51 results about "Kriging method" patented technology

The invention relates to the technical field of data processing, and discloses a bridge displacement remote intelligent monitoring method and system and computer equipment. The method comprises the following steps: setting a multi-stage trigger threshold to perform grouped polling sampling to obtain displacement data; adopting dynamic weight fusion to denoise and identify abnormities, and utilizing a Kriging method to generate a digital twinborn model; extracting features to classify and distinguish environment and structure response displacement; calculating a deviation index to estimate fatigue damage to form a health report; calculating a risk entropy value and setting an early warning generation disposal scheme; and evaluating improvement rate update configuration to complete closed-loop optimization. According to the method, the environment data and the historical monitoring data are fused, a self-adaptive sampling strategy and an intelligent analysis processing mechanism are established, displacement changes caused by environment factors and structural factors are accurately distinguished, and accurate risk quantification and automatic decision support are carried out.

The invention provides a fusion method of ground station monitoring and satellite remote sensing inversion PM2.5 data, and relates to the technical field of PM2.5 pollution prevention and control. Ground station data and satellite remote sensing data are subjected to time matching and space matching and then are fused by using a universal Kriging method to obtain a high-precision and large-area coverage PM2.5 product. The PM2.5 data spatial correlation is considered, so that the fusion result is closer to reality, the calculation complexity is reduced, and meanwhile, the reliability of the fusion result can be judged by giving an error after data fusion. According to the method, the limitations of insufficient spatial consideration and high calculation complexity of a traditional method are made up, and the PM2.5 product can be used for pollution process analysis, climate change and other researches.

The invention provides a method for constructing a physical field of a horizontal axis water turbine blade of an agent model, and relates to the technical field of digital twinning. According to the method, rotating speed data of a water turbine under different flow velocity conditions are determined, and rotating speed values corresponding to all flow velocities are obtained; then, fluid-solid coupling simulation is carried out in combination with experimental data, and stress and deformation data of the water turbine blade under the actual operation working condition are further obtained; according to the method, the KNN algorithm is utilized to carry out order reduction processing on the obtained blade structure data, a plurality of grid points are combined into representative points, the calculation complexity is low, the processing requirement of graph rendering software on the data size in the real-time rendering process is met, a combined Kriging method is adopted to carry out digital twinning agent model training on the data after order reduction, and the real-time rendering efficiency is improved. The reconstruction of a real-time physical field of the blade is realized; iteratively optimizing the digital twin physical field proxy model according to a genetic algorithm, and solving the problem of deviation between the proxy physical field model and a real physical model; and finally, deploying the digital twin physical field agent model into a digital twin system and solving physical field distribution in real time.

The invention discloses a tunnel geological information modeling and dynamic updating method and system. The method comprises the following steps: firstly, collecting drilling data before construction and stratum information exposed by a tunnel face in a construction process, defining a lithology category set after standardization processing, and converting the lithology category set into an indication variable; empirical semi-variance is calculated based on the standardized data set, and variation parameters are obtained through variation function fitting; determining a neighborhood radius according to the variation parameter, and constructing an initial geologic model by adopting an indicating Kriging method; in the tunneling process, newly revealed stratum information is fused in an incremental mode, variation parameters are adjusted in a self-adaptive mode, a geologic model is updated, and meanwhile uncertainty quantification is conducted; and finally, determining the most probable lithology category of each spatial position based on the updated geologic model, and generating a lithology distribution diagram. According to the method, lithology category probability prediction and geologic model dynamic updating are realized, tunnel geologic spatial features can be accurately described, prediction uncertainty is quantified, and prediction precision and reliability are improved.

The invention relates to the technical field of offshore wind power equipment, and discloses an offshore floating type wind turbine mooring system optimization method based on a proxy model, and the method comprises the following steps: S1, determining key design parameters; s2, sampling to generate design points; s3, establishing a total index function of multi-target coupling; s4, generating a sample database through numerical simulation; s5, constructing an agent model by using a Kriging method; s6, calling the proxy model to search an optimal solution by adopting a differential evolution algorithm; s7, performing numerical simulation verification on the optimal solution and judging convergence; and S8, if not, updating the database and returning to S5 for iteration, and if so, outputting the optimal configuration. According to the method, the problems that a traditional optimization method is high in simulation calculation cost and too long in consumed time are solved, searching is carried out through the proxy model instead of high-precision simulation, the verification iteration step is combined, the optimization efficiency is greatly improved while the result accuracy is guaranteed, and the multi-target mooring scheme with the optimal comprehensive performance can be efficiently obtained.

The invention discloses a coal mine geological data three-dimensional modeling and transparent working face construction method and system. The method comprises the steps that multi-source geological and production data are collected and preprocessed; constructing a refined three-dimensional geologic body model based on multipoint geostatistics and a collaborative Kriging method; fusing the roadway model through Boolean operation; dynamically constructing a working face model by using real-time mining data; when a model error exceeds a threshold value, dynamic updating based on data assimilation is triggered; and finally, realizing model integration and transparent display in a three-dimensional visualization engine. According to the method, multi-point geostatistics and collaborative Kriging interpolation are fused, multi-source data such as geological drilling and three-dimensional earthquakes are integrated, the depicting precision of complex geological structures such as faults and collapse columns is greatly improved, the geometrical shape of the constructed three-dimensional geologic body model is more vivid, rock mechanics parameters are integrated, and the method is suitable for large-scale popularization and application. The problem that an existing three-dimensional geologic model is limited in precision is solved.

The invention relates to the technical field of geographic information systems and disaster monitoring, in particular to a multi-disaster historical data space-time interpolation method, which comprises a space-time collaborative interpolation module, a disaster-inducing factor identification module and a quantitative characterization module. Wherein the space-time collaborative interpolation module constructs a space-time variation function based on an optimized Kriging method and spline interpolation, and realizes collaborative interpolation of space and time dimensions; the disaster-inducing factor identification module is used for carrying out system identification and weight distribution on multiple disaster-inducing factors based on a variable fuzzy optimization theory and an entropy evaluation method; and the quantitative characterization module realizes dynamic quantization and hierarchical characterization of disaster-inducing factors based on a natural breakpoint grading method and an AHP weighting method. According to the method, high-precision space-time reconstruction and disaster-inducing factor dynamic quantification of historical disaster data in a multi-disaster complex terrain region are realized, the problems of insufficient precision and insufficient dynamic factor capture of a traditional interpolation method in a space-time heterogeneity region are effectively solved, and a reliable data basis is provided for multi-disaster risk assessment and disaster chain analysis.

The invention relates to a gravity background field construction method based on iterative optimization Kriging. The method comprises the following steps: S1, obtaining in-situ gravity measurement data as a known point set; s2, based on the known point set, performing preliminary interpolation on grid points of the gridding background image by adopting a Kriging method to obtain a Kriging estimated value of each grid point; s3, by introducing a local gravity anomaly covariance and a Kriging variation function, evaluating the random uncertainty of a Kriging estimated value in an interpolation process, and dividing each grid point into a reference point and a point to be corrected based on the random uncertainty; and S4, combining the reference point and the known point set to construct a continuous distribution constraint curved surface, carrying out local continuity correction on the continuous distribution constraint curved surface based on the obtained reference point, and carrying out iterative expansion on the basis of the corrected local constraint curved surface until the continuous correction of the whole continuous distribution constraint curved surface is completed. And the construction of the gravity background field is realized.

The invention belongs to the technical field of machine tool control, and particularly discloses a machine tool hydrostatic guideway feeding system motion error compensation method and a storage medium. The method comprises the following steps: measuring and acquiring Z-direction error data of the guide rail; processing the data by Fourier series and fitting a guide rail contour error; inputting load, oil pad geometric parameters and guide rail working parameters; a workbench-guide rail balance equation based on consideration of guide rail contour errors is established, and a Z-direction motion error model perpendicular to the feeding direction of a workbench is calculated to serve as a low-fidelity model; the Z-direction deviation ez'and the coordinate Y of the workbench are measured and obtained to serve as a high-fidelity model; constructing a multi-fidelity model by combining the low-fidelity model and the high-fidelity model based on a CO-Kriging method; and inputting the multi-fidelity model into a numerical control system, reading the position of the workbench in real time, substituting the position into the multi-fidelity model, and calculating and compensating the Z-direction motion error of the workbench. The method has the advantages of being easy to operate, high in compensation precision and good in reliability and flexibility.

The application discloses a reinforced retaining wall reinforcement failure determination method based on apparent displacement monitoring, and comprises the following steps: S1, determining the apparent displacement of the monitoring point of the reinforced retaining wall on site, and performing on-site investigation; S2, screening, classifying, interpolating and integrating by using the Kriging method; S3, constructing a theoretical model of the reinforced retaining wall, and determining the theoretical distribution range of the elastic modulus of the reinforcement material; S4, performing numerical simulation analysis on the theoretical model of the reinforced retaining wall, and obtaining the corresponding analysis result of the target displacement; S5, establishing a neural network model, and learning the mapping relationship between the layered reinforcement material elastic modulus and the surface monitoring point displacement; S6, obtaining the elastic modulus of the layered reinforcement material through the neural network model inversion; and S7, analyzing the relative relationship between the elastic modulus of the reinforcement material and the elastic modulus of the soil, and predicting whether the pull-out failure occurs. The application can accurately capture the actual working state of the retaining wall, and realizes comprehensive evaluation and failure prediction on the stability of the reinforced retaining wall.

The invention discloses a millimeter wave wireless communication method based on channel reconstruction, and relates to the field of millimeter wave wireless communication, and the method comprises the steps: obtaining a communication request, calling an information matrix of a plurality of reference points in a user neighborhood, supplementing path information according to the maximum path number, and constructing a node feature matrix; inputting the node feature matrix and the initialized adjacency matrix into a path clustering device, reconstructing a prediction adjacency matrix, and clustering after threshold segmentation; user path information in each cluster is estimated by using a universal Kriging method, a channel is reconstructed based on an SV channel model, and high-precision channel reconstruction is realized on the premise that pilot frequency overhead is not needed.

This invention discloses an intelligent tool selection method based on k-nearest neighbor entropy estimation and kriging, comprising the following steps: S1, formulating a rule-based pre-screening method for cutting tools to obtain a set of candidate tools for machining; S2, performing tool performance reasoning and tool ranking based on the candidate tool set obtained in S1; S3, combining the analysis of tools and user behavior to obtain a comprehensive cutting tool recommendation method with process scenario adaptability. This invention employs the aforementioned intelligent tool selection method based on k-nearest neighbor entropy estimation and kriging, and by mining tool selection experience from cutting records, obtains a comprehensive cutting tool recommendation method with certain process scenario adaptability. This method can provide predicted machining results, possible user behaviors, and possible tool combinations based on user input when users formulate process manuals, thus achieving intelligent selection of cutting tools.

The invention relates to a control method for a road construction wind shielding device. Collected wind condition information is subjected to filtering preprocessing; constructing wind field profile information continuously spatially distributed in the vertical direction through a common Kriging method, and outputting a wind field feature vector; calculating a vortex-induced resonance risk coefficient according to the wind field feature vector, judging whether the adjustment action is good or not through a reward function, finally training and optimizing by adopting a PPO-Clip algorithm, and outputting final action control parameters of the left wing plate, the right wing plate and the top plate; the movable wind shield is adopted, real-time changes of external airflow can be adapted, and a continuous and stable working environment is provided for a rear construction area; the system no longer responds to "arrived" wind, but predicts the morphology of "upcoming" wind. And the wing plate is adjusted to a pressure relief angle in advance, so that sudden impact is avoided.

The invention provides a joint optimization method for geometric parameters of an outer rotor brushless direct current motor. According to the joint optimization method, average torque maximization, torque pulsation minimization and cogging torque minimization are taken as optimization targets; the method comprises the following steps: firstly, constructing a motor performance meta-model based on AMOP sampling and a moving least square-isotropic Kriging method, and establishing comprehensive sensitivity through Pearson correlation to distinguish primary sensitive and secondary sensitive variables; and then, a q-ROFS-TOPSIS method is introduced, so that a unified closeness degree evaluation index can be obtained. The optimization process adopts a two-layer staged optimization strategy: in the first stage, sub-sensitive variables are dominated, main sensitive is allowed to perform fine tuning, the relevance between main and sub parameters is emphasized, and optimization is performed through a particle swarm optimization algorithm; and in the second stage, the main sensitive variable is adjusted on the basis of the sub-sensitive optimization, and optimization is carried out through an evolutionary algorithm. According to the method, the optimal motor geometric parameter combination with high average torque, low torque ripple and low cogging torque can be obtained under the conditions of incomplete samples and multiple working conditions.

The invention discloses a fuel gear pump behavior prediction method based on mechanism-data fusion. The method comprises the following steps: 1, establishing a fuel gear pump internal flow mechanism model; the data acquisition module is used for acquiring low-confidence data based on physical constraints; 2, acquiring multi-working-condition experimental data of the fuel gear pump, and using the experimental data as high-confidence data for training a fuel gear pump mechanism-data fusion prediction model; and step 3, establishing a fuel gear pump mixed behavior prediction model based on a collaborative Kriging method, and utilizing the auxiliary training data set obtained based on the mechanism model and the high-confidence data set obtained based on the multi-working-condition test bed to combine with the collaborative Kriging method to predict the mixed behavior of the fuel gear pump. And the physical boundary constraint of the mechanism model and the strong nonlinear fitting capability of data driving are fused, and a fuel gear pump mechanism-data fusion prediction model is trained, so that high-precision behavior prediction of the fuel gear pump is realized. According to the invention, the prediction precision and generalization ability are significantly improved.

This application discloses a lithofacies analysis system and method based on a shale geological-mechanical coupled lithofacies classification system, relating to the field of shale lithofacies analysis. The system construction module determines the geological-mechanical coupled lithofacies classification system based on information data from the data acquisition module; the prediction module determines single-well lithofacies prediction information; the feedback adjustment module adjusts the single-well lithofacies prediction information based on set rock mechanics and geostress parameter ranges to obtain adjusted information; the three-dimensional model construction module uses a genetic algorithm to perform seismic attribute embedding and integrated inversion based on seismic attribute data and adjusted information, and uses a co-kriging method to construct a three-dimensional model of well-seismic co-modeling of geological-mechanical coupled lithofacies; the parameter model determination module uses a layer-control-facies dual-control method to control constraints and determine the heterogeneity of the geological-mechanical characteristics of shale layers in three-dimensional space, providing a basis for establishing production areas for the exploration and development of artificial oil and gas reservoirs.

The invention discloses a permanent magnet semi-direct drive transmission system frequency reliability evaluation method, which comprises the steps of S1, establishing an electromechanical coupling dynamic model of a permanent magnet semi-direct drive transmission system, and performing modal analysis on the system; s2, a coupling relation between system dynamic response and abrasion is constructed, and the meshing stiffness of the gear pair after different service times is calculated; s3, fitting a function relationship among the service time of the system, the random variable and the inherent frequency by adopting a BP neural network; and S4, constructing a system frequency reliability limit state function according to a reliability interference theory, and solving the time-varying frequency reliability of the system by adopting an active learning Kriging method. According to the method, the influence of a permanent magnet synchronous motor control strategy and an electromechanical coupling effect is comprehensively considered, the evolution rule of the meshing stiffness of the gear pair under vibration response and wear coupling is explored, and the BP neural network and the active learning Kriging method are combined; and rapid and accurate parametric analysis of modal characteristics and frequency reliability of the system under wear and dynamic response coupling is realized.

The invention discloses a method, a device and equipment for establishing a dynamic permeability model and a readable storage medium. The method comprises the following steps: selecting a target production well meeting a preset condition from each production well; calculating the dynamic permeability of each perforation section in the target production well; calculating a permeability dimensionless ratio based on the dynamic permeability of each perforation section; based on a preset logging curve and the permeability dimensionless ratio, obtaining a permeability ratio model through a Kriging method; and multiplying the static permeability model by the permeability ratio model to obtain a dynamic permeability model. According to the invention, the technical problem of low accuracy of the correction result of the permeability in the prior art is solved.

The present invention discloses a shaftless propeller blade design method based on the Isight platform, comprising the following steps: analyzing the working process of the shaftless propeller to obtain parameters of the shaftless propeller blade having a strong performance correlation with the shaftless propeller; setting the parameters of the shaftless propeller blade as optimization parameters, and setting the thrust and efficiency of the shaftless propeller as optimization targets; collecting sample data; based on a sample database, selecting a Kriging method to establish a relationship between the parameters of the shaftless propeller blade and the thrust and efficiency of the shaftless propeller and establishing an approximate model; selecting a multi-objective particle swarm algorithm as an optimization algorithm for the approximate model; performing optimization calculations to obtain the parameters of the shaftless propeller blade. The shaftless propeller blade design method based on the Isight platform of the present invention can quickly and accurately obtain the optimal parameters of the blade, which can not only reduce the calculation and experimental costs of the shaftless propeller design process, but also significantly reduce the prediction time and improve the design efficiency of the shaftless propeller.

The application relates to the technical field of data processing, and discloses a bridge displacement remote intelligent monitoring method and system and computer equipment. The method comprises the following steps: setting multiple trigger thresholds to perform grouped polling sampling to obtain displacement data; adopting dynamic weight fusion to remove noise, identify abnormalities, and generate a digital twin model by using a Kriging method; extracting features to classify and distinguish environmental and structural response displacements; calculating a deviation index to estimate fatigue damage formation and generate a health report; calculating a risk entropy value to set an early warning and generate a treatment scheme; and evaluating an improvement rate to update and configure a closed-loop optimization. By fusing environmental data and historical monitoring data, the application establishes an adaptive sampling strategy and an intelligent analysis and processing mechanism, accurately distinguishes displacement changes caused by environmental factors and structural factors, and performs accurate risk quantification and automatic decision support.

The invention relates to a reliability analysis method for a missile wing unfolding mechanism, and belongs to the field of design and reliability analysis of missile structures. Comprising the following steps: step 1, establishing a main failure mode of the missile wing unfolding mechanism according to a failure condition and a failure mechanism of the missile wing unfolding mechanism; and 2, determining the distribution type and distribution parameters of the input variables, and establishing a reliability analysis model of the missile wing unfolding mechanism. And 3, solving the proxy model of the missile wing unfolding mechanism by adopting a self-adaptive Kriging method. 4, reliability analysis is conducted on the missile wing unfolding mechanism through the agent model and the Monte Carlo method. The method provided by the invention has strong operability, can control the uncertainty factors causing the reliability problem in the design link, and has a good application prospect.

The application provides a deep-buried tunnel original rock temperature prediction method, which utilizes field drilling measured data, tunnel surrounding rock measured temperature and data provided by a ground temperature database to obtain the original rock temperature of the whole tunnel through Akima spline interpolation method, wherein the ground temperature database is obtained through the following steps: collecting measured data of published ground temperature drilling in a certain area, pre-processing the measured data, and then performing spatial interpolation in the area by using a general Kriging method. The method solves the problems of great difficulty, long time consumption and high drilling cost in original rock temperature prediction of deep-buried tunnels, reduces the workload and calculation time on the basis of scientifically predicting the original rock temperature of deep-buried tunnels, is more suitable, is more in line with the actual engineering conditions, and improves the accuracy of the prediction result.

The invention discloses a spacecraft protection structure optimization method and system based on LSDYNA impact simulation. The method comprises the following steps: establishing a model of a Nextel / Kevlar filling type protection structure by using LSDYNA; setting an impact test working condition, selecting a sample point in a design space, carrying out numerical simulation through LSDYNA, carrying out simulation for multiple times under the working condition, and verifying the reliability of the model established in the previous step; establishing an agent model by using a Kriging method to predict the kinetic energy loss of any point in the design space; and constructing an objective function, setting constraint conditions, and optimizing structural parameters. The system comprises a model structure setting unit, a simulation unit, a prediction unit and a multi-objective optimization unit. According to the invention, the optimal structure parameters can be determined at low cost. The method can be widely applied to the field of structural design.

The invention discloses a simulation prediction method based on reduced-order model machine learning, and relates to the technical field of crossing of computer-aided engineering simulation and machine learning, in particular to a simulation prediction method based on reduced-order model machine learning, which comprises the following steps: firstly, performing finite element simulation on automobile parts (such as an anti-collision beam) to obtain simulation data; secondly, selecting sample points by adopting a Latin hypercube sampling method, and verifying that a data set meets an order reduction requirement; then, carrying out dimension reduction processing on the data by applying an order reduction model (such as intrinsic orthogonal decomposition (POD) or singular value decomposition (SVD)); and finally, predicting the data after order reduction by using a Kriging method machine learning model, and evaluating the prediction precision by using a decision coefficient R2 and a root mean square error RMSE. According to the method, through combination of the reduced-order model and machine learning, the simulation calculation efficiency is effectively improved, meanwhile, the accuracy of a prediction result is ensured, and the method is suitable for rapid simulation optimization in automobile design.

The invention discloses a space wind field and environment field evaluation system and method based on unmanned aerial vehicle cluster collaboration, and belongs to the technical field of regional wind field and environment field monitoring and evaluation. The system integrates a mooring unmanned aerial vehicle, a mobile unmanned aerial vehicle, a monitoring sensor, a hyperspectral camera and a data processing and control system. Dynamically planning a monitoring path through an ant colony algorithm, and correcting abnormal data in combination with a moving average method; constructing a landform, wind field and environment field multi-parameter coupling model based on a Bayesian network, and predicting the spatial distribution of the wind field and pollutants / health-care indexes by using a Kriging method; a multi-dimensional evaluation system is established by applying an interval number analytic hierarchy process and a cloud model, and risk grading early warning is realized by integrating wind field characteristics, pollutant concentration, diffusion characteristics and health-care indexes. The problems that traditional monitoring points are sparse, cost is high, and prediction and visualization in space are difficult are solved, and the method is suitable for accurate evaluation of wind fields and environment fields in complex areas such as industrial parks, construction sites and health and maintenance sites.

The invention provides a river topography construction method and system fusing multiple nonlinear trends, and relates to the field of hydraulic engineering digitization. The curve and width change characteristics of a complex river are described through a dynamic coordinate conversion mode based on a normal vector field, a multiple nonlinear trend function parameter space is constructed, an optimal function combination is automatically optimized to generate a trend model, and spatial topology constraints are utilized to automatically adapt to the characteristics of different terrain sections of any river channel. The defects that the boundary of the traditional Kriging interpolation method is not limited, the terrain uniformity assumption is insufficient, and the system error is high when the section spacing is large are overcome. According to the method, normal vector field coordinate conversion is utilized to automatically adapt to river boundaries, multiple nonlinear trend function combinations describe comprehensive features of the river section, a Kriging method is combined to generate a high-precision interpolation result, and the method is suitable for sparse section-based river terrain reconstruction.

The application discloses a tunnel geological information modeling and dynamic updating method and system. The method first collects drilling data before construction and stratum information exposed in a working face during construction, defines a lithology category set and converts the lithology category set into an indicator variable after standardization processing; calculates an empirical semi-variance based on the standardized data set, obtains a variation parameter through variogram fitting; determines a neighborhood radius according to the variation parameter, and constructs an initial geological model by using an indicator Kriging method; in the tunneling process, newly exposed stratum information is incrementally integrated, the variation parameter is adaptively adjusted, the geological model is updated, and uncertainty quantification is simultaneously performed; finally, the most possible lithology category of each spatial position is determined based on the updated geological model, and a lithology distribution map is generated. The application realizes lithology category probability prediction and geological model dynamic updating, can accurately depict tunnel geological spatial characteristics, quantitatively predict uncertainty, and improves prediction accuracy and reliability.

The invention discloses a rapid rainfall state detection method, system and platform based on a group well effect, and the method comprises the steps: generating and obtaining corresponding first parameter data in real time based on the group well effect; according to the first reference data, generating and obtaining second parameter data which are located in precipitation wells in a target area and correspond to the first reference data; based on the first parameter data, processing second parameter data corresponding to each precipitation well in the target area in combination with a Kriging method and Monte Carlo simulation, and generating corresponding third parameter data; generating precipitation state prediction data corresponding to each precipitation well in the target area according to the third parameter data in combination with a mapping function; according to the system and the platform corresponding to the method, the stability of the precipitation effect can be guaranteed, and meanwhile construction safety is guaranteed.

An estimation method includes an acquisition step, a kriging step, and a longitudinal extension step. The kriging step includes obtaining a distribution of the environmental value on a reference plane by kriging method based on a measured value of the environmental value at a measuring point of a sensor. The longitudinal extension step includes obtaining a distribution of the environmental value in a height direction in a longitudinal region based on the environmental value obtained in the kriging step with respect to a specified point on a reference plane and a function representing a variation in the environmental value in the height direction. The longitudinal region includes the specified point on the reference plane and extends in the height direction.

The application provides a method and system for obtaining offshore wind farm site parameters, the method comprising: planning the distribution and number of overall wind farm fan positions, and selecting representative sampling points by using an optimization method; sampling static pressure at the representative sampling points to obtain low-disturbance in-situ samples; performing static sounding tests on all planned fan positions to obtain static sounding data of all positions; testing the low-disturbance in-situ samples to obtain soil strength interpretation parameters of the representative sampling points; and using the Kriging method to expand the interpretation parameters of a limited number of representative sampling points to all fan positions to interpret the static sounding data. The application can effectively improve the accuracy of site parameter prediction and evaluation, reduce the cost and workload of offshore survey, and reduce potential risks and uncertainties in the process of offshore wind power engineering design and construction.