30 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 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 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 application discloses a SOC estimation method based on a deep fusion neural network and a UKF (Unscented Kalman Filter), relates to the technical field of all-vanadium redox flow batteries, and aims to realize high-precision estimation of the state of charge of a battery. The method comprises the following steps: a second-order equivalent circuit model of the battery is established, and parameter identification is performed on the second-order equivalent circuit model; a group of sigma points are obtained through unscented transformation, the propagation state estimation and the propagation state prediction of each sigma point are calculated, the first-order statistical moments of the prior state estimation and the prior state prediction are obtained through unscented transformation, the cross covariance is obtained, and the observation difference and the state update difference are obtained; the observation difference and the state update difference are input into a neural network system to obtain Kalman gain, the prior state estimation is updated through the Kalman gain, and the state of charge of the battery is predicted.

The application discloses a navigation method based on threshold processing adaptive unscented Kalman filtering, a storage medium and equipment, comprising obtaining and preprocessing data of a navigation coordinate system and a target coordinate system, constructing a state space system and an observation equation of the navigation coordinate system and the target coordinate system, and obtaining a state distribution relationship of the navigation coordinate system and the target coordinate system; in the state space system, unscented transformation operation is performed on a target point, and a target meeting a selection condition is obtained as a sampling point; weighted calculation is performed on the sampling point, and a predicted value of a state space system state at a specified moment is obtained; based on the predicted value of the state space system, unscented transformation operation is performed on the sampling point, and a sampling point meeting the selection condition is obtained as a new sampling point; the new sampling point is brought into the observation equation, and a predicted value of an observation value at the specified moment is obtained; after updating of measurement noise and a gain matrix, the predicted value of the observation value at the specified moment is output, and navigation is performed.

A passive control method for a DC-DC boost converter based on an adaptive unscented Kalman filter belongs to the technical field of power systems. In order to develop a more accurate and real-time passive control method for a boost converter, the present invention includes constructing an average dynamic model of the DC-DC boost converter, then designing it into a state-space expression, and then using the Euler equation to discretize the state-space expression to obtain a discretized state-space expression; designing an adaptive unscented Kalman filter, performing two unscented transformations based on the discretized state-space expression, predicting the mean and covariance of the system state quantities, and obtaining estimated values of the input voltage and constant-power load power; constructing a passive controller; inputting the estimated values of the input voltage and constant-power load power into the passive controller, generating a PWM signal to drive the switch tube to work, and controlling the DC-DC boost converter. The present invention improves the robustness of the system.

The invention relates to the technical field of power system evaluation, and discloses a power transmission line lightning resistance evaluation method based on an unscented transformation method, and the method comprises the following steps: building an electromagnetic transient model of a lightning stroke power transmission system, and obtaining the core parameters of a power transmission line; generating a Sigma point set based on an unscented transformation method, and enabling the allocated Sigma point set to meet mean and covariance characteristics of original probability distribution of the core parameters; an exponential regression model is adopted to carry out curve fitting on the insulator Voltage-Time critical curve, and an intersection point of the fitted curve and the insulator Voltage-Time critical curve is calculated; and calculating the lightning trip-out rate of the power transmission line in combination with the ground lightning density and the transverse attraction width of the lightning conductor to complete the lightning resistance evaluation of the power transmission line. According to the method, the unscented transformation method and the transient program are combined, the limitation of a traditional Monte Carlo method is broken through, the number of evaluated samples in the process is greatly reduced, the calculation time is remarkably shortened, and the calculation efficiency is improved.