50 results about "Nonlinear dynamic modeling" patented technology

The invention relates to the technical field of power system monitoring, and discloses an infrared thermal image feature fusion-based power transmission line abnormal area identification method, which comprises the following steps of: acquiring infrared thermal image data of a power transmission line, and preprocessing the data; the invention further provides an infrared thermal image feature fusion power transmission line abnormal area identification system, which comprises a data acquisition module, an infrared thermal image feature fusion module, an infrared thermal image feature fusion module, an infrared thermal image feature fusion module, an infrared thermal image feature fusion module and an infrared thermal image feature fusion module, and is characterized in that the data acquisition module is used for acquiring infrared thermal image data of a power transmission line; and the preprocessing module is used for preprocessing the collected infrared thermal image data. According to the method, through combination of multi-scale wavelet transform, nonlinear dynamic modeling, graph neural network optimization and reinforcement learning, the abnormal region identification precision is improved, the robustness and adaptive ability of the system in a complex environment are enhanced, and it is ensured that the abnormal region can still be efficiently and accurately identified under dynamic change and noise interference.

The invention discloses a micro-grid dynamic scheduling method based on deep learning, and the method comprises the steps: fusing industrial Internet of Things collection and GIS positioning, and constructing a multivariable original spatio-temporal data set covering multiple nodes; extracting multi-scale features through multi-resolution wavelets and Fourier transform, combining the multi-scale features with a dynamic adjacency matrix, and realizing feature adaptive distribution and nonlinear dynamic modeling by using multi-scale attention gating, graph convolution and a time sequence neural network model; the micro-grid load and state prediction accuracy, the system generalization ability and the abnormal response level can be effectively improved, and powerful support is provided for intelligent scheduling and abnormal analysis.

The invention discloses a nonlinear dynamic modeling method of a helical gear transmission system considering wear. The method comprises the following specific steps: step (1), establishing a time-varying meshing stiffness calculation model of a helical gear pair in a healthy state; (2) establishing a time-varying meshing stiffness calculation model of the wear fault helical gear pair; (3) establishing a helical gear tooth surface friction model under the condition of mixed elastohydrodynamic lubrication; (4) establishing a deformation equation of the tapered roller bearing; (5) establishing a helical gear transmission system model considering wear, and solving the nonlinear vibration characteristics of the helical gear transmission system under different wear degree faults of the helical gear; the method has the beneficial effects that the nonlinear dynamic model of the helical gear transmission system considering wear is established, and technical support is provided for related technologies of fault dynamic modeling of the current helical gear transmission system.

The invention relates to the technical field of mobile robot control, and discloses a wheeled robot trajectory tracking anti-interference control system, which comprises a nonlinear dynamics modeling module used for establishing a nonlinear dynamics model of a wheeled robot so as to describe the motion characteristics of the robot under the influence of ground friction force, sliding force and modeling errors; the disturbance estimation module is used for estimating external disturbance and system modeling errors in real time through an extended state observer and providing disturbance compensation data for the controller; and the sliding mode control module is used for realizing rapid convergence of track deviation by constructing a sliding mode surface and enhancing the anti-interference capability of the system by dynamically adjusting the sliding mode gain. According to the invention, through nonlinear dynamic modeling, disturbance estimation, sliding mode control and model prediction control fusion and real-time trajectory optimization, high-precision trajectory tracking is realized, and the anti-interference capability and the dynamic environment adaptability are enhanced.

The invention discloses an encrypted signal receiving guide system for an unmanned aerial vehicle. According to the invention, the self-adaptive anti-interference module realizes real-time identification, feature analysis and dynamic suppression of interference signals through a multi-dimensional joint sensing and closed-loop adjustment system. The interference detector is fused with a time-frequency domain depth feature extraction technology, and existence features of complex interference such as frequency hopping and frequency sweeping can be captured in a millisecond-level time window; the interference characteristic analyzer accurately deconstructs a modulation rule and a propagation characteristic of an interference signal based on a nonlinear dynamic modeling and pattern learning algorithm; the self-adaptive filter adopts a multi-modal optimization strategy and combines space-time correlation characteristics of navigation signals, so that the integrity of original signals is reserved to the greatest extent while interference is eliminated. The whole set of mechanism forms a complete anti-interference closed loop from environmental perception to dynamic response, and ensures that the unmanned aerial vehicle can still maintain a stable encrypted communication link and centimeter-level navigation precision in extreme scenes such as strong electromagnetic suppression and dense signal conflicts.

The invention discloses a gear transmission system nonlinear dynamic modeling method and system considering thermal deformation, and relates to the technical field of gear transmission system dynamic simulation. The method comprises the steps that component parameters and operation parameters of the planetary gear system are received; based on the component parameters and the operation parameters of the planetary gear system, a tooth surface flash temperature model is constructed, and a tooth surface friction coefficient is obtained through calculation; and constructing a tooth profile thermal deformation model, and respectively calculating internal excitation of the planetary gear system considering tooth profile thermal deformation. According to the method, the gear surface flash temperature model, the tooth profile thermal deformation model and the bearing gap model are constructed, the helical tooth planetary gear train-bearing nonlinear dynamic model is established, and the technical defect that the gear temperature rise effect and the nonlinear factors of the bearing cannot be fully considered in traditional helical tooth planetary gear train dynamic modeling is overcome; and the vibration response prediction precision under the high-speed and heavy-load working condition is obviously improved.

The invention relates to the technical field of civil engineering detection, and discloses an automatic standard penetration data acquisition system based on the Internet of Things, which comprises a data acquisition module used for acquiring acceleration and vibration data in a test process through a sensor mounted on a standard penetration hammer; the data transmission module is used for transmitting the collected original data to a remote server or a cloud through a wireless communication technology for storage and processing; the data processing module is used for carrying out denoising, normalization, feature extraction and nonlinear dynamic modeling on the acquired signal data to obtain dynamic features of the standard penetration test process; and the data analysis and prediction module is used for analyzing the data based on a deep learning algorithm and predicting the soil bearing capacity and abnormal conditions occurring in the test process. A standard penetration data automatic acquisition scheme based on the Internet of Things is adopted, full automation of data acquisition, transmission and processing is achieved, and the test efficiency and accuracy are remarkably improved.

The invention relates to the technical field of communication monitoring, and discloses an optical transmitter and receiver transmission monitoring method, which comprises the following steps: S1, data acquisition and preprocessing: acquiring various operation data of an optical transmitter and receiver in real time, including temperature, voltage, current and signal intensity, and preprocessing the acquired data, including data cleaning, noise filtering and normalization; s2, nonlinear dynamic modeling and fault prediction, the invention also provides an optical transceiver transmission monitoring system, and the system comprises a data collection module which is used for collecting the parameters of temperature, voltage, current and signal intensity of the optical transceiver in real time; and the nonlinear dynamics analysis module is used for carrying out system modeling based on a nonlinear dynamics theory and calculating a Lyapunov index. By introducing the LQG control algorithm, the redundancy coding technology and the remote monitoring platform, optimization in the aspects of dynamic threshold adjustment, fault prediction and data transmission security is realized, and the intelligence, reliability and security of the system are improved.

The invention relates to the technical field of big data analysis and risk management, and discloses a risk signal identification method based on big data mining, and the method comprises the following steps: S1, data preprocessing and multi-modal feature construction: collecting structured data, unstructured data and time series data, and uniformly constructing a multi-modal high-dimensional feature vector; s2, topological space construction and persistent coherence analysis: constructing an adjacent graph by using an Euclidean distance between data, extracting high-dimensional spatial topological features, and extracting an abnormal mode through calculation of a coherence group and a Betti number; and S3, dynamic abnormal point identification based on nonlinear dynamics: constructing a phase space trajectory through time delay embedding, and calculating a Lyapunov index and a fractal dimension to identify high-risk abnormal points. According to the invention, through multi-modal data fusion, topology analysis, nonlinear dynamic modeling and dynamic visualization, accurate identification and comprehensive display of risk signals are realized.

The invention belongs to the field of machining, and discloses a modeling calculation method and system for dynamic rigidity of a cutter handle-main shaft combination part, and the method comprises the following steps: S1, modeling a geometric structure and dynamic displacement of the combination part; s2, performing multi-scale modeling on the contact surface morphology and solving the normal spacing variation of the joint part; s3, joint surface micro-convex body contact characteristic modeling is carried out; s4, modeling the non-linear stiffness of the infinitesimal junction surface; and S5, constructing an integral dynamic stiffness matrix of the joint part. According to the modeling calculation method for the dynamic stiffness of the cutter handle-main shaft combination part, high-precision modeling of the dynamic characteristics of the cutter handle-main shaft combination part is achieved through a step Timoshenko straight beam model and a distributed nonlinear spring layer in combination with a slicing method and a fractal contact theory. The method is suitable for high-precision nonlinear dynamic modeling of a numerical control machine tool, a machining center and the like, and vibration, deformation and stability problems in the machining process can be effectively predicted.

The invention provides a gas relay intelligent detection test bench based on cloud computing and an automatic calibration system, and relates to the technical field of specific cloud computing models. An intelligent decision fusion unit is constructed through cooperation of a fuzzy adaptive neural network and PINNs to realize intelligent setting of a threshold value, and standardized accurate calibration of a mechanical floater and a fault mechanism is completed by virtue of a technical route of high-precision sensor-cloud nonlinear dynamic modeling-closed-loop online optimization. And the detection consistency and the response reliability of the multi-model gas relays are obviously improved.

The invention provides a nonlinear dynamic modeling, solving and control method of a linear motor, and belongs to the field of structural dynamics, and the method comprises the steps: 1) simplifying an LRA into a two-degree-of-freedom mass-spring-damping nonlinear system, and obtaining the linear stiffness and cubic nonlinear stiffness of a beam spring; (2) nBgleff discrete data points of different relative displacements of the motor vibrator and the shell are obtained through simulation, an nBgleff fitting curve is obtained, and then electromagnetic force is obtained; 3) identifying system damping parameters by combining measured data; according to the method, the goodness of fit Rnl of the model to the LRA vibration response is larger than 0.95, vibration characteristics can be accurately predicted, the LRA vibration consistency is effectively improved through the control strategy, and the tactile feedback experience is improved.

The invention discloses a nonlinear dynamic modeling method and system for a planetary gear transmission system, relates to the technical field of transmission systems, and is used for solving the technical problems that in a planetary gear dynamic modeling mode in the prior art, errors of a meshing area and meshing force are large, and the vibration phenomenon of the planetary transmission system cannot be accurately reflected. The invention discloses a nonlinear dynamic modeling method for a planetary gear transmission system. The nonlinear dynamic modeling method comprises the steps of determining a two-stage straight-tooth planetary gear transmission system; establishing a degree-of-freedom model of the external and internal meshing gear pair according to the transmission system; improving the time-varying meshing stiffness of an internal and external meshing pair of the transmission system by using a degree-of-freedom model; obtaining multi-source excitation of the transmission system containing the time-varying meshing angle and the position angle according to the degree-of-freedom model; establishing a nonlinear function of the relative displacement and the gear backlash of the internal and external meshing pairs along the meshing line direction, and acquiring the meshing force of each meshing pair; and according to the time-varying meshing stiffness, the multi-source excitation and the meshing force, establishing a multi-stage planetary transmission kinetic model.

This invention discloses a nonlinear dynamic modeling method for crack evolution in a helical gear transmission system for vehicles, which includes the following steps: (1) establishing a calculation formula for meshing stiffness of the helical gear pair under cracked conditions using the slice integration method and the potential energy method; (2) establishing an equivalent circuit model of the motor by combining the principles of circuit theory and magnetic circuit theory with coordinate transformation relationship; (3) building an electromechanical coupling model of the motor-gear system based on the mechanical relationship between the motor and the gear transmission system; (4) establishing a model of the electromechanical coupling helical gear transmission system for vehicles and solving the influence mechanism of crack evolution of helical gears on the dynamic characteristics of the electromechanical coupling helical gear transmission system for vehicles; the beneficial effect is that this invention establishes a nonlinear fault dynamic model of the electromechanical coupling transmission system for new energy vehicles, which is of great significance for the design, optimization and life prediction of the helical gear transmission system for new energy vehicles.

The invention discloses a planetary roller screw gear-thread coupling excitation dynamics modeling method, which comprises the following steps: step (1), establishing a planetary roller screw stress model, and analyzing thread meshing elastic deformation; (2) establishing a calculation model of a gear pair system, and analyzing gear engagement excitation and time-varying engagement stiffness excitation; (3) establishing a mechanical equation set of a planetary roller screw gear pair and a thread pair, and analyzing the influence of gear meshing excitation on the thread dynamic contact force; (4) establishing a nonlinear dynamic differential equation set of the planetary roller screw transmission system; (5) reasonable parameters are selected to solve nonlinear response of the system; the method has the beneficial effects that the nonlinear dynamic response of the planetary roller screw considering the gear meshing and thread meshing coupling excitation can be solved, the current vacancy of the nonlinear dynamic modeling method of the planetary roller screw considering the gear meshing and thread meshing coupling excitation is filled, and the method has good universality.

The invention relates to the technical field of computer fault early warning, in particular to a server fault early warning system and method for a computer. Comprising a multi-source signal acquisition module, a data preprocessing module, a cross-domain signal correlation analysis module, a nonlinear dynamic modeling module, a frequency domain feature extraction module, an intermittent fault prediction model and a multi-level fault early warning module. According to the method, through technologies of multi-source signal fusion acquisition, nonlinear dynamic modeling, frequency domain feature extraction, Bayesian reasoning and the like, accurate prediction and multi-level early warning of intermittent faults of the server are realized, and the accuracy and real-time response capability of fault detection are improved, so that stable operation of the server is effectively guaranteed.

The application discloses a self-adaptive multi-modal data fusion bridge health monitoring method and system, generates a response sample database through establishment of a bridge nonlinear dynamics equation, pre-processes bridge sensor data and trains a deep learning model to extract damage features, and self-adaptively optimizes model parameters; based on model output, real-time and historical data are combined to perform bridge health evaluation, and a monitoring strategy is dynamically adjusted or early warning is triggered; an edge computing device is deployed to perform pre-processing and damage identification, and long-term trend analysis is realized in cooperation with a cloud platform; macro-structure damage and micro-crack data are fused through cross-scale feature extraction; an artificial intelligence algorithm is used to generate a bridge health evaluation result, a bridge maintenance and repair scheme and a decision-level early warning signal. The application combines nonlinear dynamics modeling, self-adaptive deep learning, edge cloud collaborative computing and cross-scale analysis, improves complex damage identification precision and monitoring intelligent level, and is suitable for large-scale traffic network bridge monitoring management.

The invention provides a multi-motor servo system preset time control method based on state and parameter estimation, and aims to improve the control precision, dynamic responsiveness and robustness of a multi-motor servo system. Firstly, nonlinear dynamic modeling is carried out by adopting a Gaussian neural network, and unknown dynamic characteristics in a system are accurately approached; secondly, an observer is designed for the multi-motor servo system, the unknown state and parameters of the system can be estimated at the same time, and the adaptive capacity and robustness of the system under the complex working condition are remarkably improved. And finally, providing a new variable gain predefined sliding mode control surface in combination with an observer estimation result. According to the method, the problem of state constraint existing in traditional sliding mode control can be effectively solved, and it is ensured that the system achieves rapid and stable tracking control within preset time. According to the technical scheme, the performance of the multi-motor servo system in the aspects of accurate control, high-speed response and robustness can be remarkably improved, and the method is widely applied to high-precision and multi-degree-of-freedom servo control systems.

The invention relates to the technical field of intelligent navigation and control, and discloses a ship intelligent monitoring method which comprises the following steps: data acquisition: acquiring an initial state variable and real-time environment data of a ship; dynamic modeling: establishing a nonlinear dynamic model of the ship based on the collected ship state variable and control input; optimization target construction: on the basis of the dynamic model, constructing an optimization target function, and generating an optimal control strategy; solving numerical values; the invention also discloses a ship intelligent monitoring system, and the system comprises a data collection module which is used for collecting the initial state variable and the real-time environment data of the ship; a dynamic modeling module; an optimization module; a numerical solution module; a real-time monitoring module; and a simulation verification module. According to the invention, through multi-objective optimization, real-time dynamic adjustment and nonlinear dynamic modeling, optimization of high efficiency, adaptability, safety and economical efficiency of ship path planning in a complex marine environment is realized.

The application provides a dynamic modeling method and system for multi-field skill AI automatic semantic labeling, and relates to information retrieval, medical health and cross-field skill fusion scene. Through nonlinear dynamic modeling and biomedical stability control, the semantic label automatic generation and real-time matching of talent skill and health intervention effectiveness are realized. The application innovatively integrates the sound wave frequency of fetal brain promoting music method, maternal action instruction and blood type nutrition scheme into the "skill-time" frequency domain space, combines the Gevrey smoothing operator and fractional derivative evolution equation, accurately separates the temporary fetal movement fluctuation and low-frequency core characteristics, and breaks through the characterization limitation of traditional models on nonlinear biological coupling effect. By introducing the "intervention effect-time" frequency domain component and local dependency constraint, the short-term effectiveness and long-term value of music intervention on fetal neurodevelopment are quantified, and the model distortion problem caused by extreme data or cross-field parameter resonance in traditional methods is solved.

The present invention provides a method for calculating the nonlinear dynamic characteristics of a pressurized water reactor fuel assembly, which belongs to the field of nuclear power technology, and includes: obtaining the boundary conditions of a grid when it is not slipping and when it is slipping; modeling to obtain the control equation of the grid; solving the control equation of the grid to obtain the bending moment of one end of the grid to the guide tube; constructing the dynamic equation of the guide tube; using the modal superposition method to obtain the first discrete linear equation group; calculating the quasi-static loading response of the pressurized water reactor fuel assembly; discretizing the dynamic equation of the pressurized water reactor fuel assembly to obtain the second discrete linear equation group; solving the second discrete linear equation group to obtain the dynamic response of the pressurized water reactor fuel assembly. The present invention solves the problem of high-precision nonlinear dynamic modeling of fuel assemblies.

The invention discloses a helical gear nonlinear dynamic modeling method based on three-dimensional anisotropy, and belongs to the technical field of dynamic simulation of gear transmission systems. Comprising the following steps: establishing a three-dimensional anisotropic tooth surface microstructure model based on a W-M fractal theory; correcting a gear backlash nonlinear displacement function and a bearing clearance nonlinear displacement function; establishing a nonlinear dynamic model of the six-degree-of-freedom helical gear transmission system containing a gear backlash, a bearing clearance and friction; performing linear change and dimensionless on the nonlinear kinetic equation of the gear pair to obtain a dimensionless nonlinear kinetic model considering the backlash and the bearing clearance based on the three-dimensional anisotropy; replacing the non-linear displacement function of the gear backlash and the non-linear displacement function of the bearing clearance in the step 2 into a dimensionless non-linear dynamic model; and calculation is carried out in MATLAB simulation software. According to the method, the gear backlash is used as a variable for modeling, and the modeling precision is improved.

The present invention relates to the field of virtual reality technology, and discloses a 3D experience interaction system based on virtual reality. The 3D experience interaction system includes a nonlinear dynamics modeling module, a synchronization control module, a chaos synchronization module, a quantum information synchronization and entanglement optimization module, and a performance evaluation and experiment module: The nonlinear dynamics modeling module: is used to establish the interaction relationship between each user and virtual objects in the virtual reality system, and describe the change of the user state over time based on the nonlinear dynamics equation to consider the mutual influence between users. Through the synergistic effect of the quantum information synchronization and entanglement optimization module and the synchronization control module, the presenter and the audience can share the same virtual 3D world and remain consistent in real-time interaction, obtaining a seamlessly synchronized virtual experience, ensuring that multiple users can smoothly interact in the same virtual space and synchronously display content.

The invention discloses an underwater flexible robot three-dimensional motion control method, device and equipment, a medium and a product, and relates to the field of flexible robot automation technology and nonlinear dynamics modeling analysis control. The method is suitable for a robot with periodic fluctuation and flexible motion characteristics. The method comprises the following steps: acquiring information data of a target underwater flexible robot; according to the information data, on the basis of a body wave equation of the target underwater flexible robot, a physical experiment and fluid mechanics simulation method is adopted, fitting of hydrodynamic parameters is conducted, and a hydrodynamic equation is obtained; based on the Lagrange theory, a kinetic model is determined according to the hydrodynamic equation; performing closed-loop motion control based on a three-dimensional space according to the kinetic model to realize three-dimensional trajectory tracking control of the target underwater flexible robot; the three-dimensional space is determined based on the speed, course and depth of the target underwater flexible robot. The invention aims to realize three-dimensional motion control of the underwater flexible robot.

The invention discloses a nonlinear coupling control method and device for piezoelectric dispensing and a medium, and relates to the technical field of precise dispensing and motion control. The method comprises the following steps: acquiring structural parameters of the dispensing system, and establishing a nonlinear kinetic model of the dispensing system; performing parameter scanning and sensitivity index calculation on the structure parameters through a nonlinear kinetic model to obtain key parameters of which the response to the dispensing system is greater than a preset threshold value; constructing a multi-objective optimization function taking the maximum displacement, the maximum speed and the vibration amplitude of the firing pin as performance objectives; building a joint simulation platform, and importing the key parameters into the joint simulation platform to obtain real-time response energy of the firing pin; adjusting a weight coefficient of the multi-objective optimization function; and performing iterative simulation and iterative adjustment through an optimization algorithm to obtain an optimal parameter combination of the key parameters. According to the method, the platform is optimized by fusing nonlinear dynamic modeling and joint simulation, and the movement performance of the firing pin and the overall stability of the dispensing system are improved.

The invention relates to the field of building construction, and discloses an ultra-large-area complex factory building high-precision wear-resistant ground construction process which comprises the following steps: establishing a predetermined height distribution model of a target ground based on topographic data and design requirements of a construction area, and estimating an actual construction height in real time in combination with sensing data acquired in a construction process; a construction error model used for reflecting the error between the target height and the actual construction height is established; and by taking the construction error model as input, performing nonlinear dynamic modeling operation on the construction equipment, establishing a multi-body dynamic model of the construction equipment, and extracting a control variable related to the motion state of the equipment as input of an equipment control strategy. By introducing a simulation optimization and real-time feedback mechanism, the dynamic optimization of a construction control strategy and material distribution is realized, the construction precision, the material utilization rate and the equipment efficiency are effectively improved, and the efficient and accurate execution of the construction process is ensured.

The invention discloses a kinetic modeling method for a pitting corrosion fault of a planetary gear transmission system. The method comprises the following steps: step (1), calculating time-varying meshing stiffness of a planetary gear under different pitting corrosion degrees based on a potential energy method; (2) introducing a gear backlash nonlinear function and a comprehensive meshing error, and determining relative displacement on a meshing line of each stage of gear pair; (3) calculating the time-varying friction force among all stages of gear pairs by utilizing a coulomb model; (4) establishing a planetary gear transmission system pitting corrosion fault dynamic differential equation set; (5) solving the nonlinear dynamic characteristic response of the system; the method has the beneficial effects that the time-varying friction force and the gear tooth pitting corrosion fault are considered, the nonlinear dynamic response characteristics of the planetary gear transmission system are solved, and the related technical blank of nonlinear dynamic modeling of the pitting corrosion fault of the current planetary gear transmission system is filled.

The present invention relates to the technical field of power system monitoring, and discloses a method for identifying abnormal regions of transmission lines by infrared thermal image feature fusion, including the following steps: collecting infrared thermal image data of the transmission line and preprocessing the data; performing multi-scale decomposition on the preprocessed infrared thermal image through wavelet transform to extract the low-frequency part and the high-frequency part. The present invention also provides a system for identifying abnormal regions of transmission lines by infrared thermal image feature fusion, including: a data acquisition module for collecting infrared thermal image data of the transmission line; a preprocessing module for preprocessing the collected infrared thermal image data. By combining multi-scale wavelet transform, nonlinear dynamics modeling, graph neural network optimization, and reinforcement learning, the present invention improves the accuracy of abnormal region identification, enhances the robustness and adaptability of the system in complex environments, and ensures efficient and accurate identification of abnormal regions under dynamic changes and noise interference.

This invention discloses a dynamic modeling method for planetary gear systems considering elastohydrodynamic lubrication, comprising the following steps: Step (1) establishing an elastohydrodynamic lubrication model for the planetary gear system; Step (2) calculating the comprehensive meshing stiffness of the planetary gear system; Step (3) calculating the time-varying friction coefficients between gear pairs at each stage under elastohydrodynamic lubrication conditions; Step (4) establishing a set of dynamic differential equations for the planetary gear system under elastohydrodynamic lubrication conditions; Step (5) solving for the nonlinear dynamic characteristic response of the planetary gear system. The beneficial effect is that this method takes elastohydrodynamic lubrication and time-varying friction into account and solves for the nonlinear dynamic response characteristics of the planetary gear transmission system, filling the current technical gap in nonlinear dynamic modeling of planetary gear transmission systems under elastohydrodynamic lubrication conditions.

The invention relates to the technical field of non-electrical variable control or regulation systems, and particularly discloses a remote control system and a remote control method for a high-pressure air blasting device. The system comprises a remote control terminal, a system main controller, a nonlinear dynamic modeling unit, a self-adaptive prediction control unit, a multi-modal execution driving unit and a high-precision sensing acquisition network. By constructing a real-time updated nonlinear dynamic model and combining adaptive predictive control and multi-mode valve cooperative driving, millisecond-level high-precision tracking and stable output of bursting pressure are achieved, and operation safety and control reliability are effectively improved.