79 results about "Nonlinear modelling" patented technology

The invention discloses a deep learning and machine learning fused soil moisture inversion construction method, and relates to the technical field of measurement of physical properties of materials, and the method comprises the steps: capturing complementary information and spatial context of multi-source data through a multi-source heterogeneous data space-time adaptive fusion step by using a cross-modal attention mechanism and a graph neural network; through a deep learning and machine learning dual-path collaborative inversion step, advantage complementation is realized by combining data-driven nonlinear modeling and a physical constraint interpretable model; according to the method, the defects of single data source, insufficient model generalization ability and incomplete physical mechanism consideration in the prior art are overcome, the inversion precision is improved by 12%-18% under the complex earth surface condition, and the method has the advantages that the method is suitable for large-scale popularization and application. And a high-precision, strong-generalization and reliable technical means is provided for precise monitoring of soil moisture.

The invention relates to the technical field of biological feature recognition, and particularly provides an anti-migration PPG recognition method based on rate perception and a state space model. The method comprises the following steps: performing physiological feature front-end extraction on an original single-channel PPG signal to obtain a high-dimensional shallow feature sequence; performing double-flow cooperative processing on the high-dimensional shallow-layer feature sequence, and distributing the high-dimensional shallow-layer feature sequence to two parallel branches, namely a control flow branch and a data flow branch; in the control flow branch, an amplitude spectrum and an instantaneous physiological rate curve are obtained; in the data stream branch, acquiring a deep global feature sequence with rate invariance; obtaining multi-scale refinement features based on the high-dimensional shallow feature sequence and the deep global feature sequence; according to the multi-scale refinement features, a final biological feature recognition result is obtained, the method can actively sense the physiological rate change, and efficient nonlinear modeling can be achieved with the extremely low parameter quantity.

The invention discloses a multi-energy system closed-loop prediction control method, system, equipment and medium, and the method comprises the steps: carrying out the decomposition, dimension reduction and nonlinear modeling of the time sequence characteristics and environmental influence factors of photovoltaic output through a photovoltaic power prediction model, and outputting a future multi-period photovoltaic power prediction sequence; establishing a state equation and an output equation, integrating equipment operation constraints, and constructing a hydrogen-containing energy storage state space model; inputting local load power and a future multi-period photovoltaic power prediction sequence into the hydrogen-containing energy storage state space model, and solving an objective function through a rolling optimization algorithm to obtain a future multi-period optimal scheduling scheme; and applying a first hour control instruction of the optimal scheduling scheme to an actual system, and proportionally superposing the deviation between an actual measurement value and a historical prediction value to a photovoltaic power prediction sequence of a next period through a feedback correction item to realize closed-loop control. According to the method, the photovoltaic local consumption rate can be improved, and the power grid fluctuation rate is reduced.

The invention discloses a split type three-box girder nonlinear vortex vibration steady-state amplitude intelligent prediction method, belongs to the field of bridge vortex-induced vibration control, and aims to solve the problem of low accuracy of three-box girder vortex vibration nonlinear modeling and amplitude prediction. The method comprises the following steps: acquiring displacement time sequence data through a reduced scale model wind tunnel test, preprocessing to obtain displacement, speed and acceleration dimensionless data, and dividing a data set; constructing a candidate function library containing high-order polynomial terms of the primary function; performing nonlinear system sparse recognition by taking acceleration data as a target item and combining an improved algorithm to obtain a preliminary feature set; a final feature set is obtained through energy-statistics-self-adaption three-stage screening; and finally, constructing a control equation to predict the steady-state amplitude. The method can accurately predict amplitude, coincide test and real bridge observation results, and is less in computing resource occupation and high in numerical value precision.

The invention relates to a Beidou / GNSS + 5G unmanned aerial vehicle cooperative positioning method based on a multi-mode neural network, and belongs to the technical field of unmanned aerial vehicle navigation and intelligent logistics. According to the invention, through nonlinear modeling, multi-source feature adaptive extraction and autonomous learning capabilities of the multi-modal neural network, Beidou, 5G, inertial navigation and visual environment perception data are deeply fused, and in combination with scene adaptive switching and cluster collaborative optimization, high-precision and high-robustness positioning support is provided for the low-altitude logistics unmanned aerial vehicle in a complex environment. According to the invention, through multi-source data deep fusion, scene adaptive switching and cluster collaborative optimization, the positioning precision and reliability of the low-altitude logistics unmanned aerial vehicle in a complex environment are effectively improved.

The invention relates to the technical field of intelligent detection, and discloses a T-SVAE feature extraction strategy and a method for improving near infrared spectrum soil rapidly available potassium measurement precision by using the T-SVAE feature extraction strategy, and the method comprises the following steps: step 1, collecting soil surface samples of different plots, obtaining near infrared spectrum data of the soil samples by using a Fourier transform near infrared spectrometer, and calculating the near infrared spectrum data of the soil samples; determining the actual content of quick-acting potassium in the soil sample by adopting a national standard method; the method comprises the following steps: 1, acquiring near infrared spectrum data, 2, preprocessing the acquired near infrared spectrum data, and removing impurity signals caused by instrument fluctuation, environmental interference and sample physical form difference, and 3, constructing a Transform and supervision constraint fused variational self-encoding model (T-SVAE). By constructing a variational self-encoding model fusing Transform and supervision constraint, the problem of feature blindness caused by high-dimensional data difficulty, nonlinear modeling limitation and unsupervised learning in near infrared spectrum data processing of a traditional feature extraction method is effectively solved.

The invention relates to the field of artificial intelligence and computer vision, in particular to an unmanned aerial vehicle multi-target tracking method based on a CNN-Transform-Mama network and a space-time Mama motion model, and the method comprises the steps: obtaining a video data set of multi-target tracking collected by an unmanned aerial vehicle; constructing a CNN-Transform-Mama network as a target detector to obtain bounding boxes and spatial position information of a plurality of targets on the ground in a single-frame image; constructing a multi-target space-time trajectory based on the space position information in the continuous space-time; a space-time Mama motion model is constructed to complete nonlinear modeling of a multi-target space-time trajectory, and multi-target tracking is achieved; and constructing a joint loss function training model, and evaluating the performance of the model through precision evaluation. According to the invention, by aggregating the advantages of the CNN, the Transform and the Mama network, the local-global-long-range dependency features are effectively fused, and the positioning precision of the multi-target position is improved; and the precision and the robustness of multi-target tracking are improved through nonlinear modeling of the space-time trajectory, so that the adaptive capacity to a complex scene is further improved.

The invention provides an intelligent control method, device and equipment for a steam crosslinking room and a medium, and relates to the technical field of intelligent control, and the method comprises the steps: collecting temperature, steam and humidity parameters in real time to form a multi-parameter observation vector with synchronous time; establishing a dynamic heat conduction model for describing the nonlinear coupling relation of the temperature, the steam flow and the thermal inertia; pID parameter output control quantity is adaptively adjusted based on the temperature deviation and the error change rate, a future temperature track is predicted by using the model, and an objective function is optimized under constraint conditions to obtain an optimal control increment; and combining the temperature deviation energy integral, the steam energy consumption and the temperature fluctuation variance to construct a multi-target optimization function to dynamically adjust the weight and output a target control signal. According to the method, the problems that a traditional control mode lacks nonlinear modeling and predictive compensation capability, temperature fluctuation is easily caused, a thermal field is uneven, and material performance is unstable can be solved.

The invention discloses a digital self-interference cancellation method based on transceiver nonlinear behavior modeling, and belongs to the technical field of electronic reconnaissance and interference. The digital self-interference cancellation method comprises the following steps of performing linear combination on memory components of a broadband signal, and generating cross terms with different phase structures by using a nonlinear effect so as to construct a Wiener model; establishing a multipath self-interference channel model; nonlinear modeling is carried out on linear components of interference signals received by a radio frequency front end at a receiver, and nonlinear component modeling is a linear model; for a nonlinear model constructed by a linear component in an interference signal received by a radio frequency front end, adopting dynamic reduction measurement to reduce the complexity of the model; parameter preliminary estimation is carried out by using the characteristics commonly represented by the intermediate variables, and parameter estimation is optimized by combining a two-step iterative algorithm; and reconstructing the transmitting signal in the digital domain according to a parameter estimation result to generate a reference signal, and realizing interference cancellation in the digital domain of the receiving end.

This invention discloses a method for dynamic prediction of reservoir production that integrates discrete gradient information, belonging to the interdisciplinary field of reservoir development and artificial intelligence. The steps are as follows: First, a numerical simulation dataset for oil-water two-phase flow in heterogeneous reservoirs is constructed based on numerical simulation methods. Second, a dual-branch network structure is designed to extract the spatial and physical features of the input field data in parallel, including a main feature encoding branch and a difference operator branch. Third, a backbone network is designed for deep nonlinear modeling. Fourth, an efficient pressure and saturation field prediction neural network model is constructed based on the dual-branch network structure and the backbone network. During the model training phase, spatial observation points from some time steps are used to participate in the calculation of data loss terms, while the residuals of the physical control equations are introduced as physical loss terms at all time steps and throughout the entire space. Finally, a well-trained efficient pressure and saturation field prediction neural network model is obtained, achieving high-precision prediction of pressure and saturation fields across the entire time series.

The application discloses a brake method and system of an automatic portal crane, and the method comprises the following steps: setting a target position of a grab bucket when braking is needed; and cyclically executing the following steps a to e until a difference between an actual position of the grab bucket and a stopping position is less than or equal to a threshold value, and outputting a frequency converter shutdown instruction and a brake starting instruction: a. collecting a real-time frequency of the frequency converter; b. calculating the actual position of the grab bucket; c. calculating a theoretical stopping distance under a current real-time frequency; d. compensating the theoretical stopping distance by considering a load; and e. calculating the stopping position based on the target position of the grab bucket and the compensated stopping distance. The application solves a long-existing problem of 'incompatibility between speed and accuracy' in the field of crane positioning by means of a trinity of non-linear modeling, dynamic calculation and data driving, and provides high-efficiency automatic operation for port automation.

The invention relates to the technical field of artificial intelligence and digital twinning crossing, and discloses an equipment fault prediction method and system based on digital twinning. Comprising the steps of collecting multi-dimensional physical data and full life cycle data of equipment, constructing a digital twin model and synchronizing physical states in real time, quantifying implicit coupling interference through three-level linkage nonlinear modeling, dynamically correcting features, calculating fault evolution probability, and optimizing parameters through reinforcement learning. The system correspondingly comprises five functional modules. The method solves the problem of inaccurate prediction caused by neglect of multi-physics field coupling interference and feature processing lag in the prior art, realizes accurate pre-judgment and early warning of faults, and is suitable for a full-life-cycle monitoring scene of industrial equipment.

The application relates to a light wavelet enhancement fusion medical image segmentation method, and belongs to the technical field of medical image processing and computer-aided diagnosis. The core of the method is to construct a light WEF-Net network, which comprises an encoder, a bottleneck layer and a decoder. The encoder adopts a dual-domain perception module, extracts complementary features from the frequency domain and the time domain through wavelet transformation and convolution, and simultaneously extracts complementary features from the frequency domain and the time domain. The bottleneck layer is designed with a feature aggregation Kolmogorov-Arnold transformation module, which is used for efficiently fusing multi-scale semantic information and enhancing the nonlinear modeling capability. The network level also integrates a sawtooth rolling feature fusion module, which enhances the global continuity of the features through the channel rolling and spatial scanning mechanism to improve the integrity of the boundary segmentation. Experiments show that the method realizes excellent performance on multiple public medical image datasets, while maintaining low parameter quantity and low computational quantity, and significantly improves the segmentation precision.

The invention discloses an energy-based economic key element linkage prediction method and system, and relates to the technical field of economics and prediction modeling, and the method comprises the steps: determining and collecting a data source, and carrying out the preprocessing of the obtained data; exporting key business data based on the multi-dimensional data model, and constructing an energy consumption data and economic growth prediction model; training an energy consumption data and economic growth prediction model, and performing result evaluation and model optimization; and economic growth and energy prediction are carried out by using the optimized model, and analysis is carried out according to a prediction result and a corresponding decision is provided. By constructing the data cube model, data from different fields can be efficiently integrated, multi-dimensional comprehensive analysis is realized, and the comprehensiveness of the prediction model is improved; according to the method, the optimized model is utilized, a more flexible nonlinear modeling method is adopted, the complex relation between economic growth and energy consumption is better captured, and the prediction accuracy is improved.

The invention discloses a probability model analysis method for a secondary accident of an over-limit transport vehicle, and the method comprises the following steps: obtaining traffic accident related data, including numerical data and classification data; inputting the data into a trained over-limit transport vehicle secondary accident probability model, wherein the model is constructed by combining a random forest model after Bayesian optimization with a logistic regression model; and carrying out influence analysis on a model prediction result through an SHAP interpretation framework. According to the method, the interpretability of logistic regression and the nonlinear modeling capability of the random forest are fused, and the model precision is improved by combining Bayesian optimization, so that accurate prediction of the secondary accident probability of the over-limit transport vehicle and quantitative analysis of key influence factors are realized, the blank of model research in the field is filled up, and the method has a wide application prospect. And a scientific basis is provided for traffic safety management and accident prevention.

The invention provides a multi-stage feature processing method and related equipment, relates to the technical field of image processing, and realizes unified coding and channel adaptive compression of output features by introducing a rear feature coding module, reduces feature redundancy, and improves normalization and stability of feature representation. A global feature weight reconstruction module is introduced to explicitly model a global association relationship among different spatial positions, so that output features can be fused with global context information, and the overall consistency and structure expression ability of the features are significantly enhanced; and by setting a reconstruction feature decoding output module, the nonlinear modeling and structured decoding capabilities of an output stage are improved, so that a prediction result is more continuous, stable and fine in a complex background scene.

The invention discloses a bearing vibration signal feature extraction method, a bearing fault diagnosis model and a diagnosis method. The feature extraction method comprises the following steps: acquiring a vibration signal detected by a sensor; performing band-pass filtering and envelope demodulation on the obtained vibration signal, and dividing the vibration signal into a plurality of signal samples; after performing multi-scale differential processing on each signal sample, inputting an obtained normalized multi-channel differential matrix into a random recurrent neural network to obtain a final hidden state sequence matrix; and determining at least two of a variance matrix, an absolute deviation matrix, a kurtosis matrix and a spectrum entropy matrix according to the final hidden state sequence matrix, and splicing the matrixes to obtain a feature vector. According to the method, feature extraction can be completed on the premise that a large amount of annotation data or tedious parameter adjustment and optimization are not needed, and the method has the advantages of multi-scale sensitivity, nonlinear modeling capability and lightweight deployment. Compared with an existing method, the method is more stable, easy to migrate and lower in calculation overhead.

The present application relates to a kind of shale oil dessert prediction method and system based on multi-attribute fusion, the present application establishes the three-dimensional distribution model of porosity, permeability, oil saturation, total organic carbon content and brittle index under lithofacies constraint, constructs the feature set including single attribute and attribute interaction term;Nonlinear mapping relationship between multiple attributes and oil production is established based on machine learning model, and the contribution value of each attribute and interaction term is calculated using SHAP method, and then the corresponding weight coefficient is determined;On this basis, the three-dimensional prediction model of dessert coefficient is constructed, and the horizontal well trajectory is dynamically optimized according to the distribution of dessert coefficient.Multiple attribute nonlinear modeling and weight objective determination improve the accuracy and reliability of shale oil dessert prediction, can effectively guide horizontal well deployment and trajectory adjustment, significantly improve sand body drilling rate and yield level.

This invention provides a fault detection method, system, device, and storage medium for pneumatic control valves, belonging to the field of industrial process fault detection. The method includes: introducing a nonlinear modeling approach based on Gaussian process regression (GPR) into the fast layer to construct the flow coefficient C. V A high-precision nonlinear model is used, whose residual statistics can sensitively capture transient anomalies in control valve performance that deviate from expectations. In the slow layer, Activity-KSFA, a kernel-based slow feature analysis, is employed to effectively extract slowly varying features during system degradation, and T² statistics are used for slow anomaly detection. Finally, a dual-threshold and continuous decision mechanism is combined to suppress false alarms while maintaining high sensitivity. Simulation results on the DAMADICS platform show that C... V While achieving zero false alarms, the residual maintains a detection rate of over 98% for most typical faults, complementing the T² statistic and significantly enhancing the adaptability of this invention to different fault modes.

This invention provides a multi-stage feature processing method and related equipment, relating to the field of image processing technology. By introducing a post-feature encoding module, unified encoding and adaptive channel compression of output features are achieved, reducing feature redundancy and improving the standardization and stability of feature representation. By introducing a global feature reweighting reconstruction module, the global correlation between different spatial locations is explicitly modeled, enabling output features to integrate global contextual information, significantly enhancing the overall consistency and structural expressive power of features. By setting a reconstructed feature decoding output module, the nonlinear modeling and structured decoding capabilities of the output stage are improved, making the prediction results more continuous, stable, and refined in complex background scenes.

The invention discloses a fuel cell health state prediction method, system and device based on space-time modeling and a graph neural network, and a storage medium, and belongs to the technical field of industrial automation and energy system optimization. According to the invention, by constructing a sparse label extension mechanism, a multi-channel correlation feature extraction system and a nonlinear dynamic mapping strategy of a hybrid architecture, the limitations of a traditional data driving and physical modeling method in the aspects of label scarcity, insufficient multi-channel correlation mining, limited nonlinear modeling capability and the like are effectively overcome; reliable technical support is provided for evaluation and predictive maintenance of the state of health of the fuel cell, and improvement of equipment reliability and system energy efficiency is facilitated.

The invention relates to the field of machine vision, in particular to a heterogenous contact lens image registration method and system based on a depth model, and the method comprises the steps: obtaining a bright field image and a point light source image of a contact lens; preprocessing the bright field image to obtain a first preprocessed image; preprocessing the point light source image to obtain a second preprocessed image; performing multi-scale feature fusion on the first pre-processed image and the second pre-processed image to obtain fusion features; a two-dimensional matrix is extracted from the multi-scale feature fusion process; and registering the bright field image and the point light source image based on the two-dimensional transformation matrix. The invention provides a depth model-based heterogeneous contact lens image registration method. The method aims at overcoming the limitation of a traditional registration technology in processing a multi-source heterogeneous image, and the robustness, accuracy and adaptability of registration are improved through feature learning of deep learning and nonlinear modeling capacity.

The invention provides a credit risk prediction method and device and electronic equipment, and relates to the technical field of credit risk prediction. The method comprises the steps of obtaining to-be-predicted target credit data; inputting the target credit data into a credit risk prediction model to obtain a credit risk prediction result output by the credit risk prediction model; wherein the credit risk prediction model is constructed based on a feature extraction model layer and a fusion layer; the feature extraction model layer comprises a plurality of feature extraction sub-models; each feature extraction sub-model is used for performing feature extraction on the target credit data to obtain a feature extraction result; and the fusion layer is used for fusing the feature extraction results of all the feature extraction sub-models to obtain a credit risk prediction result. The method is used for solving the problems that a traditional logistic regression score card method is lack of nonlinear modeling ability, depends on artificial experience and is poor in high-dimensional data adaptation.

The present application relates to the technical field of biometric identification, and particularly provides a rate-aware and state-space model-based anti-offset PPG identification method. The method comprises physiological feature front-end extraction on an original single-channel PPG signal to obtain a high-dimensional shallow feature sequence; double-flow collaborative processing is performed on the high-dimensional shallow feature sequence, which is distributed to two parallel branches of a control flow branch and a data flow branch; in the control flow branch, an amplitude spectrum and an instantaneous physiological rate curve are obtained; in the data flow branch, a deep global feature sequence with rate invariance is obtained; based on the high-dimensional shallow feature sequence and the deep global feature sequence, multi-scale refined features are obtained; and according to the multi-scale refined features, a final biometric identification result is obtained. The method can actively sense physiological rate changes and achieve efficient nonlinear modeling with extremely low parameter quantity.

The invention discloses a hydrological prediction method based on a deep learning network, and an application and a system thereof. According to the scheme, a Xinanjiang model, a Muskinggen model and an LSTM model are ingeniously combined; data (flow data and rainfall data) collected by an upstream station are imported into a Xinanjiang model and a Muskinggen model for calculation to obtain estimated confluence data, meanwhile, a transform model is used for extracting a time sequence of the collected data, and the time sequence of the collected data is calculated to obtain the estimated confluence data. The historical data features, the first confluence data and the second confluence data serve as training data to be input into the LSTM for training so as to achieve optimization of an LSTM network, the LSTM network used for predicting a flow sequence is obtained, and hydrology is predicted through the LSTM network; according to the scheme, the physical mechanism advantages of a traditional hydrological model and the nonlinear modeling capability of a deep learning model are comprehensively utilized, and the precision and reliability of hydrological prediction are improved.

The invention discloses a heat storage calculation method, control method and system of a coal-fired boiler unit, and relates to the field of thermal power unit control, and the heat storage calculation method comprises the following steps: obtaining test data of a to-be-evaluated unit after multiple tests; according to the test data of each test, calculating the average coal feeding amount before and after the load change in each test, performing function fitting by combining the first load and the second load, and constructing a power heat relation curve; on the basis of the power heat relation curve, first input heat from the test starting time to the test ending time of each test and second input heat needed by the corresponding stable working condition are calculated; the first load and the second load in each test serve as independent variables, the difference between the first input heat and the second input heat serves as a dependent variable, and a heat storage function is constructed through nonlinear modeling; according to the heat storage function, current operation data of the to-be-evaluated unit are combined, and current heat storage data are obtained through calculation.

This invention relates to a student classroom action detection method and system in the field of big data processing technology, comprising the following steps: stacking and grouping aggregated features, and calculating the behavioral feature center of each individual in the aggregated features; constructing a temporal representation of each individual based on the behavioral feature center of each individual, and after evaluating the importance using a scaled dot product attention mechanism, concatenating and linearly transforming the outputs of all attention heads to obtain the final output features of multi-head attention; reconstructing the final output features to obtain reconstructed features; performing original classification prediction on the reconstructed features to obtain initial prediction results, and performing co-occurrence matrix enhancement to obtain co-occurrence enhancement results; performing nonlinear modeling based on the co-occurrence enhancement results, calculating the final loss function, and detecting individual student actions in the video to be detected, thus solving the problem that existing classroom behavior recognition methods are highly subjective and difficult to accurately identify in multi-person scenarios.

The invention discloses a model data hybrid-driven polarization geomagnetic composite orientation method, which comprises the following steps of: after acquiring atmospheric polarization and carrier magnetic field information, calculating an initial course angle; defining a state equation based on a geomagnetic orientation method and an error model thereof, establishing a measurement equation in combination with polarization camera data, and preliminarily estimating a course angle of the composite orientation system by using a UKF (Unscented Kalman Filter); a bidirectional time sequence enhancement network model is introduced, time sequence features are extracted, through a bidirectional structure, multi-layer stacking and a nonlinear modeling mechanism, feature representation is optimized through a normalization layer, course angle errors estimated by the UKF are corrected, and an optimal solution of the course angle is obtained. According to the method, the UKF model and the BiTAN model are combined, the limitation problem that the UKF processes non-stationary time sequence data and burst noise in a dynamic time-varying environment is solved, the precision and stability of polarization geomagnetic composite orientation are effectively improved, and the orientation performance of a navigation system in satellite denial and complex environment scenes is ensured.

The invention provides a radio frequency power amplifier nonlinear modeling and digital pre-distortion method and system, a medium and equipment. The method comprises the following steps: acquiring a trained radio frequency power amplifier nonlinear model; inputting a real-time excitation signal into the radio frequency power amplifier nonlinear model to obtain a corresponding real-time phase response and a real-time group delay response; generating a phase adjustment amount and a group delay adjustment amount based on the real-time phase response and the real-time group delay response; and superposing the phase adjustment amount and the group delay adjustment amount on the real-time excitation signal to eliminate the nonlinear distortion of the real-time excitation signal passing through the radio frequency power amplifier. According to the radio frequency power amplifier nonlinear modeling and digital pre-distortion method, system, medium and equipment, radio frequency power amplifier nonlinear modeling and digital pre-distortion are realized based on the CNN and BiLSTM deep learning model, and the linearity and signal quality of a radio frequency transmitting link are effectively improved.

The application provides a high-speed electric spindle nonlinear dynamic characteristic analysis method and system, the method comprises the following steps: decomposing and simplifying an electric spindle rotor system; based on Hertz contact theory, nonlinear force models of angular contact ball bearings and linear bearings are respectively established, and a bearing seat dynamic equation is established; the dynamic equation of the rotor system is assembled; numerical solution and dynamic response analysis are performed on the dynamic equation of the rotor system to obtain a plurality of modal parameters and dynamic response results; sensitivity analysis is performed on a plurality of assembly parameters of the rotor system, the influence of each assembly parameter on the dynamic characteristics of the electric spindle is determined, and an optimization scheme of the electric spindle structure is generated according to the analysis results. The method considers the nonlinear modeling of the bearing, provides a standardized process for the dynamic characteristic analysis of the electric spindle, and can improve the efficiency of the dynamic characteristic research and performance optimization of the electric spindle.