176 results about "Global sensitivity analysis" patented technology

The invention belongs to the field of intelligent construction, and discloses a deep foundation pit displacement prediction method combining finite element and data driving, which comprises the following steps: calculating a theoretical elastic modulus according to an investigation report, and constructing a sample space by adopting a Latin hypercube sampling method and an orthogonal experiment design method; a proper constitutive model is selected, and corresponding lateral displacement is calculated through finite elements; dimensionality reduction processing is carried out on the data set based on global sensitivity analysis of variance so as to accelerate convergence during subsequent inversion; training an SKRR-LSBOOST model through an ensemble learning method to replace a traditional model; combining an improved Hemma optimization algorithm with a substitution model, and applying the combined model to parameter inversion; and performing forward analysis based on the finite element model defined by the inversion parameters to predict the deformation of the deep foundation pit in the next excavation stage. According to the method, the advantages of numerical simulation and data driving are fully exerted, the calculation cost is reduced according to the rock and soil parameters, higher prediction precision is achieved, and the accuracy of foundation pit deformation prediction is improved.

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 relates to the technical field of intelligent building environment control and energy management, in particular to a thermal comfort regulation and control method and device based on visual perception and global sensitivity analysis, and constructs an attribute recognition model based on collaborative optimization of a lightweight visual perception network and an attention mechanism. High-precision dynamic capture of human body attributes is realized through the model; human body metabolic rate information is calculated based on the recognized human body attributes, then a thermal comfort model based on global sensitivity analysis and a hybrid intelligent algorithm is constructed, human body thermal comfort is calculated through the model, and finally working parameters of an air conditioner or a heat pump are adjusted based on the human body thermal comfort. The contradiction between environment parameter real-time response and energy consumption optimization is effectively solved, and an innovative solution is provided for building energy conservation and personalized thermal comfort management.

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 discloses a slope anchor cable support optimization design method based on global sensitivity analysis, and belongs to the technical field of engineering geology. The method comprises the following steps: screening parameters by using a local sensitivity analysis method to determine parameter subsets; secondly, in the anchor cable anchoring force loss calculation model, verifying and calibrating the parallel connection mode of the anchor cable body and the West original rock-soil body rheological model, designing a plurality of sensitivity analysis schemes, and constructing a joint probability density function of uncertainty parameters; and finally, establishing an anchor cable layout and parameter optimization model by taking maximum slope stability reliability as an optimization target and considering constraint conditions such as engineering cost and construction difficulty, and formulating corresponding anchor cable anchoring force compensation measures according to changes of key uncertainty parameters. According to the scheme, the stability risks of the side slope under different working conditions are evaluated more accurately, the engineering cost is effectively reduced and the construction difficulty is reduced on the premise of guaranteeing the stability of the side slope, and the stability of the side slope is jointly improved by designing all links from design to construction.

The invention discloses a full-flow intelligent regulation and control method and equipment for sewage treatment and a medium, and relates to the technical field of sewage treatment. The method comprises the following steps: acquiring basic data of a sewage treatment plant to be regulated and controlled, and constructing a corresponding sewage treatment model based on the basic data; based on the sewage treatment model, determining input parameters and output indexes in the sewage treatment process; respectively disturbing each input parameter by utilizing a preset global sensitivity analysis method, determining key input parameters of which the sensitivity indexes are greater than a preset threshold value, and constructing a lag disturbance model of each key input parameter; monitoring data in the sewage treatment process are collected in real time, and real-time lag effluent quality prediction is carried out according to the monitoring data and the lag disturbance model; and judging whether the real-time lagging water quality prediction data exceeds an effluent water quality limit value or not, and performing control parameter dynamic optimization and regulation on the sewage treatment model based on a preset multi-target optimization algorithm under the condition that the real-time lagging water quality prediction data exceeds the effluent water quality limit value.

The invention relates to the technical field of power distribution networks, in particular to a power distribution network resource self-adaptive scheduling method, system and device and a medium. Using a support vector machine algorithm to construct an agent model representing a nonlinear coupling relationship between the novel power factor real-time parameters and the initial data set of the power distribution network; decomposing a multivariable interaction effect in the agent model by using a global sensitivity analysis method to obtain a sensitivity index; adaptively correcting the sampling space of the agent model according to the sensitivity index to obtain an optimized agent model; and on the basis of the optimization agent model and the real-time monitoring data of the power distribution network, generating a power distribution network resource adaptive scheduling strategy by adopting a particle swarm optimization algorithm, thereby carrying out optimal configuration on the power distribution network resources in the novel power factor access scene. According to the method, efficient and accurate optimal configuration of the power distribution network resources in a novel power element access scene is realized, and the operation performance and stability of the power distribution network are remarkably improved.

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 discloses a kinematics parameter identification method and system for a six-degree-of-freedom heavy-load industrial mechanical arm, and the method comprises the steps: adding rotation parameters to parallel mechanical arm joints of a six-axis robot based on a Modified D-H method, constructing a mechanical arm forward kinematics model, and obtaining a matrix conversion relational expression of the mechanical arm; a position error model of the tail end of the mechanical arm is constructed, and error parameters to be identified are obtained; on the basis of the position error model of the tail end of the mechanical arm, global sensitivity analysis is conducted on the to-be-identified error parameters, and global sensitivity values of the to-be-identified parameters are obtained; and performing two-stage identification processing on the error parameter to be identified to obtain a mechanical arm kinematics parameter identification result. According to the method, key parameters can be accurately screened out, waste of computing resources on non-key parameters is avoided, and the parameter identification efficiency and precision are improved. The kinematic parameter identification method and system for the six-degree-of-freedom heavy-load industrial mechanical arm can be widely applied to the technical field of robot parameter identification.

The invention belongs to the technical field of remote sensing application, and provides a grassland biomass inversion method based on a radiation transfer model and machine learning, and the method comprises the following steps: obtaining and processing remote sensing data, DEM data and actually measured biomass data of a target region; performing terrain correction by adopting an SCS + C method; performing global sensitivity analysis on input parameters in the PROSAIL model, and calibrating the input parameters according to the global sensitivity analysis; introducing topographic factors into the model, and correcting the solar zenith angle and the observation zenith angle; generating simulated canopy reflectivity data by using the model, and constructing a data set; constructing a lookup table LUT by combining the simulated canopy reflectivity data and the above-ground biomass data, and performing machine learning modeling according to the canopy spectral data set; and verifying the inversion precision, and outputting a grassland above-ground biomass spatial distribution map. According to the inversion method, terrain factors are introduced on the basis of a traditional model, and the adaptability and estimation precision of the model to a complex terrain area are improved by combining a machine learning method.

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 mixed tower steel cable pre-tightening force real-time monitoring method and system, and relates to the technical field of wind power generation. The method comprises the following steps: acquiring known dynamic characteristic data of the mixed tower steel cable under multiple working conditions and multiple structures; analyzing and screening key parameters which effectively represent the dynamic response, the system structure and the working condition load of the mixed tower steel cable system in the dynamic characteristic data by utilizing global sensitivity, and constructing a dynamic characteristic database according to the key parameters; constructing a structure-working condition-response relation neural network agent model by means of a neural network or a regression prediction method based on the dynamic characteristic database; and performing reverse calculation on real-time working condition data and motion response data sensed by the sensor by using the structure-working condition-response relation neural network agent model to obtain the pre-tightening force of the mixed tower steel cable. According to the invention, the pre-tightening force of the steel cable of the mixed tower can be accurately monitored in real time, and the pre-tightening force prediction of the mixed tower under the extreme working condition and the fault working condition is realized.

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 is suitable for the technical field of machine tool precision design, and provides a five-axis numerical control machine tool complete machine space error sensitivity analysis method, which comprises the following steps: abstracting a numerical control machine tool into a multi-body system based on a multi-body system theory, describing the incidence relation between the structure of the machine tool and each individual by using a topological structure chart and a low-sequence body array list, and calculating the overall space error sensitivity of the five-axis numerical control machine tool. The geometric error of the numerical control machine tool is analyzed, a generalized coordinate system is established, the position relation is expressed by using the characteristic matrix between adjacent bodies, the mutual relation between multiple body systems is expressed by using a homogeneous transformation matrix, and a space error model of the machine tool is established; and in combination with a space error model of the machine tool, an improved Sobol global sensitivity analysis method is provided, a first-order sensitivity coefficient and a global sensitivity coefficient are solved, and key geometric errors of the numerical control machine tool are analyzed. By establishing the space error model of the numerical control machine tool and the improved Sobol global error sensitivity analysis model, a new design and improvement concept of the numerical control machine tool is provided, and the problem of machining precision of the numerical control machine tool is solved.

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 relates to an optimal design method of a wind power controllable magnetic gearbox, and belongs to the field of wind power. The controllable magnetic gear box comprises an inner rotor, an outer rotor, a magnetism adjusting ring and a stator, an inner rotor average torque and torque pulsation, a stator no-load back electromotive force amplitude and a distortion rate are selected as optimization targets, ten parameters such as inner and outer rotor iron core thicknesses, a permanent magnet thickness, a magnetic adjusting ring thickness, a stator iron core outer radius and a stator winding turn number are selected as design variables to be optimized, an LHS experiment is carried out, and a BPNN proxy model is established and trained; performing Sobol 'global sensitivity analysis on the trained agent model to screen out key design variables to be optimized; optimizing the key design variables to be optimized by adopting NSGA-II to obtain an optimal solution set; and the optimal solution set is evaluated by adopting TOPSIS, and a group of optimal design variables are accurately selected, so that the optimization target is optimal. According to the method, the optimization precision and efficiency can be remarkably improved, and the purposes of reducing the cost and improving the performance are achieved.

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 discloses a PCE-HDMR proxy model-based jacket platform global sensitivity analysis method. The method comprises the following steps of S1, constructing a first-order nonlinear component function by adopting a polynomial chaos expansion model; s2, selecting a new input sample by adopting an adaptive sampling algorithm, and judging whether a first-order convergence condition is met or not; s3, judging whether a second-order nonlinear component function exists or not; s4, constructing a second-order nonlinear component function by adopting a polynomial chaos expansion model; s5, selecting a new input sample by adopting an adaptive sampling algorithm, and judging whether a second-order convergence condition is met or not; s6, synthesizing a polynomial chaos expansion-high-dimensional model to represent the proxy model; and S7, deriving a calculation formula for calculating a Sobol global sensitivity index based on the PCE-HDMR proxy model, and carrying out global sensitivity analysis. According to the method, the PCE-HDMR proxy model can be accurately and efficiently established according to the finite element response data of the jacket platform, and global sensitivity analysis of the jacket platform is realized.

The invention relates to the technical field of geometric error analysis of five-axis turning and milling combined machining centers, and discloses a geometric error global sensitivity analysis method of a five-axis turning and milling combined machining center, which comprises the following steps of: modeling a machining center into an open multi-rigid body system, and determining the relationship among bodies by adopting a low-order body array; determining the position of each coordinate system; establishing a kinematic model of the five-axis turning and milling combined machining center; determining a machine tool error item; establishing a geometric error model in a milling mode; establishing a geometric error model in a turning mode; and carrying out global sensitivity analysis on the geometric error of the machining center. The method improves the sensitivity identification efficiency of error terms in all directions, reduces the calculation cost of global sensitivity measurement based on variance, can provide theoretical guidance for engineers to purposefully design and assemble a machine tool, reduces geometric errors, improves the precision, can compensate for high-sensitivity geometric errors according to the global sensitivity measurement coefficient, and improves the accuracy of the machine tool design and assembly. And effective support is provided for error control.

The invention relates to the technical field of lithium battery state analysis, and discloses a method for estimating cyclic stress and stress reliability of a silicon-carbon lithium battery. The method comprises the following steps: firstly, constructing a thermal model, an electrochemical model and a mechanical model of the silicon-carbon lithium battery, and calculating a cell stress peak value by comprehensively considering cell structure parameters, circulating working conditions and a temperature rise effect; secondly, through global sensitivity analysis, parameter uncertainty modeling and probability density function estimation, the influence of high-sensitivity parameters on stress response is evaluated, and the stress reliability of the battery under the uncertain condition is quantified; compared with the prior art, the method integrates multi-physical field analysis modeling and cyclic stress uncertainty analysis, has the advantages of convenience in parameter sample acquisition, low calculation cost, high robustness and the like, and is suitable for various application scenes such as battery design optimization, online state evaluation, health management and the like.

The invention provides a mechanical product assembly performance simulation uncertainty analysis method. According to the method, the influence of each uncertainty influence factor on the assembly performance in different links of the simulation process is considered, the uncertainty influence factors in the simulation process are divided into two types, and quantitative analysis of uncertainty is carried out respectively. The first class comprises a measurement link, a modeling link and a simulation analysis link in the early stage of simulation, and the influence factors of the class can be used for directly calculating the uncertainty u or error; the second type of influence factors are external loads, boundary conditions and assembly performance uncertainty caused by non-linear links caused by process parameters mainly including geometric error distribution, and for the type of influence factors, the method combines optimal SVR proxy model construction and Sobol global sensitivity analysis on the basis of a simulation analysis result of a physical digital twinborn model, so that the assembly performance uncertainty caused by the non-linear links caused by the process parameters mainly including the external loads, the boundary conditions and the process parameters mainly including the geometric error distribution is obtained. And quantitatively analyzing the sensitivity of the assembly performance to the process parameters. In conclusion, the method provides guidance for the product assembly process.

The invention relates to the technical field of nuclear reactor safety analysis, and provides a reactor safety analysis model construction method, device and equipment and a storage medium, and the method comprises the steps: taking a target parameter related to a transient working condition of a reactor as a target output, and recognizing a plurality of input parameters corresponding to the target parameter by constructing a phenomenon recognition sorting table, a plurality of important parameters are screened out from a plurality of input parameters by using a moment independent global sensitivity analysis method, target input corresponding to the target parameters is constructed after the plurality of important parameters are randomly sampled for multiple times, and then a neural network model is trained by using a sample set constructed according to the target input and the target output; and obtaining a reactor safety analysis model. According to the method provided by the invention, the high-precision construction of the nuclear reactor multi-field coupling system transient safety analysis substitution model can be realized, and the constructed reactor safety analysis model can obtain relatively high precision with a relatively small sample, so that the method is used for nuclear reactor fault diagnosis and accident prediction.

The invention relates to the technical field of impeller design optimization, in particular to a fuel cell compressor impeller design optimization method, which comprises the following steps of: firstly constructing a parameter system of compressor impeller design, defining a design variable set and generating an impeller geometric model through parametric modeling; establishing a multi-objective function system, coordinating aerodynamic efficiency, structural strength and quality objective conflicts, and setting manufacturing and safety constraint conditions; then, key design variables are screened through global sensitivity analysis, and the space is optimized in a dimensionality reduction mode; a Pareto optimal solution set is generated by adopting a gradient enhanced NSGA-II algorithm, and convergence is accelerated in combination with gradient information; and finally, verifying the candidate solution through a multi-level verification system, and correcting the design space. According to the method, the design efficiency and performance of the fuel cell compressor impeller can be effectively improved, the method has wide application prospects in high-speed impeller mechanical design in the fields of aviation, automobiles and the like, and cross-platform multiplexing design criteria and empirical formula libraries are supported.

The invention relates to a low-permeability reservoir crack initiation expansion main control factor prediction method comprising the following steps: S1, collecting geological, well drilling, well logging and seismic data of a target low-permeability reservoir, carrying out data analysis, correction and synthesis, analyzing the change rule of lithology and physical property parameters, and determining the change rule of the lithology and physical property parameters; establishing a three-dimensional geologic model of the target low-permeability reservoir based on the analysis result; s2, determining rock mechanical parameters of the target low-permeability reservoir based on a sample experiment method; s3, performing stress field analysis based on the three-dimensional geological model and the rock mechanical parameters, simulating regional stress field distribution by applying a finite element numerical simulation method, and analyzing a simulation result; s4, on the basis of stress analysis, crack generation and expansion simulation is carried out by adopting an expansion finite element method; and S5, executing global sensitivity analysis, and determining the influence of the input parameters of the three-dimensional geologic model on the crack generation and expansion simulation result, so as to identify dominant factors for controlling crack formation.

The invention discloses a tire three-way force prediction method based on a piezoelectric type intelligent tire technology and a BKA-GPR. A finite element model of a tire and a PVDF (polyvinylidene fluoride) sensor is constructed, and a piezoelectric type intelligent tire model is established in combination with an Inp fusion method; through global sensitivity analysis, sensitivity of changes of a longitudinal force (Fx), a lateral force (Fy) and a vertical force (Fz) to voltage output of the PVDF sensor at different positions in the tire is evaluated, and the optimal installation position of the sensor is determined. A Sobol method is used for collecting a voltage-tire force data set, and extraction and normalization processing are carried out on signals of the ground mark area; the method comprises the following steps of: designing a tire force Gaussian process regression (GPR) model according to data characteristics, and optimizing hyper-parameters of the model through a black-wing kite algorithm (BKA); the optimal input features and the data scale are determined through a random forest (RF) algorithm, and a tire three-way force prediction model which is low in cost, high in precision and good in stability and generalization performance can be obtained by retraining the 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.