358 results about "Sensitivity analyses" patented technology

The invention relates to a dynamic risk control method and device, equipment and a storage medium. The method comprises the following steps: collecting and preprocessing multi-source data to obtain a standardized multi-dimensional data matrix; constructing a dynamic risk control model according to the standardized multi-dimensional data matrix, and calculating a risk control model parameter set; performing risk factor sensitivity analysis by using the risk control model parameter set and the standardized multi-dimensional data matrix to obtain a dynamic weight vector; executing iterative risk assessment based on the dynamic weight vector to obtain a comprehensive risk score; constructing a multi-layer threshold structure based on the comprehensive risk score and executing dynamic adjustment to obtain a personalized risk threshold; and according to the comprehensive risk score and the personalized risk threshold, triggering a risk control measure and executing a feedback mechanism to obtain a risk control execution result. The importance of different risk factors can be adjusted in real time, the market environment change is quickly responded, and the exploration capability of the system for unknown risks is enhanced.

The present invention relates to the technical field of heat sink structure design, and in particular to a micro-channel heat sink structure design method based on a multi-objective function. The method comprises the following specific steps: S1, constructing a general mathematical description of a topology optimization problem; S2, acquiring input conditions for a specific design scenario, and dividing a topology optimization design domain; S3, establishing a governing equation for a topology optimization model; S4, on the basis of an adjoint method, performing sensitivity analysis of a multi-objective function with respect to design variables; S5, solving the topology optimization model by means of a method of moving asymptotes (MMA); S6, obtaining a two-dimensional flow channel configuration of a heat sink on the basis of contour reconstruction, carrying out finite element analysis of a two-dimensional flow channel structure, and narrowing down the selection range of constraint conditions; and S7, on the basis of a two-dimensional finite element analysis result, constructing a corresponding pseudo-three-dimensional model, carrying out finite element analysis, and determining optimal constraint conditions to obtain a final topology-optimized heat sink configuration. Compared with traditional methods, the present invention can more efficiently solve the problem in respect of heat sink structure optimization under different scenarios.

The invention relates to the technical field of metallurgical raw material batching control, and discloses a bottom blowing smelting furnace automatic batching intelligent control method and system based on deep learning, and the method comprises the steps: achieving intelligent batching through multi-source data fusion, physical constraint modeling and dynamic optimization control; edge calculation is adopted to realize data space-time alignment and purification, and physical and economic mixed features are constructed; modeling a reaction path based on a graph neural network, and embedding conservation law constraint to synchronously predict key process parameters; and finally, in combination with gradient sensitivity analysis and reinforcement learning, constructing a differentiable optimization framework to realize multi-target dynamic ratio decision and real-time compensation control, and forming a perception-decision-execution closed loop. The system comprises a global sensing and data purification module, an intelligent decision-making and optimization batching module and a high-precision execution and closed-loop control module. According to the invention, the batching strategy is adaptively adjusted, and optimal resource allocation and maximum economic benefit are realized.

The invention relates to the technical field of synchronous reluctance motor structure optimization, in particular to a permanent magnet auxiliary synchronous reluctance motor structure parameter multi-objective optimization method. The method comprises the steps that motor structure optimization parameters are selected, and the change range of the parameters is determined; carrying out comprehensive sensitivity analysis according to the selected optimization target; performing hierarchical processing on the optimization variables according to the comprehensive sensitivity index, dividing the optimization variables into a strong sensitive layer and a weak sensitive layer, and taking parameters in the strong sensitive layer as to-be-optimized parameters of the next algorithm; comparing the prediction precision of the back propagation neural network, the radial basis, the extreme learning machine, the support vector machine and the kernel extreme learning machine, and selecting an agent model with an optimal prediction effect; combining the established high-precision agent model with a fast non-dominated sorting genetic algorithm, and searching an optimal combination of to-be-optimized parameters; and finally, carrying out single parameterization scanning determination on parameters in the weak sensitive layer by utilizing finite element simulation so as to obtain optimal structural parameters of the permanent magnet auxiliary synchronous reluctance motor.

The invention relates to the technical field of artificial intelligence, and discloses an agent rapid construction method and system based on enhanced retrieval, and the method comprises the steps: constructing a multi-dimensional task analysis system, retrieval task characteristics are automatically represented through characteristics such as data distribution, query complexity and semantic requirements; executing a progressive architecture evolution algorithm, and automatically searching an optimal architecture meeting multi-dimensional balance of retrieval precision, calculation complexity and memory occupation based on the task characteristic vector; constructing a hierarchical knowledge distillation transfer chain, and realizing efficient knowledge migration from a large model to a small model through feature matching and attention guidance; implementing mixing precision quantification and structured pruning on different hierarchies based on sensitivity analysis, and adapting to a target deployment environment; according to the method, the model volume is reduced, the reasoning speed is increased, the energy consumption is reduced, and the enhanced retrieval agent can efficiently operate on resource-constrained equipment.

The invention discloses a multi-dimensional data statistics intelligent analysis and prediction system and method, and particularly relates to the technical field of data prediction. Historical sales data, consumer behavior data, market environment data and external dynamic data of a target analysis object are collected, an LSTM basic prediction model is constructed, consumer behavior change trend features and market environment dynamic features are further extracted, the features are fused with the LSTM model to generate a multi-modal prediction model, and the multi-modal prediction model is analyzed. Comprehensively analyzing the interaction influence of the multi-dimensional data by using a multi-input structure; according to the method, influence weights and contribution degrees of different characteristics are evaluated through sensitivity analysis and interaction analysis, model parameter weights are dynamically adjusted, an optimized prediction model is utilized to generate a multi-dimensional prediction result in a fixed time period, and inventory management and a promotion resource allocation strategy are optimized accordingly, so that long-term drift of consumption behaviors is effectively coped with, and the consumption performance is improved. The inventory cost and the resource waste are reduced, and the operation efficiency and the business decision accuracy are improved.

The invention discloses a collaborative planning method for a regional integrated energy system, and the method comprises the steps: firstly collecting regional historical energy consumption data and various external factor data, and predicting an energy demand through employing a gray prediction and multiple linear regression combined algorithm; exploring existing energy supply facilities in the region and evaluating renewable energy potential; determining a candidate type of the integrated energy system component according to the result, establishing an optimal configuration model, and solving through a particle swarm optimization algorithm; constructing an energy network layout planning model, and determining an optimal topological structure and parameters of the network; establishing a dynamic operation model, and formulating a collaborative operation strategy by using a model prediction control algorithm; and finally, comprehensively evaluating the planning scheme, and analyzing and adjusting the scheme by utilizing sensitivity according to an evaluation result. According to the method, multiple factors are comprehensively considered, and the problems that in existing regional integrated energy system planning, demand prediction is not accurate, resource evaluation is not comprehensive, component configuration and network layout lack optimization collaboration, and the operation strategy is single are effectively solved.

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 provides an accident consequence simulation calculation method based on mathematical physical model coupling solution, and the method comprises the steps: constructing an accident chain knowledge graph and a physical trigger graph, and building a causal relationship and physical constraints among equipment, states, events and consequences; gathering and mapping historical records, expert rules and online observation data into map entities and relationships, forming a baseline accident scene and calculating a baseline index; generating candidate paths by utilizing graph reasoning and coupled multi-physics field simulation, and realizing a closed loop of graph reasoning and physical solution through consistency check; performing scoring and disturbance simulation analysis on the candidate paths, and screening robust target paths; and carrying out high-fidelity simulation on the target path, identifying key nodes in combination with sensitivity analysis and a minimum cut-off set, and generating disposal suggestions and action priorities. According to the method, high-credibility prediction of accident evolution and emergency response closed-loop linkage are realized, and the method has high precision, high robustness and engineering implementability.

The invention discloses a large language model knowledge base self-adaptive updating method combining documents and interactive feedback, and relates to the technical field of knowledge base updating, and the method comprises the steps that a real-time change detection module monitors streaming documents and interactive feedback, and captures and marks a single fact change event; the decision module calculates a shunting decision coefficient according to the heat density index and the linkage sweep index, and the event is shunted to a Micro-Edit or Incremental-Tune path; if shunting to the Micro-Edit path, the positioning module executes attention backtracking and gradient sensitivity analysis, and determines a to-be-updated weight address; the rank-one editing module implements rank-one updating and records an audit log; the verification module generates an online token or a rollback instruction through consistency comparison; the version management module manages a version, activates a new version route and cleans out expired branches. Through event granularity identification, intelligent shunting, local updating and multi-version management, the timeliness of knowledge base updating is remarkably improved, and resource consumption and maintenance cost are reduced.

The invention relates to a machining path planning system, in particular to the technical field of intelligent manufacturing and numerical control machining, and overcomes the defect that a thermal coupling effect is ignored in traditional geometric planning by constructing an evolution model fused with a physical law. A dynamic decoupling algorithm is utilized to extract a real cutting load from multi-source data and reconstruct a rigidity field changing along with material removal in real time, the problem that a physical boundary is difficult to perceive under a complex working condition is solved, and the system effectively eliminates size out-of-tolerance caused by heat accumulation through transient deformation advanced prediction. According to the method, the non-linear error is actively counteracted in cooperation with a reverse compensation strategy based on sensitivity analysis, the machining precision and surface quality of the thin-wall weak-rigidity part are remarkably improved, the rejection rate and trial cutting cost are reduced, and intelligent closed-loop control over the manufacturing process is achieved.

The invention relates to the technical field of cost assessment, in particular to a civil construction project cost assessment optimization system, which comprises a feature analysis module, a cost estimation module, a real-time monitoring module, a model training and tuning module, a risk analysis module, a decision support module, a financial simulation module and a reinforcement learning cost adjustment module. According to the invention, a convolutional neural network algorithm and a principal component analysis method in the feature analysis module excavate building information model data and screen key features, and the cost estimation module combines a multi-layer perceptron neural network and a linear regression model to enhance the cost mode analysis capability and calculate the project cost. The digital twinborn technology and the dynamic programming algorithm realize tracking of project progress and real-time monitoring of cost change in the real-time monitoring module, and the genetic algorithm and the machine learning algorithm realize continuous optimization and self-learning of model parameters in the model training and tuning module. And a Monte Carlo simulation method and a sensitivity analysis method are used for identifying and evaluating project risks in the risk analysis module.

The invention relates to the technical field of big data analysis, and discloses a prospecting target area positioning method and system based on big data, and the method comprises the steps: collecting multi-source exploration data in real time through distributed nodes, completing coordinate normalization, semantic alignment and time synchronization through a spatial heterogeneous data flow engine, and generating a standardized incremental data block; performing local feature sensitivity analysis based on the historical model library, identifying a newly added feature dimension, and performing parameter increment updating by adopting a sliding window gradient descent method; inputting the updated model into a target evolution model driven by a Bayesian space-time probability field, and dynamically calculating the metallogenic probability of each space grid in combination with a stress field, an element migration path and historical verification data; and generating high, medium and low three-level target area maps according to probability sorting, and pushing the high, medium and low three-level target area maps to a three-dimensional visual decision terminal. According to the method, minute-level dynamic response of the target region under triggering of newly-added data is realized, computing resource consumption is reduced to be less than 5% of that of an original system, and prospecting efficiency and abnormal region identification timeliness are improved.

The invention discloses a face gear worm grinding machine error compensation method based on sensitivity analysis and a multi-source error mapping model, which comprises the following steps of: 1, constructing a multi-source error-tooth surface error mapping model based on a vector decomposition and truncation function method, the truncation function method is used for quantitatively describing propagation mechanisms of geometric errors and thermal errors in a machining chain and influences of the propagation mechanisms on tooth surface deviation, and high-order error terms are eliminated to improve calculation efficiency; 2, responding to the output of the mapping model, applying an improved Morris sensitivity analysis method, distributing a weighting coefficient higher than a geometric error component for a thermal error component, and calculating a weighting sensitivity coefficient so as to identify a key error component which has the greatest influence on the tooth surface precision; and 3, based on the key error component, a motion axis compensation value is generated through an error compensation model, and the compensation value is input into a machine tool control system to adjust the relative position of the worm grinding wheel and the workpiece, so that the tooth surface error is reduced.

The invention discloses a notch position blade profile tolerance point taking device, system and method, and the method comprises the steps: enabling an actual measurement point cloud to be aligned with a theoretical model through progressive spatial registration, carrying out the initial coarse registration, and dynamically adjusting a root mean square error threshold value and a curvature change rate threshold value based on curvature sensitivity analysis, so as to achieve the self-adaptive segmented interruption control; a three-dimensional Hash mapping engine is constructed to store original point cloud data, an incremental data access mode and an iterative data structure are combined, a historical transformation matrix stack, a B spline control point and an 8-bit state register are included, and data processing integrity and high efficiency are ensured; a genetic algorithm is adopted to optimize segmented B-spline surface fitting, and contours of an air inlet edge and an air outlet edge are synchronously optimized through transverse segmentation and longitudinal segmentation; and finally, composite convergence verification is passed. According to the method, through dynamic threshold regulation and control and a hybrid optimization strategy, the detection precision and the calculation efficiency of the complex curved surface are remarkably improved.

The invention discloses a gear machining multi-source thermal error compensation system based on tensor compression edge deployment, which fuses physically guided multi-source error modeling, tensor-based model compression, edge deployment and bandwidth sensing signal scheduling to realize low-delay and high-precision error compensation. The method comprises the following steps: firstly, establishing a three-layer system architecture comprising a cloud layer, an edge layer and a sensing layer, supporting distributed model training, edge reasoning and error compensation so as to support closed-loop operation for realizing adaptive scheduling with extremely low delay, and mainly comprising: (1) a multi-source error model, thermal and geometric errors are fused into coupling high-order representation for capturing space-time interaction through an analytical method; and (2) establishing a sensitivity analysis model based on a physical guidance tensor mode decomposition method (PG-TMDM), identifying a key error source through a three-order tensor structure, and calculating a physical perception contribution rate to retain physical interpretability. And in order to realize real-time deployment, deploying to the edge layer through model compression, model packaging and an edge deployment strategy.

The invention discloses a parallel computing simulation method for thermal exploitation of a buried pipe well group in a geothermal reservoir, which comprises the following steps of: dispersing a grid volume unit of a buried pipe by adopting a parallel finite element unit method, and simplifying the buried pipe and circulating fluid in the pipe into a one-dimensional linear finite element unit; constructing a plurality of pipeline flow models arranged in an array, and combining the in-pipe one-dimensional linear units and the out-pipe three-dimensional volume units to obtain a well group type pipeline flow geometric model; a parallel finite element unit method is adopted to be combined with a non-isothermal pipeline flow physical field, an underground water seepage physical field and a porous medium heat transfer physical field to conduct multi-physical-field coupling, and a well group type pipeline flow numerical model is obtained; fluid flow and convective heat transfer in the buried pipe and pipe wall heat conduction are solved in a combined mode; and performing factor sensitivity analysis by changing different thermophysical parameters of the rock-soil body and heat exchange characteristic factors of the buried pipe well group. The invention provides a new method for researching fluid flowing and heat transfer behaviors of the buried pipe well group, and provides a new idea for optimal design and efficient simulation of thermal exploitation of the buried pipe well group in a geothermal reservoir.

The invention relates to an active power regulation and control method and system based on boundary constraint, and the method comprises the following steps: constructing a boundary constraint condition model, and achieving the dynamic updating of the boundary constraint condition model through a visual maintenance tool; the load rate of the power grid equipment is monitored in real time, whether the power grid equipment is out of limit is judged according to the scene definition table, working condition classification is performed, and generation of a control strategy is triggered; a control strategy is generated based on sensitivity analysis and resource priority, and a comprehensive index calculation model is adopted to screen and adjust resources according to a strategy constraint definition table; sensitivity analysis obtains a sensitivity coefficient based on real-time topology of power grid equipment in combination with load flow calculation, and the resource priority is determined according to a resource rule definition table; and when a single type of resources cannot meet the regulation and control requirements, generating a comprehensive control strategy containing multi-type resource regulation variables, and executing closed-loop effect verification and strategy iteration through a strategy index definition table.

The invention discloses a matrix decomposition and reconstruction method for a large model reasoning scene, and the method comprises the following steps: 1, carrying out the error statistics of conventional nodes of a network layer in a large model, and screening weight nodes capable of carrying out low-rank decomposition through a sensitivity analysis strategy; step 2, gradually performing low-rank setting, decomposing the weight matrixes in the weight nodes which can be subjected to low-rank decomposition and are obtained by screening in the step 1, and obtaining and recording respective optimal low-rank values of the decomposable weight matrixes; and step 3, performing network reconstruction on the original model based on the decomposable weight matrix and the corresponding low-rank value. The invention further discloses a system for implementing the method and a corresponding application, and the system and the application have a wide application scene.

The invention discloses a distributed power supply-containing power distribution network coordinated scheduling method, which relates to the field of power distribution network coordination, and comprises the steps of S1, multi-flexibility resource refined modeling and interaction characteristic analysis, S2, power distribution network linear load flow calculation and sensitivity analysis, and after the power distribution network linear load flow calculation and sensitivity analysis in S2 are completed, carrying out power distribution network coordinated scheduling. And S3, generating an uncertainty scene based on the generative adversarial network. According to the coordinated dispatching method for the power distribution network containing the distributed power supply, differences of various resources in control modes, response speeds and adjusting capabilities are analyzed through refined modeling, complementary characteristics of the resources are fully utilized by a collaborative optimization model, an overall optimization effect is achieved, a single type of resources are not optimized in an isolated mode, and the method is suitable for large-scale popularization and application. All adjustable resources such as a distributed power supply, an energy storage system, a reactive power compensation device, a flexible load and an intelligent soft switch are incorporated into a unified modeling and optimizing framework, and deep fusion and collaborative interaction of flexible resources can be realized.

The invention discloses a carbon dioxide geological sequestration quantity prediction method, system and device and a storage medium, and the method comprises the steps: obtaining a mathematical expression of carbon dioxide sequestration capacity through combining a coupling model of a flow field, a mechanical field and a thermal field with random parameters, and introducing a leakage risk coefficient; taking the weighted sum of the expected value of the storage capacity and the variance as an optimization objective function, and according to the mathematical expression of the carbon dioxide storage capacity, carrying out optimal solution solving on the carbon dioxide storage capacity to obtain an optimal storage strategy; performing spatial discretization on the control equation by adopting a finite element method, discretizing time by adopting a semi-implicit difference method, discretizing random variables by combining a random Galkin method, and solving a coupling model of a flow field, a mechanical field and a thermal field to obtain a prediction result of the carbon dioxide sequestration amount; and according to a prediction result of the carbon dioxide sequestration amount, outputting an expected value and a variance of the sequestration capacity, and generating a sequestration amount distribution diagram, a risk assessment diagram and a sensitivity analysis diagram.

The invention provides a sewage treatment process modeling control optimization method based on mechanism-data dual drive, and the method comprises the steps: collecting basic data of a sewage treatment process, constructing an ASM2d model of a sewage treatment plant, carrying out the sensitivity analysis and parameter calibration of ASM2d model parameters, constructing an environment and a data set, screening input features according to the data set, and carrying out the modeling control optimization of the sewage treatment process. A training set and a test set are divided according to a proportion, an ASM2d-LSTM mixed water quality prediction model is established based on an ASM2d model, the ASM2d-LSTM mixed water quality prediction model is trained based on the training set, an MPC control framework is integrated on the basis of the trained ASM2d-LSTM mixed water quality prediction model, an optimized operation sequence is generated by using the model, and the operation sequence is optimized. And evaluating the control effect of the optimization strategy by taking the collected real control parameters as a reference. According to the method, through a series of programs such as ASM2d-LSTM sewage quality prediction model construction based on mechanism-data dual drive, MPC control based on energy consumption and carbon emission reduction target optimization, key index online optimization strategy output in the sewage treatment process is realized.

The invention discloses a multi-objective optimization method for improving the comprehensive performance of a hydrostatic guideway, and the method comprises the steps: 1, carrying out the theoretical analysis and calculation of the performance indexes of the hydrostatic guideway based on a hydrostatic bearing theory, obtaining a mathematical model of each performance index, and determining design parameters which affect the working performance of the hydrostatic guideway; step 2, on the basis of a Sobol sensitivity analysis method, obtaining sensitivity indexes of each design parameter on the comprehensive performance of the hydrostatic guideway, determining key design parameters with relatively great influence, and analyzing an influence rule of each key design parameter on the performance of the guideway; and step 3, based on an NSGA-II multi-objective optimization algorithm, constructing a hydrostatic guideway comprehensive performance multi-objective optimization model, and obtaining a Pareto optimal solution set. And step 4, based on an AHP-TOPSIS method, selecting an optimal optimization scheme of the hydrostatic guideway in a Pareto optimal solution set obtained through a targeted optimization algorithm.

The invention is suitable for the technical field of permanent magnet synchronous motor performance improvement, and provides a motor multi-objective optimization method and system based on an improved dung beetle optimization algorithm, and the method comprises the steps: building a mathematical model of the torque ripple and dynamic response capability of a motor, and determining initial design parameters which affect the torque ripple and dynamic response capability; according to the simulation data of the stator skewed slot of the motor, determining an optimal angle meeting a first constraint condition so as to establish a motor model; based on the motor model, motor structure parameters influencing the initial design parameter sensitivity are analyzed, and the obtained motor structure parameters include the stator outer diameter, the permanent magnet thickness, the tooth width and the air gap length; according to the invention, research is carried out from the angle of a stator skewed slot, and an optimal angle with large torque pulsation reduction and small dynamic response capability reduction is obtained; the initial design parameters are subjected to sensitivity analysis to obtain structure parameters, algorithm optimization is carried out after fitting, and multi-objective optimization of the motor is achieved.

The invention discloses a construction method of a synthetic rock mass digital model containing a three-dimensional master control fracture, and belongs to the technical field of rock mass numerical simulation. The method comprises the following steps: collecting a complete rock mass and a crack-containing rock sample on site, and preparing a coal rock sample; reconstructing a rock core through CT (Computed Tomography) scanning and Avizo software, and segmenting a mineral matrix and fractures; based on a Fisher statistical distribution method, performing standardization processing on the orientation characteristic parameters of the three-dimensional fracture of the rock; carrying out grouping and dominance analysis on the fractures; the method comprises the following steps: carrying out coal sample fracture analogue simulation by adopting a DFN in software 3DEC, and generating a DFN-Voronoi block model; a DFN-Voronoi SRM model is manufactured; carrying out a compression experiment on the model to monitor stress and strain; and carrying out DFN-Voronoi SRM model parameter sensitivity analysis based on a single-factor experimental method, and proposing a parameter calibration method. According to the method, the mechanical property and the dominant fracture group of the rock sample are considered, and a parameter acquisition and calibration method of the model is completely given in the aspect of model calibration; fracture network construction and model synthesis are optimized, and DFN-Voronoi SRM model parameter sensitivity analysis and calibration are summarized.

The invention discloses a ship sensor measuring point optimization method and system, and relates to the technical field of sensor measuring point optimization, and the method comprises the steps: generating a fractal hierarchical weight matrix through a fractal geometric model based on pre-obtained ship navigation working condition data, and carrying out the load sensitivity analysis through the combination of pre-obtained sea area observation data, obtaining a dynamic load sensitive matrix; a morphological load coupling weight matrix is obtained, a preset ship body layout node set is combined, a layout optimization algorithm is utilized to carry out sensor measuring point layout of guide type search, and candidate measuring point layout is obtained; and carrying out time domain dynamics simulation by using the simulation model, carrying out ship whole-field structure state reconstruction in combination with a state inversion method, and optimizing the candidate measuring point layout to obtain an optimized measuring point layout. The spatial-temporal variability of external excitation is fully considered, and the problem of high-rigidity low-response misjudgment possibly caused by a traditional method only depending on structural rigidity or mass distribution is avoided.

The invention provides a method for correcting a force thermal vibration finite element model of a reinforced thin-wall cylinder, and belongs to the technical field of vibration analysis of thin-wall cylinder structures. According to the method, firstly, simulation and test modal frequencies under different working conditions are obtained by carrying out simulation and test modal analysis on the reinforced thin-wall cylinder at different temperatures and in different axial pressure ranges; secondly, defining an objective function, a decision variable and a feasible region of an optimization problem on the basis of relative errors of modal frequencies under the same vibration mode simulation and test conditions, and performing optimization analysis on key characteristic parameters influencing the modal frequencies of the structure by using sensitivity analysis, an agent model and a sparrow search algorithm; and finally, based on the optimized key characteristic parameters, establishing a force thermal vibration finite element model of the reinforced thin-wall cylinder meeting the target precision requirement. According to the method, the problem of insufficient precision of a structural finite element model correction method under the complex working condition can be solved, and high-precision simulation analysis of the hypersonic aircraft under the complex working condition is realized.

The invention provides a low-bit large model quantification method based on sensitivity analysis and abnormal value processing, and the method comprises the steps: dividing a model layer into a high-sensitivity layer, a medium-sensitivity layer and a low-sensitivity layer through employing a hierarchical mixing precision quantification strategy based on sensitivity analysis, and executing different quantification operations; carrying out weight abnormal value suppression and weight layer-by-layer quantization execution by using a quantization method based on two-stage combination; and carrying out activation outlier offline smoothing processing based on equivalent transformation. According to the method, key weights sensitive to quantization errors are identified based on a hierarchical mixed precision quantization strategy of sensitivity analysis, so that a differentiation strategy is adopted in the quantization process, the precision of high-sensitivity weights is reserved, and more aggressive quantization is performed on low-sensitivity weights to minimize model performance loss; the two-stage weight quantization collaboratively solves the problem of abnormal value and error accumulation: the quantization difficulty is migrated from activation to weight offline, the friendliness of activation quantization is improved, outliers do not need to be dynamically detected during operation, and the reasoning speed is prevented from being influenced.

The invention relates to a vibration suppression-oriented electromagnetic actuator data driving collaborative optimization design method, and belongs to the technical field of electromechanical equipment optimization design. Sample data are collected through Box-Behnken experimental design, a Gaussian process regression agent model is constructed based on the data, and a nonlinear complex mapping relation between each objective function and a design parameter is accurately represented. The influence degree of the design parameters is quantitatively evaluated through sensitivity analysis, the main design variables and the secondary design variables are distinguished accordingly, and the optimization efficiency is improved. Multi-objective optimization is carried out for the main design variables, and a design scheme with the optimal comprehensive performance is selected from a Pareto solution set through an objective decision-making mechanism based on an ideal point method in combination with the optimization result of the secondary design variables. According to the method, limitation of a traditional physical model is broken through through data-driven modeling, global optimization balance of electromagnetic performance, loss and volume is realized, and an efficient and quantifiable evaluation method is provided for design of the high-performance electromagnetic actuator.

The invention discloses a lightweight method, device and equipment for a large government affair model and a storage medium, and relates to the field of artificial intelligence model compression, and the method comprises the steps: determining metadata of the large government affair model, constructing a semantic incidence matrix based on a preset government affair field knowledge graph, and determining target information based on the metadata and the semantic incidence matrix; performing credibility evaluation on weight parameters of the government affair large model based on the target information, and determining a pruning strategy based on an obtained evaluation result; performing sensitivity analysis on the hierarchy of the government affair large model based on the target information and the weight parameter to obtain an analysis result, determining a quantization interval of the weight parameter, and determining a quantization strategy by using the analysis result and the quantization interval; and lightening the government affair large model by using the pruning strategy and the quantification strategy to obtain a corresponding target lightweight model. Therefore, the lightweight effect of the large government affair model can be improved.