144 results about "Random model" patented technology

The invention provides a method for participating in primary frequency modulation control of a power system based on a modular large-scale hydrogen production power supply, and the method comprises the steps: obtaining an original data flow from a high-precision synchronous phasor measurement unit, an electrolytic cell and a submodule, and carrying out the multi-scale decomposition and noise reduction processing after time synchronization and anomaly detection restoration; performing state estimation by using unscented Kalman filtering, extracting frequency disturbance characteristics in combination with empirical mode decomposition and Hilbert transform, and estimating the inertia of the power grid; an electrolytic cell dynamic model is established, and parameter identification and optimization are carried out through a recursive least square method; a control strategy is designed based on a multi-time scale control framework, virtual synchronous generator parameters are optimized through online Gaussian process regression, and frequency modulation control is realized in combination with scene tree random model predictive control and minimum-maximum regret learning; and finally, carrying out coordination control through a distributed consistency algorithm and carrying out multi-target performance evaluation optimization. And the frequency modulation performance and the operation efficiency of the system are improved.

The invention relates to the technical field of underwater intelligent navigation and integrated navigation, in particular to a submersible integrated navigation multi-source data fusion method based on an elastic random model, which comprises the following steps: preprocessing multi-source navigation data output by an inertial navigation system (INS), a Doppler velocimeter, an ultra-short baseline / long baseline, an altimeter and a depthometer; constructing a state space model containing navigation parameter errors and sensor errors, and screening an optimal sensor combination mode; the method comprises the following steps: constructing an elastic correction parameter model and a dynamic adjustment state and observation noise covariance matrix by combining an elastic PNT framework, performing local filtering on multi-source data by adopting a plurality of parallel elastic self-adaptive robust volume Kalman filters (CKF), outputting a fusion resolving result, and realizing optimal estimation of a global navigation state through a dynamic information distribution strategy. The method has relatively high anti-interference performance, self-adaptive capability and combination switching robustness, and is suitable for navigation and positioning tasks of a high-precision submersible in a complex underwater environment.

The invention provides a rapid random model correction method for an active learning type ocean platform structure, and the method comprises the steps: firstly carrying out the selection of correction parameters through considering the sensitivity, then carrying out the design of a test scheme, and building an initial Kriging model; a new correction parameter training sample is found, the frequency corresponding to the new sample is calculated, a Kriging model is reconstructed by adopting a new training set, random model correction is performed based on the reconstructed Kriging model, an objective function is constructed by adopting actually measured frequency data and initial finite element model frequency data, and a correction parameter mean value and a standard deviation are taken as solving objectives. And solving by utilizing an optimization algorithm to obtain a mean value and a standard deviation of correction parameters of the finite element model to be corrected, and repeatedly iterating to realize random correction of the ocean platform structure. According to the method, rapid, efficient and accurate model correction under small samples and noise errors is realized, and higher correction accuracy can be obtained by adopting fewer training samples.

Described herein are systems, methods, and other techniques for training and deploying a random forest model to predict a presence of ASD in a subject. A training dataset can be obtained including abundance measurements of a first cohort of subjects with ASD and a second cohort of subjects without ASD across the set of microbial markers. Multiple candidate random forest models are trained by generating a validation dataset comprising one of k subsets of the training dataset and a training subset comprising the remaining k-1 subsets, training a candidate random forest model of the k candidate random forest models using the training subset, and assessing the candidate random forest model using the validation dataset by computing performance metrics for the candidate random forest model. Candidate random forest models are evaluated by computing an AUC.

The invention discloses an ionospheric scintillation monitoring method based on a geodetic measurement receiver, which belongs to the technical field of satellite navigation, and comprises the following steps of: accurately eliminating non-scintillation errors by constructing an inter-station single-difference function model and an inter-station single-difference random model, extracting carrier phase residual errors by using a single epoch ambiguity fixing technology, and finally obtaining an ionospheric scintillation monitoring result. Establishing a reliable ionospheric scintillation monitoring index; according to the ionospheric scintillation monitoring method based on the geodetic measurement type receiver, high-precision real-time monitoring of ionospheric scintillation can be realized through a single-epoch ambiguity fixing technology under the condition of not depending on an external precision product, and reliable technical support is provided for space weather monitoring.

The invention discloses a gravitational field inversion method considering unsteady noise of a satellite gravity observation value. The gravitational field inversion method comprises the following steps: A, acquiring original observation data of a gravity satellite; b, setting observation value noise as white noise, and constructing a random model considering non-stationary noise; c, performing parameter estimation to obtain a post-test residual error of an observation value; d, estimating the noise auto-covariance and precision factor matrix of the observation value based on the post-test residual error of the observation value; e, repeating the steps C and D for iteration to realize refinement of the gravitational field parameter and observation value random model; and F, evaluating the time-varying gravity field model in the spectral domain. According to the invention, the defects in the prior art can be improved, and the precision and stability of gravitational field inversion are improved.

The invention provides a construction method of a fiber-matrix bond slip random model, and belongs to the technical field of composite materials. The method comprises the following steps: designing fiber burial depth and pull-out angle parameters, preparing a bidirectional single fiber pull-out test piece, carrying out a pull-out test to obtain load-displacement data, drawing a curve, analyzing characteristic value data of the curve, simplifying a bond-slip relationship into a trilinear model, introducing Weibull random distribution, and carrying out parameter estimation and inspection on the Weibull random distribution. And finally establishing a function relationship between the Weibull random distribution parameter and the fiber burial depth and the pull-out angle. According to the construction method of the fiber-matrix bond slip random model, the constructed model can generate a unique single fiber pull-out curve according to the characteristics of each fiber in the composite material, and the problem that the single fiber pull-out randomness is difficult to describe by a conventional theoretical model is solved.

The invention discloses a time-varying channel estimation method and device based on correlation prediction in an RIS auxiliary system, and the method comprises the steps: assuming that the working mode of the RIS is an ON or OFF mode, and obtaining time domain cascade channel estimation at a historical moment at a base station side; constructing a training sample set by using time domain cascade channel estimation at historical moments; randomly initializing network parameters, and training the long and short-term memory neural network by using the training sample set to obtain a network model with an optimal weight and threshold; predicting the time correlation of the channel based on the trained network model; generating an AR random model by using the time correlation of the channel, and constructing a state space model of the Kalman filter based on the AR random model and the time domain receiving signal; time-varying channel estimation is carried out based on the state space model of the Kalman filter, and cascade channel estimation passing through each RIS unit is obtained. The method is suitable for high-precision acquisition of the time-varying channel information in the RIS-assisted mobile communication system.

The invention provides a vehicle-mounted high-precision positioning method based on virtual 3D speed adaptive constraint, and relates to the technical field of vehicle navigation. The method comprises the following steps: under the condition that a navigation system GNSS (Global Navigation Satellite System) signal of a vehicle is interrupted, acquiring measurement data of an inertial measurement unit in the vehicle navigation system, constructing a regression model of a virtual 3D speed of the vehicle, establishing a mapping relation between the measurement data and the virtual 3D speed of the vehicle, and generating the virtual 3D speed of the vehicle; constructing an observation model according to the virtual 3D speed, and adaptively optimizing a random model by evaluating the regression precision of the virtual 3D speed; and determining the positioning information of the vehicle according to the observation model and the random model. According to the method, the accuracy of the positioning information provided for the vehicle can be improved under the condition that GNSS positioning is unavailable due to a complex environment.

The invention discloses a digital twinborn emergency rescue deduction optimization method for a complex disaster scene, and the method comprises the steps: constructing a composite random model for emergency resources, rescue task demands and the high dynamics and randomness of environmental elements in the disaster scene through employing a random process theory and a discretization method, and carrying out the deduction optimization of the digital twinborn emergency rescue. A deduction algorithm of a continuous state space and discrete time step length is constructed based on a digital twin deduction technology, bidirectional mapping of a physical space and a virtual space is realized, an emergency rescue optimization module based on a matching game mechanism is embedded in a digital twin deduction framework, a task demand gap and resource cost are taken as objective functions, and the task demand gap and the resource cost are taken as objective functions. And a global optimal solution is gradually approached by utilizing multi-round iteration and local strategy exchange, so that efficient optimization of resource allocation in a disaster scene is realized. According to the method, the future disaster trend can be predicted in real time, and dual optimization of task demand satisfaction and resource cost control can be realized.

The invention relates to the technical field of ore raw material analysis, and discloses an intermediate fusion strategy ore sample classification method based on random model combined data, and the method comprises the following steps: S1, collecting LIBS spectrum and Raman spectrum data of a manganese ore sample; s2, preprocessing the spectral data; s3, performing dimensionality reduction on the preprocessed LIBS and Raman data by adopting principal component analysis; s4, splicing the feature matrixes after dimension reduction to construct an intermediate fusion data set; s5, training a random forest classification model based on the intermediate fusion data set; and S6, carrying out classification and identification on the manganese ore samples by utilizing the trained model. According to the method, metal element characteristic spectral lines captured by the LIBS spectrum and molecular vibration characteristics identified by the Raman spectrum are organically integrated through an intermediate fusion strategy, two-dimensional identification is achieved, redundant information is effectively eliminated while main characteristics of the spectrum are reserved, and the accuracy and reliability of manganese ore sample classification are remarkably improved.

The invention discloses a multi-energy micro-grid energy regulation and control method and device and a storage medium. The method comprises the following steps of: constructing a scene prediction constraint optimization problem by utilizing a trained Warisstein generative adversarial network, and predicting a group of multi-dimensional uncertainty multi-energy micro-grid operation scenes in a future period by following known information at each moment to serve as prediction scenes; forming a pre-scheduling model and a real-time power compensation model based on the prediction scene, and forming a two-stage multi-energy micro-grid real-time energy regulation and control model; a pre-scheduling model obtains a pre-scheduling instruction of the multi-energy micro-grid based on random model prediction control by using the prediction scene information; the real-time power compensation model coordinates schedulable units in the multi-energy micro-grid according to real-time observation of uncertain variables; therefore, the energy in the multi-energy micro-grid is regulated and controlled. Through quick response and adjustment of real-time data, the system can effectively cope with fluctuation caused by uncertainty, thereby realizing accurate regulation and control of the energy of the multi-energy micro-grid.

The invention relates to the technical field of ocean engineering, and provides a rapid random model correction method for a step-by-step ocean platform structure. According to the design parameters of the ocean platform, establishing an ocean platform initial finite element model; selecting to-be-corrected parameters from the design parameters, and determining the maximum value and the minimum value of each to-be-corrected parameter; performing # imgabs0 # sampling on the maximum value and the minimum value of each to-be-corrected parameter to obtain a corrected parameter matrix; substituting the correction parameter matrix into the initial finite element model to obtain an updated finite element model, obtaining a structure frequency vector corresponding to a correction parameter vector, and establishing a Kriging model; calculating an initial finite element model frequency based on the corrected parameter vector and a Kriging model; constructing a distance objective function and a distribution objective function, and correcting the mean value and the standard deviation of the correction parameters twice; and obtaining a corrected ocean platform finite element model based on the finite element model frequency corrected for the second time.

The invention provides a Beidou PPP-B2b positioning compensation method and system, which are used in the field of satellite navigation, and the method comprises the steps: receiving a broadcast ephemeris, correcting the broadcast ephemeris according to a Beidou satellite PPP-B2b signal to obtain a PPP-B2b precise ephemeris, and obtaining a reference precise ephemeris; according to the PPP-B2b precise ephemeris and the reference precise ephemeris, a PPP-B2b satellite orbit error and a satellite clock error are obtained through calculation, and according to the PPP-B2b satellite orbit error and the satellite clock error, the Beidou satellite PPP-B2b global average SISRE is calculated; respectively calculating a difference SISRE sequence of each satellite epoch based on the global average SISRE of the PPP-B2b, and constructing an SISRE random model based on the difference SISRE sequence of each satellite epoch; the relation between PPP-B2b positioning precision and SISRE random model parameters is measured through experiments, and SISRE optimal process noise parameters are selected from an SISRE random model to compensate a PPP positioning model. According to the scheme, the convergence speed of PPP-B2b positioning can be increased.

The invention is suitable for the technical field of geodetic survey, and provides an InSAR turbulent atmosphere correction method based on a three-dimensional random model, and the method comprises the steps: obtaining M interferograms corresponding to N-scene SAR images of a research region, dividing each interferogram into Y windows through a window with a preset size, taking the phase information in the yth window of all interferograms as the yth observation window; for each observation window, constructing a space-time three-dimensional random model in the observation window according to the M interferograms, constructing a function model of all pixels in the observation window, and solving by using a weighted least square method to obtain a deformation parameter corresponding to the observation window; the deformation parameters corresponding to all the observation windows are spliced, and the deformation phase of the research area is obtained through calculation based on the splicing result. The influence of turbulent atmosphere can be effectively inhibited.

The invention discloses a lithology identification method and system based on TLS, and belongs to the technical field of lithology intelligent identification, and the method comprises the steps: S1, collecting all rock data of a target region, and carrying out the preprocessing of the rock data, and obtaining outcrop point cloud data; s2, constructing a lithology identification unit, and dividing the outcrop point cloud data into point cloud blocks of regular grids according to a preset grid; s3, extracting features with lithologic sensitivity from the point cloud blocks, wherein the features comprise geometric features and spectral features; and S4, inputting the geometric features and the spectral features into a trained random model forest model, and fitting the geometric features and the spectral features by adopting a random forest to obtain lithology categories of various rocks in the target area. According to the method, automatic classification of outcrop lithology is realized, powerful support is provided for geological exploration and lithology analysis, understanding of the geological field is introduced into effective exploration of the artificial intelligence field, and interdisciplinary fusion development is promoted.

The invention discloses the field of automatic control systems, and particularly relates to a random model predictive control method and equipment triggered by a self-adaptive event. The method comprises the following steps: S1, dynamically adjusting the sampling time of a control system and the updating frequency of control input through a self-adaptive event triggering mechanism; the self-adaptive event triggering mechanism is adjusted according to a system state and a change condition of control input; s2, using a distributed robust optimization method to convert a prediction control problem of the random model with opportunity constraint into a second-order conic convex optimization problem for solving; and S3, at each sampling time, determining the optimal control input by solving the second-order cone convex optimization problem, performing feedback control on the state of the control system, and adjusting the communication sampling frequency. According to the method, unnecessary sampling and communication costs are reduced, the feasibility and robustness of the optimization problem are improved, the calculation burden of the system is reduced, and meanwhile, the stability of the control system is ensured.

The invention provides a high-precision precise point positioning method based on BDS-3, and relates to the technical field of wireless communication. The method comprises the following steps: firstly, introducing a traditional ionosphere-free combination model to eliminate the influence of ionosphere delay on positioning precision, and eliminating or weakening a satellite orbit error and a clock error error by using satellite orbit and clock error data provided by an IGS; secondly, constructing a troposphere correction model of adaptive weighted wet delay for eliminating troposphere delay errors; thirdly, establishing a self-adaptive random model, and reasonably weighting pseudo-range and carrier phase observed quantity; and finally, estimating the parameters to be estimated through extended Kalman filtering so as to obtain a positioning calculation result. The method has obvious advantages in the aspect of improving the positioning precision, can meet the requirement of high-precision positioning, can realize centimeter-level positioning precision no matter in a dynamic mode or a static mode, and can even reach millimeter-level positioning precision in the static mode in some observation stations and directions.

The invention relates to a river channel sand body random model generation method and device based on hidden Markov model data filling, a medium and equipment. The method comprises the steps that logging data of a target area are collected and arranged; dividing logging data according to a logging gas-bearing interpretation conclusion, and carrying out analytical statistics on the velocity and density of longitudinal and transverse waves in the logging data; initializing an initial state probability, a state transition matrix, and a mean value and a covariance of Gaussian distribution in the hidden Markov model; inputting a three-dimensional observation vector composed of the velocity and density of the longitudinal and transverse waves into the initialized hidden Markov model for iterative training; performing down-sampling on the lithology data, then constructing a state transition probability matrix, and further constructing a lithology sequence conforming to a geological law through simulation; according to the lithologic sequence, calling a hidden Markov model to generate longitudinal wave velocity, transverse wave velocity and density corresponding to the lithologic sequence; and calculating a reflection coefficient of the synthetic stratum, setting a Ricker wavelet dominant frequency, and obtaining synthetic seismic response data through convolution.

A multi-modal uncertainty perception mechanical life prediction method based on random model updating comprises the steps that firstly, a drift network is constructed, a representation extraction block RDB comprises four layers including a data structure DS, short-term local feature refinement, long-term time trend development and down-sampling, and residual connection is used for capturing cross-layer information and promoting gradient flow; a diffusion network is constructed, Lipschitz continuity is followed, and ReLU is selected as an activation function; then each subnet is trained, the first two parts of a target function of the subnet are regression terms of a distributed drift network and an adjustment diffusion network, and the latter component belongs to an OOD sample; then, the uncertainty of each subnet is quantified, and finally, multi-modal fusion is carried out based on the respective uncertainty; according to the method, modeling is carried out on the uncertainty of each mode through the fractional order stochastic differential equation sub-network, dynamic fusion is carried out according to the uncertainty of modal characteristics, and a final prediction result and the corresponding uncertainty are obtained through regression reasoning.

The present invention belongs to the technical field of generating heterogeneous structures of aquifers, and provides a method for generating heterogeneous structures by coupling a potential diffusion model and a random model. The present invention can utilize the prior distribution of aquifer structural parameters and the geostatistical parameters of permeability coefficients corresponding to different lithologies to generate heterogeneous structures of aquifers at different scales, and can constrain the spatial distribution of permeability coefficients according to the spatial distribution of large-scale lithological structures, thereby ensuring that the spatial distribution characteristics of permeability coefficients within different lithologies meet the given geostatistical distribution of permeability coefficients of different lithologies, thereby realizing the reasonable characterization of heterogeneous structures of aquifers at different scales.

The invention discloses a post-processing device of a true random number generator, which comprises a random seed simulation module used for generating random seeds; the first processing module is used for performing accelerated multiplication on each random seed group formed by the random seeds and each original random number sub-vector formed by the original random number vector output by the true random number generator based on fast Fourier transform to obtain a first result corresponding to each random seed group; or extracting bits in the original random number vector through a single-bit extractor according to each random seed group formed by the random seeds to obtain a first result corresponding to each random seed group; splicing all the first results to obtain an intermediate result; and the second processing module obtains an actual random number vector according to the intermediate result. According to the method, bit correlation defects can be processed, original random numbers from a plurality of correlative entropy sources can be processed, and the method has unconditional security independent of any calculation hypothesis or random model.

The present invention belongs to the field of federated learning technology and discloses a method, apparatus, device, and medium for constructing a random model sharing pool for federated learning. The method comprises: randomly selecting at least one base model from the model sharing pool; randomly assigning the at least one base model to multiple participants; obtaining the model parameters of the local model recorded by each participant after training, aggregating the model parameters of all local models corresponding to the same base model after training, and obtaining a global model after the aggregation of each base model; then randomly selecting at least one global model and re-randomly assigning it to multiple participants to update the local model of each participant, and each participant then performs the next iterative training with the updated local model until the global models corresponding to each base model converge. The present invention can provide better guarantees for privacy protection in federated learning and provide a better privacy protection solution for digital governance.

The invention provides a PPP-RTK random model construction method and system for weakening ionospheric scintillation influence, and belongs to the technical field of navigation positioning, the PPP-RTK random model is applied to an observation equation of a PPP-RTK rapid precision positioning model to carry out PPP-RTK positioning, and the method comprises the following steps: when the carrier-to-noise ratio of satellite observation data is higher than a preset threshold value, determining that the PPP-RTK random model is a PPP-RTK random model; determining weight factors of the pseudo-range and the phase observation value based on the satellite elevation angle so as to set random noise distribution obeyed by the pseudo-range and the phase observation value in the observation equation; and when the carrier-to-noise ratio of the satellite observation data is lower than a preset threshold value, determining weight factors of the pseudo-range and phase observation values according to the carrier-to-noise ratio intensity and the satellite elevation angle so as to set random noise distribution obeyed by the pseudo-range and phase observation values in the observation equation. According to the invention, the influence of ionospheric scintillation on PPP-RTK positioning is effectively weakened, and rapid, accurate, continuous and steady PPP-RTK positioning in a scintillation environment is realized.

The application provides a verification method and system of a maximum likelihood sequence detection chip, the verification method comprises creating a random environment, and a random model is created in the random environment; a verification environment is created, a reference model is run in the verification environment, and a device to be tested is run in the verification environment; the random environment is run, random configuration information is generated from the random environment and is sent to the reference model; the reference model forms a test signal according to the received configuration information, and inputs the test signal to the device to be tested; the reference model generates reference data according to the configuration information and outputs the reference data to a checker, the device to be tested generates running data after running according to the test signal and outputs the running data to the checker, and the checker compares the reference data with the running data and generates a verification result. The application also provides a verification system for realizing the above method. The application can realize decoupling of generation of configuration information and the verification environment, and improves the accuracy of chip verification.

The invention relates to a stochastic simulation method considering dimensional deviation and concrete material variability, and belongs to the field of concrete structure analysis. According to the method, accurate simulation of the mechanical properties of the concrete structure under the influence of construction errors and material randomness is realized by determining random factors, establishing a random field matrix, carrying out sample transformation, establishing random models in batches, solving in batches and carrying out post-processing. A random simulation result is used as an output parameter, a random sample is used as an input parameter, and a PRRM-EPR algorithm is adopted to establish a machine learning prediction model. Compared with a traditional method, the method has the advantages that the generation quality of the random sample is improved, the calculation cost is reduced, the randomness in actual engineering can be better reflected, and the established prediction model can directly predict the mechanical response (displacement, load and the like) of the component through actually measured random parameters. The method is suitable for design, evaluation and optimization of a concrete structure, and particularly has a remarkable effect on improvement of structural safety.

The application provides a single-step absolute antenna phase center calibration method and system with PCV constraint, comprising the following steps: establishing an antenna phase center calibration field based on a mechanical arm, forming a short baseline by using the mechanical arm and a static observation base pier, and collecting calibration data outdoors; constructing three-difference observation values, determining an observation model and a random model, and calculating the attitude of the antenna through mechanical arm data; for the three-difference observation values, a PCO approximate value is given, the observation value residual is calculated, then a spherical harmonic function is used to model the PCV, and the PCV value on the grid point is solved and output; according to the coupling between the PCO and the PCV, the phase model is expressed as different combinations of the PCO and the PCV, the minimum PCV constraint is applied, and the final consistent phase model result is obtained, so that the antenna phase center calibration is realized. The application has the advantages that the two-step estimation is reduced to one step, the data processing flow is simplified, the convergence speed of the antenna phase solution is improved, and the consistency of the antenna phase model calibration result is enhanced.

The present invention provides a real-time adjustment method for the random walk variance of the BDS PPP ionospheric delay, which relates to the technical field of GNSS satellite navigation and positioning. This method constructs a random walk form of the ionospheric delay, utilizes the parameter correlation between the epoch-to-epoch ionospheric delay EDI change value and the true ionospheric delay in the short term, weakens the noise in the ionospheric observations, calculates the power spectral density PSD of the ionospheric delay, and then combines the relationship between the PSD and the random walk variance to adjust the variance constraint value in real time, thereby effectively shortening the integer ambiguity fixing time and improving the positioning accuracy. The present invention realizes the real-time adjustment based on the random model of the BDS ionospheric parameters, effectively improves the positioning accuracy, and has strong practical application value.

The application relates to the technical field of satellite orbit calculation, and discloses an inter-satellite baseline solution weight determination method based on elevation angle grouping and variance component estimation, which comprises the following steps: S1) obtaining inter-station single-difference observation values; S2) pre-processing the observation values; S3) determining the weights of the observation values used for parameter estimation based on the elevation angle grouping and variance component estimation method, iteratively performing parameter estimation and orbit integration until convergence, and generating high-precision inter-satellite baseline and outputting the same. The inter-satellite baseline solution weight determination method based on the elevation angle grouping and variance component estimation determines the noise variances of observation values in different elevation angle intervals by using the variance component estimation method, establishes a more accurate and objective random model, and greatly improves the inter-satellite baseline precision.

The present invention relates to the field of ocean engineering technology, and provides a method for rapidly and randomly correcting the model of a step-type ocean platform structure. According to the design parameters of the ocean platform, an initial finite element model of the ocean platform is established; among the design parameters, the parameters to be corrected are selected, and the maximum and minimum values of each parameter to be corrected are determined; sub-sampling is performed on the maximum and minimum values of each parameter to be corrected to obtain a matrix of corrected parameters; the matrix of corrected parameters is substituted into the initial finite element model to obtain an updated finite element model, and the structural frequency vector corresponding to the vector of corrected parameters is obtained, and a Kriging model is established; based on the vector of corrected parameters and the Kriging model, the frequency of the initial finite element model is calculated; a distance objective function and a distribution objective function are constructed to perform two corrections on the mean and standard deviation of the corrected parameters; based on the frequency of the finite element model after the second correction, the corrected finite element model of the ocean platform is obtained.