34 results about "Unscented transform" patented technology

The invention provides an attitude monitoring method and system in navigation of an aircraft, and the method comprises the steps: determining a process noise covariance matrix through a high-frequency energy component of an angular velocity signal measured by a current gyroscope in each filtering period; predicting a prior state vector and a prior covariance matrix at the current moment according to the state vector at the previous moment, the angular velocity measured by the current gyroscope and the process noise covariance matrix; generating an asymmetric Sigma point set and determining a measurement noise covariance matrix; the asymmetric Sigma point set is substituted into a measurement model, and a predicted measurement value and a predicted measurement covariance are obtained through unscented transformation; calculating Kalman gain based on the predicted measurement covariance and the measurement noise covariance matrix, and performing weighted gating test on innovation formed by a real measurement value and a predicted measurement value; and updating a state vector and a covariance matrix on the basis of the adjusted Kalman gain and information so as to obtain attitude information of the aircraft at the current moment.

The invention discloses a generator state estimation method and system considering noise and parameter uncertainty constraints. The method comprises the following steps: acquiring model parameters and dynamic state vectors of a generator, and establishing augmented state vectors; performing unscented transformation on the augmented state vector to obtain a particle set; improving to obtain robust mixed Kalman particle filtering, and in the process of performing unscented Kalman filtering on an augmented state vector, taking correlation entropy maximization of a measurement information sequence as a target function, and solving by adopting a fixed point iteration method to obtain a filtering gain; determining a filtering gain according to the updated state of the measurement information; robust mixed Kalman particle filtering is executed, physical constraints of model parameters serve as a feasible region, after resampling, projections of the model parameters in new-generation particles exceed the feasible region, the model parameters are set to be closest boundary points, and then resampling is conducted again; a weighted average value of the particle set is an optimal joint estimation value, a dynamic state estimation value and a model parameter identification result are separated, and reliable uncertainty quantization is provided for state and parameter estimation.

The invention discloses a probabilistic optimal power flow calculation method based on a second-order cone and unscented transformation, belongs to the field of power system optimization, and efficiently processes the uncertainty of new energy output and load by coupling the probabilistic sampling capability of unscented transformation (UT) and a convex optimization framework of second-order cone programming (SOCP). According to the method, unscented transformation efficient sampling is utilized, a covariance matrix is directly embedded to simplify correlation processing, and accurate probability distribution of network loss, voltage and line power can be output. Test results show that the calculation efficiency is improved by dozens of times compared with Monte Carlo simulation, the error is lower than 3%, and the method is suitable for risk assessment and operation optimization of the power system.

The invention provides a failure spacecraft relative pose estimation method based on dual algebra, and belongs to the field of spacecraft navigation, and the method comprises the following steps: building a failure spacecraft six-degree-of-freedom relative motion and dynamics model under dual algebra description; then, through a matrix vectorization operator, determining analysis forms of a state equation and an observation equation, and establishing a multiplicative extended Kalman filter; finally, unscented transformation is introduced, a process noise covariance and measurement noise covariance adaptive law is designed, the problems that parameter adjustment of a complex system is tedious and convergence is slow are solved, and accurate estimation of the near-operation real-time pose of the failed spacecraft is achieved. According to the invention, successful execution of the capture maintenance task can be ensured.

The invention discloses an IGBT life prediction method based on adaptive unscented particle filtering of time sequence form perception optimization, and belongs to the technical field of health prediction and reliability engineering. The method aims at the problems that a traditional filtering algorithm is prone to falling into local optimum in a high-dimensional parameter space and prediction trajectory forms are discontinuous, and comprises the steps that firstly, an IGBT nonlinear degradation model is established through a state-space equation; performing Sigma point sampling and nonlinear propagation at a particle level by adopting an unscented transformation and particle filtering cooperation mechanism, and fusing process noise to realize dynamic state prediction; constructing a Gaussian likelihood function by using the deviation between a predicted value and an actual observed value to generate a system residual sequence; an FCD fusion index is innovatively introduced, the index fuses a Pearson correlation coefficient and a Frechet distance to synchronously quantify trend consistency and form similarity, and self-adaptive genetic optimization is driven accordingly; and repeating the behaviors before a preset termination condition is met, and continuously updating the parameters of the state-space equation.

The invention discloses a GEO binocular orbit determination method and system based on inter-satellite communication, and belongs to the technical field of aerospace, and the method comprises the steps: predicting the priori estimation of the state variable of a binocular orbit determination system through unscented transformation according to an orbit dynamics model of the binocular orbit determination system; simulating and generating space-based observation data based on an observation model of a binocular orbit determination system; according to the principle of unscented Kalman filtering, the priori estimation of the state variable and the space-based observation data are fused, and the posteriori estimation of the state variable of the binocular orbit determination system is obtained; repeatedly iterating until the posteriori estimation orbit determination precision of the state variable of the binocular orbit determination system meets the task requirement; according to the method, target observation data is obtained through binocular observation, observation data between observation satellites is obtained by considering inter-satellite communication, prediction data and the observation data are fused through an unscented Kalman filtering algorithm, and a high-precision orbit determination method is provided for a GEO spacecraft.

The application discloses a power distribution network prediction auxiliary state estimation method and system based on an improved iteration process, and the method comprises the following steps: establishing a nonlinear discrete time state space model; generating a group of Sigma points through an unscented transformation, and substituting the Sigma points into a state transition equation to calculate a state prediction value and a state prediction error covariance matrix at a current time; substituting the Sigma points into a measurement equation to obtain a measurement prediction value, and calculating a Kalman gain; calculating a process noise covariance matrix at a next time; and correcting the state prediction value and the state prediction error covariance matrix at the current time to obtain an optimal state estimation value and an error covariance matrix thereof. The application avoids the risk of losing semi-positive definiteness of the covariance matrix in the iteration process from the algorithm mechanism, significantly enhances the numerical stability and robustness of the UKF algorithm, and ensures reliable application in high-dimensional complex power distribution network state estimation.

The present application belongs to the technical field of unmanned aerial vehicle navigation, and particularly relates to an online trajectory prediction and parameter optimization method coupling multiple output Gaussian processes. The method first constructs a multiple output coupling kernel function fusing a square exponential kernel and a neural network kernel, and uses a symmetric positive definite output covariance matrix to explicitly model the dynamic coupling relationship between the lateral position (axis) and the longitudinal position (axis) of the unmanned aerial vehicle, thereby overcoming the coupling information loss caused by the traditional independent modeling method. Secondly, a recursive Gaussian process online learning framework is established, and an augmented state space model containing the flight trajectory latent function value and the kernel hyperparameter is constructed. Finally, the unscented transformation technology is introduced to process the nonlinear propagation of the hyperparameter, and the real-time prediction of the flight state of the unmanned aerial vehicle and the online adaptive update of the model hyperparameter are realized through the block Kalman filtering mechanism. The present application significantly improves the precision, real-time performance and robustness of the unmanned aerial vehicle trajectory prediction under complex flight environment and high maneuvering task.

The invention belongs to the technical field of rigid body posture estimation, and discloses a rigid body posture estimation method, system and device based on generalized correlation entropy geometric filtering, and the method comprises the steps: obtaining target motion information, and building a discrete nonlinear kinematics model; the discrete nonlinear kinematics model is initialized; sigma points of a posterior state covariance matrix and a process noise covariance matrix are generated respectively, and the sigma points are propagated and updated through manifold fast unscented transformation; obtaining a prediction state mean value, a priori state covariance matrix and a square root of the priori state covariance matrix; updating the central parameters based on the information of the historical moments; calculating a pseudo measurement matrix and updating the gain; correcting the state estimation by using the gain; judging whether convergence occurs or not, and if not, continuing iteration; otherwise, outputting a posterior state estimation value as a final rigid body posture estimation result at the moment. According to the method, the problem that the robustness and precision of rigid body posture estimation in a non-zero mean value and non-Gaussian noise environment are reduced is effectively solved.

The present invention relates to the technical field of probabilistic power flow calculation for interconnected power grids, and specifically discloses a probabilistic power flow calculation method for interconnected power grids based on static external network equivalence and unscented transformation method, including using the unscented transformation method to generate a sample point set X; obtaining the nodal admittance matrix Y of the internal network after equivalence eq ; performing AC power flow calculation on the sample point X0 representing the mean value to obtain the basic operating point, and then using the DC-like power flow method to obtain the operating points corresponding to other sample points; obtaining the sample point set Z of the equivalent parameters through equivalent calculation; based on Z and the subset X of X I , obtaining the sample point set R after equivalence. Then calculate the mean value #imgabs0# and covariance P of the input random variables of the internal network after equivalence RR ; using the unscented transformation method again to generate a new sample point set X eq ; taking Y eq and X eq as inputs, performing 2n2 + 1 deterministic AC power flow calculations to obtain the output sample point set Z eq , and calculating the mean value #imgabs1# covariance matrix #imgabs2# and standard deviation #imgabs3# of the output random variables of the internal network. This method realizes double dimensionality reduction in terms of the amount of calculation.

The application discloses a multi-target probability hypothesis density DOA tracking method under a wireless sensor network. Firstly, a uniform array is used to receive a non-circular signal under the wireless sensor network to obtain extended measurement information. Secondly, state parameters of signal particles at an initial moment are set as initial distribution of the signal, and signal sampling particles surviving at a current moment are measured. A set of predicted sampling particles is obtained based on an unscented transformation. Then, a covariance matrix of the received signal is calculated by using the extended measurement information at the current moment, and a noise subspace is obtained. A PHD filter is used to update the predicted sampling particles of the signal at the current moment. Finally, more effective particles are found by using resampling, a new signal sampling particle set is composed, a weighted sum of the signal sampling particle states is calculated, and the signal state at the current moment is tracked until the end. The application can more accurately estimate the number of signals and track the state of the signal source.

The invention discloses a sliding window anomaly detection method and system based on a chi-square cumulative mean value, and belongs to the technical field of aerospace, and the method comprises the steps: generating simulation space-based observation data based on an observation model; determining priori estimation of a state variable of the observation system by using unscented transformation, and according to a Kalman filtering principle, fusing the priori estimation of the state variable and the simulation space-based observation data to obtain posteriori estimation of the system state variable; based on a chi-square test principle, determining an accumulated chi-square mean value according to prior estimation and posterior estimation of a system state variable and simulation space-based observation data; and setting the width of the sliding window, and judging the abnormal behavior of the target satellite according to the change trend of the chi-square cumulative mean value in the sliding window. According to the method, the problems of data jitter and difficulty in critical value selection in chi-square detection are effectively solved, and the robustness of spacecraft abnormal behavior detection is improved.

The invention provides an inertia and acoustics integrated navigation method based on enhanced hybrid minimum error entropy and unscented Kalman filtering. The method comprises the following steps: establishing a state equation model of an inertia and acoustics integrated navigation system; establishing an enhanced mixed minimum error entropy measurement model considering a sound ray bending effect and a multi-modal noise characteristic; based on a state equation model and an enhanced hybrid minimum error entropy measurement model, an EnMMEE-UKF framework is constructed, nonlinear mapping is processed through unscented transformation, an attenuation factor in a strong tracking filtering theory is introduced to correct an error covariance matrix, and an expectation maximization algorithm is adopted to adaptively adjust a hybrid coefficient of a hybrid kernel function. And state estimation and a covariance matrix are recursively updated in combination with a fixed point iteration method, so that real-time updating and feedback of errors are realized.

The invention discloses a power distribution network prediction auxiliary state estimation method and system based on iteration process improvement. The method comprises the following steps: establishing a nonlinear discrete time state space model; generating a group of Sigma points through unscented transformation, substituting the Sigma points into a state transition equation, and calculating a state prediction value and a state prediction error covariance matrix at the current moment; substituting the Sigma point into a measurement equation to obtain a measurement predicted value, and calculating a Kalman gain; calculating a process noise covariance matrix at the next moment; and correcting the state prediction value and the state prediction error covariance matrix at the current moment to obtain an optimal state estimation value and an error covariance matrix thereof. According to the method, the risk that the covariance matrix loses the semi-positive qualitative property in the iteration process is avoided from the algorithm mechanism, the numerical stability and robustness of the UKF algorithm are remarkably enhanced, and reliable application in high-dimensional complex power distribution network state estimation is ensured.

The invention discloses a nonlinear pipeline system state estimation method and system based on unscented Kalman filtering and a storage medium, relates to the field of network control, and aims to solve the problems of limited state estimation precision and high power consumption caused by signal attenuation, nonlinear characteristics and limited communication resources in the existing method. Comprising the following steps: 1, establishing an oil pipeline system dynamic model, and discretizing to obtain a state space model; 2, designing a dynamic event triggering mechanism, and constructing an amplification forwarding relay system to obtain amplification measurement signal data; step 3, setting initial values of state estimation and a covariance matrix; step 4, weighting 2n + 1 Sigma sampling points obtained by unscented transformation according to a weight coefficient to obtain one-step prediction and a one-step prediction error covariance Pj + 1j; 5, designing a filter according to a system phenomenon, calculating an estimation error, calculating an estimation error covariance upper bound matrix by using a matrix inequality, and solving a filtering gain matrix Kj + 1; and step 6, executing the step 4 and the step 5 until the total duration is reached.

The invention relates to the technical field of hydrogen-electricity coupling, and provides a hydrogen-electricity coupling micro-grid energy management model adaptive updating method, which comprises the steps of generating a Sigma point based on unscented transformation, predicting a state and an observation value through a state and observation equation, and calculating statistical characteristics of the state and the observation value; according to the method, state-observation cross covariance and Kalman gain are further calculated, weighted fusion correction is carried out on state prior estimation by using actual observation residual, noise is effectively suppressed, nonlinear dynamics are accurately processed, and high-credibility state estimation is provided for energy management decision. A feature space is constructed based on real-time state estimation feedback information, fusion features are extracted through incremental principal component analysis and input into an energy management model to calculate a loss function, and a learning rate is adaptively adjusted by calculating first-order and second-order moments of gradient information in parameter vector loop iteration. The model parameters are updated according to the learning rate and the gradient of the loss function, and the problem that the model applicability in a traditional model updating mode is reduced is solved.

The invention discloses a probabilistic optimal power flow calculation method based on differential evolution and a discrete Fourier matrix, which efficiently processes the uncertainty of new energy output and load by coupling the probabilistic sampling capability of a discrete Fourier matrix method and a differential evolution optimization framework. According to the method, a discrete cosine transform matrix (DCTM) sub-method in DFTM is utilized to generate sample points, and the sample size is reduced to m + 1 from 2m + 1 of a traditional unscented transform (UT) method. The probabilistic optimal power flow problem of the island micro-grid containing high-proportion renewable energy sources can be efficiently and accurately solved, and meanwhile, the adaptability to asymmetrically distributed random variables is ensured.

The invention discloses a self-adaptive UKF fusion positioning method for an outdoor cleaning robot. The method comprises the following steps: acquiring inertial measurement unit IMU data, point cloud data and RTK positioning data through a sensor; according to the system state of the robot, unscented Kalman filtering (UKF) is adopted for unscented transformation, IMU data serve as control input, a predicted sigma point set is obtained, and sigma points serving as the system state are updated; evaluating the quality of the point cloud data; according to the point cloud data quality evaluation and the RTK positioning data, a current positioning mode is adaptively selected through a state machine, and the positioning mode comprises a first mode with the point cloud data quality as the main mode, a second mode with the RTK positioning data as the main mode and a third mode fusing the point cloud data quality and the RTK positioning data; and according to a corresponding mode, observation updating and time-sharing dimension multiplexing are carried out based on unscented Kalman filter (UKF).

The invention provides an unmanned aerial vehicle pose integrated unscented Kalman filtering method based on a torque, and the method comprises the following steps: building a pose integrated motion model of an unmanned aerial vehicle based on the torque, and enabling the pose state quantity to describe the position and the pose in a unified manner through a pose dual quaternion; converting the pose state quantity into an error torque quantity; unscented transformation is carried out based on the error torque, and Sigma sampling points meeting pose geometric constraints are generated; performing state prediction and measurement updating by using the Sigma sampling points to obtain pose estimation data; and converting the updated error torque into a pose dual quaternion.

The invention discloses an ATR engine control system state estimation method based on adaptive robust UKF. The method comprises the steps that a nonlinear discrete model of an ATR engine control system is established; the time updating process comprises the steps of initializing an engine state quantity and a state error covariance matrix, selecting a sampling point, constructing a state quantity of the sampling point, and predicting an estimated value of the engine state quantity and the error covariance matrix; the measurement updating process comprises the steps of updating the state quantity of the sampling point, converting the updated state quantity of the sampling point, calculating an engine output quantity estimated value and a covariance matrix through unscented transformation, and calculating a state-measurement cross covariance matrix; calculating a Kalman gain matrix process through a maximum correlation entropy criterion; and updating a state estimation and covariance matrix process. The method can solve the problems that an existing nonlinear system state estimation method is insufficient in robustness in a complex noise environment, sensitive to abnormal measurement values, difficult to cope with the influence of non-Gaussian noise and the like.

The application discloses a kind of water unmanned vehicle trajectory tracking method and system based on distributed fusion strategy, belong to automatic control field.First, the state estimation problem of water unmanned vehicle based on multiple radars is constructed;Then, by means of radar measurement vector, the mean and variance of random variable after transformation by nonlinear radar measurement model are approximated using unscented transformation, and a local nonlinear filter is designed under the least mean square error criterion;Further, the distributed fusion estimation value is obtained by fusing local estimates based on distributed fusion strategy, and the real-time tracking of unmanned vehicle motion trajectory is realized.The simulation results show that the application realizes the real-time tracking of water unmanned vehicle trajectory.

This invention discloses a visual inertial wheel velocity meter SLAM method based on an improved adaptive UKF in the field of visual navigation technology, including the following steps: 1) establishing the state equation for the robot SLAM system; 2) establishing the measurement model of the visual sensor and wheel velocity meter; 3) determining the unscented transformation sampling points and weights according to the state space model; 4) transferring the sampling points through a nonlinear function and updating the system state prediction and state prediction covariance matrix; 5) updating the measurement and updating the process noise and measurement noise covariance matrix using an improved Sage-Husa algorithm; 6) introducing a convergence factor to update the state estimation, filter gain, and state estimation covariance matrix. This invention uses an improved Sage-Husa adaptive Kalman filter, which optimizes the filtering effect by adaptively adjusting the parameters of the UKF algorithm, thereby improving the real-time performance and accuracy of the robot SLAM system.

The invention discloses an SOC (State of Charge) estimation method of deep fusion neural network unscented Kalman filtering, relates to the technical field of all-vanadium redox flow batteries, and aims to realize high-precision estimation of the state of charge of the battery. The method comprises the following steps: establishing a second-order equivalent circuit model of a battery, and performing parameter identification on the second-order equivalent circuit model; obtaining a group of sigma points through unscented transformation, calculating propagation state estimation and propagation state prediction of each sigma point, obtaining a first-order statistical moment of prior state estimation, a first-order statistical moment of prior state prediction and a cross covariance through unscented transformation, and obtaining an observation difference and a state updating difference; and inputting the observation difference and the state updating difference into a neural network system to obtain a Kalman gain, and performing state updating on the priori state estimation through the Kalman gain to obtain prediction of the state of charge of the battery.

The present application relates to a method, device and equipment for evaluating the performance of intelligent dialogue based on large language models, which solves the problem of performance evaluation of large language models by research, distinguishes the topics of questions and answers of large language models, studies the uncertainty of large language models under different topics, establishes the uncertainty expression of large language models in each topic based on information entropy under the assumption of introducing multivariate Gaussian distribution, and then uses unscented transformation estimation to solve and obtain the estimated entropy value, so as to obtain the uncertainty of large language models in different topics, and accurately determine the performance of intelligent dialogue of large language models at the topic level.

The application discloses a pose solution method based on complementary filtering and unscented Kalman fusion, and particularly relates to the technical field of waterways, and the specific solution steps are as follows: S1: for a nonlinear system, the system state vector is iteratively updated by using unscented Kalman filtering; the application fuses Mohony complementary filtering and unscented Kalman filtering (UKF) algorithm, replaces the pose angle directly calculated from acceleration and magnetic field intensity information with the pose angle obtained by solving based on the complementary filtering algorithm from the perspective of making the measurement information more accurate, improves the overall pose solution accuracy; meanwhile, the framework of the fusion algorithm is based on the unscented Kalman filtering algorithm, the probability distribution of the nonlinear function is approximated by unscented transformation, the high-order truncation error caused by Taylor expansion of the extended Kalman filtering algorithm is avoided, and the pose solution accuracy for the nonlinear system is improved.

The invention discloses a correction method, device, equipment and medium for an inertial measurement unit of an unmanned aerial vehicle, and relates to the field of data processing, the correction method for the inertial measurement unit of the unmanned aerial vehicle comprises the following steps: constructing a sigma point at a k-1 moment through unscented transformation according to posterior state data at the k-1 moment of the inertial measurement unit of the unmanned aerial vehicle, the sigma point covers the sensor parameters of the inertial measurement unit and the probability distribution of the state of the unmanned aerial vehicle; performing prediction processing on the posterior state data at the k-1 moment by using unscented Kalman filtering based on the sigma point to obtain prior state data at the k moment of the inertial measurement unit of the unmanned aerial vehicle; according to the preset measurement data of the plurality of sensors at the moment k, constructing a sensor measurement model at the moment k; projecting the sigma point at the k-1 moment to a measurement space of a sensor measurement model, and determining a pre-measurement mean value and a measurement covariance of the sensor at the k moment; determining the state correction of the inertial measurement unit according to the pre-measured mean value, the measurement covariance and the sigma point; performing state parameter correction on the prior state data at the k moment according to the state correction quantity, and determining posterior state data of the inertial measurement unit of the unmanned aerial vehicle at the k moment; and determining the state information of the unmanned aerial vehicle at the moment k and the zero offset of the inertial measurement unit at the moment k according to the posterior state data, and performing sensor parameter correction on the inertial unit of the unmanned aerial vehicle according to the zero offset at the moment k.

This invention belongs to the field of rigid body pose estimation technology, and discloses a rigid body pose estimation method, system, and device based on generalized correlation entropy geometric filtering. The method includes: acquiring target motion information and establishing a discrete nonlinear kinematic model; initializing the discrete nonlinear kinematic model; generating sigma points for the posterior state covariance matrix and the process noise covariance matrix, respectively, and propagating and updating the sigma points through a manifold fast unscented transformation; obtaining the predicted state mean, the prior state covariance matrix, and the square root of the prior state covariance matrix; updating the center parameters based on the information from historical moments; calculating the pseudo-measurement matrix and updating the gain; correcting the state estimate using the gain; determining whether convergence has occurred, and if not, continuing the iteration; otherwise, outputting the posterior state estimate as the final rigid body pose estimation result for that moment. This invention effectively solves the problems of robustness and accuracy degradation in rigid body pose estimation under non-zero mean and non-Gaussian noise environments.

The application provides a kind of attitude monitoring method and system in aircraft navigation, in each filtering period, the high frequency energy component of current gyroscopic measurement angular velocity signal is used to determine process noise covariance matrix, according to the state vector of last time and the angular velocity obtained by current gyroscopic measurement and process noise covariance matrix, the prior state vector and prior covariance matrix of current time are predicted;Asymmetric Sigma point set is generated and measurement noise covariance matrix is determined;The asymmetric Sigma point set is substituted into measurement model, and the predicted measurement value and predicted measurement covariance are obtained by unscented transformation;Based on the predicted measurement covariance and the measurement noise covariance matrix, the Kalman gain is calculated, and the innovation composed of real measurement value and predicted measurement value is weighted gate inspection;Based on the adjusted Kalman gain and innovation, the state vector and covariance matrix are updated, and then the attitude information of current time aircraft is obtained.

The invention discloses a multi-target probability hypothesis density DOA tracking method under a wireless sensor network, and the method comprises the steps: receiving a non-circular signal through employing a uniform array under the wireless sensor network, and obtaining extended measurement information; secondly, state parameters of signal particles at the initial moment are set as initial distribution of signals, signal sampling particles continuously surviving at the current moment are measured, and a prediction sampling particle set is obtained based on unscented transformation; and then calculating a covariance matrix of a received signal and obtaining a noise subspace by using extended measurement information at the current moment, and updating signal prediction sampling particles at the current moment by using a PHD filter. Finally, more effective particles are found out through resampling, a new signal sampling particle set is formed, and the weighted sum of the signal sampling particle states is calculated to serve as the signal state tracked at the current moment till the end. According to the invention, the number of signals can be estimated more accurately, and the state of the signal source is tracked.

The invention belongs to the technical field of ships, and discloses a ship uncertainty optimization method based on improved unscented transformation and application, the unscented transformation method is introduced into statistical moment estimation of any uncertainty system, and uncertainty optimization is further carried out by taking the statistical moment as a target; compared with a traditional statistical moment estimation method, the unscented transformation method can consider both precision and efficiency, and efficient calculation of the statistical moment of the random system is achieved; the unscented transformation method provided by the invention can be suitable for any random variable type and uncertainty that only a small amount of sample data is available and accurate probability distribution cannot be established; when the unscented transformation method is applied to uncertainty optimization, the uncertainty does not need to be repeatedly sampled in the uncertainty analysis link, the statistical moment is calculated only through a small number of fixed node response values, the uncertainty optimization structure is simplified, and the uncertainty optimization efficiency is improved.