198 results about "Online identification" patented technology

The invention discloses a dynamic monitoring-based deaerator water supply control method and system for a nuclear power plant, and relates to the field of deaerator water supply control, and the method comprises the steps: firstly, through a hybrid online identification algorithm, analyzing a historical valve position instruction and a water supply flow sequence in real time, and dynamically estimating the local gain and hysteresis width of a valve under the current working condition; then, the parameters identified in real time are utilized to carry out prospective compensation on the expected flow variation calculated by the main controller. In other words, the instruction amplitude is adjusted according to the estimated local gain, and it is ensured that consistent flow response can be obtained under different loads; meanwhile, when the instruction is reverse, the compensation amount is actively applied according to the estimated hysteresis width so as to eliminate the adjustment dead zone and oscillation caused by hysteresis. In this way, the nonlinear object of the valve is equivalent to a linear link with consistent response, and the stability and accuracy of the deaerator water level control system in the full working condition range are remarkably improved.

The invention relates to the technical field of underground engineering safety monitoring and disaster simulation, in particular to a digital twinborn deduction platform for underground engineering disaster chain evolution simulation, which comprises a multi-disaster coupling numerical simulation module used for acquiring multi-source data including design drawing data, geological data and engineering monitoring data, and based on the multi-source data, establishing an initial stress field model by using a numerical calculation method combining a finite element and a finite difference, and carrying out leakage-settlement-structure failure multi-disaster coupling numerical simulation. According to the method, the partial differential equation for describing the evolution law of the underground engineering is used as a constraint term to be embedded into the deduction model, and online identification and dynamic extrapolation of parameters are executed by fusing real-time monitoring data, so that the accuracy of disaster evolution simulation under complex working conditions is remarkably improved; and the problem that the traditional numerical calculation method is difficult to meet the real-time requirement of digital twinning is solved.

The invention relates to a low-voltage transformer area phase sequence recognition graph connection prediction method fusing time sequence coding and graph attention. The method comprises the following steps: step S101, acquiring voltage time sequence data of all users; step S102, converting a traditional'node classification 'problem of allocating a phase label to each user into a'graph connection prediction' problem of judging whether any two users are connected to the same phase; step S103, constructing an end-to-end deep learning model; step S104, constructing a pairwise prediction module (MLP classifier); and step S105, deploying the finally trained model to an embedded computing device, and performing low-cost and high-efficiency online identification and effect test on the user phase relationship of an unknown transformer area in an actual or simulated environment. The method gets rid of dependence on physical parameters of a power grid, and realizes high-precision automatic phase identification through an innovative problem modeling mode and a deep learning architecture.

The invention relates to the field of motor fault diagnosis, and particularly discloses a permanent magnet synchronous motor fault diagnosis method and system, which dynamically update motor physical model parameters through an online identification algorithm so as to reflect the characteristic change of a motor in real time. Thirdly, predicting the theoretical voltage of the healthy motor under the working condition by using the self-adaptive model and the real-time operation data, and calculating a residual sequence subjected to working condition normalization between the theoretical predicted voltage and the actual voltage; the residual signal is essentially free from the influence of working condition change, and only the abnormal characteristics caused by the fault are highlighted. And finally, inputting the high-robustness residual error sequence into a convolutional recurrent neural network, deeply fusing space-time fault features, and realizing accurate judgment on a motor fault type and confidence thereof, thereby effectively improving variable working condition adaptability and weak fault detection capability of diagnosis.

The invention provides a digital twinning five-axis machine tool contour error prediction method and system and electronic equipment, and belongs to the technical field of numerical control machine tool precision control and digital twinning. According to the method, servo axis data are collected in real time at a numerical control system end, electromechanical parameters are identified on line, comprehensive disturbance is estimated, and an autoregressive exogenous (ARX) model is updated accordingly; an interpolation instruction is generated in a digital twin body end mirror image numerical control system process, an updated model is utilized to predict a shaft position with deviation, a tool path is obtained through kinematics positive solution, and finally contour errors are calculated through geometric alignment. According to the digital twinning five-axis machine tool contour error prediction method and system and the electronic equipment, dynamic and high-precision prediction of the five-axis machine tool machining track contour error is achieved through the online identification and digital twinning technology, the limitation of a traditional static compensation method is overcome, and the precision of the digital twinning five-axis machine tool contour error prediction is improved. And a key basis is provided for prospective process optimization and precision control, and the machining precision and efficiency are effectively improved.

The invention relates to a mechanical arm self-adaption compliance control method and system based on model reference self-adaption and impedance parameter online identification, and belongs to the field of robots. The method comprises the steps that a mechanical arm task space dynamics model is established; designing a reference model; a recursive least square method is adopted, and an equivalent stiffness matrix and a damping matrix of the interaction environment are identified online; designing a composite adaptive law; the expected rigidity of the reference model is dynamically adjusted according to the environment rigidity obtained through identification; and according to sensor data collected in real time, the mechanical arm task space dynamics model, the robot parameter self-adaption law, the environment rigidity, the environment damping and the environment parameter identification robust item, control force is generated, and the mechanical arm is driven through the control force. According to the method and the system, the environment characteristics can be sensed online, and the compliant behaviors can be adaptively adjusted, so that safe, efficient and high-robustness interaction of the mechanical arm in a complex and unknown environment is realized.

The invention relates to a self-adaptive control method, system and device for environment test equipment, and the method comprises the following steps: S1, data collection: collecting environment detection data and data of a corresponding actuator according to a to-be-controlled physical quantity; s2, selecting or constructing a control object according to the physical quantity to be controlled; s3, performing online identification operation when an identification condition is met, otherwise, directly executing the step S4; s4, feedforward and feedback: calculating feedforward compensation and feedback compensation according to the value of the to-be-estimated quantity, and superposing the feedforward compensation and the feedback compensation to obtain a control instruction; and S5, performing constraint allocation on the actuator: performing saturation constraint processing on the control instruction to obtain a control quantity of the corresponding actuator. Joint online identification can be carried out on the to-be-estimated quantity only through perturbation excitation; different loads and test object states are automatically adapted; saturation constraint processing is adopted; the model and the algorithm are low in calculation amount, and precision, speed and engineering realizability are considered.

The invention discloses a load self-adaption device based on online system identification. The device comprises a track-Jacobian matrix offline generation module used for obtaining a pseudo-inverse matrix of a track-Jacobian matrix offline; the online excitation and measurement module is used for online controlling the robot to execute excitation motion and measuring to obtain an actual joint response error vector; and the rapid parameter identification module is used for combining the actual joint response error vector with the pseudo-inverse matrix to obtain an inertial parameter of a target load. The track-Jacobian matrix represents a linear sensitivity relation between inertia parameter disturbance of a load at the tail end of the robot and a joint response error. According to the method, the dynamic solving process with high calculation complexity is transferred to the offline stage to be completed, and the deterministic matrix-vector operation only needs to be executed once in the online stage, so that the calculation delay of online identification is greatly reduced on the premise of ensuring the identification precision.

The invention discloses a stainless steel welded pipe drawing process regulation and control method and system based on online identification, and the method comprises the steps: synchronously collecting a temperature field distribution image, axial and circumferential strain time sequence signals and a microscopic plastic deformation acoustic emission signal of the surface of a welded pipe, and generating a time-space synchronous multi-mode perception data flow; performing cross-domain feature fusion processing on the multi-modal sensing data stream, and extracting a fusion feature vector; inputting the fusion feature vector and the real-time drawing process parameters into an online rolling identification module, and outputting a flow stress curve and an anisotropy coefficient; based on the flow stress curve and the anisotropy coefficient, generating a cooperative regulation and control instruction set; and executing the coordinated regulation and control instruction set, and driving the drawing machine main motor, the mold posture adjusting mechanism and the minimal quantity lubrication system to perform linkage adjustment. By utilizing the embodiment of the invention, the self-adaptive closed-loop optimization regulation and control in the drawing process of the stainless steel welded pipe can be realized, and the product quality, the process stability and the production efficiency are improved.

The invention discloses a motor multi-parameter online identification and robust control method based on multi-observer cascade. The method comprises the steps of obtaining real-time operation data of the permanent magnet synchronous motor; obtaining a preliminary observation value through a sliding mode observer group; the sliding-mode observer group is constructed based on a mathematical model of the permanent magnet synchronous motor and is used for receiving real-time operation data and synchronously outputting initial observation values of a permanent magnet flux linkage, q-axis inductance and d-axis inductance; then analyzing the preliminary observation value to obtain a flux linkage accurate value, a q-axis inductance accurate value and a d-axis inductance accurate value, and finally performing adaptive control on the permanent magnet synchronous motor according to the flux linkage accurate value, the q-axis inductance accurate value and the d-axis inductance accurate value. According to the method, multiple motor parameters can be synchronously estimated, the limitation that other parameters are assumed to be accurate when a single parameter is identified in a traditional identification method is avoided, adaptive current reference and voltage decoupling are further generated based on the estimated parameters, and robust and stable field weakening control under variable working conditions can be achieved.

The invention relates to the technical field of power system stability analysis, and provides a broadband oscillation modal online identification method, which comprises the following steps: constructing a dynamic heterogeneous graph model according to the physical topology and measurement data of a power system, the dynamic heterogeneous graph model comprising a node set, an edge set and dynamic attributes; inputting the dynamic heterogeneous graph model into a physical information embedded space-time graph convolution circulation network, extracting space-time features and outputting potential states; based on the potential state and a learnable state matrix in the network, broadband oscillation modal parameters are calculated, and the broadband oscillation modal parameters comprise oscillation frequency, a damping ratio and a participation factor; and according to the real-time measurement data, carrying out online updating on the space-time diagram convolution circulation network embedded with the physical information so as to maintain the identification precision and enable the closed-loop operation of the digital twin model of the power system. And furthermore, the problem of online identification of the broadband oscillation mode of the high-proportion new energy power grid is solved.

The invention discloses a wear online identification and compensation control system and method for a high-pressure distribution plunger and barrel assembly, and the system comprises a data collection module which is used for collecting working condition data and state data; a health state dynamic baseline model obtained through offline training is arranged in the data processing module, and the wear level of the plunger and barrel assembly is recognized by calculating the residual error between actual state data and health state data predicted by the model under the current working condition; and the compensation control module is used for generating a self-adaptive compensation control instruction according to the identified wear grade and the current working condition and sending the self-adaptive compensation control instruction to an execution mechanism. According to the method, the dynamic base line synchronous with the real-time working condition is constructed, early weak wear characteristics can be separated from strong working condition interference, the recognition sensitivity and accuracy are improved, the influence of wear on the system performance is actively counteracted through closed-loop adaptive compensation control, the service life of equipment is expected to be prolonged, and the system performance is improved. And technical support is provided for predictive wear monitoring management.

The invention discloses an industrial mechanical arm joint rigidity online identification method based on an optimal excitation track, and belongs to the field of mechanical arm parameter identification. The method comprises the following steps: firstly, establishing a mechanical arm rigid-flexible coupling dynamic model, and constructing a linear regression equation taking joint rigidity and viscous damping as parameters; a Fisher information matrix is derived based on the regression model to quantify the amount of information of the excitation trajectory. And further, parameterizing the excitation trajectory by using finite term Fourier series, and solving the optimal excitation trajectory by using a scalar index of a Fisher information matrix and a sustainable excitation condition as optimization targets under the condition of meeting joint motion constraints. And finally, the mechanical arm is driven to execute the track, motion data are collected and input into parameter identification algorithms such as unscented Kalman filtering and recursive least square, and online and high-precision estimation of the time-varying joint stiffness is achieved. According to the method, the excitation track is designed directly from the information theory, and the precision, robustness and real-time performance of joint rigidity identification are effectively improved.

The invention discloses an AI vision intelligent inspection and foreign matter recognition emergency shutdown system for a belt conveyor. The system comprises data acquisition and sample construction; constructing a DETR foreign matter recognition model; images are collected in real time, and online recognition is conducted through a DETR foreign matter recognition model; carrying out target tracking and behavior analysis based on a multi-target tracking algorithm of ByteTrack; a distance threshold value, a speed threshold value and a score threshold value are set; graded alarming is carried out, and a shutdown signal is sent to the PLC control unit under the shutdown condition. The system comprises an alarm module, an edge calculation unit, a communication module and a PLC control unit. The AI inspection camera, the edge calculation unit, the communication module and the PLC control unit are connected in sequence; the edge calculation unit comprises an AI identification module and a tracking and behavior analysis module. According to the invention, real-time identification, behavior judgment and automatic safe shutdown linkage control of foreign matters or abnormal states on the conveying line can be realized.

The application discloses a kind of porous metal deformation mechanism acoustic emission online identification method, device and electronic equipment, including collection porous metal material acoustic emission signal and characteristic parameter data and pre-processing, obtain each acoustic emission signal feature vector;Determine the loss function of porous metal material deformation, build acoustic emission machine learning classifier candidate model;Train multiple different parameters machine learning classifier candidate model;Evaluation and optimization are carried out, and preferred machine learning classifier model is selected;Preferred machine learning classifier model is applied to online identification of the deformation mechanism of porous metal material acoustic emission signal.This method can be combined with the loss function determined based on the distribution characteristics of acoustic emission signal of different deformation mechanisms, build machine learning classifier model, have the characteristics of not needing label data and high identification accuracy and effectiveness.

A kind of milling cutter installation error online identification method based on milling force signal, a series of angular microelement is obtained by discretization to the rotation angle under the rotation period of milling cutter, and the tool shape and machining parameters of milling cutter are discretized along the axial direction to obtain a series of axial microelement;After calculating the instantaneous cutting thickness of each axial microelement of each tooth at the discrete time and the total milling thickness of the corresponding tooth in each tooth period in turn, the theoretical value of the total milling thickness corresponding to different tooth periods is determined, and then the milling force integral ratio corresponding to different teeth is obtained;According to the actual measured milling force in the milling process, the integral of milling force curve corresponding to each tooth with respect to time is calculated, then the milling force integral ratio corresponding to different tooth periods is obtained, and an optimization function about milling force integral ratio and milling force integral ratio is established, the PSO algorithm is used to optimize to reach minimum, and then the parameters of tool installation error are obtained.The present application can significantly improve the installation accuracy without knowing the milling force coefficient of workpiece-tool pair in advance.

The invention relates to the technical field of digital twinning and system health state prediction, in particular to a steering system model construction method based on digital twinning. Comprising the steps of 1, constructing a high-fidelity physical mechanism model representing the dynamic characteristics of the steering system; acquiring a working condition data flow of the steering system in real time; constructing a state observer; resolving a residual signal through a state observer; step 2, constructing an augmented state vector; performing recursive estimation on the augmented state vector by adopting a nonlinear filtering algorithm; 3, injecting the online identification parameter sequence into the high-fidelity physical mechanism model in real time to realize adaptive updating of the physical mechanism model; collecting historical data of the online identification parameter sequence; training a degradation prediction model by using historical data; and predicting and outputting a degradation trend prediction time domain of the steering system. According to the method, the fidelity and credibility of the model are remarkably improved, and a solid foundation is laid for subsequent analysis and prediction.

The application discloses an online checking method and system for an SF6 gas detector, and the method comprises the following steps: controlling a standard gas source to output an asymmetric triangular concentration waveform, wherein the rising rate of the waveform is not equal to the falling rate; collecting real-time concentration of the detector; drawing rising and falling response curves with standard concentration as the horizontal coordinate and real-time concentration as the vertical coordinate; calculating the deviation area between the two curves and an ideal straight line; if the area ratio is not greater than a dynamic threshold, extracting all intersection points of the two curves and sorting them; calculating the algebraic area of the region surrounded by adjacent intersection points to generate a surrounding area symbol string; and determining the fault type according to the intersection point number parity and zero value, intersection point horizontal coordinate interval dispersion degree and symbol string distribution according to the topological classification rule. The asymmetric excitation fully exposes the adsorption-desorption asymmetric characteristics of the sensor, and the response curve topological characteristics realize online identification and classification of baseline separation, translation drift, response lag, nonlinear superposition and noise jamming and the like.

The invention provides a dual-core hemispherical inertial navigation voltage sensitivity coefficient online identification and self-calibration method, which comprises the following steps: respectively constructing equivalent virtual angular rate excitation under electrostatic driving for each hemispherical resonator gyroscope, and determining a driving voltage for calibration; excitation is applied to the two coaxial hemispherical resonator gyroscopes according to a preset rule, and the output angular velocity and the standing wave azimuth angle are collected; establishing a difference observation model; establishing a Kalman filtering equation based on the difference observation model, wherein the Kalman filtering equation comprises an observation matrix, observed quantity and state quantity; kalman filtering is carried out on angular velocities and standing wave azimuth angles output by the two coaxial hemispherical resonator gyroscopes based on the established Kalman filtering equation, and a voltage sensitivity coefficient and a zero offset coefficient are solved. And writing the calculated voltage sensitivity coefficient and zero offset coefficient into a calibration register of the corresponding hemispherical resonator gyroscope. According to the technical scheme, the technical problem that in the prior art, a single gyroscope is prone to external interference, and consequently calibration precision is poor is solved.

The invention discloses an automobile ornament laser welding surface defect online identification method and system, and the method comprises the steps: projecting a structured light field to a welding surface: obtaining the coordinate information of a welding region, driving to carry out the spatial displacement compensation, and adjusting the aperture range of a diaphragm; capturing reflected light of the structured light field in real time to form an image sequence, which comprises the following steps: receiving a pulse signal output by a welding equipment encoder to trigger shooting, and adjusting exposure time to a preset mapping interval according to real-time light intensity feedback; performing defect feature extraction and defect classification identification on the image sequence, namely performing edge extraction by adopting an edge detection algorithm, and calculating contour geometric parameters and gray-level co-occurrence matrix parameters to extract features; performing defect classification and identification through a residual neural network integrated with a feature attention mechanism; and a defect recognition result is obtained, and welding process parameters are adjusted based on the defect recognition result. According to the invention, the laser welding surface defect on-line identification of the automobile ornament can be realized, and the identification precision is improved.

This invention belongs to the field of conveying equipment technology, specifically disclosing a conveying device and method for egg supply, including a conveyor belt machine, a chamber, and a blower. The conveyor belt machine's external belt consists of three sets of circular tracks that wrap around the conveyor belt machine. The circular tracks have notches spaced evenly along their circular paths. The chamber is fixedly installed above the conveyor belt machine and near its center. A ventilation component is installed above the chamber. A U-shaped frame is fixedly mounted above the conveyor belt machine and below the ventilation component. A crack visual detection marking component is installed above the U-shaped frame and corresponding to each circular track. This invention integrates wiping, flipping, drying, visual detection, and marking functions, enabling online identification and marking of cracked eggs produced after cleaning. This effectively prevents cracked eggs from being mixed with qualified products, significantly improving the overall quality and automation level of egg supply.

The invention is suitable for the technical field of automatic control of mining auxiliary transportation equipment, and provides an underground monorail crane predictive control method and system based on a digital road spectrum, and the method comprises the following steps: obtaining a position and a road spectrum; a parameter online identification step; a target speed dynamic correction step; a composite torque instruction generation step; a driving execution and braking cooperation step; the method has the beneficial effects that speed self-adaptive correction is realized through road spectrum pre-reading and parameter online identification, and the smooth torque is output in combination with composite predictive control, so that pure-electric-drive stable speed regulation of the monorail crane is realized. According to the scheme, instability slipping under complex working conditions is effectively overcome, the safety risk that mechanical braking intervention is triggered due to the fact that the speed is out of control is avoided, and the response speed, the operation stability and the intrinsic safety level of a whole machine system are remarkably improved.

The invention belongs to the technical field of power supply, and discloses a fault identification-based self-adaptive switching control method and system for a network-building converter, and the method comprises the following steps: S1, carrying out the online identification of the dominant features of a transient fault; s2, selecting a dominant support strategy so as to switch the converter to an operation mode matched with the dominant support strategy; s3, providing optimal support for the transient process; and S4, after the transient fault is cleared, the converter is switched to a conventional operation mode, the system comprises a transient feature identification module, a self-adaptive strategy selection and recombination module and a safe switching and recovery module, and the method has the following beneficial effects: 1, the transient fault response accuracy is improved; 2, the control continuity and safety are guaranteed; and 3, the recovery process is safer and more reliable.

The application discloses an online identification method for pyrolysis and ablation interface evolution of carbonized ablation material based on X-ray image fusion enhancement. In the ground thermal test of the carbonized ablation material, a plurality of groups of images of the carbonized ablation material at different moments are obtained through X-ray imaging, an image fusion enhancement algorithm is adopted to improve the image quality, the local features of the pyrolysis and ablation interface are enhanced, the image noise is reduced through local sliding window averaging, and the pyrolysis and ablation interface is identified in real time based on a gradient algorithm. The application can realize real-time automatic measurement of the ablation amount, the interface between the pyrolysis layer and the carbonization layer, and the interface between the pyrolysis layer and the original layer in the ground thermal test of the carbonized ablation material, overcome the problems that the traditional measurement method is difficult to identify the interface between the pyrolysis layer and has low accuracy, and improve the measurement technical level of the ground thermal test of the thermal protection material, thereby providing support for the design of the ablation thermal protection system.

This invention discloses an online method and system for identifying coal powder fineness by fusing multi-source images and operating parameters, belonging to the fields of thermal power generation detection and artificial intelligence technology. The method includes: simultaneously acquiring backlight transmission and annular diffuse reflection images of the same coal powder particle group within a detection chamber using an air curtain isolation and mechanical cleaning self-cleaning mechanism; extracting morphological features and surface texture features of the particle group using a dual-branch deep neural network; generating a fused feature tensor by combining the above features with the coal mill operating parameters through a gated fusion network, and then outputting real-time fineness values ​​and early warnings through a regression prediction and early warning network; when update conditions are triggered, incremental fine-tuning of the fusion and regression prediction and early warning networks is performed on an edge computing device, and a containerized grayscale strategy is used to achieve hot service updates. This invention enables long-term, maintenance-free, high-precision, and adaptive online monitoring under high dust conditions.

This invention discloses an online measurement device and method for coal dust parameters based on multi-physics field fusion and adaptive decoupling. The device includes: a co-location fusion sensing probe, which integrates a charge sensing module and a microwave resonant sensing module, the microwave resonant sensing module operating at a frequency of 1.0-5.0 GHz; an adaptive decoupling calculation unit, equipped with an adaptive decoupling algorithm, configured to simultaneously receive electrostatic and dielectric characteristic signals, identify coal type characteristic parameters online through a pre-stored dielectric characteristic-coal type parameter mapping relationship, substitute these parameters into the charge signal physical model, and decouple and calculate the estimated coal dust concentration and flow rate values ​​to eliminate the influence of coal type through the online parameter identification method; and a data communication unit for outputting the estimated coal dust concentration and flow rate values. This invention, through online identification of coal type characteristics using microwave dielectric measurement and dynamic correction of the charge model, fundamentally eliminates measurement drift caused by changes in coal type, achieving long-term calibration-free, high-precision online measurement.

This invention discloses an online method and device for underwater target recognition using forward-looking sonar images. The method involves inputting a collected forward-looking sonar image dataset into a pre-trained YOLOv3 deep learning detection model for fine-tuning. The model file, weight file, and configuration file are then converted using an ATC tool to obtain an offline model, which is then deployed to an embedded system. The data stream acquired online by the forward-looking sonar device is converted to generate sonar images. The embedded system then uses the detection model to detect targets in the images, and the detection results are packaged and transmitted to the display and control software. This makes the results more intuitive and reduces the difficulty of target recognition in sonar images. It assists operators in judging targets on the display and control system, avoiding missed and false detections, reducing operator error rates, and solving the problem of limited scalability due to the difficulty of manual identification.

The invention relates to the technical field of mixed loading explosive trucks, and discloses a five-stage telescopic arm and dynamic stability control coupling system of an on-site mixed loading explosive truck. The system comprises a high-precision sensing system, a dynamic modeling system, a coupling control system, a precise execution system and a safety monitoring system. The arm length, the gravity center position and the rotational inertia are calculated in real time through the time-varying parameter online identification module, the coupling control system executes feed-forward compensation, envelope calculation and gain scheduling at the same time based on the time-varying parameter online identification module, and the safety monitoring system calculates the safety operation range and triggers emergency arm falling when the range exceeds the limit. A multi-sensor data fusion and weighted Kalman filtering algorithm is adopted to realize accurate positioning and dynamic stability control of the five-stage telescopic arm under a complex working condition. The technical problems that in the prior art, due to flexible deformation, parameter time varying and external disturbance, the positioning precision is low, vibration suppression is poor, and the instability risk is high are solved, and the advantages that the tail end positioning error is smaller than or equal to 5 mm, the vibration attenuation rate is larger than or equal to 88%, and the control response lag is smaller than or equal to 50 ms are achieved.

A transmission characteristic parameter identification method for a ball screw feeding system comprises the following steps: in the normal operation process of the ball screw feeding system, acquiring voltage, current, rotating speed and other signals through a servo motor and a driving system thereof, and realizing online identification of key electromagnetic parameters and mechanical parameters in stages; under the static working condition of the motor, controlled voltage excitation is applied, stator current is measured, and identification of stator winding resistance and stator inductance is completed; under the steady-state operation condition, the flux linkage amplitude of the permanent magnet is calculated based on a q-axis voltage equation; under a dynamic operation condition, a friction torque model is established through segmented constant-speed operation, a rotor motion equation is combined, a recursive least square algorithm with a forgetting factor is introduced, and online identification of the equivalent rotational inertia of the system is realized; according to the method, complete and accurate transmission characteristic parameters can be obtained in the ball screw feeding system, the identification process is stable, the calculation complexity is low, and the method is suitable for being embedded into a servo control system.

This application discloses a multi-scenario adaptive thermal management control method, device, medium, and product, relating to the field of electric vehicle thermal system management. The method includes: constructing a thermal system model library and obtaining dynamic response characteristics; obtaining a thermal system controller based on a hierarchical control strategy using a dynamic programming algorithm; determining personalized control parameters using an asymmetric Gaussian process regression model and a perceived temperature prediction model; integrating multi-mode switching logic to dynamically adjust personalized control parameters, resulting in a panel controller; determining thermal scenario prediction data using an online identification model and clustering thermal system sensitive features using an improved K-Means++ algorithm to generate a typical scenario feature vector library; and determining a multi-scenario adaptive thermal management control strategy using a hierarchical controller constructed based on the thermal system controller and the panel controller. This application can determine a dynamic control strategy based on real-time data, achieving thermal system adaptation under real-time conditions.