41 results about "Nonlinear kinetics" patented technology

The invention discloses an attention mechanism and LSTM fused industrial bearing vibration time sequence signal fault prediction method and system. The method comprises the following steps: collecting a bearing vibration signal and carrying out filtering, noise reduction and normalization preprocessing; constructing a deep learning model combining the bidirectional BiLSTM and a coordinate attention mechanism to extract bidirectional time sequence features and enhance key fault features; carrying out model training by adopting a multi-target composite loss function and an Adam optimizer, and introducing an early stop mechanism to prevent overfitting; performing fault type identification and degree evaluation on the real-time vibration signal by using the trained model, and performing quantitative analysis by fusing multi-scale spectrum kurtosis features and nonlinear kinetic parameters; and finally, outputting a fault diagnosis report, and triggering multi-stage early warning based on an adaptive threshold. The method can realize high-precision and high-reliability bearing fault prediction and health state evaluation, and is suitable for intelligent operation and maintenance of industrial equipment.

The invention discloses a micro-cantilever geometric structure parameter inversion system, method and device, and belongs to the technical field of micro-electro-mechanical systems. According to the system, a dynamic model containing geometry, material and electromechanical coupling nonlinearity is constructed, a PINNs network fused with physical constraints is designed, a staged training optimization strategy is adopted, and efficient inversion of micro-cantilever geometric parameters is achieved. The core of the method is that a nonlinear kinetic equation is used as a constraint to be embedded into a neural network for training, experimental data and physical residual errors are combined to construct a composite loss function, and the problems that a traditional method neglects a nonlinear effect, depends on finite element simulation and is low in inversion precision are solved. The average relative error of inversion of the system is lower than 1%, which is obviously superior to that of a traditional method, and the method can be widely applied to MEMS device design, online detection and closed-loop control scenes.

The invention relates to the field of nonlinear system modeling, and particularly provides a global linear modeling method and system for a nonlinear system based on Lie derivative and sparse recognition, a terminal and a medium, and the method comprises the steps: obtaining an explicit nonlinear kinetic equation of a target system, calculating the Lie derivative of a state variable or an output variable of the system based on the kinetic equation, constructing a candidate observation function library directly associated with the physical mechanism of the system; performing screening and dimension reduction on the candidate observation function library by adopting a sparse recognition method to obtain a low-dimensional observation function set; and on the basis of the screened observation function and system operation data, a finite-dimensional global linear model of the target system in the dimension raising observation space is obtained through identification by using a data driving algorithm. According to the method, the online solving efficiency and the control real-time performance are improved, and efficient and reliable multi-target collaborative optimization control of systems such as a wind driven generator is realized.

The invention discloses a consistency control method and system for second-order multi-agent iterative learning and a medium. The method comprises the steps that a second-order nonlinear CMANSs kinetic equation which is composed of N follower bodies and a leader body and has uncertainty disturbance is constructed; constructing a double-layer adaptive iterative learning control input model; controlling an input model based on the double-layer adaptive iterative learning so as to enable the tracking consistency error of the iterative learning of the second-order nonlinear CMANSs kinetic equation within a preset time range to be within a preset range; and on the basis of a Lyapunov function, the tracking consistency error of iterative learning in a preset time range is demonstrated in a preset range. Therefore, the method can control the input model through double-layer adaptive iterative learning, and ensures that the second-order nonlinear CMANSs can still realize robust tracking in an uncertain environment, thereby realizing tracking consistency of the second-order nonlinear CMANSs with uncertain disturbance in finite time.

The invention discloses an intelligent self-healing power grid metrological verification control method, system and device and a medium, and the method comprises the steps: carrying out the phase-space reconstruction based on the fused time sequence data, judging whether the time sequence has a nonlinear dynamic characteristic or not, and outputting a predicted value and a residual feature of a system state; performing anomaly identification on the operation state of the metering device by adopting an improved anomaly detection algorithm, and outputting an anomaly type, a confidence coefficient and a risk level; dynamically generating a verification task priority list and a resource scheduling scheme in combination with a historical verification record of the equipment and a current resource state; and a self-adaptive self-healing control mechanism is triggered, corresponding self-healing operation is executed, an optimized control strategy is fed back to all the links, and closed-loop self-recovery and continuous optimization of the system are achieved. The reliability, the self-adaptability and the intelligent level of the verification system are remarkably improved.

The present application relates to the technical field of nonlinear dynamics experimental equipment, in particular to a V-shaped spring oscillator programmable excitation device and method based on reference frame transformation. The device comprises a V-shaped spring structure, a driving module, a control module and a measurement module. The V-shaped spring structure comprises two springs, one end of each spring is connected to a movable suspension point, and the other end is connected to a mass. The driving module is used to drive the two movable suspension points to move synchronously. The control module is used to generate a control signal according to a target external force waveform to control the motion trajectory of the movable suspension points. The measurement module is used to collect the motion data of the mass. By transforming the reference frame to a non-inertial reference frame with the movable suspension point as the origin, the mass is subjected to an equivalent inertial force in the non-inertial reference frame, which is the programmable external force applied to the mass.

The application discloses a kind of fusion attention mechanism and the industrial bearing vibration time series signal fault prediction method and system of LSTM.This method includes: collecting bearing vibration signal and carrying out filtering, noise reduction and normalization preprocessing;BiLSTM and coordinate attention mechanism combined deep learning model is constructed to extract bidirectional time series features and enhance key fault features;Multi-objective composite loss function and Adam optimizer are used for model training, and early stopping mechanism is introduced to prevent overfitting;Real-time vibration signal is used to identify fault type and degree evaluation using the trained model, and multi-scale spectral kurtosis features and nonlinear dynamics parameters are used for quantitative analysis;Finally, output fault diagnosis report, and trigger multi-level early warning based on adaptive threshold value.The application can realize high-precision, high-reliability bearing fault prediction and health state evaluation, and is suitable for industrial equipment intelligent operation and maintenance.

The invention relates to the technical field of battery management, in particular to a novel self-adaptive control and synchronization method for nonlinear dynamic behaviors of a sodium-ion battery, which comprises the following steps: designing a controller by combining a self-adaptive inversion technology, and constructing a strategy by adopting a multi-step Lyapunov function; in order to ensure that a system state error and a parameter estimation error can be converged to zero within free intention time pre-specified by a user, a self-adaptive parameter adjustment rate is designed to ensure the continuity and the bounded property of a control signal, and in order to cope with parameter uncertainty caused by battery aging and environment change, a self-adaptive parameter adjustment rate is designed. The method further introduces a real-time monitoring and dynamic adjusting mechanism. According to the method, the control capability of a highly-uncertain and highly-nonlinear system is remarkably enhanced, the control flexibility is improved through setting of free intention time, the global stability is enhanced through multi-step inversion design, the control precision and robustness are remarkably improved, and the method can be widely applied to various scenes such as fixed energy storage and electric automobiles and is prominent in practical value.

The utility model relates to a non-linear dynamic test bench, which comprises a non-linear dynamic test assembly, a surrounding frame and an experiment table, and is characterized in that a motor is started through the arranged experiment table, the output end of the motor drives a linkage rod to steer under rotation, a push-pull arm pushes a door plate, and the door plate swings and folds on the rear end face of a surrounding plate; the rear end face of the peripheral plate is in an opening state, personnel can directly enter the interior of the peripheral plate through the opened opening to overhaul algorithm hardware, after overhaul is completed, the convenient opening and closing structure is made to operate reversely, and after the door plate is closed, the heat dissipation body is started, and the front end of the heat dissipation body is made to rotate at a high speed; according to the utility model, hot gas generated in the peripheral plate can be accelerated and dissipated conveniently, gas cooling is realized, meanwhile, the plate body is provided with a plurality of through holes, the hot gas can be discharged outwards through the through holes conveniently, and thus the test bed is ensured to have excellent heat dissipation performance.

The invention discloses a beneficial disturbance perception-oriented pneumatic artificial muscle manipulator control method and system, and belongs to the technical field of pneumatic system control, and the method comprises the following steps: constructing a nonlinear kinetic model of a pneumatic artificial muscle manipulator system according to a three-element model of pneumatic artificial muscle and a kinetic equation of a manipulator, defining an angle error and an angular velocity error when the manipulator moves; a nonlinear controller based on beneficial disturbance perception is constructed by taking the control targets that the motion angle of the manipulator reaches an expected position and the tracking angle error converges to 0 and considering unknown dynamic state and external disturbance in the system; and according to the angle, obtained through actual measurement, of the manipulator during movement, the constructed nonlinear controller is used for determining the input air pressure of the pneumatic artificial muscle so as to drive the manipulator to move. Accurate and efficient angle tracking control of the manipulator under external disturbance can be achieved, and stable and efficient operation of the system is guaranteed.

The application discloses a gear box fault diagnosis method and system based on scatter recurrence analysis, and belongs to the technical field of wind turbine fault diagnosis, and comprises the following steps: collecting vibration signals of a wind turbine gear box under different health states; utilizing scatter recurrence analysis to represent sensitive fault features in the gear box vibration signals from multiple dimensions; proportionally dividing the multi-dimensional features into a training set and a test set; inputting the training set to train an intelligent fault diagnosis model based on a support vector machine; and finally inputting the test set data into the trained SVM to obtain a gear box fault diagnosis result. The application combines nonlinear dynamics and machine learning to provide a diagnosis method and system capable of accurately identifying various faults of a wind turbine gear box, provides a reference for realizing efficient operation and maintenance of the wind turbine, and guarantees reliable and stable operation of the wind turbine.

The invention discloses a mechanism and deep learning fused machine tool precision out-of-tolerance fault diagnosis method, and belongs to the technical field of precision numerical control machine tool health management. Comprising the following steps: S10, constructing a tool nose nonlinear dynamic model of a nonlinear stiffness item K (x) considering thermal deformation influence; s20, the inherent frequency, damping and Taylor empirical formula coefficients of the whole machine are obtained in combination with the hammering excitation test and the cutting test; s30, the displacement change of a tool nose is obtained; s40, constructing an out-of-tolerance recognition library; and S50, calculating a precision index of the X (t) obtained under different working conditions, comparing the precision index with a threshold value to generate a qualified / out-of-tolerance label, and carrying out fault prediction based on a 1D-CNN-BiLSTM fusion network training classification model. And finally verifying the precision out-of-tolerance problem of the machining process in combination with field experimental data. The method can solve the problems that the traditional diagnosis technology is difficult to capture in real time, the precision is out-of-tolerance, and generalization is insufficient due to separation of a physical model and a data driving method.

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 belongs to the technical field of non-linear dynamic numerical calculation and rigid differential equation solution, and discloses a non-matrix rigid self-adaptive non-linear dynamic numerical calculation method and a non-matrix rigid self-adaptive non-linear dynamic numerical calculation system. According to the method, equivalent product calculation of the Jacobian matrix and the vector is realized based on the directional derivative through a matrix-free calculation framework, and a complete Jacobian matrix does not need to be stored; high-efficiency and high-precision estimation of the spectral radius is realized through quantum annealing driven Krofft power iteration; constructing an adaptive mixing precision calculation engine based on a spectral radius result to reduce memory occupation; and realizing stable solution of the extreme rigid system through rigid self-adaptive step length control. According to the method, the solution convergence rate and the calculation efficiency of the extreme rigid nonlinear system can be effectively improved, the memory occupation and the long-term integral error are greatly reduced, and the method can be widely applied to the fields of aerospace, mechanical engineering, intelligent equipment, chemical reaction kinetics and the like.

The application discloses an angular acceleration estimation method based on accelerometer correction, which comprises the following steps: S1, establishing a process model of an estimated signal, including a state equation and an observation equation; S2, performing first prediction based on a nonlinear dynamic model, obtaining a first k+1 prior state estimation matrix and a prior estimation error covariance matrix; S3, performing first correction, establishing an observation equation, calculating a Kalman gain K, and updating a posteriori estimation matrix and a posteriori estimation error covariance matrix P; S4, performing second prediction; S5, performing second correction; setting the posteriori estimation matrix after the first Kalman filtering as the initial value of the second correction process, performing second correction, and completing correction; and S6, returning to step S2 and performing iterative estimation. The application has the characteristics of further reducing time delay and more accurately and effectively estimating the angular acceleration under interference.

The application discloses a design method of an alternating current stimulation Hodgkin-Huxley cardiac Purkinje fiber model, and the dynamic characteristics of the model under different alternating current parameters and different initial conditions are observed through a phase plane orbit, a waveform, a bifurcation diagram and a Lyapunov exponent spectrum, and multiple coexisting membrane action potential modes are revealed, corresponding to the same or different periodic coexisting attractors in a phase diagram. The model experiences period, quasi-period and forward or reverse period-doubling bifurcation with the change of an amplitude A or a frequency f, and it is proved that the cardiac Purkinje fiber model with alternating current injection has complex nonlinear characteristics. By changing a current I m , a sodium ion equilibrium potential E Na and a potassium ion equilibrium potential E K , heartbeat frequency rules are observed, and state conversion of the cardiac Purkinje fiber between normal, abnormal and sudden cardiac arrest is found. The application provides a reference for exploring the evolution rules of nonlinear dynamics in the HH CPF model, protecting life and heart health.

The invention relates to the technical field of modern application of traditional Chinese medicine theories, and aims to solve the problem of insufficient quantitative research of the five-element theory of traditional Chinese medicine and the meridian theory under modern scientific contexts. Clinical twelve-meridian bio-electricity signals are collected, the chaos theory is used as the basis, a five-element generation-restriction mechanism is fused, and a five-dimensional nonlinear kinetic equation system is constructed; by establishing an acupuncture point-meridian-five-element two-stage mapping relation, quantitative characterization of five-element characteristic values is realized; parameter identification is completed by adopting a PSO-GA integrated optimization strategy, and chaos characteristics and evolution laws of a five-element system of a human body in healthy, sub-healthy and disease states are represented by calculating a Lyapunov index, calculating fractal dimensions, drawing a chaos bifurcation diagram, a phase diagram and the like. The method can be used for human health status assessment, provides a modeling thought for modernization of traditional Chinese medicine theories, and has clinical application value.

The invention belongs to the technical field of non-linear dynamics, and particularly relates to a V-shaped spring system, a lagging and bifurcation experiment device and an experiment method.The V-shaped spring system comprises a base, two telescopic arms and a spring, the two telescopic arms are the same in length, the first ends of the two telescopic arms are connected to a first connecting piece, and the second ends of the two telescopic arms are connected to a second connecting piece; at least one of the two telescopic arms is rotationally connected with the first connecting piece, the second ends of the two telescopic arms are respectively connected with a second connecting piece, the telescopic arms are rotationally connected to the base through the second connecting pieces, and the first connecting piece is movably arranged on the base along the perpendicular center line of the connecting line of the second ends of the two telescopic arms; the two telescopic arms are sleeved with springs, and the springs can stretch out and draw back along with the telescopic arms. According to the V-shaped spring system and the lagging and bifurcation experiment device applying the V-shaped spring system, the situation that the spring deviates from the axis of the spring during deformation can be reduced, and the result accuracy during theoretical verification is improved.

The application discloses a discharge mode controllable memristor coupling discrete neuron system, which comprises three delay units U1, U2 and U3, an invariable parameter C2, variable parameters C1, C3, C4, C5 and C6, a hyperbolic tangent module T, a square module S, multipliers M1, M2 and M3, a divider D, adders A1, A2, A3, A4 and A5, and three outputs X, Y and Z. A magnetic control memristor is introduced into a Rulkov discrete neuron, and corresponding parameters are adjusted, so that the system exhibits complex dynamic behaviors. By changing the initial value and the corresponding parameters of the system, the system exhibits transient chaotic discharge, transient periodic discharge, intermittent chaotic discharge, coexistence of periodic and chaotic discharge modes, discharge mode transition and other phenomena, and has multi-stability. The discharge mode controllable memristor coupling discrete neuron system provides a new idea for the development of neural dynamics, intelligent control, bionics and discrete neurons, provides theoretical guidance for the research on discrete neurons in medical experiments, and promotes the development of nonlinear dynamics of the memristor coupling discrete neuron direction.

The invention belongs to the technical field of random system control and sewage treatment, and discloses a T-S fuzzy Markov jump system control method based on an activated sludge system. According to the method, a nonlinear kinetic model is constructed from key state variables of the system, and the complex biological reaction and substance transfer process in sewage treatment is accurately described. By introducing the Markov jump theory, parameter random jump caused by inlet water quality sudden change and the like is effectively described, and the adaptability and accuracy of the model under the dynamic working condition are enhanced. For possible faults of an actuator (such as a dilution valve and an aerator), a fault model is established and a corresponding fault-tolerant control strategy is designed, so that the reliability and robustness of the system are improved. On the basis of the Lyapunov stability theory, a linear matrix inequality condition which enables the system to be randomly stable under the fault and disturbance and meets the H-infinity performance index is deduced, the effectiveness of the linear matrix inequality condition is verified through strict mathematical derivation, and a theoretical basis is provided for implementation of a controller and stable operation of the system.

The invention provides a safety boundary dynamic updating method and device of a four-wheel steering system. The method comprises the steps that current vertical loads and side slip angles of four wheels are calculated; calculating the current critical slip angle of the four wheels, detecting whether the current slip angle of the four wheels reaches the critical slip angle, if not, calculating the lateral deviation force of the four wheels under the current slip angle based on a first wheel lateral deviation force calculation model, and if the detection result is yes, calculating the lateral deviation force of the four wheels under the current slip angle; calculating the lateral deviation force of the four wheels under the current slip angle based on a second wheel deviation measurement force calculation model; calculating the current effective cornering stiffness of each wheel; the current safety boundary condition of the vehicle is updated based on the effective cornering stiffness of each wheel at the current moment, a nonlinear dynamic prediction model is constructed, key parameters such as load transfer and cornering stiffness change are dynamically identified, high-precision modeling of the vehicle motion state is achieved, and a safety boundary is dynamically constructed according to the current vehicle motion state. And the driving safety of the vehicle is comprehensively improved.

The application discloses a kind of end-to-end quantum simulation methods and systems for nonlinear dynamics, belong to quantum circuit compilation technical field.The method comprises: obtaining the original physical state of each time in input time sequence, and generating the observable feature of original physical state and the required circuit parameter of quantum initial state preparation;Based on the evolution relationship between the observable feature at different time steps, the evolution rule is obtained, and the evolution rule is converted into an independent executable quantum circuit or several weighted quantum circuits according to whether the evolution rule needs unitary conversion, combined with the circuit parameter;Based on quantum processor, the evolved observable feature is obtained by executing the independent executable quantum circuit or several weighted quantum circuits corresponding to each evolution rule;Based on the evolved observable feature, system simulation result is generated.The application significantly reduces the computational overhead of complex nonlinear system simulation and improves the processing capacity.

The present application relates to the field of intelligent medical multi-modal data analysis and nursing auxiliary decision-making, in particular to a neurology nursing risk dynamic early warning and intelligent decision support system. The system comprises a data acquisition module, a feature extraction module, a causal reasoning module, a decision module and a feedback module. The data acquisition module collects physiological time series and nursing logs to obtain a multi-modal original data set. The feature extraction module performs nonlinear dynamics analysis on physiological data to generate a state manifold vector and performs semantic embedding on logs to generate an intervention state tensor containing time and space features. The causal reasoning module constructs a dynamic causal graph, maps vectors and tensors as nodes, and uses tensors to adjust transmission weights to suppress non-pathological fluctuations. The decision module is based on the graph counterfactual reasoning, calculates the dynamic risk and difference, generates a graded early warning and the optimal nursing path, and updates the graph parameters according to the treatment results. The system effectively solves the high-frequency false alarm problem caused by daily nursing actions of traditional monitoring equipment, reduces the alarm fatigue of medical staff, and ensures the focus on real critical events.

This invention discloses a multi-scale phase space correlation dimension analysis method for jet reactors under negative pressure conditions. By introducing phase space reconstruction and attractor analysis methods (i.e., the correlation dimension method), the nonlinear dynamic characteristics of the internal flow field of the jet impacting the reactor are systematically studied. Phase space reconstruction technology reconstructs time-series data into trajectories in a high-dimensional phase space using a delayed embedding method, thereby revealing the system's intrinsic dynamic behavior. Attractors, as geometric structures in phase space, reflect the long-term dynamic characteristics of the system; their shape and distribution can provide important clues for understanding the complexity of the flow field. Through phase space reconstruction and attractor analysis, chaotic behavior, fractal characteristics, and the geometric structure of attractors in the flow field can be identified, thus providing theoretical support for optimizing reactor design and operating conditions.

The invention relates to the field of nonlinear dynamics, in particular to a coupled piecewise power function chaotic system. According to the method, compression, translation and segmentation mechanisms are firstly adopted, a segmented power function chaotic system is designed, and the system can generate a random number sequence with good chaotic characteristics. Then, based on the piecewise power function chaotic system, a coupled piecewise power function chaotic system is constructed in a parameter coupling mode, the system has two coupling modes of global coupling and local coupling, and the parameter space is shown in the specification. The invention provides an excellent multi-dimensional random number sequence generation method which can provide powerful support for application in the fields of scientific calculation, engineering control, information technology and the like.

This invention belongs to the field of rigid light rod trajectory optimization technology, aiming to solve the problems of numerical convergence instability, parameter sensitivity, and poor suppression effect in existing anti-sway technologies. It provides a rigid light rod trajectory optimization method and system that combines a nonlinear dynamic model and an adaptive mesh enumeration strategy. By constructing a physical model of the rigid light rod system, a nonlinear dynamic equation characterizing the coupling relationship between the link angle and the hoisting moving platform is derived. Multiple candidate solutions are generated by discretizing the total motion time, and a velocity trajectory is constructed using a cubic polynomial. The peak velocity and acceleration sequence of the hoisting moving platform are adaptively adjusted based on displacement deviation. The residual oscillation parameters of the load are extracted using the nonlinear dynamic equation, and the total motion time is adaptively adjusted. Candidate solutions are screened through preset acceleration and angle constraints, and the optimal solution is selected with the goal of minimizing the total motion time. This invention can achieve efficient, low-oscillation, and precise hoisting control.

The invention discloses a multiple sclerosis intervention suggestion generation system based on individual immune features. The multiple sclerosis intervention suggestion generation system comprises a multi-source immune data acquisition and time sequence standardization processing layer, a multi-scale immune feature extraction and immune state index calculation layer and a non-therapeutic intervention scheme multi-target optimization and generation layer. The method has the following advantages and effects: time sequence alignment, dimension normalization and missing value interpolation of a multi-dimensional immune feature original data set are realized through the multi-source immune data acquisition and time sequence standardization processing layer; through a multi-scale immune feature extraction and immune state index calculation layer, features are extracted from a time domain, a frequency domain and nonlinear dynamics in a multi-scale parallel mode, and immune state indexes are calculated in a fusion mode; and a non-therapeutic intervention scheme is generated through the non-therapeutic intervention scheme multi-objective optimization and generation layer, so that the problems that multi-source heterogeneous immune data integration is difficult, immune state evaluation is not comprehensive and intervention suggestions are lack of individuation and practicability are effectively solved.

The invention belongs to the technical field of electro-hydraulic servo control, and discloses a high-precision position compound control method based on an ESO-MPC electro-hydrostatic actuator, and the method comprises the steps: building a nonlinear dynamic model of the electro-hydrostatic actuator based on the working principle and structural composition of the electro-hydrostatic actuator; based on the nonlinear dynamic model of the electro-hydrostatic actuator, constructing a state-space equation of the electro-hydrostatic actuator; designing an extended state observer (ESO), regarding unmodeled dynamics and external disturbance in the system as total disturbance d, and obtaining a total disturbance estimated value through real-time observation; designing a model predictive controller (MPC) to realize rolling optimization; and the model prediction controller dynamically adjusts control input in combination with compensation information of the extended state observer, so that high-precision tracking and strong-robustness control of the displacement of the electro-hydrostatic actuator piston are realized. According to the invention, on the basis of the nonlinear model and the extended state observer, internal and external disturbance of the system is estimated and compensated in real time, and high-precision displacement tracking and control of the electro-hydrostatic actuator are realized.

This invention discloses a multiple sclerosis intervention suggestion generation system based on individual immune characteristics, comprising: a multi-source immune data acquisition and temporal standardization processing layer, a multi-scale immune feature extraction and immune status index calculation layer, and a non-therapeutic intervention scheme multi-objective optimization and generation layer. This invention has the following advantages and effects: the multi-source immune data acquisition and temporal standardization processing layer achieves temporal alignment, dimensional normalization, and missing value imputation of the original dataset of multi-dimensional immune features; the multi-scale immune feature extraction and immune status index calculation layer extracts features from the time domain, frequency domain, and nonlinear dynamics in parallel and fuses them to calculate the immune status index; and the non-therapeutic intervention scheme multi-objective optimization and generation layer generates non-therapeutic intervention schemes, thereby effectively solving the problems of difficulty in integrating multi-source heterogeneous immune data, incomplete immune status assessment, and lack of personalization and practicality in intervention suggestions.