216 results about "Model fitting" patented technology

The present disclosure provides a model training method and device, and a data processing method and device. The method of the present disclosure uses a reference model to screen an existing instruction dataset for an instruction whose model fitting difficulty meets a preset condition, and the method can screen the instruction dataset for a challenging instruction having high model fitting difficulty as a seed instruction. A plurality of similar instruction samples are generated by means of expansion on the basis of the seed instruction, thereby obtaining more challenging instruction samples, a training set comprising the instruction samples and reference responses for the instruction samples is constructed, and knowledge distillation can be achieved by using the reference model on the basis of the existing instruction dataset, thereby obtaining a training set containing higher-quality instruction data. Furthermore, using the training set to train a deep learning model enhances the capability of the deep learning model to handle more complex and challenging tasks, and improves the performance of a trained target model.

The invention discloses a degraded cultivated land soil organic matter three-dimensional mapping method fusing remote sensing, near-earth sensing and historical data, and relates to the technical field of digital soil mapping, the method is based on an INLA-SPDE three-dimensional model framework, remote sensing, near-earth sensing and historical data are fused, and high-precision three-dimensional mapping is achieved. The method comprises the following steps: collecting organic matter data and near-sensing visible near-infrared reflection spectrum data of different depths of a soil profile at each sampling point in a research area, then screening visible near-infrared reflection spectrum characteristic wavebands significantly related to organic matters, collecting and integrating historical data, and completing standardization processing on remote sensing and near-earth sensing data; dividing a modeling set and a verification set, calculating a range value based on a semi-variable function, determining triangular mesh parameters, and constructing an SPDE model; the near sensing data and the remote sensing data serve as covariables and are fused into the INLA-SPDE three-dimensional model, and a soil organic matter three-dimensional space distribution diagram is generated after model fitting and uncertainty is quantified. According to the method, the problem that the vertical change of the soil attribute cannot be accurately simulated by a traditional method is solved, and high-precision and high-efficiency three-dimensional mapping of the regional scale soil organic matter in the horizontal and vertical dimensions is realized.

The invention relates to the technical field of data processing, and particularly discloses a data analysis method and device fusing a large number rule, equipment and a medium, and the method comprises the steps: determining a minimum convergence amount through hierarchical aggregation; calculating a cumulative sample mean trajectory and carrying out convergence diagnosis; performing noise attenuation weighting based on LLN convergence characteristics; performing distribution and component decomposition under steady moment constraint; performing consistency check and re-extraction robustness of LLN guidance; lLN-constrained model fitting and uncertainty calibration output are carried out; according to the method, based on noise attenuation weighting of LLN convergence characteristics, samples which are close to a steady state obtain greater influence in estimation, and unstable or sparse samples are naturally weakened, so that self-adaptive suppression of heterogeneity and small sample noise is realized, and the robustness of overall estimation is improved; steady moment constraint is introduced into distribution / component decomposition, it can be guaranteed that components obtained through decomposition are consistent with observation convergence characteristics in the aspect of high-order statistics, and component mismatching caused by extreme values or local fluctuation is reduced.

The invention relates to a quadruped robot laser odometer method based on ground point optimization, hardware and application, and the method comprises the steps: obtaining an original point cloud collected by a laser radar, carrying out the preprocessing, dividing a polar coordinate region, jointly extracting ground plane feature points and ground geometric edge feature points based on normal vector and local curvature features, and carrying out the calculation of the ground plane feature points and the ground geometric edge feature points; ground geometric edge feature points are utilized to optimize ground model fitting; optimizing a point set of the ground based on a time sequence reference plane of the ground; the optimized ground points are removed, and non-ground point features are extracted from the remaining point clouds; establishing feature association between the feature points in the current frame and the target point cloud, and screening effective matching pairs; a cascade weight optimization objective function associated with the ground point plane constraint, the non-ground point line feature constraint and the surface feature constraint is constructed, and continuous pose transformation of the quadruped robot is solved; hardware is realized based on the method, and the method is applied to real-time pose estimation and environment mapping of the quadruped robot in outdoor unstructured terrains and complex dynamic environments.

The invention discloses a numerical control machine tool autonomous tool setting method based on 3D visual guidance. The method comprises the following steps: firstly, acquiring three-dimensional point cloud data of a cutter and a workpiece by using a 3D visual sensor and preprocessing; then, segmenting the point clouds through a region growing algorithm, realizing the separation of the tool point clouds and the workpiece point clouds, and extracting the point clouds of each cylindrical section of the workpiece; thirdly, based on a cylinder fitting algorithm, cylinder model fitting is conducted on the segmented tool and workpiece point cloud, and geometric parameters such as the axis direction, the radius and the center coordinate are obtained; calculating a relative position relation between the tool and the workpiece according to the geometric parameters, generating a tool setting path and converting the tool setting path into an NC code; and finally, the NC code is transmitted to a grinding machine control system through a grinding machine communication module, and tool setting operation is completed. The automatic tool setting device has the advantages that the relative position relation between the workpiece and the tool can be automatically measured in the machining process, automatic and rapid tool setting is completed, and the machining quality and efficiency are guaranteed.

The invention relates to the technical field of data processing, and discloses a hydrocephalus correlation analysis method and system based on multi-dimensional data. The method comprises the steps that a feature matrix is generated by obtaining and standardizing hydrocephalus multi-dimensional data, after the matrix is discretized, an association rule mining algorithm is adopted to extract an association mode, a rule set is obtained in combination with knowledge graph verification, data clusters are divided based on mahalanobis distance clustering, mixed effect time sequence model fitting parameters are established for all the clusters, and a mixed effect time sequence model is obtained. And calculating a target sample attribution cluster and predicting a symptom improvement trajectory and a confidence interval. According to the method, automatic integration, credible association rule extraction, precise subtype division and individualized symptom trajectory prediction of the multi-dimensional data of the hydrocephalus patient are realized.

The invention discloses a power distribution network area low voltage prediction method, and relates to the technical field of voltage prediction, and the method comprises the steps: S1, data preprocessing; s2, establishing a GA-BP neural network load prediction model; s3, GA-BP load prediction model simulation is carried out; s4, establishing an LSTM neural network voltage prediction model; s5, performing model simulation and result analysis; and S6, voltage prediction and treatment suggestion. According to the power distribution area low voltage prediction method, firstly, a GA-BP neural network is utilized to predict area loads, influence factors such as temperature, humidity, date types and different moments in one day are fully considered, and predicted load data are utilized as input to construct an LSTM model to predict area voltages; the transformer area low voltage is effectively predicted through the GA-BP-LSTM combined model, the model is high in fitting degree and low in prediction error, the low voltage degree of a user is reflected in real time, and more information support is provided for follow-up transformer area low voltage treatment.

The invention discloses an unmanned aerial vehicle autonomous information perception path optimization method fusing sparse Gaussian estimation and RLSAC, and particularly relates to the technical field of unmanned system multi-target tracking, and the method comprises the steps: employing Kalman filtering to carry out the optimization processing of target initial data obtained by a ground laser radar, achieving the filtering of the noise of a target, obtaining the dynamic trajectory of the target, and obtaining the optimal path of the target. Therefore, the output insufficiency of the RLSAC to the dynamic trajectory is made up. The data after Kalman filtering processing is used as original input data of a multi-unmanned aerial vehicle target tracking I PP algorithm based on information path planning, the own unmanned aerial vehicle adopts the I PP algorithm to track a target in real time and transmit target related data to provide input data for RLSAC, the RLSAC is used to process the input data, model fitting is carried out, and the RLSAC is used to carry out target tracking. And the RLSAC outputs rewards of different geometric morphology models adopted by the own unmanned aerial vehicle during mode recognition, and the model which is more consistent with the output of the RLSAC is determined by calculating the confidence coefficient.

Methods, systems, and computer storage media for providing iterative data processing optimization using an iterative data processing optimization engine in a data intelligence system are described. Iterative data processing refers to handling data where the processing steps are repeated multiple times, across multiple views or modalities, to train machine learning models, filter and score data or generate output. The iterative data processing optimization engine employs expectation step machine learning models that are simple but with fast language models to efficiently and effectively probe and analyze data, while iteratively refining maximization step machine learning models that are optimized and fast to approximate the probing mechanism of the expectation step machine learning models more efficiently, for example, using metadata, external information, and compressed representation. The iterative data processing optimization engine can operate based on an agentic framework using lightweight artificial intelligence (AI) agents to perform model fitting, featurization, and report generation autonomously.

The invention discloses a multi-modal depth sensing and grabbing system based on a transparent object, which relates to the field of robot operation and comprises a multispectral sensing module, a depth correction module, a grabbing posture generation module and a control module. The visual information and the thermal radiation information of a transparent object are comprehensively obtained by combining two perception modes of an RGB-D image and a thermal imaging (TIR) image, systematic error analysis is performed on a depth map, and error sources of an RGB-D camera and a TIR camera are detected. The system adopts an encoder-decoder model as a depth correction core, the model extracts complementary features of an RGB-D image and a TIR image through a modal exclusive encoder, feature alignment and integration are completed by using a feature fusion module, and the depth estimation precision on the transparent surface is effectively improved. And meanwhile, a Bayesian optimization method is adopted to carry out hyper-parameter optimization on the depth correction model, through hyper-parameter optimization and model fitting processing, the system effectively avoids the problems of over-fitting and under-fitting, and the robustness of the depth correction model and the transparent object grabbing precision are further improved.

The invention discloses an allergic rhinitis diagnosis and allergen traceability system based on dynamic text guidance, and belongs to the technical field of intelligent medical treatment. According to the method, the problems that in the prior art, patient description is inaccurate, allergens are various in variety and have differences, and allergens are difficult to accurately determine and trace are solved, the initial situational sub-graph is verified by introducing periodic features of the patient, the causal contribution degree of the initial situational sub-graph is evaluated through a Bayesian reasoning algorithm, and a mode association result library is formed; according to the method, the crossing of the allergic rhinitis diagnosis and tracing from the traditional static judgment to the dynamic accurate inference is realized, and a personalized and scientific allergen avoidance scheme is provided for patients; through similarity retrieval, environmental information injection verification and calculation of a mode goodness-of-fit score, under the condition that patient symptoms are in multi-factor mixing, a mixed causal graph is created through a graph fusion technology, and the mode goodness-of-fit score is calculated again, so that the accuracy of obtaining an allergen traceability result in practical application is ensured.

The invention discloses an automatic boiler dosing method based on response surface modeling-PID coupling control, and relates to the field of thermal power boiler feed water treatment, and the method comprises the steps: collecting historical operation data, and employing a Pearson correlation coefficient method to screen out the correlation between the frequency of a boiler dosing pump and each parameter index; bBD design is carried out through response surface analysis, response values and factors in the dosing process are determined, and three different levels are selected for the factors; establishing a mathematical model between the response value and each factor through model fitting; setting a target value, substituting the target value into the response surface model for calculation, and taking the calculated value as a feedforward input quantity of a PID controller of the boiler dosing system; setting the deviation between the target value and the feedback value to obtain the adjusting frequency of dosing; and obtaining the final frequency of the ammonia adding pump by adopting a weighted average algorithm. According to the method, a mathematical model is constructed, an algorithm model and control logic are fused, and real-time response and control optimization of the boiler dosing control process are achieved.

The invention discloses a dense topology model lightweight method, and belongs to the technical field of three-dimensional model processing. The problems that an existing scheme is low in efficiency, large in structural damage, poor in mapping quality and the like are solved. The method comprises the following steps: importing a dense grid model containing high-precision geometry and texture; selecting an automatic (adaptive lattice clustering algorithm) or manual mode to generate a target simplified topology; constructing a simplified model fitting the original model through a multi-direction projection technology; uV expansion is realized through automatic blocking, LSCM algorithm optimization and manual adjustment, and textures are baked in combination with RendertoTexture and SSAA technologies; and after performance optimization, outputting a lightweight model compatible with a mainstream format and an engine. According to the invention, high efficiency, stability and light weight are realized, key features are accurately reserved, visual consistency is guaranteed, and multi-scene application is adapted.

Embodiments create models configured to predict behavior of real-world systems. An example embodiment receives input and output data for a real-world system and, next, subdivides the input and output data received into a plurality of subsets in accordance with a criterion. For each subset of the plurality, a regression model is fit to data of the subset. For each data point in each subset of the plurality of subsets, a respective weight is assigned to the data point for each regression model. In turn, the model configured to predict the behavior of the real-world system is generated by calculating a weighted average of each regression model using the assigned respective weights.

A method and a system for generating a 3D hand model are provided. The method includes: receiving heterogeneous hand keypoints collected from a plurality of tracking systems; performing a coarse optimization process to align the heterogeneous hand keypoints into an anatomical reference frame to produce unified hand keypoints; performing a fine optimization process to fit a hand mesh model to the unified hand keypoints; generating a 3D hand mesh using the hand mesh model fit to the unified hand keypoints; obtaining anatomical joint positions from the 3D hand mesh using a trained model; and outputting the 3D hand model including the 3D hand mesh and the anatomical joint positions.

The invention relates to a numerical control machine tool reliability modeling method and system considering a fault trend. The method comprises the following steps: checking the homogeneity of a numerical control machine tool based on the fault trend; constructing an AMSAA model of the multiple samples subjected to truncation at unequal time; evaluating the precision of the reliability model; aiming at the problem of insufficient reliability model precision of a multi-sample condition of a numerical control machine tool, a multi-sample AMSAA model modeling method of the numerical control machine tool considering a fault trend is provided, sample data classification is carried out through trend inspection and homotype inspection, and fault data of same-type machine tools are preprocessed by adopting a fault total time method; adopting a maximum likelihood method to estimate AMSAA model parameters of the same-type machine tool, and using Cramer-Von Mises to test the goodness of fit of the model; evaluating the precision of the reliability model by taking an average absolute percentage error (MAPE) of an instantaneous MTBF point estimation value and an MTBF observation value as an index; compared with a multi-sample AMSAA model established by a direct maximum likelihood method, the method is higher in prediction precision.

A method for assessing a lens capsule stability condition in an eye of a human patient includes simultaneously directing electromagnetic energy in a predetermined spectrum via an energy source onto a pupil of the eye after movement of the eye results in eye saccades occurring therein. The method further includes acquiring images of the eye indicative of the eye saccades using an image capture device and calculating a motion profile of the lens capsule using the images via an ECU. Additionally, the method includes extracting time-normalized lens capsule oscillation trajectories based on the motion profile via the ECU and then model fitting the lens capsule oscillation trajectories via the ECU, thereby assessing the lens capsule instability condition. Also disclosed herein is an automated system for performing an embodiment of the method, the automated system including an energy source, an image capture device, and an ECU.

A method for determining a model for an unknown function is described comprising training a neural network for selecting inputs at which to evaluate the unknown function. The training includes a plurality of iterations of sampling, from a set of Gaussian processes, at least one initial guess for the unknown function, using the neural network to select inputs and evaluating the selected inputs using the at least one initial guess, determining a value of an objective function from the evaluated selected inputs, adjusting the neural network to improve the value of the objective function and determining the model by evaluating the unknown function at a sequence of inputs given by the trained neural network and fitting the model to the evaluated inputs.

The present application belongs to the technical field of medical monitoring, and particularly relates to a pipe diameter flow monitoring method and system for ECMO, which comprises the following steps: a video image of an ECMO pipe is acquired, a dynamic contour model containing a statistical shape model and a Kalman filter is constructed to adapt to non-rigid deformation of the pipe and track the non-rigid deformation; macro tracking features such as model fitting residuals and kinematic deviations, and micro features such as textures and optical flows inside the pipe are combined to calculate a multi-dimensional tracking confidence, which is used to adaptively adjust parameters of the Kalman filter, so that optimal compensation for pipe vibration and deformation is realized; furthermore, the system can accurately extract the pipe diameter and flow rate, and calculate real-time instantaneous flow. The accuracy, robustness and intelligent level of ECMO monitoring are improved.

The invention relates to a method for determining an individualized nerve regulation target spot, and the method comprises the steps: obtaining nerve image data, which are repeatedly measured for multiple times, of a subject and corresponding clinical psychological assessment data in a preset time period; calculating a brain activity index of each voxel in a preset brain region; establishing a plurality of candidate mathematical models to describe the relationship between the brain activity indexes and the clinical scores; selecting an optimal model through statistical test of fitting residual errors; generating a parameter weight map representing'symptom-activity 'association strength based on the optimal model; and finally, determining an individualized nerve regulation target according to the numerical distribution of the map. The invention also relates to a corresponding device, a processor and a computer readable storage medium thereof, and solves the problem of inaccurate target positioning caused by neglecting symptom heterogeneity and individual brain function difference in the prior art through a core methodology of ''longitudinal tracking-multi-model fitting-residual optimal selection''. The curative effect of nerve regulation and control treatment is obviously improved.

The invention is suitable for the technical field of data identification, and provides a service life test method and device for a wireless charger, and the method comprises the steps: executing a test process at a preset test temperature, and collecting observation data corresponding to a plurality of sampling moments; wherein the preset test temperature is higher than normal temperature; calculating a health index corresponding to each sampling moment according to the observation data corresponding to each sampling moment; fitting the plurality of sampling moments and the health indexes corresponding to the plurality of sampling moments based on a plurality of degradation track models to obtain a target fitting model; and calculating the service life of the wireless charger according to the target fitting model. Through systematic data acquisition, dynamic evaluation of health indexes, scientific degradation model fitting and reliable life prediction, the accuracy and effectiveness of life evaluation of the wireless charger are significantly improved.

A steel structure digital restoration device and method for digital twin construction, the device comprises a structure recognition module, the structure recognition module is configured to: the point cloud information of the acquired steel structure is executed in a virtual three-dimensional space according to its embedded space coordinate information based on the corresponding restoration operation of position, the total model field about the distribution of point cloud in space is acquired, and the restored point cloud information is in the form of a point model in the total model field;Based on the distribution of point cloud, the position with the best point model density is automatically selected, slicing is carried out to obtain the basic cross-section data of the structure;Based on the image in the basic cross-section data, the identification and judgment of the type of steel structure are executed, after correctly identifying the type of steel structure, the three-dimensional model fitting restoration of slicing is executed based on the identified type of steel structure.The present application can directly restore the three-dimensional pipeline model with high precision, and the engineering personnel can obtain the information of all steel structures in the whole factory by directly observing the three-dimensional steel structure model with sufficient rich details.

The invention provides a model training method and device and a data processing method and device. According to the method, the instruction with the model fitting difficulty meeting the preset condition is screened out from the existing instruction data set by means of the reference model, and the challenging instruction with the high model fitting difficulty can be screened out from the instruction data set to serve as the seed instruction; a plurality of similar instruction samples are generated based on seed instruction expansion, so that more challenging instruction samples can be obtained, a training set containing the instruction samples and reference responses of the instruction samples is constructed, knowledge distillation can be realized based on an existing instruction data set by means of a reference model, and a training set containing higher-quality instruction data is obtained; further, by using the training set to train the deep learning model, the ability of the deep learning model to process more complex and challenging tasks can be improved, and the performance of the target model after training is completed is improved.

The invention discloses a radio astronomical interference data processing and analyzing method and system based on AIPS, and relates to the technical field of astronomical data processing. The method comprises the steps of data loading, imaging processing, image information extraction, source detection and parameter extraction, model fitting and result output. By integrating an AIPS data processing function and automatic workflow management, tasks such as fit, imagr, sad, jmfit and omfit of the AIPS are called, and end-to-end automatic processing from original observation data to scientific results in the whole process from astronomical data loading to source parameter extraction, model optimization and result visualization is achieved. The system comprises functional modules corresponding to the steps, supports multi-band data processing, can automatically identify a radio power source, performs Gaussian model fitting, analyzes parameters such as flow, position, form and polarization of the source, and generates a high-quality visual result. According to the method, the efficiency and precision of astronomical data processing are improved, the risk of manual operation errors is reduced, the efficiency and consistency of radio astronomical data processing are remarkably improved, and the method is suitable for batch processing and analysis of radio astronomical observation data.

The application discloses an interpretable heart sound anomaly recognition method and system based on a fractional domain Fourier transform, which comprises preprocessing, feature extraction, model establishment and model interpretation. The preprocessing comprises shearing, downsampling, filtering, amplitude normalization, heart cycle segmentation, frame segmentation and windowing in sequence; the feature extraction is configured to firstly perform fractional domain Fourier transform on the preprocessed heart sound, then extract frame-level Shannon entropy features of one-dimensional fractional domain heart sound signals, and calculate 13 statistical functions on the frame-level features as final features; the model establishment selects an XGBoost classifier; and the model interpretation selects a SHAP (SHapley Additive exPlanation) interpretation model. The application is easy to realize, simple in method, low in feature dimension, fast in model fitting, and has model prediction interpretability.

The invention discloses a method for determining homomorphic encryption machine learning deployment configuration and computing equipment. The method comprises the following steps: determining a security demand and basic configuration of a user; according to the security requirement, determining a limiting condition for the homomorphic encryption parameter; the calculation accuracy and deployment flexibility corresponding to each parameter combination meeting the limiting conditions and the basic configuration are determined, and each parameter combination comprises a set of homomorphic encryption parameters and model fitting parameters; determining a plurality of coarse screening combinations in each parameter combination based on calculation accuracy and / or deployment flexibility, determining calculation time delay under each bootstrap insertion scheme supported by the coarse screening combination for any coarse screening combination, and determining a target scheme in each combination scheme formed by the plurality of coarse screening combinations and the bootstrap insertion scheme based on the calculation time delay, through the two-stage screening of the security requirement and the deployment flexibility, the screening calculation amount of the deployment configuration is greatly reduced, and the efficiency of determining the deployment configuration of the HEML system can be improved.

The invention discloses an anomaly monitoring method for panoramic smart energy cloud edge collaboration, and the method comprises the following steps: S10, edge side credibility perception calculation: carrying out the real-time analysis of locally collected energy time sequence data through an edge side device, generating a preliminary anomaly probability Pe, and carrying out the calculation of the edge side credibility based on the probability Pe and the corresponding data; calculating an edge credibility score Re corresponding to the preliminary anomaly probability Pe by analyzing the difference with a historical normal mode and the uncertainty of local model fitting; and step S20, cloud credibility enhancement analysis: the cloud performs deep analysis according to the data uploaded by the edge side, generates a cloud anomaly probability Pc, and calculates a cloud credibility score Rc corresponding to the cloud anomaly probability Pc by analyzing the novelty of the data in global distribution and the stability of model prediction based on the probability Pc and the corresponding data. According to the invention, abnormity monitoring of panoramic smart energy cloud edge collaboration can be carried out more comprehensively.

The invention discloses a control method and equipment of a knowledge distillation system and a storage medium, and belongs to the technical field of data processing. The method comprises the steps of obtaining a condition vector of a middle layer of a to-be-learned model based on a classification processing action of the to-be-learned model on sampling sub-graphs; carrying out noise addition on the sampling sub-graph to obtain a noise-added sub-graph; through the first teacher model, performing a denoising action on the noise-added sub-graph according to the condition vector to obtain a reconstructed sub-graph, and calculating reconstruction loss of the reconstructed sub-graph and the sampling sub-graph; calculating the model loss of the to-be-learned model according to the reconstruction loss and the fitting loss of the to-be-learned model and a second teacher model; and iterating the to-be-learned model according to the model loss to obtain a student model. Through the double-teacher model, in the student model fitting learning process, the student model is guided to construct an understanding mechanism for the graph result, so that the essential performance is improved.

The invention discloses a multi-linear welding seam extraction method and system, and relates to the technical field of welding extraction, and the method comprises the steps: building a data set based on a point cloud neighborhood multi-dimensional feature vector corresponding to a single point and a type label, and carrying out the model training, verification and testing, and obtaining a welding seam point recognition machine learning model, and a multi-size prediction module is combined to carry out weld joint extraction. According to the method provided by the invention, a traditional weld joint extraction thought of carrying out overall geometric model fitting on the workpiece point cloud is abandoned, and a weld joint point recognition machine learning model is established based on multi-dimensional feature learning of a point cloud neighborhood corresponding to a single point; and learning and understanding the internal characteristic difference between the weld joint points and the non-weld joint points through a weld joint point recognition machine learning model, so that high-robustness and high-precision weld joint extraction is realized.

The invention provides a packer rubber cylinder optimization design method, device and equipment and a storage medium, and belongs to the technical field of oil and gas well engineering oil testing and well completion. The method comprises the following steps: acquiring measured mechanical property data of a rubber cylinder rubber material at an expected service temperature of a research well; rubber constitutive model fitting is carried out, and a target rubber constitutive model is determined according to a fitting result; rubber sleeve structure parameters and metal framework structure parameters of the research well are obtained; constructing a rubber sleeve combination finite element parameterized model; obtaining the temperature and the pressure load of the rubber sleeve under the specific well completion and oil testing working condition of the research well, and carrying out simulation analysis on the rubber sleeve combination finite element parameterized model under the temperature and the pressure load to obtain a rubber sleeve simulation analysis result; the sealing effect of the packer is evaluated according to the rubber sleeve simulation analysis result; and determining an optimal design scheme of the rubber sleeve according to an evaluation result. By means of the method, optimal design of the high-temperature-resistant and high-pressure-resistant packer rubber barrel is achieved, and the application requirements of ultra-deep and extra-deep wells are met.