1067 results about "State variable" patented technology

The invention provides a generator excitation regulation and control optimization system based on model prediction, and relates to the technical field of data processing, and the system comprises a data acquisition module which is used for collecting the operation parameters of a generator; the state estimation module is used for calculating a state variable of the generator excitation system and constructing a state space model of the generator excitation system; the model prediction control module is used for calculating excitation current prediction values at multiple moments in the future by adopting a rolling horizon optimization method, and performing dynamic correction based on a prediction error correction mechanism; the optimization calculation module is used for constructing a target optimization function which comprises a generator voltage stability target item and an excitation current adjustment smoothness target item, and calculating an optimal excitation current adjustment amount by adopting a constraint optimization method; the excitation adjusting module is used for controlling a generator excitation system so as to adjust excitation current; according to the invention, the autonomy and accuracy of the generator excitation regulation and control optimization system are improved.

The invention discloses a forklift operation control optimization method and system based on a disturbance observation technology, and relates to the technical field of electric forklift control, and the method comprises the steps: collecting the operation data of a forklift operation process in real time, sensing and fusing through multi-source sensing information, analyzing the state variable of forklift movement, and building a dynamic parameter mapping relation; taking the measured value and the predicted value of the forklift all-condition dynamic model as input, constructing a high-order sliding-mode observer, evaluating disturbance source interference, and generating a motion compensation correction amount; a feed-forward-feedback fused composite controller is constructed, a yawing moment pre-compensation instruction is generated based on a disturbance source disturbance evaluation result, and a forklift trajectory tracking error is dynamically corrected; and monitoring the distance between the operation point of the forklift and the instability boundary in real time, and triggering a torque redistribution strategy when the distance is close to the instability boundary. Through deep fusion of real-time sensing data, dynamic modeling and an intelligent control algorithm, the working performance of the forklift under complex working conditions is remarkably improved.

The invention discloses a vehicle state estimation method based on adaptive strong tracking extended Kalman filtering. The method comprises the following steps: constructing a nonlinear three-degree-of-freedom dynamic model containing a state equation and an observation equation based on an extended Kalman filtering algorithm to describe longitudinal, lateral and yaw states of a vehicle; state variables of the state equation are a side slip angle, a yaw velocity and a longitudinal vehicle speed; constructing a time-varying measurement noise statistical estimator based on a Sage-Husa algorithm to adaptively correct a measurement noise covariance matrix in the extended Kalman filtering algorithm; state prediction is carried out on the vehicle through the state equation, and a state prediction covariance matrix is calculated; and calculating the ratio of the sum of quadratic terms of the information sequence to the trace of the variance matrix of the information sequence based on an extended Kalman filtering algorithm, and judging whether filtering is really divergent or not. According to the method, the state quantity which is difficult to measure in the vehicle driving process is estimated in real time by establishing an adaptive strong tracking extended Kalman filter, and the vehicle state is accurately estimated.

The invention relates to the technical field of flour processing, and discloses an intelligent control method and system for a flour workshop, and the method comprises the following steps: collecting raw material characteristic data and equipment operation state data in real time through a sensor, calculating the state variable of the current system according to the collected data, and calculating the state variable of the current system according to the current state variable. Performing rolling optimization on a control strategy in a future time domain, dynamically adjusting grinding parameters according to a rolling optimization result, correcting a prediction model according to a data deviation between real-time acquired data and nonlinear model prediction data, and calculating an optimal control parameter combination based on a stochastic optimization theory; the system comprises a sensing module, a control module, an execution module, a feedback correction module and a random optimization module. By adopting the technical scheme that a high-precision sensor is combined with intelligent control, comprehensive automation and accurate control of the flour production process are achieved. The stability and consistency of flour quality are remarkably improved, and quality fluctuation is avoided.

Disclosed in the present invention is an output feedback control method for a pilot-operated electro-hydraulic proportional servo valve considering disturbance suppression. According to the control method, a nonlinear active disturbance rejection controller with both disturbance suppression and unknown state estimation is designed on the basis of a constructed disturbance observer in combination with an extended state observer. To address the problem of a valve core position control system of the pilot-operated electro-hydraulic proportional servo valve, the present invention not only ensures suppression of matched interference in the system, but also compensates for mismatched interference in the system, and utilizes a state observer to estimate unmeasurable state variables, so as to construct full-state feedback control, thereby ultimately achieving high-precision tracking control of the valve core position.

The invention discloses a dough humidity and temperature dynamic regulation and control system based on multi-modal sensor fusion, and relates to the technical field of operation parameter regulation and control, and the method comprises the steps: carrying out the non-blind area monitoring of a three-dimensional temperature and humidity field in a fermentation box, and eliminating the local failure risk of a point-type sensor; capturing a space-time coupling relationship of temperature and humidity-carbon dioxide concentration-dough volume through a self-attention mechanism; constructing a differential equation set comprising at least one state variable; establishing a fermentation equipment thermodynamic model, optimizing refrigeration and heating time sequences through reinforcement learning, and designing an energy consumption optimization strategy based on digital twinning; performing cross-device cooperative control; constructing a hierarchical diagnosis tree containing fault features; performing microbial activity enhanced humidity regulation and control, and forming a protective layer on the surface of the dough based on a humidity gradient control algorithm of a water molecule diffusion coefficient. And by arranging the environment regulation and control module, the time compression module and the time sequence optimization module, the product quality, the production efficiency and the energy utilization rate are remarkably improved.

The invention discloses an aircraft control method and device based on switching chaotic system encryption and a medium, and relates to the field of data encryption, and the method comprises the steps: injecting plaintext data corresponding to measurement data sent by a sensitive device into an output equation of a chaotic system, and obtaining corresponding ciphertext data; obtaining a corresponding switching signal according to the chaotic system state variable of the basic judgment model, and switching the currently activated second chaotic system according to the switching signal; sending the ciphertext data to a control component to obtain plaintext data corresponding to the measurement data; and performing flight control on the aircraft. The characteristic that a chaotic nonlinear system is extremely sensitive to an initial value is utilized, and an encryption algorithm based on a switching chaotic system is designed and used for unmanned aerial vehicle control. According to a designed switching strategy, a switching encryption chaotic system is introduced into an encryption algorithm, so that a key stream is more random, and the security of aircraft measurement data encryption is improved.

The invention provides an intelligent spraying robot multi-task collaborative operation method and system. The method comprises the steps that a discrete sliding mode controller considering a sampling period is constructed based on a discrete state equation; introducing an auxiliary state variable to design an adaptive barrier function; establishing a T product dynamic system control model to realize online parameter identification; and a hierarchical control architecture is adopted to realize multi-robot cooperative control. According to the scheme, the problems that in the prior art, discrete implementation precision is reduced, controller parameters are difficult to adjust in a self-adaptive mode, and an effective data driving control strategy is lacked are solved, and efficient collaborative operation of the multiple spraying robots is achieved.

The invention discloses a bridge crane anti-swing control method based on a neural network algorithm, and relates to the technical field of crane control, and the method comprises the following steps: collecting suspended load swing state variables of a bridge crane and establishing a dynamic model; the dynamic model is used for predicting the suspended load swing trend of the bridge crane, and a swing trend prediction result is obtained; feed-forward compensation control is carried out according to the swing trend prediction result, and a feed-forward control signal is obtained; performing real-time feedback correction on the movement of the bridge crane trolley according to the feedforward control signal to obtain a real-time correction control signal; and performing nonlinear compensation calculation according to the real-time correction control signal to obtain a final anti-swing control result. According to the invention, the method achieves the precise estimation of the future swing state, improves the prediction accuracy of the future swing state, achieves the advanced compensation, improves the response speed of a control system, enables a compensation strategy to adapt to different operation environments, and improves the control precision and robustness.

The invention discloses a system and a method for analyzing stability and oscillation transmission of a network-forming converter system, and belongs to the technical field of virtual synchronous generator control, and the system comprises an active power control loop which takes an active power deviation value as input, and outputs a power angle of a VSG system through an active integration part and a feedback part; the reactive power control loop takes the reactive power deviation ratio as input, and outputs the voltage amplitude of the VSG system through an active integration part and a feedback part; according to the analysis method, whether each state variable is stable or not is judged firstly, and then the intensity of oscillation transmission between the state variables is judged. According to the method, the stability of the system can be evaluated more accurately, and the accuracy of system stability analysis under dynamic disturbance is ensured; an oscillation transfer effect function is introduced, and the mutual influence between different state variables is quantitatively analyzed; and the dynamic fluctuation range of each state variable in the system can be accurately evaluated.

The invention provides an overhead and portal crane lifting appliance control method based on multi-stage model predictive control, and relates to the field of data processing. The method comprises the following steps: firstly, acquiring a control parameter and an initial state variable so as to define an operation boundary and an initial state, and establishing a dynamic model containing a coupling relationship among a trolley acceleration, a sling rope length acceleration and a sling swing angle; acquiring obstacle distribution by using space sensing equipment, and calculating the dynamic safety height of the lifting appliance by combining obstacle parameters and control parameters; judging whether the lifting appliance is in an obstacle avoidance stage or a target tracking stage according to the initial state variable and the dynamic safety height; constructing a model prediction controller and dynamically adjusting a state weight matrix and a control input weight matrix; finally, a control input sequence is generated through an optimization algorithm, and track safety and attitude stability control of the lifting appliance is achieved. According to the technical scheme, the swing angle of the lifting appliance is restrained, meanwhile, the obstacles are effectively avoided, and the safety of the lifting appliance in the whole operation process is guaranteed.

The invention is suitable for the technical field of vehicle intelligent control, and provides a model prediction controller parameter self-calibration method based on deep reinforcement learning. According to the method, modeling expression is carried out on the delay characteristic of a vehicle steering system by utilizing a first-order inertial link, the delay characteristic is used as a state variable to be augmented to an error tracking control architecture, and a model prediction controller considering the steering delay characteristic is designed according to the state variable; the problem that the transverse motion control performance of the automatic driving vehicle is deteriorated due to hysteresis between the target wheel rotation angle and the actual vehicle rotation angle is solved. Meanwhile, a deep Q network (DQN) of a multi-target reward mechanism is designed, and an optimal strategy is trained to quickly calibrate multiple parameters of model predictive control (MPC). The generalization performance of reinforcement learning enables the vehicle to adapt to most complex working conditions after sufficient training, the problems that in a traditional method, the parameter calibration time of a control system is long, and robustness is insufficient are solved, and high-precision and stable transverse control over the full-drive-by-wire four-wheel steering vehicle under the complex working conditions is achieved.

The invention relates to the technical field of industrial mechanism models, in particular to a shock absorber performance optimization control method based on model fusion, which comprises the following steps: extracting a low-frequency disturbance state variable and inputting the variable-topology industrial mechanism model to generate a nominal reference state trajectory; utilizing a depth state observation network fused with energy passivity constraint to calculate a non-linear model mismatch compensation amount and an adaptive weighting parameter; performing dynamic fusion on the nominal reference state trajectory and the compensation amount based on the adaptive weighting parameter to generate a generalized state estimation value; and executing dynamic multi-objective optimization based on the generalized state estimation value, and generating mixed mode control input acting on an execution end. According to the invention, through adaptive fusion of a mechanism model and a data driving method, physical consistency, calculation real-time performance and robustness of a control process are considered.

The invention provides a multi-stage heat release regulation optimization method and system based on steam extraction energy storage, and relates to the technical field of heat regulation, and the method comprises the steps: building a dynamic thermodynamic model of a heat release process of a steam extraction energy storage system containing a plurality of state variables of heat release power, heat release time, heat release efficiency and temperature change of a heat storage water tank; establishing a multi-objective optimization model by taking minimization of total energy consumption of the system and minimization of entropy production of the heat exchanger as optimization objectives and combining constraint conditions corresponding to the optimization variables; solving the multi-objective optimization model by adopting a hybrid optimization strategy combining an improved non-dominated sorting genetic algorithm and a multi-objective particle swarm optimization algorithm, and determining an optimal regulation strategy set of the multi-stage heat release process of the steam extraction energy storage system; the valve position opening degree of the steam extraction electric adjusting valve and the water supply flow of the steam extraction temperature and pressure reduction device are controlled through the steam extraction energy storage control system, optimized adjustment of steam extraction parameters is achieved, meanwhile, the rotating speed of the cold source side water supply and return pump is controlled, and optimized matching of water supply and return parameters is achieved.

The invention discloses a rope-driven mechanical arm motion control method based on model predictive control, which comprises the following steps: taking a joint angle error as a state variable, taking a rope-driven rotation angle as an input variable, and establishing a nonlinear state-space equation of a rope-driven mechanical arm according to the state variable and the input variable; the nonlinear state-space equation is subjected to linearization processing, a linear approximation model is generated, and the linear approximation model comprises a linearization matrix; defining a prediction interval, expanding the linear approximation model into a multi-step prediction form, and generating a prediction model; constructing a cost function, and combining the prediction model into the cost function to solve and obtain an optimal control sequence; and extracting the optimal control increment at the current moment from the optimal control sequence, and updating the state variable, the input variable and the linearization matrix according to the optimal control increment to form closed-loop control. Accurate path tracking control can be carried out on the rope-driven mechanical arm under complex dynamic constraints, and it is ensured that the mechanical arm moves according to the planned trajectory.

The invention provides a distributed electric propulsion aircraft lateral course model prediction control method and system, and the method comprises the steps: completing the six-degree-of-freedom linear state space modeling of a distributed electric propulsion aircraft, obtaining the aerodynamic parameters of the aircraft, and constructing a flight dynamics model containing multiple propellers; under the flight dynamics model of the multiple propellers, according to a flight task instruction and state variables of the current position and speed of the airplane, calculating an expected flight path, and converting the expected flight path into an attitude angle; the edge computing platform computes an optimal control instruction based on the flight state variable and the attitude angle, and the target attitude is realized by integrating the propeller, the control surface and the flight state constraint; and the edge computing platform realizes the optimal control of the lateral heading attitude of the aircraft according to the optimal control instruction. The precision, stability and fault tolerance of flight control are remarkably improved, and an efficient and reliable solution is provided for practical application of model prediction control in a distributed electric propulsion aircraft.

The invention relates to the technical field of industrial control, in particular to an industrial stacker intelligent control system based on machine vision, and the system comprises a state resolving module which is used for carrying out the feature region decoupling of original observation data, and resolving a position deviation matrix and a deformation state variable for a rigid subspace and a flexible subspace respectively; the dynamic modeling module is used for identifying the first-order inherent frequency and the damping ratio of the mechanical structure on line and establishing a second-order impedance contact model; the impedance control module is used for generating an expected speed based on the position deviation and adaptively adjusting an impedance parameter according to a deformation state so as to carry out compliant modulation on a speed vector; and the estimation shaping module is used for correcting system time lag by using a Kalman observer and inhibiting residual vibration in combination with a zero-vibration input shaper. Through multi-dimensional physical field fusion and dynamic modeling, the problem that a rigid control model is mismatched with a flexible operation object is solved, and self-adaptive compliant control and vibration-free accurate operation of the stacking machine in the unstructured environment are achieved.

The invention relates to the technical field of process optimization control, in particular to a single crystal furnace crystal pulling process control method and system based on an intelligent optimization algorithm. Identifying to obtain equipment operation parameters and process state variables; obtaining a state estimation value of the controlled medium based on the equipment operation parameter and the process state variable; determining target operation parameters of the controlled equipment according to the technological process and the equipment operation parameters; on the basis of the target operation parameters, expected changes of process state variables are estimated in combination with system dynamic response characteristics, and predicted state variables are obtained; calculating a medium distribution parameter according to the equipment operation parameter, the process state variable and the state estimation value; generating a state prediction value of the controlled medium based on the medium distribution parameter, the target operating parameter and the predicted state variable; and integrating the state estimation value and the state prediction value, and outputting a control instruction for adjusting the execution structure according to a preset adjustment rule. The closed-loop regulation of the controlled medium is realized through the control instruction.

The invention relates to a multi-physics field coupling simulation method and equipment for industrial digital twinning. The method comprises the following steps: automatically dividing a plurality of heterogeneous physical sub-domains based on actual physical field data of industrial equipment, and defining a solving region and a boundary condition for each sub-domain; and then, a variable-driven region division mechanism is utilized to construct a parallel computing map of multiple physical domains, and efficient synchronization of inter-region coupling state variables and real-time interaction of boundary data are realized. In the parallel simulation process, calculation tasks and resources are dynamically and optimally distributed through a self-adaptive scheduling algorithm, bottleneck areas are effectively recognized and eliminated, and the overall simulation efficiency and stability are improved. According to the invention, co-evolution of a plurality of physical processes such as structure, heat, flow, magnetism, electricity and the like can be realized, and unified, flexible and extensible simulation support is provided for digital twinning, state monitoring, performance prediction, control optimization and other applications of a complex industrial system.

The invention relates to a coaxial multi-rotor unmanned aerial vehicle control method based on a Koopman operator, and the method comprises the steps: constructing a deep neural network based on the Koopman operator, and mapping a state variable to a high-dimensional observable space through an encoder; in a high-dimensional observable space, state variables are predicted through a linear connection layer based on dimension rising state variables and rotor rotating speeds; the decoder restores the prediction result to an original state space; training a deep neural network by using historical flight data to obtain codec parameters and a weight of a linear connection layer, and constructing a linear dynamic model of the observation function and the unmanned aerial vehicle; and the prediction controller predicts state variables of future multiple steps of the unmanned aerial vehicle by using a linear dynamics model, and enables the unmanned aerial vehicle to track a given trajectory by minimizing a cost function to obtain optimal control input. Compared with the prior art, the method has the advantages that complete data-driven modeling is realized, and the method can adapt to unmanned aerial vehicles with different sizes, loads and flight conditions.

The invention relates to the technical field of battery energy storage station heat management, and discloses a battery energy storage station heat management optimization method based on multi-target control, which comprises the following steps: constructing a mathematical model of an energy storage station heat management system, and defining a state variable and a control variable of the model; the mathematical model comprises a battery heat production model, a battery heat exchange model, an SOC dynamic model and an energy storage station power balance model; based on the mathematical model of the thermal management system of the energy storage station, establishing a multi-target comprehensive evaluation function and setting constraint conditions; according to the defined state variables and control variables, the multi-objective comprehensive evaluation function and set constraint conditions, learning iteration is carried out based on a Q learning algorithm, a Q value is obtained when a preset convergence condition is reached, and an optimal control variable distribution strategy is output; and performing thermal management optimization on the energy storage station based on the optimal control variable distribution strategy. According to the invention, the balance among the targets is realized based on the combination of the multi-target control strategy and the optimization algorithm, and guarantee is provided for the performance and safety of the battery energy storage station.

The invention relates to the technical field of industrial control systems, in particular to an ink path control system of a full-color printer, which comprises the following specific implementation steps of: measuring a plurality of physical quantities in a controlled physical process in real time, and outputting the physical quantities as state variables and external disturbance signals; receiving a state variable and an external disturbance signal, performing future state prediction through a nonlinear dynamic process model, and generating a prediction result; constructing a process performance index function, optimizing the performance index function through a sparrow search optimization algorithm based on a prediction result, and generating an optimal target set value; through a model based on Bayesian reasoning, parameters of a PID control law are updated in a self-adaptive mode according to external disturbance signals; the controller is used for receiving the optimal target set value and the adaptive PID control law parameters, executing the PID control law in combination with a preset undisturbed switching module, and generating a final control instruction; and receiving a final control instruction, and converting the final control instruction into driving signals acting on a plurality of physical actuators so as to realize closed-loop feedback control of the controlled physical process.

The invention relates to a tin smelting process dynamic optimization control method, and belongs to the technical field of crossing of metallurgical engineering and artificial intelligence. Firstly, data are collected and preprocessed, abnormal values are removed, missing values are generated through cGAN, and data vacancy is made up; secondly, modeling is conducted on key state variables in the tin smelting process through an LSTM model, a complex time sequence change rule is captured, and metallurgical knowledge is embedded to serve as a constraint; then, a state updating model based on data driving is added into the reinforcement learning environment; and finally, a deep reinforcement learning algorithm is introduced, the operation parameters of the spray gun serve as an action space, and the state in the tin smelting process serves as a state space. And in the reward function design, the prediction result of the key parameter is fused, and a dynamic optimization control strategy is realized through intelligent agent learning. The method has the remarkable advantages in the aspects of dynamically optimizing the tin purity and reducing CO emission, and an innovative solution is provided for intelligent and efficient production of the metallurgical industry.

The invention discloses an LSTM (Long Short Term Memory)-based harmonic frequency coupling model optimization method for a network construction type converter, which considers the influence of a dead zone and establishes a converter model controlled by a virtual synchronous generator based on a harmonic state space method. In the power electronic modeling process, due to approximate simplification, switch neglecting and other nonlinear relations, the model is not accurate enough, meanwhile, in the harmonic state space model modeling process, system state variable parameters need to be considered, and the precision of the model affects harmonic transmission characteristics and coupling analysis, so that the modeling efficiency is greatly improved. Errors caused by power electronic modeling are reduced through an LSTM neural network algorithm, the harmonic amplitude estimation difference is reduced, state variable parameter values in a system model are accurately estimated, the model precision is further improved, and then the harmonic coupling condition under the dead zone influence is effectively and accurately analyzed.

The invention relates to the field of mine geological disaster monitoring and early warning, and discloses a mine geological disaster early warning method based on artificial intelligence, and the method comprises the steps: describing the dynamic change of a state variable and a multi-physics coupling effect in a disaster evolution process through constructing a kinetic model; carrying out discrete solution on the model by adopting a numerical method to obtain a disaster evolution path; a disaster instability critical point is accurately identified in combination with a bifurcation theory and Lyapunov stability analysis; designing an optimal early warning strategy by using a reinforcement learning algorithm, and dynamically selecting an alarm signal triggering opportunity; fusing monitoring data and a model in real time, and dynamically updating a disaster state and a risk level; and outputting an alarm signal and providing emergency suggestions. According to the method, real-time response of accurate disaster prediction and early warning is realized, and the mine geological disaster prevention and control capability is improved.

The invention discloses an online self-calibration filtering method for inertial navigation, and relates to the technical field of inertial navigation, and the method comprises the following steps: obtaining multi-modal data, and establishing an inertial device error mathematical model and an inertial navigation system error mathematical model; carrying out recursive estimation on the state variables by the extended Kalman filtering model to obtain error parameters of the inertial device and the navigation system; constructing a fuzzy adaptive model based on the error parameters, generating an observation noise correction factor through a fuzzy logic rule, and correcting an observation noise covariance matrix in the extended Kalman filtering model in real time; carrying out online estimation and compensation on error parameters of the inertial navigation system by utilizing an extended Kalman filtering model, and outputting compensation parameters containing zero offset and scale factors in real time; correcting the original data based on the compensation parameters, injecting the original data into the navigation solution, and updating the fuzzy adaptive model according to the compensated error data.

The invention belongs to the technical field of energy storage, and particularly relates to a hybrid energy storage system energy management strategy based on adaptive weight and a related device. The invention provides a self-adaptive weight model based on a multi-layer perceptron by adopting a model prediction control method, and the self-adaptive weight model is used for real-time control of a hybrid energy storage system. Specifically, firstly, terminal constraint is applied to the state of charge of the electric energy storage equipment in an optimization function, then optimal weights corresponding to different initial state variables are calculated off line, and the multi-layer perceptron is trained according to the optimal weights. After training is completed, the multi-layer perceptron calculates the weight of the model prediction control module in real time based on the current state variable. And finally, the model predictive control module outputs an input variable of the system as a control reference value based on the obtained weight so as to control the system to operate. Compared with a traditional fixed weight model prediction control method, the weight can be adaptively adjusted according to the real-time state of the hybrid energy storage system, so that the control performance of the hybrid energy storage system is effectively improved.

The invention discloses an SPMSM robust speed control method based on an adaptive high-order expansion sliding-mode observer, and the method comprises the following steps: designing and building a third-order LESO system according to a motor mathematical model, and the third-order LESO system is used for outputting an estimated rotating speed actual value, an estimated acceleration and estimated total disturbance; combining a non-singular fast terminal sliding mode algorithm with a disturbance observer, and designing a non-singular fast terminal sliding mode disturbance observer which is used for observing an acceleration state variable of the motor; the acceleration state variable is input into the third-order LESO system and replaces the estimated acceleration, and a high-order expansion sliding mode observer is generated; and constructing an active-disturbance-rejection controller by using the high-order expansion sliding-mode observer, linear state error feedback and total disturbance compensation, and performing speed control on the motor by using the active-disturbance-rejection controller. According to the method, high-precision observation of disturbance can be realized under the conditions that only less interference information exists and the method is not sensitive to interference frequency change.

The invention discloses an engine state estimation and system modeling correction method based on double-layer variational inference, which relates to the field of engine state estimation and comprises a variational inference stage aiming at component performance states and kinetic model parameters; a system output prediction stage based on an observation equation; and a solving stage of performing objective function optimization through an evidence lower bound. The structure clearly presents information flow and key calculation links of the proposed algorithm in state estimation and model learning. According to the method, combined reasoning of state variables and model parameters is achieved by building a probability modeling structure, the modeling problem when system dynamics is partially or completely unknown is solved by combining a modeling method of a stochastic differential equation, and while the state variables and the model parameters are optimized, the modeling efficiency is improved. Precise inference of component states and reliable identification of fault features are achieved, the fault detection accuracy of sudden gas circuit abnormity reaches the standard, and meanwhile the performance is better in the aspect of tracking long-term performance degradation.

The invention relates to the technical field of virtual marshalling train operation control, and discloses a carbon emission-based virtual marshalling high-speed railway train cooperative operation control method, which comprises the following steps of: establishing a dynamic model of a virtual marshalling high-speed railway train, and calculating an expected distance of the virtual marshalling high-speed railway train; calculating line additional resistance in the braking process of the virtual marshalling high-speed railway train; a state variable is selected, a state space equation is established and discretized, and a model prediction control virtual marshalling high-speed railway train prediction equation is obtained; establishing a carbon emission-based intelligent control objective function and constraint conditions for cooperative operation of the virtual marshalling high-speed railway train group, and carrying out quadratic transformation to obtain a carbon emission-based intelligent control optimization model for cooperative operation of the virtual marshalling high-speed railway train group, and solving the model; according to the method, cooperative operation control over the virtual marshalling high-speed railway train group can be achieved under the condition that the line condition changes, and the carbon reduction effect can be achieved to a certain extent while the train operation efficiency is improved.