790 results about "Surrogate model" patented technology

A structural-health-monitoring system is disclosed for real-time detection and autonomous maintenance of physical structures. The system includes a sensor network comprising at least one strain gauge and one tri-axial accelerometer mounted on the structure to generate real-time sensor signals. A perception module filters and normalizes the signals and extracts numerical features such as peak amplitude and dominant frequency. A digital-twin module maintains a finite-element model updated in response to the extracted features. A data-driven surrogate model predicts sensor behavior and refines itself using machine-learning techniques. An anomaly-detection module computes an anomaly score from model residuals or classifier outputs. Upon exceeding a threshold, a maintenance module initiates a maintenance action, including generating an inspection schedule or issuing a control signal to an autonomous inspection or repair device. A learning module continuously improves system performance using reinforcement learning based on historical outcomes. The system supports predictive diagnostics, robotic repair, and automated optimization for long-term structural integrity.

The invention relates to a thermal coupling over-reduced order prediction method and system for a multi-component assembled nonlinear system. The method comprises the following steps: collecting multi-scale physical field data; constructing an intrinsic orthogonal decomposition basis function space of a temperature field and a stress field, and establishing a double-field coupling constraint equation; constructing contact thermal resistance parameterized proxy models of a cylinder contact area, a bolt area and a free deformation area by adopting a domain discrete empirical interpolation method; constructing a parametric intrinsic mode tensor network to obtain a decline model which is used for realizing real-time reconstruction of a mode basis function through acquired tensor slices in an online stage; performing dynamic inversion based on a modal basis function reconstruction result, and outputting a predicted transient displacement field, a predicted temperature gradient field and a predicted contact stress field; and obtaining real-time parameters, and calculating a residual error with a corresponding prediction result so as to dynamically update the primary function and interpolation point distribution. Compared with the prior art, the real-time prediction of the transient thermal coupling of the multi-component contact system is realized on the premise of ensuring the precision.

The invention discloses a mold and formwork design system based on virtual simulation, and particularly relates to the field of mold design, the mold and formwork design system comprises a multi-mode perception fusion module, a digital twin modeling module, a multi-physics field coupling simulation module, a hybrid intelligent optimization module, a digital twin closed loop verification module and a data center module, and each module forms a design closed loop through a data center. The multi-modal sensing fusion module adaptively collects and fuses multi-source signals to generate high-credibility data; the digital twin modeling module constructs and iteratively corrects a model based on the data; the multi-physics field coupling simulation module realizes multi-solver co-simulation under a dynamic boundary; the hybrid intelligent optimization module accelerates to generate an optimal solution through secondary optimization and an agent model; the digital twin closed-loop verification module detects defects and generates a correction instruction; the data center module is responsible for data management, cross-module scheduling and precision control; the system improves the design precision and efficiency of the mold frame, reduces the physical mold testing cost, and is suitable for a high-precision mold development scene.

The invention discloses an aircraft aerodynamic performance analysis method, which comprises the following steps: constructing a multi-dimensional parameterized model integrating aerodynamic / structure / thermal control, and outputting a global design space; based on a global design space, fusing sparse fluid dynamics and engineering data to train an agent model, and verifying the cross-speed domain reliability; decoupling the centroid / lift-drag ratio / thermal load conflict by using the proxy model, and generating a speed domain adaptive configuration library; executing fluid-structure-thermal field strong coupling simulation by utilizing a shock wave-boundary layer self-adaptive grid technology aiming at the speed domain self-adaptive configuration library; based on a fluid-structure-thermal field strong coupling mechanism, utilizing a dynamic sensing technology to predict the aerodynamic performance of the aircraft; and integrating dynamic prediction data and digital simulation, calibrating model parameters, and outputting an aerodynamic performance report and an optimization scheme. According to the method, the problems of insufficient multi-physical field integration, low cross-speed domain reliability, poor configuration self-adaption and low coupling simulation precision in aerodynamic performance analysis of a traditional aircraft are solved.

The invention discloses a high-precision static aeroelastic model optimization design method based on a model correction technology, and relates to the technical field of aircraft design, and the method comprises the following steps: S1, firstly constructing an initial model, and carrying out statics pre-analysis to verify integrity; s2, executing SOL 101 statics analysis based on the initial model and outputting a physical field result; s3, carrying out consistency analysis in combination with test data and generating a correction decision; s4, screening high-priority correction parameters through local or global sensitivity analysis; s5, correcting model parameters by adopting a mixed algorithm of a gradient method and an agent model, and verifying precision and generalization ability; s6, the corrected model is output as a Nastran file and a reduced-order model in a standardized mode, and a parameter change log is recorded; s7, executing static aeroelastic coupling and flutter analysis, and feeding back a result to drive optimization iteration; s8, constructing a multidisciplinary coupling optimization model in combination with aeroelastic and flutter results to realize collaborative optimization; and S9, finally performing engineering standardization packaging on the optimization model and outputting a verification report.

A method, computer program product, and computing system for processing a query provided to a generative AI model. A content portion retrieved by a Retrieval Augmented Generation system for the query is processed. User context information associated with a user providing the query is determined. Hyperparameters are generated for processing the prompt with the generative AI model by processing the query, the content portion, and the user context information using run-time surrogate model inversion optimization.

The invention belongs to the field of uncertainty quantification and robustness design optimization of aero-engine air-cooled turbines, and particularly discloses a one-dimensional uncertainty design optimization method and system for a reverse cooling turbine based on a one-dimensional aerodynamic analysis method for the reverse cooling turbine. Forming an augmented space by the optimization variables and the uncertainty parameters, and generating a sample set; establishing a Kriging global agent model, and generating an uncertainty parameter sample set; an ASPC model is established, and one-dimensional uncertainty quantification of the reverse cooling turbine is completed; an NSGA-II multi-objective optimization algorithm is coupled, and one-dimensional robustness design optimization of the reverse cooling turbine is completed. The one-dimensional uncertainty design optimization method and system framework of the reverse cooling turbine are provided and established, aerodynamic performance analysis of the reverse cooling turbine can be completed through simple parameters, a geometric entity is not needed, and tasks such as one-dimensional uncertainty quantification and robustness design optimization of the reverse cooling turbine can be achieved.

The invention discloses an optimization design method for a control surface of a wide-speed-range large-attack-angle reusable carrier, and relates to optimization design of aerodynamic configuration of an aircraft. The method comprises the following steps: S1, setting a control surface aerodynamic configuration design variable range, and generating an initial sample library by adopting Latin hypercube sampling; s2, aerodynamic parameters are obtained through CFD simulation, and a Kriging proxy model is trained; s3, evaluating the precision of the proxy model by taking a U learning function as a criterion, and stopping adding points when the minimum U function value is smaller than a threshold value; s4, constructing an optimization model which takes maximization of the lift-drag ratio and the static stability margin under the hypersonic speed as a target and takes the condition that the aerodynamic parameters of the supersonic speed / subsonic speed are not lower than a reference value and the hinge moment as constraints; and S5, performing iterative optimization by adopting an improved multi-target particle swarm algorithm based on genetic algorithm crossover mutation operation, updating the proxy model after the optimal solution of each generation is subjected to CFD verification, and outputting an optimal solution set. The problem of wide-speed-range aerodynamic configuration contradictions is solved, and the comprehensive performance of the carrier is remarkably improved.

The invention discloses an optical system multi-objective optimization method based on computer vision, and relates to the technical field of intelligent optics, and the method comprises the steps: collecting initial design parameters and an installation and adjustment tolerance range of an optical device, and obtaining an initial design vector set; based on the initial design vector set, performing simulation and image processing under different working conditions, extracting image quality and image features, and obtaining same-domain features; constructing a multi-target agent model fusing optical indexes and task performance indexes through same-domain features; obtaining a target function interface according to the multi-target agent model; on the basis of the target function interface, global optimization and local refinement are adopted to obtain a candidate optimal solution set; and through the candidate optimal solution set, re-evaluation is carried out in uncertainty evaluation and robustness screening, and a final nominal solution is obtained. According to the method, the multi-target agent model fusing the optical indexes and the task performance indexes is constructed, and prediction, constraint evaluation and risk sensitivity scoring are performed on the candidate design vectors, so that joint optimization of the optical performance and the task performance is realized.

The invention provides a centrifugal pump performance optimization method and device, computer equipment and a storage medium, and belongs to the field of centrifugal pump design. The method comprises the steps that a parameterized model of a centrifugal pump impeller is established to define the geometrical shape of the centrifugal pump impeller, a design space is determined, and a plurality of sample points are generated; for each sample point, performing computational fluid dynamics simulation, adjusting inlet pressure, monitoring a ratio of simulation lift to rated lift, determining critical cavitation pressure corresponding to the sample point, and storing flow field data to construct a data set; training an agent model by using the data set; the lift, the efficiency and the critical cavitation pressure serve as targets, design variables serve as optimization variables, performance is evaluated through an agent model, global optimization is conducted through a multi-target optimization algorithm, and a target design scheme is selected from a Pareto solution set obtained through optimization. Therefore, the centrifugal pump performance under different design variables can be quickly and accurately predicted, multiple targets are effectively balanced, and a comprehensive optimization solution is provided.

To create high-performance metasurface devices (110) in an inverse design process over a large design space (100), a deep neural network may be used as a surrogate model in lieu of a full physics simulation to more efficiently predict figures of merit for given input metasurface topologies during the iterative topology optimization. The neural network may also serve to efficiently compute, via backpropagation, gradients of the figures of merit with respect to design parameters, as are used to update the topology in each iteration.

The invention discloses a wing design optimization method based on agent-assisted multi-initial-point simulated annealing, and belongs to the technical field of optimization design. Comprising the steps of 1, initializing algorithm parameters and a training data set; 2, constructing an agent model; 3, executing multi-initial-point parallel simulated annealing, and generating a batch of candidate new solution sets; 4, executing a double-elite active learning strategy based on the new solution set, screening the most potential sample to carry out real evaluation, and updating the agent model; 5, cooling the temperature and reducing the step length; 6, judging whether the cumulative evaluation times of the expensive objective function reach the set maximum evaluation times or not; if not, returning to the step 2; if yes, optimization is stopped; and finally, traversing the training data set, and selecting a sample point with the minimum real objective function value as a global optimal solution. According to the method, a multi-initial-point parallel simulated annealing search mechanism and a double-elite active learning strategy are combined, and the global optimal solution is quickly approached under the limited simulation times.

The invention discloses a power module multi-objective optimization method and device, and the method comprises the steps: determining a key structure parameter and a constraint range of a to-be-optimized power module, and constructing a multi-dimensional parameter space based on the key structure parameter and the constraint range; performing global uniform sampling on the multi-dimensional parameter space based on a Sobo sequence to generate a sample point set; performing electromagnetic-thermal coupling simulation based on the sample point set to obtain a target performance index, and constructing a training data set based on the target performance index and the sample point set; constructing an agent model of the MLP, and training the agent model based on the training data set to obtain a target optimization model; and constructing a multi-target optimization model based on the target performance index, solving the multi-target optimization model based on a tabu search algorithm, a fast non-dominated sorting genetic fusion algorithm and the target optimization model, and obtaining optimal key parameters of the to-be-optimized power module.

The invention discloses a post-pumping air tank robustness collaborative design method considering hydraulic parameter time-varying characteristics, which comprises the following steps: defining a hydraulic parameter uncertainty fluctuation interval of a water delivery system in a full life cycle, and setting a body structure decision variable range of a post-pumping air tank; performing combined sampling in the water conservancy parameter uncertainty fluctuation interval and the body structure decision variable range by using a test design method to generate an initial sample set; performing steady-state and transient-state coupling simulation on the initial sample set, constructing a constant-flow operation condition of the water delivery system by using a hydraulic equation, updating a water pump working point and pipeline pressure distribution, performing transient simulation by using a characteristic line method to obtain a hydraulic response index, and training to obtain a water hammer response agent model; constructing a robustness optimization objective function based on failure probability constraint; and performing global optimization on the target function by using an intelligent optimization algorithm, calling the water hammer response agent model to perform random simulation, evaluating a failure probability, and outputting a target design scheme.

The invention relates to a multi-target aerodynamic-structure optimization method based on a deep neural network proxy model, and solves the problem that an existing method is insufficient in efficiency, feasibility and credibility, and the method comprises the steps: firstly, carrying out the sampling in a parameterized design target, building an aerodynamic-structure database through CFD / FEA high-fidelity simulation; the method comprises the steps that firstly, a multi-task neural network is used for predicting the lift-drag ratio, quality and stress at the same time, uncertainty is output, then, in NSGA-III multi-target optimization, a proxy model is used for rapidly evaluating and searching out a Pareto leading edge meeting constraints, finally, the precision of the model is gradually improved through high-fidelity checking and incremental updating, and an optimal design scheme is output. According to the method, sub-optimization and repeated iteration caused by'pneumatic first and then structure 'are avoided; the design universality and robustness are improved; a compromise scheme set of the system is directly obtained; and the optimization cost and the optimization period can be obviously reduced.

The invention discloses a 1D-3D coupled centripetal turbine multi-objective collaborative optimization method based on an agent model, which comprises the following steps: step 1, based on a heat source condition and an operation condition, determining optimization objective functions and constraint conditions thereof, the related optimization objective functions including efficiency, wheel cycle power and flow stability; and 2, generating an initial parameter set containing thermodynamic parameters, aerodynamic parameters and structural parameters through a one-dimensional aerodynamic design model and a multi-objective optimization design function. The invention relates to the technical field of centripetal turbine optimization in medium and low temperature heat energy utilization. According to the 1D-3D centripetal turbine collaborative optimization method, one-dimensional aerodynamic design and three-dimensional flow characteristic analysis are deeply fused, an experimental design idea is introduced to establish a high-precision agent model, efficient collaborative optimization of thermodynamic parameters, kinetic parameters and structural parameters is achieved, the optimization period can be shortened by 40% in ORC centripetal turbine design, and the optimization efficiency is improved. And the design point efficiency is improved by more than 2%.

The invention discloses a fault-tolerant neural network optimization method based on automatic architecture search. The method comprises the following steps: introducing a plurality of operators and diversified neural network unit structures in a search space construction process, and constructing a neural network search space supporting multi-dimensional balancing of accuracy, calculation overhead and fault-tolerant capability; in the multi-target architecture optimization process, topological structure coding is performed on a neural network, three performance indexes are introduced in a combined manner based on a Bayesian optimization theory, an agent model is constructed to simulate network performance, and a candidate architecture is generated by using an acquisition function and a multi-target optimization algorithm; in the architecture evaluation and iteration process, candidate architectures are trained through a weight-shared super network, key indexes of the candidate architectures are evaluated, an architecture pool is updated, an optimal compromise is obtained through iteration, and a final neural network architecture is given; the method has high efficiency, flexibility and universality, can be used for automatically exploring the fault-tolerant neural network architecture required by the safety key field, and can adapt to multi-scene and multi-requirement fault-tolerant neural network optimization design.

The invention discloses an underwater gliding robot anchoring process energy consumption optimization method and system based on a subdomain grey box model. The energy consumption optimization method comprises the steps that a white box energy consumption mechanism model in the underwater gliding robot anchoring process is established; the whole anchoring energy consumption stage of the underwater gliding robot is divided into a plurality of sub-domains, and a Latin hypercube design method is adopted to simulate and generate a plurality of sub-domain samples meeting variable range constraints by using a hardware-in-the-loop simulation platform; selecting a Kriging model as a sub-domain agent model, fusing the white-box energy consumption mechanism model and constraint conditions of an oil bag volume and a movable mass position to form a sub-domain grey-box model, and performing segmented energy consumption fitting by using the sub-domain grey-box model; and with minimization of energy consumption fitted by the sub-domain grey box model as a target, performing iterative optimization on the sub-domain proxy model by adopting a dynamic guidance self-adjusting particle swarm algorithm, and outputting optimal planning parameters. The method realizes anchoring full-process low-energy-consumption control, and is suitable for marine resource exploration, hydrological monitoring and other tasks.

The invention belongs to the technical field of advanced manufacturing and intelligent design, and provides a topological optimization method and system for a liquid cooling plate of a lithium ion battery pack, and the method comprises the steps: building a parameterized simulation model of a battery pack liquid cooling system, constructing a multi-dimensional design variable space of the liquid cooling plate, and formally defining a multi-objective optimization problem; on the basis of the initial training data set, independently constructing a probabilistic agent model capable of predicting a target value and quantifying uncertainty for each optimization target function; performing quantum behavior enhanced multi-universe optimizer iterative optimization on the constructed agent model, selecting new sample points from a multi-dimensional design variable space of the liquid cooling plate according to a set criterion after a set number of iterations is completed, performing high-fidelity CFD simulation to obtain real data, supplementing the real data to a data set, and updating or reconstructing the agent model; and judging whether an iterative optimization condition is met or not, and if so, screening and outputting a final Pareto optimal solution set from all samples subjected to high-fidelity simulation verification. And efficient multi-objective optimal design of the liquid cooling plate is realized.

The invention discloses a biomass gasification experimental design and performance prediction method based on support vector and transfer learning, which comprises the following steps: establishing an Aspen plus simulation model, adjusting the gasification temperature to air equivalence ratio in the Aspen plus model, and obtaining a biomass gasification simulation data set covering wide boundary operation conditions; taking the simulation data set as a training sample, constructing a simulation agent model, and searching an optimal hyper-parameter; extracting a support vector sample through a support vector regression method, designing experimental working condition points, and collecting experimental data to obtain an experimental data set; taking the experimental data set as a model input sample, and outputting a predicted value by the model; performing linear migration on a model prediction result; and performing secondary correction on the difference between the migrated result and the experimental data to obtain a high-precision calibration model fusing linear migration and residual correction. According to the method, the problems of high acquisition cost of biomass gasification experiment data, limited simulation data precision and the like are effectively solved, a model with better generalization ability and higher interpretability is constructed under limited experiment samples, and accurate prediction of product distribution is realized.

A system may comprise a neural network trained to predict mechanical damage to field-deployed industrial equipment with a main or supplementary function to transfer heat change phase, or drive / limit a reaction. The neural network may be trained using an idealized geometry. A processor may receive process input data from the equipment and may provide the data to the trained neural network. The processor may generate a prediction of mechanical damage using the trained neural network and the process input data. The process input data may comprise temperature data collected during operation. A scanning device may generate a digital twin of at least a portion of the equipment through scanning and ultrasonic testing. A monitoring module may monitor processes through a control system to collect the process input data. The neural network may comprise a convolutional neural network configured to calculate damage using a surrogate model.

The invention belongs to the technical field of aircraft strength design, and particularly relates to an aircraft structure strain field reconstruction method and device based on a deep learning neural network, and the method comprises the steps: S1, carrying out the dimension reduction mapping of irregularly distributed three-dimensional grid nodes of a finite element model, and obtaining regular two-dimensional grid nodes; s2, obtaining calculation results of each two-dimensional grid node under a plurality of working conditions based on finite element simulation, and constructing a training set; s3, training the U-NET deep convolutional network through the training set to obtain a strain field proxy model; and S4, based on the strain field agent model, processing an input image with a small number of measurement point position measurement results to obtain an output image containing an aircraft structure strain field. According to the invention, the strain field of the whole aircraft can be reconstructed through displacement data of a small number of points measured by the static test of the whole aircraft.

The invention provides a sampling method suitable for continuous-discrete mixed variable design optimization of an advanced reactor. The sampling method comprises the following steps: (1) screening an effective discrete variable combination; (2) constructing a cumulative probability distribution function of the discrete variable combination; (3) hierarchically designing a space based on a cumulative probability distribution function interval; (4) distributing the number of samples according to the interval width proportion of the cumulative probability distribution function of each layer; (5) Latin hypercube sampling is carried out on the continuous variables in each layer; (6) integrating a global sample set and verifying the quality of the global sample set; and (7) constructing an agent model and evaluating precision. According to the method, the probability characteristic of the discrete variable and the space filling characteristic of the continuous variable are integrated, efficient exploration of the nuclear reactor design space is achieved, and compared with a traditional method, the sample quality and the calculation efficiency are remarkably improved.

An equipment system optimization method driven by deep reinforcement learning comprises the following steps: sequentially constructing an equipment system architecture model and a combat scene simulation model based on a DoDAF framework, then carrying out parameter space sampling in the equipment system architecture model by using a uniform design method, and according to capability items and equipment elements defined by the equipment system architecture model, carrying out parameter space sampling on the equipment system architecture model; constructing a combat effectiveness evaluation model and a system cost evaluation model for evaluating the system; constructing a parameter-modulated deep reinforcement learning (PM-DRL) model to explicitly embed system parameters into an agent state space, and performing data collection and evaluation through the trained PM-DRL model to obtain lt; system parameter-evaluation result gt; a data set; and finally, constructing an optimization model by taking the agent model as a target function, and determining an optimal solution, namely system parameter configuration, by combining the solved Pareto frontier with the preference of a decision maker. According to the method, under the same combat effectiveness requirement, system parameter configuration with lower construction cost can be obtained through optimization.

The invention discloses a telescope parameter efficient optimization method and system based on a Bayesian algorithm and a fluctuation optical TTL coupling noise calculation model, and aims to solve the problem that a fluctuation optical high-precision calculation module is used for improving TTL coupling noise calculation precision in an existing telescope parameter optimization method. And due to frequent calling of a high-time-consumption module, the optimization time is sharply increased, and the efficient design requirement of a space gravitational wave detection task is difficult to meet. According to the method, on the basis of the principle of a Bayesian optimization algorithm, potential optimal to-be-evaluated telescope parameters are searched through an agent model, a collection function and the optimization function of Zermarkes software; and calculating TTL coupling noise of the telescope to be evaluated by combining a light field propagation calculation method based on a wave optics theory with a light field-phase resolving method, carrying out iterative optimization on telescope parameters, and outputting optimal TTL coupling noise and optimal telescope optical parameters.

The invention discloses a manufacturing defect-oriented multi-scale composite material mechanical property prediction method, and belongs to the technical field of composite material mechanical property analysis. The method comprises the following steps: based on a multi-scale representative volume element (RVE) theory, respectively constructing parameterized models containing pores and fiber dislocations in a microscopic scale and a mesoscale; the equivalent elastic parameters under each scale are calculated through a finite element numerical homogenization technology, and an agent model is constructed in combination with an artificial neural network, so that the calculation cost is remarkably reduced, and the high-efficiency cross-scale prediction of the rigidity and strength performance of the composite material is realized. The method overcomes the defect of defect modeling of a traditional model, has high precision and high efficiency, and is suitable for performance evaluation and optimization design of composite material structures in the fields of aerospace, rail transit and the like.

The invention discloses a partition dot matrix filling air rudder lightweight design method. The method mainly comprises three parts of establishment of a dot matrix unit cell equivalent mechanical performance proxy model, topological optimization design of an air rudder structure and optimization design of dot matrix filling parameters. Comparing compression tests of different rod diameters with simulation results of variable cross-section beam units with different parameters, and searching optimal beam unit cross-section parameters to construct and calculate a lattice unit cell equivalent mechanical performance agent model; structure topological optimization design is carried out on the determined air rudder appearance and load conditions, dot matrix filling area division is carried out on a design area according to a topological optimization result, and the design area can be divided into areas with different filling densities according to design requirements; and establishing a parameterized finite element model for the dot matrix filling structure and the air rudder model, and performing optimization design on rod diameters of different partitions on the basis to obtain a final dot matrix filling design model.

The invention discloses a welding simulation heat source parameter calibration method and system based on a hybrid agent model, and belongs to the technical field of welding numerical simulation and optimization, and the method comprises the following steps: S1, problem definition and experiment design; s2, analyzing a product structure and constructing a joint model; s3, parameter space sampling; s4, performing simulation calculation; s5, constructing a mixed agent model; s6, proxy model optimization based on a genetic algorithm; s7, parameter verification and model updating; and S8, optimizing and outputting optimal parameters. According to the method, most time-consuming full-order finite element calculation is replaced by the proxy model and the simplified joint model, the parameter calibration process is shortened from several days or weeks to several hours, and the optimization efficiency is improved; a simplified model and a complete model are used for optimization, firstly, the simplified model is used for rapidly obtaining an optimal parameter approximate value, and the complete model is used for obtaining accurate parameters.

A system may comprise a neural network trained to predict mechanical damage to field-deployed industrial equipment with a main or supplementary function to transfer heat change phase, or drive / limit a reaction. The neural network may be trained using an idealized geometry. A processor may receive process input data from the equipment and may provide the data to the trained neural network. The processor may generate a prediction of mechanical damage using the trained neural network and the process input data. The process input data may comprise temperature data collected during operation. A scanning device may generate a digital twin of at least a portion of the equipment through scanning and ultrasonic testing. A monitoring module may monitor processes through a control system to collect the process input data. The neural network may comprise a convolutional neural network configured to calculate damage using a surrogate model.

The invention relates to the technical field of aero-engines, and discloses a proxy model-based wheel disc over-rotation fracture rotation speed prediction method, which utilizes a strain prediction proxy model to perform analysis and calculation, and can improve the precision and efficiency of wheel disc fracture rotation speed prediction. The strain prediction agent model is adopted to replace a traditional finite element method for strain prediction calculation, a large number of simulation models for simulating wheel disc over-rotation fracture are not needed, only finite element calculation of a small number of sample points is needed, the strain prediction result under any working condition can be rapidly output, calculation efficiency is high, calculation cost is low, and the method is suitable for large-scale popularization and application. And the real-time requirement of calculation can be met. According to the method, the influence of the uncertain parameters on the wheel disc fracture behavior is considered, the fracture rotation speed interval under the influence of the uncertain parameters is predicted, the accuracy and reliability of fracture rotation speed prediction are improved, and a more scientific basis can be provided for design and optimization of a wheel disc.