64 results about "Kriging surrogate model" patented technology

The present invention relates to an adaptive assessment method and apparatus for nuclear power plant component reliability, a storage medium, and an electronic device. The method comprises the following steps: constructing a Kriging initial model; gradually updating the Kriging initial model on the basis of first-layer samples of ICE to obtain a Kriging surrogate model; gradually updating the Kriging surrogate model on the basis of last-layer samples of the ICE; after the updating of the Kriging surrogate model is completed, obtaining a current Kriging model; and on the basis of the current Kriging model, assessing the reliability of a component to be assessed. In the present invention, first-layer adaptive Kriging gradually updates and explores a failure domain on the basis of the first-layer samples of the ICE until a set stopping criterion is satisfied, and on the basis of a currently constructed Kriging model, the last-layer samples of the ICE are also updated accordingly. Thus, it is ensured that an estimated failure probability converges unbiasedly to a real failure probability, and unnecessary updating of Kriging is avoided, thereby greatly improving calculation efficiency and saving calculation costs.

The invention discloses a differential active temperature control matching method and system based on fluid thermosetting coupling analysis. The method comprises the following steps: determining a multi-physical field action form of a machine tool structure; a fluid-heat-structure multi-physics field coupling simulation model is obtained, specifically, coupling simulation of a cooling fluid field, an endogenous heat field and a machine tool structure field is conducted in sequence, and an effective multi-physics field coupling simulation model is obtained; adopting a response surface design method to generate multiple groups of DOE analysis working conditions; a Kriging algorithm is adopted to construct an agent model and establish a mapping relation: based on the DOE sample points and simulation results thereof, the Kriging agent model is trained, and a mapping relation between machine tool detection parameters and active temperature control parameters is established through an automatic refining process; based on the mapping relation, performing multi-objective optimization on temperature control parameters by taking thermal deformation of the key position as a constraint, and realizing differential control strategy matching of each temperature control loop; the problem of thermal deformation caused by thermal characteristic change of a machine tool structure is solved.

The invention relates to the technical field of extremely-small curved surface structures, in particular to an optimal design method, system and equipment for an extremely-small curved surface structure and a storage medium. The minimum curved surface structure optimization design method comprises the steps that a multi-objective optimization problem is determined according to design variables and optimization objectives of a three-period minimum curved surface lattice structure; constructing an approximate function relationship between the optimization target and the design variable by utilizing a self-adaptive multi-target engineering optimization method of a Kriging agent model; latin hypercube sampling and a sequential quadratic programming optimization algorithm are combined to carry out optimization design on the three-period minimal curved surface lattice structure. According to the design method, the light / bearing / wave-absorbing minimum curved surface lattice sandwich structure with good structure-performance dual characteristics can be designed, the wave-absorbing structure is filled with the minimum curved surface lattice structure in a shape follow-up mode, design and manufacturing of a wave-absorbing and bearing integrated structure are achieved, and rapid iteration and performance improvement of design of the wave-absorbing and bearing structure are supported.

The invention provides a composite beam reliability optimization method based on a failure probability function. The method comprises the following steps: 1) defining a random input variable, a design variable, a target function and a performance function; 2) calculating a failure sample and an extended failure probability based on an autonomous learning Kriging agent model and a Monte Carlo method according to the Bayesian theory; the method comprises the steps of (1) calculating a design parameter failure sample, (2) calculating a joint probability density implicit function on the basis of an adaptive K-nearest neighbor method and autonomous learning Kriging according to the calculated design parameter failure sample, and (3) calculating a failure probability implicit function on the basis of the Bayesian theorem.By means of the method, the problem that in the reliability optimization process, the calculation cost is difficult to bear due to reliability analysis is solved.

The invention discloses a light high-strength orthogonal isotropic steel bridge deck structure and a parameter optimization method thereof, and belongs to the technical field of bridge engineering, the structure comprises a top layer steel plate, a lower layer steel plate, longitudinal plate ribs, a diaphragm plate and a diaphragm plate bottom plate, and the longitudinal plate ribs and the diaphragm plate are separated to form closed plate grid cavities. A flexible transition rib array composed of a plurality of independent ribs is arranged in the bridge direction in an opening area formed when a transverse partition plate penetrates through longitudinal plate ribs and is located below a top-layer steel plate, and a response surface model is coupled according to a preset multi-parameter. Through finite element analysis, Latin hypercube sampling, Kriging agent model construction and multi-target genetic algorithm global optimization, inversion optimization from a preset performance target to an optimal geometric parameter combination of each flexible transition rib is realized, the fatigue performance at the opening of the diaphragm plate is improved, and meanwhile, the lightweight and economical efficiency of the structure are realized.

This invention belongs to the field of electrical digital data processing technology, specifically relating to a multi-objective optimization method for improving motor torque performance. The method includes: establishing a parameterized model of the motor; extracting geometric harmonic prior features based on the harmonic analysis principle of the motor's air gap magnetic flux density, and using these as a screening criterion to obtain an initial high-quality sample set; constructing a Kriging surrogate model reflecting the mapping relationship between design variables and performance response, introducing an energy efficiency boundary risk function during the construction process; based on the Kriging surrogate model, using a multi-objective optimization algorithm to search for a Pareto front solution set, selecting potential sample points from the Pareto front solution set for simulation verification, and then adaptively updating the Kriging surrogate model until the stopping criterion is met, at which point the target motor design parameters are output. This invention can achieve accurate optimization of electromagnetic parameters by screening samples through geometric harmonic prior features and utilizing the energy efficiency boundary risk function.

The invention discloses a turbine disc fatigue reliability life influence factor importance analysis method, which comprises the following steps of: converting fatigue reliability life sensitivity into unconditional fatigue reliability life and conditional fatigue reliability life calculation under characteristic points through Gauss integral, and establishing a self-adaptive updating criterion of a finite element Kriging agent model; the finite element Kriging agent model is iteratively updated, so that the analysis time of single-time fatigue life reliability in the fatigue reliability life analysis process is shortened, and unconditional fatigue reliability life analysis and conditional fatigue reliability life analysis are based on a structural finite element model of the same structure; the idea of sequence updating structure finite element is provided and is applied to the fatigue reliability analysis life process of the semi-proxy algorithm, so that the sequence sharing of the training sample set in the fatigue life reliability analysis method under different fatigue life thresholds is realized; and the solving efficiency of the unconditional fatigue reliability life and the conditional fatigue reliability life is improved.

The application discloses a new energy station frequency control parameter collaborative setting method and application thereof, and the method comprises the following steps: constructing an optimized parameter vector x containing a network type and a network type station heterogeneous control parameter; establishing a comprehensive objective function f(x) which fuses the system frequency deviation minimization and the parameter adjustment cost minimization, and generating an initial training sample database corresponding to the comprehensive objective function based on sampling; constructing an anisotropic Kriging surrogate model based on the initial training sample database; constructing a sparse degree weighted adaptive expected improvement SW-AEI adding point criterion based on the anisotropic Kriging surrogate model, and screening an optimal candidate vector with the maximum sparse degree weighted adaptive expected improvement value; updating a new sample to the training sample database to recalibrate the anisotropic sensitivity hyperparameters in the anisotropic Kriging surrogate model until a convergence condition is met, and outputting the optimal collaborative control parameter.

The invention provides a high-efficiency magnetic field optimization method for a wind power electromagnetic brake, and belongs to the technical field of wind power electromagnetic brakes. Air-gap magnetic field distribution is calculated by using a fast multi-pole expansion algorithm, and feedforward compensation is performed on coil excitation current through a Preisach inverse model adaptive identification algorithm to improve the uniformity of a magnetic field; an operator splitting method is adopted to solve a magnetic-thermal coupling problem, material parameters are corrected according to temperature, an optimization objective function including air gap magnetic flux density maximization, magnetic field uniformity minimization and temperature constraint is established, Latin hypercube sampling is utilized to generate a training data set, optimization iteration is carried out through a Kriging agent model in combination with an expectation improvement criterion, and a training result is obtained. And outputting the optimal design parameters. The technical problems that the calculation efficiency of the wind power electromagnetic brake in the magnetic field optimization process is low, and the hysteresis nonlinear characteristic and the multi-physical field coupling effect are difficult to accurately consider are solved.

This invention discloses an optimization design method for lightweight torsion springs used in robots. The method includes the following steps: First, setting design objectives and constraints, with minimizing spring mass as the optimization objective. Constraints include stiffness, maximum stress, torque capacity, and maximum thickness. Then, material selection and topology design are performed, using chromium vanadium steel and designing three sets of symmetrically distributed wave-shaped spring structures. Next, a three-dimensional parametric model is established in a finite element environment, and simulation analysis is conducted to obtain key performance indicators. Further, based on the Latin hypercube sampling method and the Kriging surrogate model, the spring thickness and transition fillet radius are modeled and predicted, and a multi-objective genetic algorithm is used for global optimization to obtain the optimal structural parameters. Finally, physical processing and experimental verification are performed based on the optimization results. This invention can achieve significant weight reduction, reduce stress concentration, and extend service life while ensuring low stiffness and high torque capacity.

This invention belongs to the field of vehicle shape optimization design and discloses a method, system, device, and medium for global optimization design of vehicle aerodynamic shape. The method includes: constructing a parametric model of the vehicle based on free deformation technology; dividing the parametric model into regions and arranging control points in each candidate deformation region; analyzing the drag reduction rate of each candidate deformation region under typical deformation modes to obtain an initial design space; reducing the dimensionality of the design variables in the initial design space using principal component analysis to obtain a dimensionality-reduced design space; constructing a Kriging surrogate model and performing global optimization on the Kriging surrogate model using a genetic algorithm to obtain a preferred region; and applying adjoint optimization to the preferred region to obtain the globally optimal shape design scheme. The technical solution of this invention achieves intelligent and efficient global optimization of complex shapes under given constraints.

The application discloses a kind of complex aviation structure under mixed uncertainty Time-varying reliability analysis method, and its basic flow is as follows: the finite element simulation model of complex aviation structure is established, the examination site is selected and the failure criterion is determined, the finite element simulation model is parameterized, and the response function of complex aviation structure is constructed;Generate training sample set, and establish the multiple Kriging surrogate model of complex aviation structure response function according to training sample set and corresponding structure response, and the KL divergence is combined with limit state surface projection profile method to construct adaptive learning function under mixed uncertainty, and the new training sample is screened to update and enhance Kriging surrogate model;Global accuracy index of multiple Kriging surrogate model is used to adjust multiple surrogate models in the proxy model set, and local accuracy index is used to solve the time-varying reliability result of complex aviation structure under mixed uncertainty.

The application discloses a performance optimization method for a heavy machine tool hydrostatic guide rail system, comprising the following steps: S1. establishing a multi-objective optimization model: determining design variables of the hydrostatic guide rail system X , constructing an optimization objective function F(X) , and establishing an equality constraint based on force and torque balance H(X and an inequality constraint for preventing dry friction G(X) ; S2. constructing a multi-fidelity database: obtaining a high-fidelity data set through high-fidelity numerical model simulation, and obtaining a low-fidelity data set through low-fidelity numerical model simulation with the same calculation logic but a lower grid density; S3. constructing a data fusion prediction model: utilizing the high-fidelity data set and the low-fidelity data set, constructing a collaborative Kriging surrogate model based on an autoregressive structure, and outputting a predicted mean and a predicted variance; S4. generating a candidate solution population; S5. multi-criteria point selection; S6. simulating and updating a Pareto frontier solution set; and S7. repeating steps S3 to S6 until a preset convergence condition or a calculation budget is met, and outputting a final Pareto optimal solution set.

The invention discloses an AK-MCS reliability analysis method based on a dynamic critical reinforcement learning function, and belongs to the field of mechanical equipment structure reliability analysis. According to the method, a DC learning function is provided, and the coupling influence of a sample point prediction value and a prediction variance on the failure probability estimation precision is comprehensively considered through an adaptive weight mechanism. Preferentially selecting sample points with large prediction variance in the exploration stage to quickly improve the global approximation capability of the model; in the refinement stage, a learning function is converted to be dominant by a mean item, the selection weight of sample points near a limit state curved surface is dynamically increased, local fine description of a critical region is enhanced, and a global relative uncertainty index is introduced to realize smooth transition from global exploration to local refinement. According to the method, the problems of excessive iteration and local optimization of a traditional learning function are effectively avoided, the construction efficiency of the Kriging proxy model and the robustness of failure probability estimation are remarkably improved, and the method is suitable for efficient reliability analysis of a complex mechanical equipment structure.

This invention relates to the field of intelligent transportation technology, and provides a method and system for dynamic control of emergency lanes in highway accident scenarios. The method includes: acquiring accident reporting information; using a deep learning network model to predict the accident handling time; based on the accident handling time, using a traffic wave model to predict the queue length and dissipation time under the impact of the accident; using the queue length under the impact of the accident as the total length of the controlled road segment and discretizing it into segments; using the dissipation time under the impact of the accident as the control duration and intervalizing it; constructing a binary control strategy combining time windows and spatial segments; constructing an objective function with the goal of minimizing the weighted average of total travel time and total collision exposure time; solving the objective function using a Kriging surrogate model; and obtaining the dynamic control strategy for the emergency lane based on the binary control strategy. This improves the response speed of accident handling.

The application discloses a three-dimensional asymmetric excitation rotor uncertainty steady-state response analysis method and system, relates to the energy power technology field, and comprises the following steps: a three-dimensional finite element dynamics model of an excitation rotor with an axial slot is established; uncertainty quantification is carried out on the excitation rotor system, and a dynamics model of the excitation rotor system containing interval uncertainty is established; a Latin hypercube sampling method is adopted, and a Kriging surrogate model is used to solve the uncertainty steady-state response; considering the interval uncertainty, a dynamic response decoupling coordinate method is used to suppress false resonance peaks; a fast analysis method of the three-dimensional asymmetric rotor system uncertainty steady-state response is established by combining the Kriging and Latin hypercube sampling method-based surrogate model; and the three-dimensional asymmetric rotor system steady-state response is analyzed; and the method solves the phenomenon that the interval method is prone to false resonance peaks when used for uncertainty analysis of the asymmetric rotor steady-state response.

The invention discloses a multi-parameter collaborative wind speed optimization method for a spillway tunnel hoist chamber. The method comprises the following steps: determining an optimization variable and an optimization target based on multiple operating parameters; establishing a plurality of groups of flood discharge tunnel operation parameter samples; according to the three-dimensional geometric model of the tunnel spillway, constructing a tunnel spillway full-size three-dimensional numerical model matched with each group of operation parameter samples in the step 2, and determining the computational domain range of the three-dimensional numerical model; working condition coupling is conducted on the determined multiple groups of flood discharge tunnel operation parameter samples and the three-dimensional numerical model, numerical calculation is conducted on the three-dimensional numerical model through the computational fluid dynamics technology, and an operation parameter-hoist chamber wind speed distribution mapping data set is formed; taking each group of flood discharge tunnel operation parameter samples as an optimization variable sample set, taking the maximum wind speed of the hoist room in one-to-one correspondence with each group of flood discharge tunnel operation parameter samples as an optimization target value sample set, constructing a proxy model by adopting a Kriging interpolation method, and performing precision evaluation on the Kriging proxy model by using a cross validation method, if the precision does not reach the standard, returning to the step 2 to increase the sampling points until a Kriging agent model with the precision reaching the standard is formed; and constructing a wind speed optimization model of the flood discharge tunnel hoist room, solving by adopting an intelligent optimization algorithm to obtain multiple groups of optimal solutions, and performing multi-attribute decision on the multiple groups of optimal solutions to screen out an optimal scheme. According to the method, the wind speed optimization scheme can be effectively screened out.

The invention discloses a rapid equivalent analysis method and system for a heat dissipation cold plate based on a Kriging agent model, and belongs to the field of heat dissipation design of electronic equipment, and the method comprises the following steps: obtaining a geometric model of the heat dissipation cold plate, and carrying out grid division and simulation to obtain design points of a grid model of the heat dissipation cold plate; after the key simulation data and the corresponding temperature response values serve as a training set to be normalized, a distance matrix is obtained through calculation, a regression matrix and a correlation matrix are constructed in combination with the key simulation data of the design points, finally, parameters of a Kriging agent model are optimized in combination with boundaries of the key simulation data, and the Kriging agent model is obtained. And obtaining a Kriging agent model for rapid equivalent analysis of the heat dissipation cold plate, and inputting a to-be-tested design point into the Kriging agent model to obtain a predicted temperature response value. According to the method, the processes of geometric modeling, grid division and numerical solution do not need to be carried out again after subsequent parameter adjustment, and the efficiency of equivalent analysis of the heat exchange performance of the heat dissipation cold plate is improved.

The present disclosure relates to a method for analyzing sensitivity of distribution parameter uncertainty of a radome structure, and relates to the field of uncertainty analysis, which comprises: constructing an adaptive Kriging surrogate model based on a first initial training sample set; calculating a total variance value and a first conditional variance value of an output failure probability of a real radome structure model according to the adaptive Kriging surrogate model, and calculating a main sensitivity of the real radome structure model according to the total variance value and the first conditional variance value; calculating a second conditional variance value of the output failure probability according to the adaptive Kriging surrogate model, and calculating a conditional expectation value of the output failure probability according to the second conditional variance value; calculating a total sensitivity of the real radome structure model according to the second conditional variance value and the conditional expectation value, and analyzing the sensitivity of the real radome structure model according to the main sensitivity and the total sensitivity. The present disclosure improves the sensitivity analysis efficiency.

The application provides a full-size subsonic wind tunnel loop aerodynamic profile multi-objective optimization method based on steady CFD, and belongs to the technical field of wind tunnels.The application reduces the initial geometric parameters to a sensitive parameter subspace through principal component analysis, generates initial sample points by using Latin hypercube sampling, extracts response values by executing steady Reynolds average Navier-Stokes equation solving, constructs a Kriging surrogate model, performs multi-start parallel search based on an expected improvement function, extracts a separation zone length and a backflow zone height to input an unsteady correction factor model to calculate a correction coefficient for correcting a diffusion section pressure recovery coefficient, searches a Pareto front solution set by using a non-dominated sorting genetic algorithm, selects an optimal parameter combination through a comprehensive performance index, and reconstructs the wind tunnel loop aerodynamic profile through a hierarchical parameterization method and performs verification calculation, so that the problem that the steady calculation framework cannot accurately predict the separation flow and the optimization result deviates from the true performance is solved.

The invention discloses a full-process prediction method based on broaching parameters, surface integrity and fretting wear, and relates to the field of fretting wear. The method comprises the following steps: S1, acquiring multiple groups of surface integrity parameters and fretting wear parameters of a broached material; s2, the broaching parameters as input and the surface integrity parameters as output are substituted into a first Kriging agent model for training to obtain a first model, and the surface integrity parameters as input and the fretting wear parameters as output are substituted into a second Kriging agent model for training to obtain a second model; and S3, associating the first model with the second model to obtain a whole-process prediction model, and predicting the whole-process prediction model. According to the method, the broaching parameters, the surface integrity and the fretting wear parameters are subjected to full-process prediction, the limitation of traditional single parameter mapping is broken through, and full-process interpretability prediction of machining parameters, the surface state and the wear performance is achieved.

The application discloses a kind of hypersonic vehicle multi-source energy management methods, belong to the technical field of calculation, reckoning or counting.For the nonlinear optimization problem of multi-source energy distribution in mode conversion process, the method first establishes the mechanism model of TBCC engine and multi-source power generation system, quantifies the correlation between power generation and fuel consumption;Subsequently, an adaptive sparse Kriging surrogate model is proposed, which improves the generalization ability of large-scale data through low-rank approximation of covariance matrix and dynamic selection strategy of inducing points;Further, an optimization function is designed to minimize the cumulative fuel consumption, and an improved DQMR-DDPG algorithm is integrated to solve the problems of policy oscillation and Q-value overestimation through state hierarchical experience pool, double Q-network and adaptive soft update mechanism.The application realizes real-time optimization of energy management under complex conditions, while meeting the thrust, power and safety constraints, significantly reducing fuel consumption during mode conversion phase.

A reliability design optimization method suitable for separating impact components of a shedding connector belongs to the technical field of engineering optimization. In order to solve the problems that traditional reliability optimization design is highly dependent on physical test, and the test cycle is long and the cost is high, the present application proposes a spatial self-adaptive sampling strategy for Kriging surrogate model, which combines model prediction uncertainty and gradient information, dynamically identifies active constraints, thereby effectively reducing misclassification and redundant sampling; in view of the different response characteristics of the objective function and the constraint function, an anisotropic sampling space is constructed for each function, guiding the sample to focus on the area with higher optimization potential; based on the maximum chord deviation criterion, the sampling points are placed in the nonlinear significant area, so as to improve the accuracy of the local surrogate model and accelerate the convergence speed, thereby realizing reliability design optimization.

The application provides a large-span steel structure closed loop analysis method and system, the analysis method comprises the following steps: constructing a finite element model and a design parameter space, identifying key design parameters and key regions affecting the first N modal strain energy of the structure through initial sensitivity and modal analysis, and establishing a mapping relationship between the two; according to the parameter sensitivity contribution, a non-uniform sampling strategy is formulated, the sampling is encrypted in the high sensitivity parameter subspace, an initial sample set is generated, based on the sample and the finite element response, a hierarchical mixed kernel Kriging surrogate model containing a global long-range kernel and a multi-region local short-range kernel is constructed, the local kernel weight increases as the input point approaches the high sensitivity region, the iteration termination and the performance limit state are set, the composite point adding criterion integrating the prediction variance, the limit state distance and the cross-validation error is used to iteratively update the sample and the model, and after the condition is met, the structure performance analysis is completed by using the surrogate model.

The invention provides a high-speed train suspension parameter evaluation method which combines a particle Markov chain Monte Carlo model, a Kriging model and a particle swarm optimization algorithm. According to the method, firstly, based on a vehicle vertical six-degree-of-freedom dynamical model, a PMCMC algorithm is used for carrying out preliminary evaluation on suspension parameters; then, key suspension parameters, namely, the vertical stiffness of a primary spring and the vertical stiffness of an air spring, serve as design variables, the Pearson's correlation coefficient of the vertical acceleration power spectral density of the simulation and actual measurement vehicle body serves as output, and a Kriging proxy model is constructed; meanwhile, with the purpose of minimizing simulation and actually-measured vertical acceleration PSD errors of a vehicle body, key suspension parameters matched with the actual service state are obtained through a PSO algorithm. And taking the obtained key parameters as prior information of a PMCMC algorithm, and taking actually measured vibration data of the train as an observation value to complete posteriori estimation of the suspension parameters.

The invention is suitable for the technical field of fan design, and provides a fan airfoil optimization method and system based on a Kriging proxy model and a genetic algorithm, and the method comprises the following steps: carrying out the parameterized expression of an airfoil curve based on a classification function and shape function transformation method, and obtaining the expression of the airfoil curve; selecting sample points, calculating a power coefficient of each sample point, and constructing sample point data; based on the sample point data, constructing and training a Kriging agent model; power coefficient maximization is used as a target function, parameters and installation angles in an expression of an airfoil curve are used as design variables, constraint conditions are set, and global optimization is carried out on the basis of a Kriging agent model based on a multi-island genetic algorithm; and based on an optimization result, through comprehensive evaluation, selecting an optimal airfoil profile. According to the optimized airfoil profile, the power coefficient of the fan is remarkably improved, a systematic theoretical basis is provided for the design of the airfoil profile of the fan, and meanwhile, an effective technical support is provided for improving the aerodynamic performance of the fan.

The application discloses a curvature driving dynamic compensation motor multi-physical field modeling method, and relates to the field of motor simulation modeling.The method comprises the following steps: constructing a bidirectional coupling basic model of an electromagnetic field, a temperature field and a structural mechanics field; correcting thermal parameter drift and mechanical deformation error in the electromagnetic field through a dynamic parameter compensator; based on local curvature characteristics of a parameter curve, adopting a curvature driving bimodal adaptive switching mechanism to smooth the electromagnetic parameter curve, and generating a dynamic weight factor according to the curvature distribution characteristics to fuse the output results of the two types of modes; generating a parameter response surface based on a Kriging surrogate model to replace nonlinear equations for calculating key physical fields by finite elements; and outputting the joint simulation results of electromagnetic torque ripple spectrum, winding temperature distribution and rotor vibration mode.The application scheme can solve the three-way contradiction among precision, efficiency and stability in traditional motor modeling, and has high application value.

This invention belongs to the field of composite material structure optimization design technology, and discloses an integrated design method for composite material pipe winding parameters based on surrogate models and multi-objective optimization. The steps include: First, through parametric modeling and automated finite element simulation, a rapid mapping from design variables to performance responses is achieved; second, samples are generated based on optimal Latin hypercube sampling, and a high-precision Kriging surrogate model is constructed to replace time-consuming high-fidelity simulation; third, using the surrogate model as an evaluation tool, the NSGA-II multi-objective genetic algorithm is used for optimization, obtaining a series of Pareto optimal solution sets that achieve the best trade-off among multiple performance objectives; finally, the entropy weight method is used to objectively calculate the weights of each performance index, and the TOPSIS method is combined to make a comprehensive decision on the Pareto solution set, selecting the final optimal parameter combination. This achieves efficient, high-precision, and automated multi-objective optimization design of composite material pipe winding parameters, and ensures the comprehensive optimality and objectivity of the design scheme.

The invention belongs to the technical field of electric digital data processing, and particularly relates to a multi-objective optimization method for improving the torque performance of a motor, and the method comprises the steps: building a parameterized model of the motor; based on a harmonic analysis principle of motor air gap flux density, geometric harmonic prior characteristics are extracted, and an initial high-quality sample set is obtained by taking the geometric harmonic prior characteristics as a screening criterion; constructing a Kriging agent model reflecting a mapping relation between a design variable and a performance response, and introducing an energy efficiency boundary risk function in the construction process; and based on the Kriging agent model, searching a Pareto frontier solution set by using a multi-target optimization algorithm, selecting potential sample points from the Pareto frontier solution set for simulation verification, and then adaptively updating the Kriging agent model until a stop criterion is met, and outputting target motor design parameters. According to the method, samples can be screened through geometric harmonic prior characteristics, and accurate optimization of electromagnetic parameters can be realized by using an energy efficiency boundary risk function.