the target particle.

[0013] As a further solution of the present invention, the specific steps for generating the optimized formulation result are as follows: Based on the macroscopic stress distribution model, extract the influence data of the water-cement ratio, admixture ratio, and aggregate gradation on the crack resistance and bearing capacity. By analyzing the correlation relationship between the parameters and the macroscopic stress distribution item by item, summarize the key parameters affecting the crack resistance and bearing capacity, screen the initial ranges of the water-cement ratio, admixture ratio, and aggregate gradation, and generate parameter correlation data; Based on the parameter correlation data, multiple groups of proportioning test schemes are adopted to gradually change the water-cement ratio, admixture ratio, and aggregate gradation. For each proportioning, experimental evaluations of crack resistance and bearing capacity are carried out. The bearing performance is measured through a pressure test bench, and the crack resistance performance is recorded through a crack detection system. The performance manifestations of each proportioning parameter are summarized and the range is adjusted to generate optimized parameter data; Based on the optimized parameter data, combined 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. The crack resistance and bearing capacity are simulated and verified region by region. By screening the proportioning data that meet the conditions, a proportioning scheme that meets the design requirements for both crack resistance and bearing capacity is finally determined, and the optimized formula result is output; Among them, the optimized formula result includes the optimized water-cement ratio, the optimized admixture ratio, and the optimized aggregate gradation.

[0014] As a further scheme of the present invention, the specific steps for generating the experimental verification result are as follows: Based on the optimized formula result, a precision weighing device is used to batch weigh cement, aggregates, and admixtures. After verifying the material weights through a metering device, they are sequentially added to a mixer. The speed and time of the mixing device are set for material mixing. Observe the uniformity of the materials during the mixing process and record the mixing data to generate mixing test data; Based on the mixing test data, a material performance tester is used to test the crack resistance, strength, and fluidity of the mixed materials. The strength data is recorded through a pressure loading device, the crack resistance performance is collected through a crack monitoring device, and the deviation data is sorted and analyzed. By adjusting the water-cement ratio and admixture ratio, the formula parameters are improved to generate experimental adjustment formula data; Based on the experimental adjustment formula data, after repeating the formula adjustment, mixing and performance testing are carried out. The material performance is gradually verified through a pressure loading and crack monitoring device. Finally, the formula parameters are determined through comprehensive analysis, and the crack resistance and strength data of the materials are recorded to output the experimental verification result; Among them, the experimental verification result includes the crack resistance performance test result, the bearing capacity test result, and the durability test result.

[0015] As a further scheme of the present invention, the specific steps for generating the performance evaluation result are as follows: Based on the experimental verification result, the strength and durability test data of the materials are extracted, and a data analysis tool is used to sort out the test results item by item. By comparing the crack resistance performance and bearing capacity data, a material performance record form 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, through item-by-item comparative analysis of matching the material performance with the environmental conditions, the adapted data is sorted out through a data recording tool and the final conclusion is summarized to generate environmental adaptation data; Based on the environment adaptation data, comprehensively considering the strength, crack resistance and durability data of the material, verify whether the performance indicators meet the design requirements by verifying each performance indicator one by one, record the final performance indicator evaluation results and file them, and output the performance evaluation results; Among them, the performance evaluation results include the compressive strength, tensile strength and long-term durability indicators of the material.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are as follows: 1. In the present invention, by quantifying the proportions 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 genetic algorithm is combined to adjust the gap distribution between particles, making the microstructure of the material more uniform and dense, and improving the stability of the internal structure; 2. In the present invention, the application of the discrete element method also refines the mechanical behavior of the particles, avoiding the problem of local stress concentration that may be caused by the randomness of particle arrangement. Through the radial basis function interpolation method, continuous mapping is performed on 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; 3. In the present invention, a macroscopic stress distribution model is established by finite element analysis, providing data support for the crack resistance and bearing capacity design of the material. By optimizing the synergistic effect of the microscopic structure and macroscopic stress, the comprehensive performance of the cement-based grouting material in aspects such as anti-fatigue, anti-deformation and anti-environmental erosion is improved, ensuring mechanical stability and long-term durability. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a schematic diagram of the main steps of the present invention; Figure 2 is a detailed schematic diagram of S1 of the present invention; Figure 3 is a detailed schematic diagram of S2 of the present invention; Figure 4 is a detailed schematic diagram of S3 of the present invention; Figure 5 is a detailed schematic diagram of S4 of the present invention; Figure 6 is a detailed schematic diagram of S5 of the present invention; Figure 7 is a detailed schematic diagram of S6 of the present invention; Figure 8 is a detailed schematic diagram of S7 of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0018] To make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be further described in detail below in conjunction with 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 used to limit the present invention.

[0019] Please refer to Figure 1 , the present invention provides a technical solution: a preparation method of a super-high-strength cement-based grouting material for wind power generation, comprising the following steps: S1: According to the mixing ratio requirements of the cement-based grouting material, select a specific water-cement ratio, admixture ratio and aggregate gradation, accurately quantify the ratio of cement to aggregate, and generate an initial mixing ratio result; S2: Based on the initial mixing ratio result, simulate the stress and bonding conditions in the contact area between cement particles and aggregate particles by the discrete element method, and at the same time use the genetic algorithm to adjust the particle gap according to the stress and bonding conditions to obtain a microstructural adjustment result; S3: According to the microstructural adjustment result, calculate the micro-contact force distribution by adjusting the contact model parameters, simulate the forces between particles under different pressures, and obtain a mechanical behavior prediction result; S4: Based on the mechanical behavior prediction result, extract the micro-stress distribution data, perform interpolation calculation on the micro-stress by using the radial basis function interpolation method, map the discrete micro-stress data into a continuous distribution field, and perform correlation analysis on the micro-stress and macro-stress by the finite element method to generate a macro-stress distribution model; S5: Based on the macro-stress distribution model, design a formula suitable for crack resistance and bearing capacity for the specific environment of the wind farm by optimizing parameters including water-cement ratio, admixture ratio and aggregate gradation to obtain an optimized formula result; S6: Based on the optimized formula result, conduct a mixing test on the actual material, adjust the experimental formula according to the material performance indicators and through actual application tests to generate an experimental verification result; S7: According to the experimental verification result, evaluate the material performance according to the strength and durability of the material, and confirm whether it meets the specific requirements of wind power generation to obtain a performance evaluation result.

[0020] Please refer to Figure 2 , the specific steps for generating the initial mixing ratio result are: S101: According to the mixing ratio requirements of the cement-based grouting material, select cement, aggregate and admixture, use measuring equipment for weight measurement, adjust the water-cement ratio, aggregate gradation and admixture ratio to ensure accuracy and record the ratio parameters at the same time to generate ratio parameter data; S102: Based on the ratio parameter data, add cement, aggregate and admixture to the mixing equipment in sequence according to the ratio, use mechanical stirring for mixing, adjust the speed and time of the mixing equipment, check the mixing state and adjust the operation until the material is uniform to generate a uniformly mixed material; S103: Based on the uniformly proportioned materials, use a test mold to fill the materials, test the forming performance with standard test tools, and at the same time perform optimization operations by adjusting the water-cement ratio and aggregate gradation data until the grouting material ratio requirements are met, and output the preliminary ratio result; Based on the ratio requirements of the cement-based grouting material, use metering equipment to measure the weights of cement, aggregate and admixture. The metering equipment includes an electronic balance and an automatic weighing device. The accuracy of the electronic balance is 0.01 g, and the weighing range of the automatic weighing device is 1 g to 50 kg. Measure according to the weight ratio that cement accounts for 40%-60%, aggregate accounts for 30%-50%, and admixture accounts for 5%-10%, and record the actual weight values of each component. Ensure the accuracy of the ratio data by adjusting the water-cement ratio range of 0.3-0.5, the aggregate particle grading range of 5-20 mm and the admixture ratio parameter of 5%-10%, and generate ratio parameter data; Based on the ratio parameter data, sequentially add cement, aggregate and admixture to the mixing equipment in proportion. The mixing equipment is a twin-shaft mixer, and the rotation speed control range of the mixing equipment is 60-120 rpm. The addition order is to add the aggregate first, then the cement, and finally the admixture. Perform the mixing operation by mechanical stirring. Set the rotation speed of the mixing equipment to 90 rpm and the mixing time to 5 minutes. Adjust the operation of the equipment by setting the mixing intermittent time to 1 minute. Use the video monitoring system to monitor the mixing state to check the mixing uniformity. Adjust the operation by adjusting the mixing time range of 3-7 minutes and the rotation speed range of 60-120 rpm until the mixing is uniform, and generate uniformly proportioned materials; Based on the uniformly proportioned materials, perform the material filling operation with a test mold. The size of the test mold is 40 mm×40 mm×160 mm. Use a pressure filling device to layer-press the materials into the mold at a pressure of 20 kPa, control the forming density of the filled materials, use standard test tools such as an electronic fluidity tester to test the forming performance, and measure the flow performance of the filled materials. The measured parameters include the flow spread value range of 200 mm-250 mm. Optimize the material ratio by adjusting the data of the water-cement ratio range of 0.3-0.5 and the aggregate grading range of 5-20 mm. During the optimization process, repeatedly test by gradually changing a single parameter and measuring the corresponding fluidity data until the grouting material ratio requirements are met, and generate the preliminary ratio result; Among them, the preliminary ratio result includes the mass ratio of cement to aggregate, the water-cement ratio and the admixture ratio.

[0021] Please refer to Figure 3 , the specific steps to generate the microstructural adjustment result are: S201: Based on the initial proportioning results, import the parameters of cement particles and aggregate particles, measure the particle shape, size, and distribution characteristics using a scanning electron microscope, discretize the particles through the discrete element method, establish a particle point distribution model, and define the particle contact area and contact point properties to generate a particle contact model; S202: Based on the particle contact model, conduct a force analysis between particles, apply pressure to the particles using a constant pressure loading, determine the force transfer path between particles by measuring the force changes at the contact points, and analyze the bonding strength between particles to generate particle force-bonding data; S203: Based on the particle force-bonding data, optimize the arrangement and gaps between particles through a genetic algorithm. Generate an initial population using the genetic algorithm, where each individual represents a particle arrangement. At the same time, evaluate the combined performance of particle porosity and bonding strength, select excellent particle arrangement schemes for crossover and mutation operations, optimize the particle arrangement and gap configuration generation by generation, and adjust the positions and contact point properties of the particles to output the microstructural adjustment results; Based on the initial proportioning results, import the parameters of cement particles and aggregate particles, measure the particles using a scanning electron microscope. The measurement contents include particle shape, particle size, and particle distribution characteristics. The resolution of the scanning electron microscope is set to 1 nm. The particle shape is characterized by fitting the shape boundary using a polygon fitting algorithm. The particle size is measured by the minimum circumscribed circle method to obtain its diameter range. The particle distribution characteristics are calculated using a uniformity distribution index. Discretize the particles using the discrete element method, establish a point distribution model based on the spatial coordinates of the particles. The particle points are described using a three-dimensional Cartesian coordinate system, and the coordinate range is 0 - 100 mm. Define the radius of the particle contact area as 1 mm. The contact point properties include contact stiffness and bonding strength. The contact stiffness is set to 100 N / mm, and the initial value of the bonding strength is 10 MPa to generate a particle contact model; Based on the particle contact model, analyze the forces between particles. Apply pressure to the particles using a constant pressure loading method. The loading pressure is set to 50 MPa. The loading process is achieved through a simulated distributed loading technique, and the loading range covers the particle contact area. Determine the force transfer path between particles by measuring the change data of the contact point forces. The sampling frequency of the measurement data is set to 10 Hz. The force transfer path is identified using a maximum force value point-by-point tracking algorithm, and the identification range includes all contact points between particles. Analyze the bonding strength between particles. The bonding strength calculation is based on the pressure distribution at the contact points, and the pressure distribution is completed by Gaussian distribution fitting. The fitting parameters include the pressure average value and standard deviation to generate particle force-bonding data; Based on the particle force-bonding data, the genetic algorithm is used to optimize the arrangement and gaps between particles. The genetic algorithm is used to generate an initial population with a population size of 100. Each individual in the population represents a particle arrangement. The gene encoding of each individual adopts binary encoding. Evaluate the combined performance of particle porosity and bonding strength. The calculation of porosity is based on the space occupancy of particle arrangement, and the bonding strength is statistically based on the average pressure of particle contact points. Select particle arrangement schemes with excellent performance, and the selection ratio is 20% of the total population. Perform crossover operations on the selected individuals. The crossover method is single-point crossover, and the crossover probability is set to 0.8. Perform mutation operations on the crossed individuals, and the mutation probability is set to 0.05. The mutation operations include adjusting particle positions and redefining contact point attributes. Optimize the particle arrangement and gap configuration generation microstructure adjustment results; Among them, the microstructure adjustment results include the size distribution of particles, the distribution of particle gaps, and porosity.

[0022] Determine the force transfer path between particles by measuring the force changes at the contact points. According to the contact points between particles, extract the geometric parameters of the contact points, including position, contact surface normal direction, and contact radius. Define the initial conditions of normal force and tangential force for each contact point. Apply an external force through loading to make the contact points generate a force response and record the force change data. Place virtual force sensors at the contact point positions, gradually apply an external force and record the dynamic changes of normal force and tangential force. Establish a force change curve by decomposing the normal and tangential components of the force, and connect the force paths between particle contact points according to the force changes. Extract the transfer order of the force between particles and record the complete path. Calculate the stress distribution at the contact points, and at the same time extract the stress values according to the relationship between the contact area and the normal force. Record the stress changes at the contact points, and verify the force balance state between particles by combining the force path and stress distribution.

[0023] Please refer to Figure 4 , and the specific steps to generate the mechanical behavior prediction results are as follows: S301: Based on the microstructure adjustment results, extract the contact point positions, contact areas, and contact normal forces between particles. Use standardization tools to perform point-by-point analysis on the geometric relationships and mechanical parameters between particles, record the change ranges of the normal force and tangential force of the particles, adjust the contact parameter values in the model, set the external loading conditions and action ranges, and generate contact model parameter data; S302: Based on the contact model parameter data, use a loading tool to apply different levels of pressure between the particles. Gradually increase the external force to observe the stress state at the contact points. By recording the dynamic responses of the normal force and tangential force between the particles point by point, analyze the changes in the stress distribution at the contact points. According to the requirements of mechanical analysis, summarize the microscopic contact force data between the particles to generate microscopic contact force distribution data; S303: Based on the microscopic contact force distribution data, use a loading simulator to calculate the dynamic responses of the forces between the particles under multiple pressure conditions. Gradually adjust the mechanical relationship at the contact points to establish the distribution pattern of the force pairs between the particles. Analyze the overall stress trend of the particles and predict the future stress state, and output the mechanical behavior prediction results; Based on the microscopic structure adjustment results, extract the contact point positions, contact areas, and contact normal forces between the particles. Use a geometric analysis tool to perform point-by-point analysis of the geometric relationships between the particles. The analysis tool parameters include the particle coordinate positions, and the particle radius ranges from 0.1 mm to 10 mm. The contact area is calculated based on the overlapping area of the two particles. Use an analytical algorithm to solve the contact point area through three-dimensional coordinates, analyze the mechanical parameters between the particles. The normal force is calculated based on the pressure distribution between the particles in the normal direction of the contact point, and the tangential force is calculated through the contact point friction model. The friction coefficient is set to range from 0.3 to 0.5. Record the change ranges of the normal force and tangential force point by point. The normal force change range is from 0 to 100 N, and the tangential force change range is from 0 to 50 N. Adjust the contact parameter values in the model. The adjustment contents include setting the contact point stiffness range to 10 to 100 N / mm and the bond strength range to 1 to 10 MPa. Set the external loading conditions and action range. The loading condition is a constant pressure, and the loading range covers all the contact points of the particle distribution model to generate contact model parameter data; Based on the contact model parameter data, use a loading tool to apply different levels of pressure between the particles. The pressure level is set to range from 10 MPa to 50 MPa. The loading tool realizes the loading process through a finite loading plate. The moving speed of the loading plate is set to 1 mm / s. Gradually increase the external force to the target pressure value. Record the dynamic responses of the normal force and tangential force between the particles point by point through a pressure sensor. The sampling frequency of the pressure sensor is set to 100 Hz. The recorded data includes the time, position, normal force, and tangential force values of the contact points. Calculate the stress distribution at the contact points. The normal force distribution is analyzed using a pressure distribution diagram, and the tangential force is linearly fitted through a friction force model. Summarize the microscopic contact force data between the particles. The summarized data content includes the time series of particle stress, the contact point distribution area, and the mechanical response trend 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 through a loading simulator. The set pressure range of the loading simulator is from 5 MPa to 50 MPa, the loading increment is 5 MPa, and the calculation duration for each pressure level is 60 seconds. The mechanical relationship of the contact points is adjusted step by step. The adjustment content includes the position coordinate range of the contact points and the distribution of the pressure values. The position coordinate range is adjusted to 0 to 50 mm, and the pressure value distribution generates a continuous pressure field through the Gaussian fitting method. A distribution pattern of the inter-particle force pairs is established. The force pairs include the paired distributions of the normal force and the tangential force. The pairing relationship is constructed through the maximum pressure value and the friction force distribution points. The overall force trend of the particles is analyzed, and vector synthesis is performed through the force field of the particles in the overall model to predict the future force state and generate the mechanical behavior prediction result; Among them, the mechanical behavior prediction result includes the particle contact force distribution, the force uniformity, and the mechanical equilibrium state between particles.

[0024] Please refer to Figure 5 , and the specific steps to generate the macroscopic stress distribution model are as follows: S401: According to the mechanical behavior prediction result, interpolation calculation is performed on the point position data of the particles through the radial basis function interpolation method, and a particle microscopic stress field is established through region division. The force changes in the contact area between particles are analyzed and regional stress integration is completed to generate microscopic stress distribution data; S402: Based on the microscopic stress distribution data, by establishing a mapping relationship from the microscopic to the macroscopic, the microscopic force change data of the particles are merged. At the same time, the macroscopic stress input between particles is allocated according to the coordinate region, and the distribution trend of the macroscopic stress is analyzed to generate stress correlation data; S403: Based on the stress correlation data, the macroscopic stress values are calculated for each coordinate region through the finite element method and the construction of the stress distribution field is completed. At the same time, the macroscopic mechanical change trend of the overall particles is recorded, and the macroscopic stress distribution model is output; Based on the mechanical behavior prediction result, interpolation calculation is performed on the particle point position data by using the radial basis function interpolation method. The radial basis function interpolation method selects the Gaussian basis function. The basis function parameters include the shape parameter 0.5 and the influence radius of 10 mm. The coordinate range of the interpolation points is 0 to 50 mm. Interpolation operations are performed using the coordinates of each contact point and the corresponding force values. During the interpolation process, the stress value of the target point position is obtained by using the point-by-point calculation method. Through region division of the interpolation result, the particle region is divided into multiple sub-regions by using the quadtree partitioning algorithm. The size of the smallest region unit for division is 1 mm × 1 mm. The force changes in the contact area between particles are analyzed point by point, and the gradient algorithm is used to calculate the stress change trend in the contact area. Regional stress integration is completed and summarized as the total force data of each region to generate microscopic stress distribution data; Based on the micro stress distribution data, the data is merged by establishing a mapping relationship from micro to macro. The data of the change in the micro force on particles is merged using the linear weighting method. The weighting factor is calculated according to the area ratio of the sub-region where the particle is located, and the weighting range is from 0.1 to 1. The macro stress input between particles is distributed according to the coordinate region. The stress value at the center point of the region is used as the initial value of the input point and is extended to the entire region through the interpolation algorithm. The distribution trend of the macro stress is analyzed. The principal component analysis method is used to extract the main characteristics of the stress change in different regions, and the number of characteristics is set to 2 to 3. The overall change trend of the macro stress is summarized to generate stress correlation data; Based on the stress correlation data, the macro stress value is calculated for each coordinate region by the finite element method. The three-dimensional finite element method is used to calculate the stress within the region. The element type is selected as the eight-node three-dimensional isoparametric element, and the size of each element is 5mm×5mm×5mm. The input stress parameters include the normal stress range from 0 to 50MPa and the shear stress range from 0 to 20MPa. The point-by-point iterative solution method is used during the calculation, the number of iterations is set to 100 times, and the convergence error range is set to 0.001. The construction of the stress distribution field is completed, and the three-dimensional grid diagram is used to visually record the distribution field. At the same time, the overall macro mechanical change trend of the particles is recorded, and the recorded content includes the force time series of the particles, the stress change rate, and the directional characteristics of the macro force distribution, generating a macro stress distribution model; Among them, the macro stress distribution model includes the macro stress distribution diagram, the stress concentration area, and the stress transmission path.

[0025] Radial basis function interpolation method, according to the formula: Where: is the coordinate of the target particle point, is the known coordinate of the contact point between particles, is the Euclidean distance between the target particle point and the known contact point between particles, is the weight coefficient, is the radial basis function, is the smoothing factor, is the polynomial function, is the weight adjustment coefficient, is the dynamic weight function, is the velocity vector of the target particle; Execution process: First, according to the distribution characteristics of the particles in the grouting material, determine the coordinate of the target particle point and the coordinate of the known contact point between particles, calculate the distance between the particles , solve the interpolation function value between the target point and the known contact point, and then, through the least square fitting method, establish a polynomial function based on the overall microscopic stress data of the grouting material , supplement the global trend of the stress distribution between particles, and then introduce the velocity vector of the target particle , dynamically correct the microscopic stress deviation caused by the fluidity of the grouting material, where γ is the velocity field diffusion factor, determined by combining the fluidity experimental data of the grouting material is the center point of the velocity field, representing the center of the main flow direction. Subsequently, calculate the weight adjustment coefficient according to the stress change amplitude of the contact points between particles , obtained by fitting through particle contact tests. Finally, the calculated weight coefficient , interpolation function value , polynomial function , and dynamic correction function are substituted into the formula to generate the microscopic stress distribution value of the target particle point, which is used to comprehensively describe the microscopic mechanical properties of the grouting material. Please refer to Figure 6 , the specific steps to generate the optimized formula result are as follows: S501: Based on the macroscopic stress distribution model, extract the influence data of the water-cement ratio, admixture ratio, and aggregate gradation on the crack resistance and bearing capacity. By analyzing the correlation between the parameters and the macroscopic stress distribution item by item, summarize the key parameters affecting the crack resistance and bearing capacity, screen the initial ranges of the water-cement ratio, admixture ratio, and aggregate gradation, and generate parameter correlation data; S502: Based on the parameter correlation data, adopt multiple groups of mixing ratio test schemes, gradually change the water-cement ratio, admixture ratio, and aggregate gradation, conduct experimental evaluations of crack resistance and bearing capacity for each mixing ratio, measure the bearing performance through a pressure test bench, record the crack resistance performance through a crack detection system, summarize the performance of each mixing ratio parameter and adjust the range, and generate optimized parameter data; S503: Based on the optimized parameter data, combined with the specific environmental conditions of the wind farm, adjust the water-cement ratio, admixture ratio, and aggregate gradation according to the environmental load requirements and temperature and humidity characteristics, conduct simulation verification of crack resistance and bearing capacity region by region, and finally determine the mixing ratio scheme that meets the design requirements for both crack resistance and bearing capacity, and output the optimized formula result; Based on the macroscopic stress distribution model, extract the influence data of water-cement ratio, admixture ratio, and aggregate gradation on crack resistance and bearing capacity. Use the stepwise regression analysis method to analyze the correlation between water-cement ratio, admixture ratio, aggregate gradation, and macroscopic stress distribution item by item. The model equation form of stepwise regression analysis is a linear polynomial form. The input parameters include a water-cement ratio range of 0.3 - 0.5, an admixture ratio range of 5% - 10%, and an aggregate gradation range of 5 - 20 mm. The target parameters are the stress distribution changes of crack resistance and bearing capacity. Screen the key parameters affecting crack resistance and bearing capacity through the magnitude and significance test of partial regression coefficients. The statistical significance of partial regression coefficients is tested using the P-value. Set the P-value less than 0.05 as the screening criterion. Adjust the initial ranges of water-cement ratio, admixture ratio, and aggregate gradation according to the screening results, record the specific values of each parameter within the range, and generate parameter correlation data; Based on the parameter correlation data, adopt multiple sets of mixing ratio test schemes, gradually change the water-cement ratio, admixture ratio, and aggregate gradation, and conduct experimental evaluations of crack resistance and bearing capacity for each mixing ratio. The experiment is jointly completed using a pressure test bench and a crack detection system. The loading range of the pressure test bench is set to 0 - 50 MPa, the loading method is stepwise loading, the increment of each level is 5 MPa, and the loading time of each level is 10 seconds. Record the bearing performance data including the bearing capacity limit value and the corresponding loading pressure. The crack detection system uses a high-definition camera to monitor the crack generation time, location, and width. The camera resolution is set to 0.1 mm, and the crack width range is set to 0.01 - 5 mm. By summarizing the crack resistance and bearing performance of each mixing ratio parameter, use the stepwise screening method to adjust the parameter values within the range, and retain the parameter combination with the best performance during the adjustment process to generate optimized parameter data; Based on the optimized parameter data, adjust the water-cement ratio, admixture ratio, and aggregate gradation 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 - 50 MPa, the temperature range is -20°C to 40°C, and the humidity range is 10% - 90%. Gradually adjust the water-cement ratio, admixture ratio, and aggregate gradation by region. During the adjustment process, use simulation calculations to verify crack resistance and bearing capacity. The simulation calculations are completed through finite element simulation. The input parameters include the specific values within the adjusted mixing ratio range and the environmental condition characteristics within the region. Screen the mixing ratio data that meet the requirements of crack resistance and bearing capacity, record the specific parameter values after the final screening, determine the mixing ratio scheme that meets the environmental requirements of the wind farm, and generate the optimized formula result; Among them, the optimized formula result includes the optimized water-cement ratio, the optimized admixture ratio, and the optimized aggregate gradation.

[0026] Please refer to Figure 7 , and the specific steps to generate the experimental verification result are as follows: S601: Based on the optimized formula results, use precision weighing equipment to batch weigh cement, aggregates, and admixtures. After verifying the material weights through metering equipment, add them to the mixer in sequence. Set the speed and time of the mixing equipment for material mixing, observe the material uniformity during the mixing process, and record the mixing data to generate mixing test data; S602: Based on the mixing test data, use a material property tester to test the crack resistance, strength, and fluidity of the mixed materials. Record the strength data through a pressure loading device, collect the crack resistance performance through a crack monitoring device, and organize and analyze the deviation data. Improve the formula parameters by adjusting the water-cement ratio and admixture ratio to generate experimental adjustment formula data; S603: Based on the experimental adjustment formula data, repeat the formula adjustment, then conduct mixing and performance tests. Gradually verify the material performance through the pressure loading and crack monitoring devices. Finally, determine the formula parameters through comprehensive analysis, record the crack resistance and strength data of the materials, and output the experimental verification results; Based on the optimized formula results, use precision weighing equipment to batch weigh cement, aggregates, and admixtures. The weighing equipment includes a high-precision electronic balance and an automatic weighing system. The accuracy of the electronic balance is set to 0.01 g, and the maximum weighing range of the automatic weighing system is 50 kg. Verify the weight of each batch of materials one by one. Use the metering equipment to record the weighing range of cement as 10 - 20 kg, the weighing range of aggregates as 20 - 30 kg, and the weighing range of admixtures as 1 - 5 kg. Add the weighed materials to the mixer in sequence. The rotation speed setting range of the mixer is 60 - 120 rpm, and the mixing time is set to 5 minutes. The mixing equipment operates in an intermittent operation mode, with an intermittent time of 1 minute. Observe the material uniformity during the mixing process in real time through a mixing state monitoring device, and record the mixing data including rotation speed, time, and uniformity level to generate mixing test data; Based on the mixing test data, use a material property tester to test the crack resistance, strength, and fluidity of the mixed materials. The crack resistance test uses a crack monitoring system, and the system collects crack propagation data through a high-definition camera. The resolution of the camera is set to 0.1 mm, and the measurement range of the crack width is 0.01 - 5 mm. The strength test is completed through a pressure loading device, and the loading range of the device is 0 - 50 MPa. The loading mode is step-by-step loading, with an increment of 5 MPa for each step and a loading time of 10 seconds for each step. The fluidity test is completed using an expansion value measuring instrument, and the expansion value measurement range is 200 mm - 300 mm. Analyze the collected crack resistance and strength data one by one, use a data processing tool to organize the deviation values in the material properties, and use the one-way analysis of variance method to identify the main mixing parameters affecting the performance. Adjust the water-cement ratio range to 0.3 - 0.5 and the admixture ratio to 5% - 8% to generate experimental adjustment formula data; Adjust the formula data based on experiments. After repeatedly adjusting the formula, conduct mixing and performance tests. Use a pressure loading device and a crack monitoring system to record the bearing capacity data and anti-crack performance of each test respectively. The bearing capacity data includes the maximum bearing capacity and the corresponding deformation. The anti-crack performance data includes the crack generation time and the change in crack width. By summarizing the performance of each test's mix ratio, use the weighted average method to comprehensively calculate the anti-crack and strength data of different mix ratios. Determine the calculation weights based on the influence coefficients of crack width and strength limit. Gradually screen the range of mix ratio parameters that meet the requirements. Finally, determine the specific values of the water-cement ratio, admixture ratio, and aggregate gradation of the mix ratio plan. Record the anti-crack and strength data of the final material to generate experimental verification results; Among them, the experimental verification results include anti-crack performance test results, bearing capacity test results, and durability test results.

[0027] Please refer to Figure 8 , and the specific steps to generate the performance evaluation results are as follows: S701: Based on the experimental verification results, extract the strength and durability test data of the material. Use a data analysis tool to sort out the test results item by item. Establish a material performance record form by comparing the anti-crack performance and bearing capacity data to generate performance evaluation data; S702: Based on the performance evaluation data, combine the climate and load environment data of the wind farm. Through item-by-item comparative analysis of matching material performance with environmental conditions, use a data recording tool to sort out the adapted data and summarize the final conclusion to generate environment-adapted data; S703: Based on the environment-adapted data, comprehensively consider the strength, anti-crack, and durability data of the material. By verifying each performance index one by one to confirm whether it meets the design requirements, record the final performance index evaluation results and file them to output the performance evaluation results; Based on the experimental verification results, extract the strength and durability test data of the material. Use a data analysis tool to sort out the test results item by item. The strength test data includes the compressive strength range of 0 - 50 MPa and the tensile strength range of 0 - 10 MPa. The durability test data includes the number of anti-fatigue cycles and the anti-corrosion ability level. Use the Python data analysis library Pandas to tabulate the test data. Summarize the anti-crack performance data by crack width and crack generation time into the same column, and summarize the bearing capacity data by loading pressure and corresponding deformation into another column. Define data cleaning rules to delete duplicate and invalid data. When comparing the anti-crack performance and bearing capacity data, use Matplotlib to draw a line chart to visualize the performance trend according to the load conditions and crack generation time of the material. By comparing the average value and standard deviation of each group of data, screen the test results with a data deviation range less than 10%. Establish a material performance record form, and the recorded content includes compressive strength, tensile strength, crack generation time, and the corresponding load value to generate performance evaluation data; 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 from -20°C to 40°C and the humidity range from 10% to 90%, and the load data includes the foundation pressure range from 10 MPa to 50 MPa. By comparing and analyzing item by item by matching the material properties with the environmental conditions, the regression analysis method is used to verify the correlation between the material properties and the environmental parameters. The form of the regression model is a quadratic polynomial form. The input variables are temperature, humidity and load data, and the output variables are crack resistance performance and bearing performance indicators. The Scikit-learn tool is used for model training. The training data set includes the material strength data and crack propagation data under different climate conditions. The regression model parameters are set as the maximum number of iterations 500 and the learning rate 0.01. The results of each group of regression analysis are classified and sorted, and the adaptation of each material ratio under different environmental conditions is recorded. An adaptation table is generated through the sorted results, the data of the adapted materials are recorded in the table, and the environmental adaptation data is generated; Based on the environmental adaptation data, integrating the strength, crack resistance and durability data of the material, when verifying the performance indicators one by one, the finite element analysis tool is used to simulate the performance of the material according to the regional environmental conditions. The input parameters of the finite element analysis model include the compressive strength range of the material from 0 to 50 MPa, the tensile strength range from 0 to 10 MPa and the crack propagation width range from 0.01 to 5 mm. The temperature, humidity conditions and load range of each region are loaded into the model one by one for simulation verification. Each group of data is dynamically recorded during the simulation process. Through the verified material performance indicators, the final crack resistance, strength and durability indicators are recorded, and the performance simulation and experimental results of all regions are archived to generate the performance evaluation results; Among them, the performance evaluation results include the compressive strength, tensile strength and long-term durability indicators of the material.

[0028] The above is only the preferred embodiment of the present invention, and it is not used to limit the present invention in other forms. Any person skilled in the art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope 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: According to the requirements of the mix ratio of cement-based grouting materials, select a specific water-cement ratio, admixture ratio and aggregate grading, accurately quantify the ratio of cement to aggregate, and generate the initial mix ratio result; 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 gap is adjusted according to the stress and bonding conditions by genetic algorithm to obtain the microstructure adjustment result; Step 3: According to the microstructure adjustment result, the micro contact force distribution is calculated by adjusting the contact model parameters, the force between particles under different pressures is simulated, and the mechanical behavior prediction result is obtained; Step 4: 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; Step 5: Based on the macro stress distribution model, by optimizing parameters including water-cement ratio, admixture ratio and aggregate gradation, a formula suitable for crack resistance and bearing capacity is designed for the specific environment of the wind farm to obtain an optimized formula result; 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 needs of wind power generation and obtain the performance evaluation results.

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, aggregate and admixture are selected, and the weight is measured by metering equipment. The water-cement ratio, aggregate grading and admixture ratio are adjusted to ensure accuracy and record the ratio parameters at the same time to generate the proportion parameter data; Based on the mix parameter data, cement, aggregate and admixture are sequentially added to the mixing device in proportion, mixed by mechanical stirring, the speed and time of the stirring device are adjusted, the mixing state is checked and the operation is adjusted until the materials are uniform, thereby generating uniformly proportioned materials; Based on the uniformly proportioned materials, a test mold is filled with materials, and the molding performance is tested by a standard test tool. At the same time, the water-cement ratio and aggregate grading data are adjusted to perform optimization operations until the grouting material proportion requirements are met, and the initial proportion results are output; The initial mix ratio includes the mass ratio of cement to aggregate, the water-cement ratio and the 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 result, cement particle and aggregate particle parameters are imported, particle shape, size and distribution characteristics are measured by scanning electron microscopy, particles are discretized by discrete element method, a particle point distribution model is established, and particle contact area and contact point properties are defined to generate a particle contact model; Based on the particle contact model, force analysis between particles is performed, constant pressure loading is used to apply pressure to the particles, the force transmission path between particles is determined by measuring the force change at the contact point, and the bonding strength between particles is analyzed to generate particle force bonding data; Based on the particle force bonding data, the arrangement and gap between particles are optimized by genetic algorithm, and the initial population is generated by genetic algorithm, each individual represents a particle arrangement mode, and the combined performance of particle porosity and bonding strength is evaluated at the same time, and the particle arrangement scheme with excellent performance is selected for crossover and mutation operations, and the particle arrangement and gap configuration are optimized generation by generation, and the position and contact point properties of the particles are adjusted, and the microstructure adjustment result is output; The microstructure adjustment result includes 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, 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, causes the contact point to generate 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, extracts the stress value through 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 particles are extracted, the geometric relationship and mechanical parameters between 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 the 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 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 the 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 force between particles 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 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 as follows: According to 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 the 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 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; 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, while the macro mechanical change trend of the whole 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: in: is the coordinate of the target particle point, are the known coordinates of the contact points between particles, is the Euclidean distance between the target particle point and the contact point between known particles, is the weight coefficient, is the radial basis function, is the smoothing factor, is a polynomial function, is the weight adjustment coefficient, is the dynamic weight function, 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, extract the influence data of water-cement ratio, admixture ratio and aggregate gradation on crack resistance and bearing capacity, summarize the key parameters affecting crack resistance and bearing capacity by analyzing the correlation between parameters and macro-stress distribution item by item, screen the initial range of water-cement ratio, admixture ratio and aggregate gradation, and generate parameter correlation data; Based on the parameter correlation data, a multi-group mix test scheme is adopted to gradually change the water-cement ratio, admixture ratio and aggregate gradation, and experimental evaluation of crack resistance and bearing capacity is carried out for each group of mixes. The bearing performance is measured by a pressure test bench, and the crack resistance performance is recorded by a crack detection system. The performance of each mix parameter is summarized and the range is adjusted to generate optimized parameter data; Based on the optimization parameter data, combined with the specific environmental conditions of the wind farm, the water-cement ratio, the admixture ratio and the aggregate gradation are adjusted according to the environmental load requirements and the temperature and humidity characteristics, and the simulation verification of the crack resistance and the bearing capacity is carried out region by region. By screening the proportion data that meets the conditions, the proportion scheme that meets the design requirements in terms of crack resistance and bearing capacity is finally determined, and the optimization formula result is output; The optimized formula results include an optimized water-cement ratio, an optimized admixture ratio and an 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 as follows: Based on the optimized formula results, cement, aggregates and admixtures are weighed in batches using precise weighing equipment, and the weight of the materials is verified by the metering equipment before being 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 mixed test data, the mixed material is tested for crack resistance, strength and fluidity using a material performance tester, the strength data is recorded using a pressure loading device, the crack resistance performance is collected using a crack monitoring device, and the deviation data is collated and analyzed, the formula parameters are improved by adjusting the water-cement ratio and the admixture ratio, and the experimental adjustment formula data is generated; Adjust the formula data based on the experiment, perform mixing and performance testing after repeated adjustment of the formula, gradually verify the material performance through pressure loading and crack monitoring equipment, and finally determine the formula parameters through comprehensive analysis, record the crack resistance and strength data of the material, and output the experimental verification results; The experimental verification results include anti-cracking performance 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 and environmental conditions, comparative analysis is performed item by item, and the adaptation data is sorted and the final conclusion is summarized through data recording tools to generate environmental adaptation data; Based on the environmental adaptation data, the strength, crack resistance and durability data of the material are integrated, and the performance indicators are verified one by one to confirm whether the design requirements are met, and the final performance indicator evaluation results are recorded and archived, and the performance evaluation results are output; Wherein, the performance evaluation results include the material's compressive strength, tensile strength and long-term durability indicators.

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