112 results about "Global sensitivity analysis" patented technology

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.

The invention belongs to the technical field of urban rail vehicle detection and maintenance, and discloses a full-process optimization method and system for polygonal wear of a metro vehicle wheel. The method comprises the following steps: firstly, constructing a digital twin-driven train rigid-flexible coupling dynamic model, carrying out global sensitivity analysis, establishing a sensitivity index model, screening key dynamic performance indexes, and carrying out batch simulation to construct a dynamic response database; feature extraction and classification model training are carried out on the index data, multi-layer wavelet packet decomposition is carried out on the one-dimensional vibration signals, and a multi-channel feature vector is constructed and input into a one-dimensional residual network model; inputting actually acquired data into the trained model, calculating a relative close degree to generate a comprehensive index and a grading result, and generating turning repair suggestions based on grading; meanwhile, multi-source monitoring data are collected, a long-short-term memory network is used for predicting the abrasion evolution trend, finally, turning repair suggestions and trends are integrated, an accounting model and an evaluation system are constructed, and an optimal maintenance decision is generated through a multi-target optimization algorithm.

The invention discloses a data and knowledge dual-drive wheel set multi-parameter comprehensive state evaluation method and system, and belongs to the technical field of railway vehicle maintenance. The method comprises the following steps: firstly, constructing a mapping relation between geometric parameters and dynamic performance indexes through sampling and dynamic simulation, and carrying out global sensitivity analysis to screen key dynamic performance indexes; secondly, determining subjective and objective weights of the indexes in combination with an analytic hierarchy process and an entropy weight method, and introducing a dynamic optimization model based on a Bellman equation to generate a final combined weight; then, constructing a multi-dimensional state space division model by applying adaptive kernel density estimation and fuzzy C-means clustering, and determining the probability density and membership function of each index under different health levels; and finally, performing simulation prediction on the target wheel set, inputting a predicted value into the state space model, fusing a dynamic combination weight and a D-S evidence theory, calculating a comprehensive health index, and outputting a grading result, so that the health state of the wheel set can be accurately and efficiently evaluated.

The invention relates to the technical field of engine testing, and discloses a reliability test method and system for a small general engine, and the method comprises the steps: collecting an engine vibration signal through a superconducting quantum interferometer, and collecting a temperature distribution parameter through a quantum dot temperature sensor; performing combined testing of cold and hot shock, high-load operation and random working conditions, and synchronously monitoring dynamic response characteristic data; constructing a Weibull probability model, and determining key influence factors through a global sensitivity analysis method; acquiring a surface roughness parameter by adopting a field emission scanning electron microscope, obtaining a material component parameter through energy spectrum analysis, and establishing a parameter mapping relation; establishing a vibration and temperature early warning system, executing a graded sweep frequency excitation and abrasive particle acceleration test, and recording a parameter degradation process; and generating a three-dimensional parameter cloud picture, and outputting a reliability test report. According to the method, the reliability of the small general engine can be comprehensively and efficiently evaluated, and a scientific basis is provided for design, maintenance and improvement of the engine.

The invention provides a battery electrochemical parameter identification method, system and device and a program product, and the method comprises the steps: carrying out the discharge test of a plurality of discharge rates on a battery, and obtaining the experimental data of the discharge test; constructing a battery electrochemical model based on experimental data; performing cross-working-condition sensitivity analysis on the model input parameters of the battery electrochemical model by adopting a global sensitivity analysis method to obtain global sensitivity parameters; constructing a target function based on key region constraint based on experimental data; and alternately adopting a constraint Bayesian optimization method based on a trust domain and a granular self-adaptive local search method to explore the optimized target function, and iteratively optimizing the target function to obtain an optimal solution of the global sensitivity parameter. According to the method, a set of parameter identification system with high precision, cross-working-condition robustness and calculation efficiency is constructed, and cross-working-condition high-precision identification of the electrochemical parameters of the high-capacity lithium ion battery is realized.

The invention relates to the technical field of ship navigation risk assessment, in particular to a ship navigation risk assessment system based on multi-source heterogeneous data fusion, which comprises a multi-source data integration module, a spatial-temporal feature mapping module, a dynamic risk detection module, a linkage decision control module and a feedback optimization module. According to the method, standardized operation data is generated through multi-source data cleaning and fusion, a spatial-temporal feature distribution map is generated by using a multi-dimensional dynamic clustering algorithm, a risk index set is extracted in combination with adaptive boundary adjustment and a nonlinear optimization algorithm, and accurate path planning and real-time regulation are realized. In addition, a global sensitivity analysis framework and an early warning module are introduced into the system, and the ship navigation safety and reliability are improved. According to the method, the risk prediction accuracy can be remarkably improved, the navigation accident probability is reduced, the navigation efficiency is optimized, and safe operation of the ship is guaranteed.

The invention discloses an alpine meadow aboveground biomass monitoring method based on PROSAIL-BP, and belongs to the field of ecological remote sensing information processing. In order to overcome the defect that samples in the alpine region are insufficient and have high precision and high reliability, the method comprises the steps that an alpine meadow mask, remote sensing images and field biomass data in a target region are obtained, the remote sensing images are spliced, the wave band reflectivity is normalized, and the grassland region reflectivity is reserved in combination with mask cutting; predefining a PROSAIL model matched with the region features; performing Sobol global sensitivity analysis to obtain a first-order sensitivity index and a total-order sensitivity index of the parameter; screening a key wave band and four high-sensitivity parameters; uniformly sampling to generate a parameter group, simulating a hyperspectrum through PROSAIL, extracting the reflectivity of a key wave band, and constructing a data set by multiplying a leaf area index by a dry matter content as a target variable; and training a three-layer BP neural network, and processing the reflectivity output pixel biomass of the remote sensing key wave band. The method is applied to a remote sensing information processing system and has high precision and high reliability.

The invention discloses a method and a system for improving carbon dioxide mineralization and storage efficiency. The method comprises the following steps: building a microfluidic experiment platform integrated with an online CO2 mineralization and storage monitoring system; on a microfluidic experimental platform, simulating a formation temperature and pressure condition to carry out a CO2 mineralization corrosion experiment to obtain pore scale reaction kinetic parameters and a pore structure evolution rule; macromineralization efficiency data are obtained through an indoor physical simulation mineralization reaction experiment of the core scale; a CCM-GEM reaction flow numerical model is constructed; determining a Pareto optimal solution set by using a CMG-GEM reaction flow numerical model; and based on the pore scale reaction kinetic parameters, the pore structure evolution law, the macromineralization efficiency data and the Pareto optimal solution set, determining a CO2 mineralization storage efficiency improvement path of the target rock sample. According to the method, global sensitivity analysis and automatic optimization of multiple parameters are rapidly completed, the research and development period is shortened, the economic cost and the time cost are reduced, and the research efficiency is improved.

The invention provides a small sample AI proxy model construction method based on sensitivity analysis and supplementary sampling, and the method comprises the steps: carrying out the global sensitivity analysis through the automatic simulation process of parameterized parts of wind power equipment in combination with test design, and removing insensitive variables, thereby guaranteeing the accuracy and standardization of a sample, avoiding the invalid consumption of irrelevant variables, and achieving the automatic simulation of the parameterized parts of the wind power equipment. Latin hypercube sampling is adopted based on key variables, a core design area is uniformly covered, the number of initial simulation times is greatly reduced, the cost is controlled, the problem that the generalization ability is poor due to uneven small sample distribution is solved, a deep neural network regression model is trained through a training set, the strong nonlinear fitting ability of the deep neural network regression model adapts to a complex mapping relation, the core law is efficiently learned, and the robustness is high. Based on initial model prediction precision and sensitivity information, samples are accurately supplemented in weak areas, samples are not increased blindly, investment is reduced, prediction blind areas are made up, errors are gradually reduced through iterative training closed-loop optimization, and finally the small sample AI proxy model meeting preset requirements is obtained.

The invention discloses an arch dam aging deformation mechanism analysis method, and relates to the field of hydraulic structure safety. The method comprises the steps that based on three-dimensional terrain and geological data, a high-precision arch dam-foundation overall finite element mesh model is established, concrete materials and permeation partitions are divided carefully, and an anti-seepage and drainage system structure is embedded; by means of multi-source monitoring data, real boundary conditions such as water temperature and air temperature and thermodynamic parameters are dynamically recognized and optimized through the intelligent inversion technology; respectively analyzing the aging influence of single factors such as a temperature field and valley amplitude deformation on arch dam deformation by adopting finite element simulation; on the basis of the normalized deformation response data, a Bayesian optimized long-short-term memory neural network and a hierarchical correlation propagation algorithm are adopted, contribution weights of all factors to deformation are calculated, and weight rationality is verified through global sensitivity analysis. According to the method, quantitative separation and evaluation of the main deformation influence effect of the arch dam are realized, and a theoretical basis can be provided for structural health monitoring and safety management.

The invention discloses a model intelligent verification and parameter correction method, which comprises the following steps of 1, building an unmanned ship task high-value verification point set based on expert experience and a large model collaboratively, screening boundary points and extreme points, and performing simulation-actual measurement data consistency verification; if the normalization error of the simulation data and the actual measurement data exceeds a threshold value, triggering correction; step 2, constructing a sensitive factor set according to global sensitivity analysis and a parameter association relationship mining result, screening high-sensitivity parameters as a priority correction target, and avoiding redundancy optimization; and step 3, optimizing the high-sensitivity parameters, and forming a verification-correction-update closed loop by verifying and iteratively updating the priority of the factors to realize the intelligent correction of the parameters of the unmanned ship model.

The invention provides a sewage biological treatment model prediction control method. According to the method, firstly, parameters are sorted through global sensitivity analysis, and high-sensitivity parameters are emphatically processed; dynamically evaluating the local sensitivity of the parameters based on a multi-scale time window; the DDQN network based on sensitivity guidance is fused with data-driven learning and physical mechanism knowledge; and the model prediction control system realizes multi-objective optimization of stable and up-to-standard effluent quality and energy-saving operation of the system based on the calibrated ASM3 model. According to the method, a complete closed-loop system from sensitivity analysis to parameter correction to prediction control is constructed, and compared with a traditional off-line parameter correction and experience control method, the model prediction precision, the control response speed, the operation stability, the energy efficiency level and the like are remarkably improved; and a complete technical support is provided for intelligent operation of an activated sludge process.

The invention discloses a multi-target robust optimization method for gulf nutritive salt pollution treatment, and the method comprises the following steps: S1, carrying out the global sensitivity analysis through a Sobol variance analysis method, setting a threshold value, and screening out a high-sensitivity parameter; s2, adopting a Latin hypercube sampling method for the high-sensitivity parameters to generate a plurality of disturbance scene parameter combinations, and constructing to obtain a unified disturbance sample space; s3, constructing a multi-target optimization model for nutrient salt pollution treatment, and determining water quality optimization and treatment cost minimization targets; s4, calling an NSGA-II algorithm to respectively solve a multi-objective optimization problem, and obtaining a Pareto solution set under different disturbance scenes; s5, calculating an expected value and a standard deviation of each solution under the representative disturbance sample and a gradient change rate under decision disturbance; s6, evaluating the robustness of each candidate solution to obtain a representative solution set; s7, checking target function response performance of each representative solution in a non-original disturbance scene; and S8, outputting an optimal scheme for treating the nutritive salt pollution of the watershed gulf.

A method for evaluating terrain uncertainty in flood warning and forecasting is provided. The method includes: S1, acquiring three types of digital elevation model (DEM) data from a shuttle radar topography mission (SRTM), an advanced spaceborne thermal emission and reflection radiometer (ASTER), and an advanced land observing satellite (ALOS); and preprocessing the three types of DEM data; S2, optimizing urban terrain characteristics; S3, constructing a multidimensional parameter space by using Latin hypercube sampling (LHS); S4, calculating flood hydrodynamics numerical value based on multidimensional sample points; and S5, constructing a global sensitivity analysis method frame suitable for urban terrain characteristics-related factors, where a Sobol quantitative method is used in the global sensitivity analysis method frame, and the Sobol quantitative method is used to evaluate uncertainties and sensitivity characteristics of multiple factors of terrain data based on a variance decomposition theory.

A method can include accessing data for a number of reservoir sites, where the data include at least property data for reservoir properties; performing a determination, using the property data for the reservoir sites, as to whether the reservoir properties for the reservoir sites are independent; responsive to the determination, implementing a Sobol' indices technique or a Kucherenko indices technique to generate global sensitivity analysis results that include property indices for the reservoir properties; generating confidence intervals for the property indices; and generating a graphical user interface for rendering the property indices with the confidence intervals for a number of the reservoir properties.

The invention discloses a method and a system for predicting throttling performance of a waterproof hammer air valve based on multi-working-condition simulation, and relates to the technical field of waterproof hammer air valves. The method comprises the following steps: establishing a simulation reference model containing adjustable parameters for the waterproof hammer air valve; defining a multi-working-condition driving matrix, performing global sensitivity analysis on each working condition, and extracting dominant and redundant parameters; the reference model is equalized by redundant parameters, and working condition configuration and calibration are completed based on dominant parameters to generate a matching model; and real-time working conditions are collected and matched with corresponding models to complete throttling performance prediction. The technical problems of low calculation efficiency and insufficient prediction precision caused by an undefined parameter influence mechanism and model redundancy in a multi-working-condition scene of a traditional waterproof hammer air valve throttling performance prediction method are solved. The technical effects of accurately quantifying parameter influence through working condition global sensitivity analysis, equivalently processing redundant parameters to construct a lightweight working condition simulation model, and realizing high-precision and high-efficiency throttling performance real-time prediction under multiple working conditions are achieved.

This application provides a method for uncertainty analysis in mechanical product assembly performance simulation. This method considers the impact of various uncertainty factors at different stages of the simulation process on assembly performance, classifying these factors into two categories for quantitative analysis. The first category includes the measurement, modeling, and simulation analysis stages in the early stages of simulation; the uncertainty u or error of these factors can be directly calculated. The second category consists of assembly performance uncertainties caused by external loads, boundary conditions, and nonlinear processes primarily based on geometric error distribution. For this category, this method uses simulation analysis results from a physical digital twin model, combined with the construction of an optimal SVR surrogate model and Sobol global sensitivity analysis, to quantitatively analyze the sensitivity of assembly performance to process parameters. In summary, this method provides guidance for the product assembly process.

The invention provides a blue crab saline-alkali soil seedling desalination and domestication interactive training system, a salinity regulation and control model optimization method, equipment and a medium, a dynamic physiological response model is obtained by establishing a blue crab seedling salinity stress response model and performing parameter estimation and calibration based on actual breeding data; carrying out global sensitivity analysis according to the dynamic physiological response model, and determining a physiological tolerance interval of key regulatory factors related to the desalination and domestication salinity of the juvenile green crabs; carrying out numerical optimization calculation by taking the daily decreasing amplitude of salinity and the stage stable time as decision variables and the maximization of the survival rate of the fries as an optimization target to obtain an optimal combination; and determining an optimal target salinity set value of each desalination stage according to the physiological tolerance interval and an optimization result, and constructing a gradient desalination regulation and control strategy. By adopting the scheme of the application, the optimization of the salinity regulation model can be realized, and the stability and the efficiency of desalination and domestication of the young green crabs are improved.

The application provides a global optimization method of an air conditioning system and a storage medium, and relates to the technical field of intelligent control of air conditioning systems. The application first screens target variables through global sensitivity analysis and generates high-quality training data. Then, the grid search is used to optimize the hyperparameters and verify the generalization ability. Then, the multi-class global optimization algorithm is used to optimize the optimization variables and extract performance data. Based on multi-dimensional trade-off analysis, the target combination of the model and the algorithm is determined to achieve the collaborative optimization of the prediction accuracy, optimization accuracy and optimization speed. Finally, the target combination is embedded in the global optimization architecture and combined with the interference factor prediction value to output the set value, ensuring that the optimization scheme can dynamically adapt to environmental and load changes, significantly reducing the total energy consumption of the air conditioning system, while ensuring the system operation stability and indoor thermal comfort, and realizing the global optimal operation effect.

The present application relates to the field of automobile chassis electric control, and particularly relates to a chassis electric control system parameter sensitivity analysis method based on a deep agent model, adopting a three-level progressive strategy of'single factor preliminary screening-modeling-Sobol analysis', first screening by single factor, adjusting each parameter value one by one and observing the system response, eliminating the parameters with less influence on performance, and reducing the range of parameters to be calibrated; then constructing a system response agent model based on BP neural network to replace the simulation model for fast calculation, ensuring the accuracy of the analysis while greatly reducing the calculation cost; finally, through Sobol global sensitivity analysis based on the agent model, the key parameters under each working condition are further extracted. The present application solves the contradiction between calculation efficiency and accuracy in high-dimensional parameter space, and takes into account efficiency and accuracy, providing standardized method support for virtual calibration under complex working conditions.

This invention relates to the field of geotechnical engineering monitoring and slope stability analysis technology, specifically to a method and system for the layout of slope monitoring points throughout the entire construction and operation cycle. The method acquires multi-source monitoring data and constructs a unified data warehouse through cleaning, format conversion, and spatiotemporal registration. Based on this unified data warehouse, a dynamic risk assessment model coupled with the limit equilibrium method and the finite element method is used to identify target risk areas, and key monitoring variables are identified through global sensitivity analysis. Finally, a multi-objective optimization algorithm is used to solve for the Pareto optimal solution set to determine the location and density of monitoring points. This invention achieves adaptive matching of the monitoring network to changes in geological conditions and dynamic engineering needs throughout the entire cycle, improving the targeting of monitoring and the efficiency of resource allocation.

The invention discloses an injection product production process adjusting method and system, and relates to the field of process optimization. The method comprises the following steps: acquiring and analyzing drawing information of a non-labeled plastic product to obtain product information; according to the product information and a preset production process framework, adopting a multi-objective optimization algorithm to generate a process parameter table of the production process; performing global sensitivity analysis on the process parameter table of the production process by adopting a sobol algorithm to obtain a first-order sensitivity index and a total-order sensitivity index of a plurality of process parameters to a product quality index; according to the first-order sensitivity index and the total-order sensitivity index of the multiple process parameters to the product quality index, a debugging strategy of the multiple process parameters is determined, and the debugging strategy comprises a debugging priority, an interaction parameter debugging proportion table and a debugging range; and outputting a debugging strategy of the plurality of process parameters to guide a process engineer to adjust the production process. The problems of long debugging period and high cost of the current non-labeled plastic product production process are solved.

This disclosure relates to the field of reliability technology, specifically to a method for global sensitivity analysis of a radome structure. The method includes: obtaining important sampled samples of the input variables of the radome structure and a sample pool of the distribution parameters of the input variables; obtaining a training set for a Kriging failure model of the radome structure based on the important sampled samples, and training the Kriging failure model using the training set; when it is determined that each sample in the sample pool meets preset conditions, substituting the samples of the distribution parameters into the Kriging failure model to obtain the global sensitivity analysis result of the radome structure. This disclosure can improve the performance and reliability of radome structures.

The invention provides a method and equipment for constructing a battery electrochemical-thermal coupling model, and a medium, and relates to the technical field of batteries. The method comprises the following steps: constructing an initial electrochemical-thermal coupling model of a battery according to input parameters of the battery; performing global sensitivity analysis on the initial electrochemical-thermal coupling model; according to a global sensitivity analysis result, determining high-sensitivity parameters influencing model output under different working conditions; and adjusting the high-sensitivity parameters by using an optimization algorithm, and determining an electrochemical-thermal coupling model after battery optimization according to the adjusted parameters. According to the method, the key parameters influencing the battery performance can be accurately identified, and the prediction precision and reliability of the battery model are improved.

This paper presents a sparse grid polynomial-based orbital game situation analysis method, belonging to the field of aerospace orbital adversarial and intelligent decision analysis technology. Addressing the problems of lack of systematic representation of multi-source uncertainties, low computational efficiency of traditional quantification methods, and difficulty in achieving multi-objective equilibrium optimization in existing high-orbit multi-to-multi dynamic adversarial scenarios, this paper constructs a POMDP multi-to-multi dynamic game model incorporating uncertainties in high-orbit dynamics and control execution, strategy behavior, and communication delay. A surrogate model is established using sparse grid sampling and polynomial chaotic expansion, and global sensitivity analysis is performed. Based on this, a multi-objective optimization model is constructed, and the Pareto optimal policy set is solved. This achieves game strategy generation that balances performance, robustness, and economy, and is applicable to spacecraft strategy optimization and space situation analysis in high-orbit multi-to-multi dynamic adversarial missions.

The invention discloses an expressway maintenance carbon emission metering and accounting method based on deep learning. The method comprises the following steps: 1, data collection and preprocessing: collecting historical data of an expressway maintenance construction area; step 2, global sensitivity analysis based on a neural network: establishing a mapping relation between input parameters and output carbon emission, calculating Sobol indexes or SHAP values of the parameters, quantifying the sensitivity of the parameters to the carbon emission, and identifying key parameters having significant influence on the carbon emission; step 3, constructing a carbon emission metering model: based on a result of sensitivity analysis, selecting key parameters which have obvious influence on carbon emission as input, constructing the carbon emission metering model, and enabling the carbon emission metering model to predict carbon emission under different construction conditions; 4, GAN-driven driving behavior modeling: inputting the generated driving track data into a carbon emission metering model; 5, GNN enhanced dynamic interaction modeling is carried out; and step 6, carrying out time-space Transform real-time data fusion.

The application provides a global sensitivity analysis method for ocean satellite radiation reference transmission, which comprises the following steps: selecting initial parameters which preferably affect the atmospheric top reflectivity from four dimensions; constructing a monthly-grid radiation characteristic data set; performing sensitivity pre-screening on the initial parameters coupled with each ocean satellite band; constructing a radiation transmission simulation scene; performing low-difference sampling on the input variable parameter space; performing global sensitivity analysis on the input variable combination and the atmospheric top reflectivity output data set; adjusting the sampling scale through a double-threshold method; combining the global sensitivity index with the atmospheric top reflectivity index of the initial parameters coupled with each ocean satellite band to calculate the total error of each band reference transmission; and analyzing and judging the uncertainty sources in the ocean satellite radiation reference transmission process. The application can quantitatively identify the main error sources and the contribution of the coupling effect between variables to the atmospheric top reflectivity deviation, and improve the calculation efficiency while ensuring the analysis accuracy.

The invention relates to an index system dynamic construction method and device based on a CNN-Kriging model. The method is applied to a center node of a distributed intelligent spectrum access system comprising the center node and a plurality of distributed edge nodes. Comprising the following steps: preprocessing a data sample reported by a distributed edge node, dividing a preprocessing result into a training set and a test set, and training a CNN-Kriging model; a synthetic data set is generated according to the trained CNN-Kriging model; and performing statistical analysis and index optimization by adopting a global sensitivity analysis method according to the synthetic data set, updating a data sample according to an optimized index system, and updating the index system to obtain an optimized index system oriented to a spectrum access decision. According to the method, the problems of low spectrum efficiency, poor reliability and incapability of self-adaption to a dynamic environment caused by a static and uninterpretable interference management strategy under dense deployment of massive Internet of Things equipment are solved.

The invention discloses a polynomial chaos expansion wind power plant generating capacity prediction method and system fused with an active learning algorithm, and the method comprises the steps: obtaining a small number of parameter samples of a wind power plant through a Latin hypercube sampling method, generating wind rose data representing the statistical characteristics of wind climate through wind direction sector division and wind speed interval joint statistics, and carrying out the prediction of the generating capacity of the wind power plant. Therefore, a probability model of wind speed and wind direction is established, a key wind regime area with the largest contribution to prediction precision is automatically identified and calculated, a polynomial chaos expansion proxy model building module is formed, an active learning module fusing exploration and utilization criteria is introduced, and rapid and accurate prediction of the generating capacity of the wind power plant is achieved. According to the scheme, the simulation cost can be remarkably reduced, the calculation efficiency is greatly improved on the basis of ensuring the prediction precision, the output uncertainty can be effectively quantified, and efficient and reliable technical support is provided for layout optimization, operation evaluation and global sensitivity analysis of the wind power plant.

The invention provides a multi-axis numerical control machine tool precision error distribution method and device and belongs to the technical field of medium precision manufacturing and machine tool design. The method comprises the steps that a machine tool comprehensive error transmission model is established; the machine tool comprehensive error transfer model maps the geometric error of each motion axis of the machine tool into a space contour error of a tool nose point relative to a workpiece based on a multi-body system theory and a homogeneous coordinate transformation principle; performing global sensitivity analysis on the comprehensive error transfer model by adopting a Sobol global sensitivity analysis method, and calculating a total global sensitivity coefficient of each geometric error term to the contour error; by taking the total global sensitivity coefficient as a weight, constructing an optimization objective function, setting a constraint condition, and establishing an error allocation optimization model; solving the error allocation optimization model by adopting a heuristic optimization algorithm to obtain a tolerance allocation value of each geometric error term; according to the method, collaborative optimization of precision and cost is achieved, and scientificity and economical efficiency of machine tool design are remarkably improved.