39 results about "Interval estimation" patented technology

The invention discloses a new engineering course teaching evaluation method based on a knowledge-ability-quality triple atlas, and belongs to the technical field of intelligent education and intelligent control. Collecting data of three dimensions of knowledge, ability and quality of students to construct a unified state vector, and describing dynamic evolution of a learning process by combining a state updating and prediction model of a residual network; a multi-cell filtering method is adopted to carry out state set estimation, linear propagation and residual local linearization are combined in the prediction step, and a Lipschitz upper bound is utilized to carry out external connection on a nonlinear residual to ensure the safety and credibility of state interval estimation; in the updating step, set tightening is achieved through prediction strip intersection and generator contraction, coverage rate calibration based on quantiles is introduced, and the confidence level of set estimation is ensured. According to the method, the learning state of the student can be dynamically, stably and interpretably estimated, accurate and efficient course adjustment optimization is realized, and the method has a good application prospect and popularization value.

The invention discloses an aviation fuel gear pump confidence degree evaluation method based on the Bayesian theory. The method comprises the following steps: step 1, firstly, determining Weibull distribution as a theoretical basis of reliability modeling according to the life characteristics of the aviation fuel gear pump, and providing probability model support for subsequent analysis; 2, on the basis of Weibull distribution, adopting a Bayesian method to fuse historical data and field test data, and achieving point estimation and interval estimation of shape parameters and scale parameters of the aviation fuel gear pump; and step 3, based on the point estimation and the interval estimation, constructing an MTBF confidence interval and verifying by adopting a Bootstrap method, and finally completing quantitative evaluation of the reliability confidence of the aviation fuel gear pump. According to the method, the specific problems of low evaluation precision, poor confidence interval reliability and insufficient historical data utilization rate of a traditional method under the small sample condition are solved.

The invention relates to a cable tunnel bridge fire prediction method and device, electronic equipment and a storage medium. The cable tunnel bridge fire hazard prediction method comprises the following steps: establishing a cable tunnel bridge fire hazard numerical simulation model, establishing a cable tunnel bridge fire hazard simulation model based on FDS software, obtaining key parameters such as heat release rate, temperature distribution and flame spread boundary under different working conditions, and constructing a multi-time sequence sample data set; performing state discretization on continuous variables such as temperature and a spreading range, and estimating a node prior probability in combination with a statistical frequency; constructing a dynamic Bayesian network topological structure according to a causal relationship among a heat source, temperature and spread, and setting a cross-time slice node dependence path to realize time sequence modeling; inputting a training sample and performing parameter learning to generate a conditional probability table; in the prediction stage, multi-step reasoning is achieved through forward propagation, flame spreading range probability distribution and interval estimation of multiple time steps in the future are obtained, and the specific position where the flame arrives is predicted.

The invention discloses a bridge bearing capacity dynamic evaluation method based on multi-source monitoring data driving, and aims to solve the problem that the bearing evaluation precision is difficult to guarantee under a sparse sensing coverage condition. According to the method, through cross-modal attention alignment guided by event anchor points, mask codes influencing line and propagation time delay constraints and geometric position coding, Green function kernels and time-varying boundary flexibility latent variables are introduced into a physical constraint neural operator model, symplectic integral propulsion is adopted, and block shape-preserving interval estimation and order-preserving calibration weighted according to working conditions are adopted. According to the method, the full-bridge space-time response and the bearing utilization coefficient are calculated, the risk level and the load limiting suggestion are output, and the technical effects of reliability improvement, physical consistency and long-time energy stability evaluation under the complex working conditions and the low signal-to-noise ratio are achieved.

The invention relates to the field of electric vacuum devices, and provides a method, a device and equipment for evaluating the service life of a space traveling wave tube, and the method comprises the steps: generating an equivalent comprehensive stress profile according to an on-orbit working profile of the space traveling wave tube; under the action of the equivalent comprehensive stress profile, continuously collecting a life characteristic parameter set; performing data preprocessing on the life characteristic parameter set, performing stable degradation interval identification on the obtained purification parameter set, and determining a starting moment of a stable degradation interval; performing filtering smoothing processing on the purification parameter set from the starting moment, inputting obtained smooth degradation data into a preset machine learning model library, determining an optimal degradation function according to an information criterion, and obtaining a life point estimation value based on the optimal degradation function; and obtaining life interval estimation according to the life point estimation value. According to the invention, the problems of long test period and low accuracy of space traveling wave tube life evaluation in the prior art are solved, and rapid and accurate prediction of the long life of the space traveling wave tube in limited test time is realized.

The application provides a small sample constraint SGEMP damage probability evaluation method and system, adopts probability characteristics to evaluate the damage of SGEMP to a satellite electronic system, and obtains the damage probability of SGEMP to the satellite electronic system; is more consistent with the randomness of an actual SGEMP source and a space electromagnetic field, and the accuracy of evaluation is improved. The damage evaluation method firstly obtains the probability density function of sample data through parameter estimation; for sample data of a known probability distribution type, the confidence interval of unknown parameters in the probability density function is obtained by adopting an interval estimation method; for sample data of an unknown probability distribution type, multi-parameter optimization is adopted for estimation to obtain the probability density function; and then damage probability calculation is performed. The method can solve the practical problems of lack of a large amount of data in actual engineering application, and can avoid a large amount of tests to reduce the cost.

The invention provides an aviation bearing health evolution change point online identification and residual life prediction method, and the method comprises the steps: firstly, achieving the online precise identification of the aviation bearing health evolution change point through a Welch's t hypothesis testing method under a sliding window mechanism; secondly, constructing a degradation model based on a multi-stage linear Wiener process, and accurately describing evolution characteristics of the aircraft bearing from a normal stage to a defect stage; a sliding window parameter optimization framework is established, and optimal window parameters are determined by comprehensively evaluating the detection success rate, the average absolute error and the average detection delay; and finally, deriving complete probability distribution of the residual life based on inverse Gaussian distribution, and realizing point estimation and interval estimation of the residual life. According to the method, the health state evolution change point can be timely and accurately identified, the reliability of residual life prediction is improved, effective technical support is provided for safety control and maintenance decision of an aviation system, and the method has important popularization and application values.

The application discloses a natural gas load interval estimation method based on large model coupling working condition clustering, and belongs to the technical field of natural gas pipeline network operation optimization and artificial intelligence load prediction. The method extracts working condition semantic constraints by using a large model, and obtains fuzzy working condition clusters by combining historical operation data clustering; a natural gas load point prediction model is trained for each cluster, and residual probability density distribution is estimated; semantic and numerical weights are fused in real time, and the final point prediction value and the natural gas load prediction interval at the prediction time are obtained by dynamic weighting, which are taken as the natural gas load prediction result and output. The application introduces a large model into the natural gas working condition expression and clustering constraint construction process, and no longer uses the large model as a simple downstream feature generation tool, but solves the problem of interval estimation failure of the natural gas load under fuzzy working conditions such as holiday switching, peak-valley transition and extreme weather through deep coupling of the large model and working condition clustering, so that high-reliability dynamic prediction interval output under complex working conditions is realized.

The invention discloses a construction and control method of a land pollution abatement system, and belongs to the technical field of environmental pollution abatement, and the method comprises the steps: constructing a land pollution abatement model described by a singular partial differential equation; the model is converted into a control system with a movable sprayer as an original point through coordinate transformation; carrying out approximate linearization on a nonlinear term in the system by utilizing a T-S fuzzy method, and designing an interval observer and a controller capable of estimating upper and lower bounds of a system state based on the approximate linearization; the bounded tolerance of the system is analyzed based on the Lyapunov stability theory, the gain of an interval observer and the gain of a controller are obtained by solving a linear matrix inequality, and the gains are applied to the designed observer and the designed controller to complete closed-loop control. According to the method, unknown soil conditions and external interference can be effectively handled, the control robustness is ensured through interval estimation, the limitation that a traditional model only depends on a partial differential equation is avoided, and accurate and stable treatment on polluted land is achieved.

The present invention discloses a method for predicting the number of steel plate defects based on a Gaussian-Poisson hybrid variational autoencoder. Aiming at the fact that the mapping relationship between production process data and the number of steel plate defects is different under different steel plate rolling conditions, this method proposes a Gaussian-Poisson hybrid variational autoencoder as a data-driven soft sensor model. This model uses multiple parallel Gaussian-Poisson variational autoencoders to fit the mapping relationships under different conditions, fuses the parallel outputs with the help of a weight network, and constructs a loss function with the likelihood function values of all data. The proposed method realizes the online prediction of the number of steel plate defects in actual industry through data-driven soft sensor technology and can provide an interval estimate of the number of steel plate defects.

The invention discloses an uncertainty evaluation method for a quality parameter early warning method in a crude oil distillation process, and the method comprises the steps: firstly, predicting the total uncertainty through variance quantification of interval estimation, and overcoming the defect that an existing method only provides point prediction and cannot evaluate the credibility; secondly, in combination with parameter posterior distribution and model output probability distribution, model uncertainty and random uncertainty are decomposed, and the problem that a traditional method is difficult to distinguish uncertainty sources and cannot take targeted compensation measures is solved; and finally, dynamically dividing the credibility grade of the early warning signal according to the uncertainty proportion, and providing a decision basis for an operator. According to the method, the interpretability and reliability of an early warning result are improved, and a guarantee is provided for safe and stable operation of a refinery enterprise.

The application provides a rotor assembly geometry precision prediction and optimization method considering multi-source uncertainty, comprising: analyzing uncertainty affecting assembly precision in a rotor assembly process; representing the uncertainty as random variables and interval variables respectively through a random model and an interval model; establishing a precision prediction model considering random-interval hybrid uncertainty to predict rotor assembly coaxiality error; solving the uncertainty model based on a Monte Carlo method to obtain probability statistical results of upper and lower bounds and midpoint values of the coaxiality error interval; performing coaxiality out-of-tolerance probability interval estimation, constructing a rotor coaxiality double-layer optimization model under hybrid uncertainty, and optimizing to obtain optimal installation phases of each stage disk. The application applies an uncertainty quantification method to prediction and optimization of rotor assembly geometry precision, can realize accurate prediction and reliable optimization of rotor assembly geometry precision, and meets high-reliability assembly requirements of an aero-engine rotor.

The application relates to a suspension bridge flutter response prediction method based on an enhanced Bayesian physical constraint extreme learning machine, and belongs to the technical field of suspension bridge wind resistance design. The method constructs a mixed base function form fusing a Fourier base function and a hyperbolic tangent base function for the established bridge flutter dynamic equation, and builds an enhanced extreme learning machine network. Then, a physical constraint condition dominated by a flutter dynamics control equation is introduced, a special form of a trial solution is constructed to perform hard constraint embedding processing on initial conditions, and a regularization ridge regression method is used to solve the output layer weight of the enhanced extreme learning machine network. Finally, based on a Bayesian inference framework, the posterior distribution of the output layer weight coefficient is estimated, and a bridge flutter response prediction result and an uncertainty interval are obtained. The application can realize high-precision prediction of bridge flutter time domain response, and can give reliable probability interval estimation. The application shows good robustness under the conditions of wind speed fluctuation and noise level change.

The application discloses a deep-cut valley stress field inversion method based on a neural network and a numerical model, and comprises the following steps: deep-cut valley stress field inversion region determination and numerical model construction; stress interval estimation, stress boundary parameter sample generation; batch forward calculation, measurement point stress-boundary stress data set construction; BP neural network construction for inversely deducing stress boundary conditions from geostress measurement point data; actual stress boundary conditions are inversely deduced from the measured geostress of the measurement points, and the final geostress field is calculated by combining the physical information neural network and the numerical model in a forward direction. The application embeds physical constraints such as balance differential equations and compatible equations into the loss function of the physical information neural network, solves the problem that the existing method is biased towards data statistics and the physical mechanism is unclear, realizes the physical rationality of the inversion result, generates parameter samples through orthogonal test design and carries out batch numerical forward calculation, and fully covers the parameter space under the condition that the measurement point data is sparse and the distribution is limited.

The application discloses a flexible joint mechanical arm state estimation method and system based on an ellipsoid beam, relates to the technical field of mechanical arm control, and comprises the following steps: the performance of a Luenberger state observer is quantified by estimating an error, a target function is generated and solved, and a gain matrix of the Luenberger state observer is obtained; an ellipsoid beam propagation law of the estimation error is established based on the gain matrix, a disturbance is expressed as a disturbance ellipsoid beam, and an error ellipsoid beam at the next moment is obtained based on the ellipsoid beam propagation law and the disturbance ellipsoid beam; a state estimation set at the next moment is obtained based on a generated matrix set in the error ellipsoid beam at the next moment; and a certainty upper limit and a lower limit of the state estimation set at the corresponding moment are obtained based on the state estimation set at the next moment, so that an interval estimation result of the system state is obtained. The application realizes a collective disturbance description of the ellipsoid beam, only needs to know the boundary information of the disturbance, avoids the limitation of the Gaussian noise assumption, and provides a certain compact error limit.

The present application provides a sewage treatment biological process fault detection method and system based on interval estimation, belonging to the technical field of automatic monitoring of sewage treatment process. The present application is to solve the problem that the existing sewage treatment detection is strongly dependent on model precision and noise statistical characteristics, and is easy to cause divergence and false alarm and miss alarm when facing channel fading and nonlinear uncertainty. The method comprises the following steps: S1: T-S fuzzy model modeling; S2: robust observer design; S3: interval propagation design, obtaining state interval and residual interval; S4: establishing double fault detection strategy, outputting fault category, fault size and fault occurrence time. The system comprises: modeling module, channel processing module, observer design and operation module, interval propagation module and fault detection decision module.

The present invention belongs to the technical field of model parameter identification and discloses a method for identifying and estimating the output interval of an aero-engine system that comprehensively considers performance degradation and model mismatch. For an aero-engine system, a linear variable parameter modeling method is introduced to combine the state variable models of multiple operating points of the aero-engine through scheduling parameters to construct a global model with a large operating range. To address issues such as errors and model mismatch in the modeling process, a quantile regression neural network is used to perform interval estimation of the system output uncertainty, thereby improving the model's accuracy and robustness. The standard state-space equation of the aero-engine system is augmented, and the health parameters of each component of the system are treated as states for estimation. A variational Bayesian adaptive Kalman filter method is used to estimate the health parameters in real time, thereby constructing a system adaptive model. This invention provides a research foundation for fields such as fault diagnosis and nonlinear system control of aero-engine systems.

The invention relates to the technical field of fuel cells, and discloses a proton exchange membrane fuel cell long-term prediction method based on a multi-step prediction strategy, comprising the following steps: preprocessing experimental data to obtain a model training data set and long-term prediction initial input data; establishing a model and a training model to obtain a trained hybrid probability data driving voltage decline trend prediction model; performing long-term prediction based on a multi-step prediction strategy; and prediction result and uncertainty analysis: taking the average value of the same prediction position as a point estimation result, taking a standard error range of 95% confidence level as an interval estimation result, analyzing the point estimation result and the interval estimation result through an error evaluation index, and calculating an interval estimation concentration rate to evaluate the prediction uncertainty. The proton exchange membrane fuel cell long-term prediction method based on the multi-step prediction strategy is good in long-term prediction effect and high in prediction result precision and credibility.

The invention provides a UWB positioning method fusing CIR interval constraint and ToA ranging optimization. The UWB positioning method is characterized in that a CIR positioning interval estimation module, a ToA ranging error module and a fusion position prediction module are included; the CIR positioning interval estimation module comprises a CNN (Convolutional Neural Network) model and a Copula model; the ToA ranging error module comprises a GMM model; the positioning method comprises the following steps: a CIR positioning interval estimation module processes CIR data of a positioning target to obtain a first positioning error interval and a second positioning error interval, and a ToA ranging error module processes ToA data of the positioning target to obtain an optimized target function and a weighted cost function; and the fusion position prediction module adopts a PSO algorithm, performs particle optimization in a first positioning error interval according to an optimization objective function, and then performs weighted average operation processing on particles in a second positioning error interval according to a weighted cost function to obtain a positioning prediction value of a positioning target. By adopting the positioning method, the positioning precision and robustness of the positioning target in a complex indoor environment can be greatly improved.

This invention relates to the field of product reliability analysis technology, specifically to a reliability assessment method for a non-fixed shape parameter dependent competing failure model. The method includes the following steps: constructing a dependent competing failure model, designing an adaptive stepwise type II hybrid truncated constant-load life test, estimating parameter points based on test data, establishing an acceleration equation, calculating product reliability under normal stress, and performing interval estimation. This invention constructs a MOBG distribution description that assumes fixed shape parameters and does not consider the dependencies between unknown failure mechanisms and causes. It designs an adaptive stepwise type II hybrid truncated constant-load life test to obtain data, solves for parameters based on maximum likelihood estimation and Bayesian estimation, and combines the acceleration equation to obtain parameter estimates under normal stress. This leads to the calculation of product reliability and interval estimation, improving the accuracy and applicability of reliability assessment. It is suitable for reliability analysis of various products with multiple failure mechanisms and potentially unknown causes.

The invention relates to the technical field of bed separation grouting, in particular to an evaluation method for determining a bed separation grouting range based on intelligent drilling parameter inversion lithologic change. The core of the method is to introduce a hidden structure dip angle parameter, establish an information magnitude correlation model of a strong transformation ratio coefficient, a deformation ratio coefficient and a stratum structure, distinguish lithologic anomaly caused by the structure and characteristics of a rock stratum, and solve the problem of inversion deviation of a traditional method. Dynamic data comparison is formed through two times of drilling and calculation before and after grouting, the statistical significance of interval estimation quantification difference is combined, evaluation is converted into a quantifiable confidence level index, and the limitation of experience judgment is broken through. And directly outputting a filling effect conclusion and an adjustment suggestion based on the comparison of the confidence level and the discrimination threshold, optimizing the decision-making efficiency, and reducing the cost and the construction period. And meanwhile, drilling multi-dimensional parameter values are deeply excavated, and upgrading of data from collection and storage to decision support is achieved.

The application provides a power electronic device inertia and primary frequency modulation coefficient estimation method, comprising: collecting unit, regional and system real-time data; inputting the unit, regional and system real-time data into a trained collaborative adaptive counter particle filtering module, and outputting a posterior particle set; the collaborative adaptive counter particle filtering module comprises an agent network and a particle transformation network; performing statistical operation on the posterior particle set to obtain a power electronic device inertia and primary frequency modulation coefficient estimation value. The method provided by the application can realize long-term, adaptive, point estimation and interval estimation of inertia and primary frequency modulation coefficient in a multi-level unit, region and system, solve the problems of model dependence and particle degradation of existing methods, and improve the robustness and engineering applicability of power electronic device dynamic parameter evaluation.

The application relates to the technical field of fuel cells, and discloses a long-term prediction method for a proton exchange membrane fuel cell based on a multi-step prediction strategy, which comprises the following steps: experimental data preprocessing, obtaining a model training data set and long-term prediction initial input data; establishing a model and training the model to obtain a trained mixed probability data-driven voltage degradation trend prediction model; long-term prediction based on the multi-step prediction strategy; prediction result and uncertainty analysis: taking the average value of the same prediction position as a point estimation result, taking the standard error range of the 95% confidence level as an interval estimation result, analyzing the point estimation and the interval estimation result through an error evaluation index, and simultaneously calculating the interval estimation concentration rate to evaluate the prediction uncertainty. The long-term prediction method for the proton exchange membrane fuel cell based on the multi-step prediction strategy has good long-term prediction effect, and the prediction result has high precision and reliability.

The invention discloses an acceleration coefficient interval estimation system, which comprises the following steps of: acquiring sufficient complete failure sample data through a Testto-Failure test, designing a'stress layer and degradation characteristic layer 'double-layered Bootstrap sampling rule, and generating a plurality of groups of failure sample combinations; meanwhile, whole-course degradation data of failure samples under double stress are integrated, a cross-stress integrated joint likelihood function is constructed, the acceleration coefficient of each combination is directly solved, and finally the acceleration coefficient interval is determined through statistical analysis. By the adoption of the technical scheme, the problems that existing acceleration coefficient estimation can only achieve point estimation and lacks effective interval estimation adaptive to Testto-Failure data features, an interval result cannot cover failure scene parameter fluctuation, and error accumulation exists in traditional step-by-step solving are solved, and the accuracy of acceleration coefficient estimation and the interval reference value are improved.

The invention discloses a crop single-leaf photosynthetic rate estimation method considering small sample learning and uncertainty prediction, and the method comprises the steps: obtaining photosynthetic rate data of different crops under different environment conditions to construct a data set, and dividing the data set of each crop into subsets; constructing a basic prediction model based on a multi-layer perceptron, taking the divided subsets as a test domain and a training domain, and updating model parameters by utilizing meta-learning training; aiming at an unknown task, constructing a loss function in combination with a quantile regression method to finely adjust the basic prediction model so as to realize interval prediction of the photosynthetic rate; and finally, introducing Gaussian kernel density to estimate probability distribution of the photosynthetic rate under different environmental conditions, and synchronously realizing point prediction and interval prediction visualization of the photosynthetic rate by taking a median as a point prediction value. Compared with the prior art, the method has the advantages that the photosynthetic rate in an unknown environment can be accurately predicted only by using a small amount of sample data for training, and the interval estimation method is adopted, so that the method can adapt to various application scenes.

The invention discloses a centrosymmetric multi-cell-body-based Kalman filter AUV Doppler velocimeter fault interval estimation method, a program, equipment and a storage medium, and belongs to the field of autonomous underwater robot fault diagnosis. The method comprises the following steps: firstly, acquiring a state space system model through a system identification method, and constructing an augmented system equivalent to an original system by taking a sensor fault as an auxiliary state vector; then, in combination with Kalman filtering and a centrosymmetric polytope, all possible states are contained in the centrosymmetric polytope through prediction and updating; and finally, performing interval envelope operation on the updated centrosymmetric multi-cell body to obtain the interval estimation of the Doppler velocimeter fault. An optimal gain matrix is obtained through optimization by adopting a Frobenius norm minimization method, the influence of unknown disturbance and noise is reduced, and the precision is improved. According to the method, under the condition that noise and disturbance are unknown but bounded, it can still be ensured that a fault true value is strictly wrapped by an estimation interval, and the method is more practical and reliable in a complex disturbance scene.

The application provides a data crowdsourcing labeling distribution method and device, electronic equipment and a storage medium. The method comprises distributing a task title and receiving a current labeling result of the task title, updating a labeling result set of the task title based on the current labeling result, performing true value inference on the updated labeling result set to obtain a current probability of a label corresponding to the task title, performing interval estimation on the probability of the label based on the current probability to obtain an interval range of the probability of the label, and stopping distributing the task title in response to determining that the interval ranges of the probabilities of all labels of the task title are not greater than a preset convergence threshold. Thus, the labeling quantity of the task title is reduced under the premise of ensuring the quality of data crowdsourcing, and the labeling cost is saved.

The invention belongs to the technical field of engineering data processing, and particularly relates to a consistency temperature interval estimation method based on temperature difference grading. According to the method, collaborative optimization of interval estimation precision and cross-domain consistency can be realized, dependence on artificial experience parameter adjustment and equipment specific calibration is reduced, and the method has good expandability and interpretability; meanwhile, the method can effectively reduce the operation and maintenance cost of power equipment and improve the operation safety of a power grid by improving the grading accuracy and consistency, and has remarkable technical and economic values.

This invention discloses a fault detection method and system for Mecanum wheel automated guided vehicles (AGVs), relating to the field of industrial automation fault diagnosis technology. Based on differential inclusion theory and the Euler-Lagrange equations, this invention establishes a Lur'e differential inclusion-type state-space equation characterizing system nonlinearity, parameter uncertainty, and external disturbances. An adaptive event-triggered mechanism with dynamically adjustable parameters and exponentially decaying terms is designed to optimize data transmission, reduce resource consumption, and strictly exclude Zeno behavior. An adaptive event-triggered interval observer is constructed to achieve robust interval estimation of the system state, and actuator fault detection is achieved through the zero-value inclusion property of the residual interval. This invention solves the problem of traditional methods struggling to balance fault detection accuracy and resource utilization, and is suitable for state monitoring and fault diagnosis of Mecanum wheel AGVs in industrial scenarios.

The invention relates to a power assembly suspension system uncertainty optimization method based on fast interval estimation. The method comprises the steps that design variables of a power assembly suspension system, the value range of the design variables and the uncertainty of the design variables are determined; constructing an agent model of a decoupling rate weighted value and a vibration isolation rate of the suspension system, and performing optimization through an interval multi-objective evolutionary algorithm IMOEA by taking the agent model as an objective function; initializing a population; for individuals in the current population, calculating a sensitivity coefficient of each design variable to the target function; quickly estimating upper and lower bounds of an interval of the target function under the influence of parameter uncertainty; performing non-dominated sorting on individuals in the population by utilizing an interval partial order relationship; performing evolution operation according to a non-dominated sorting result to generate a filial generation population; and repeatedly executing the steps until a termination condition is met, and outputting a Pareto solution set, namely an optimization parameter of the suspension system under an uncertain condition. According to the method, the optimization precision is improved while the uncertainty optimization efficiency of the suspension system is improved.