1951 results about "State-space representation" patented technology

The invention relates to the technical field of numerical control machine tools, and discloses a five-axis high-precision numerical control machine tool control method, device and equipment and a storage medium, and the method comprises the steps: collecting temperature data and displacement data of a five-axis high-precision numerical control machine tool, and carrying out state space modeling to obtain a machine tool motion state model; stress data and thermal error influence factors of the feed shaft are calculated; feeding shaft stress data and the thermal error influence factors are input into the compensation model, real-time compensation parameters are calculated, compensation control is conducted on the machine tool feeding shaft, and a compensation control result is obtained; according to the compensation control result, an initial machining track optimization model is constructed, and optimized machining parameters are generated; performing segmented machining control based on the optimized machining parameters to obtain real-time machining data; the initial machining track optimization model is optimized based on the real-time machining data, a target machining track optimization model is obtained, and the dynamic response performance and the control precision of the five-axis high-precision numerical control machine tool are improved through the method.

The invention discloses a drilling key parameter automatic regulation and control method based on an improved DDPG algorithm. The method comprises the following steps: collecting and preprocessing drilling data, and constructing a complete data set for training; a drilling speed (ROP) time sequence prediction model is constructed and trained on the basis of a self-attention mechanism (self-attention); building a simulation environment by using the trained ROP prediction model, and defining a state space, an action space and a multi-target reward function of a reinforcement learning DDPG algorithm model; a deep deterministic strategy gradient algorithm (DDPG) is adopted to train a reinforcement learning model, through an Actor-Critic network structure, an intelligent agent learns an optimal bit pressure (WOB) and rotating speed (RPM) regulation strategy in a simulation environment, and dynamic optimization of the drilling speed (ROP) is achieved. According to the method, through automatic and intelligent parameter regulation and control, the efficiency and stability of the drilling process are improved, the drill bit wear rate and the operation cost are reduced, and the method has good adaptability and multi-target optimization capacity and is suitable for drilling operation under the complex stratum condition.

The invention provides a servo hydraulic system optimization control method and system for a press machine test platform, and relates to the technical field of press machines, and the method comprises the steps: building a state space expression and building a mathematical model of a servo hydraulic system based on dynamic characteristics by deducing mathematical expressions of all components of the system; building a predictive control algorithm based on the mathematical model, solving a quadratic programming problem by adopting a rolling optimization strategy through building a state space predictive model and determining a multi-objective optimization function to obtain an optimal control sequence, and taking a first element of the optimal control sequence as control input at the current moment; feedforward compensation and pressure feedback compensation are introduced, the feedforward compensation constructs a compensation model based on an inverse system of a press transfer function and pre-corrects a control signal, and the pressure feedback compensation designs a digital filter based on a pressure error, fuses a feedforward compensation signal with a pressure feedback compensation signal and superposes the fused signal with the control signal; and obtaining a final control signal.

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, which relates to the technical field of air transportation and income management, discloses a dynamic generation method and system for an air travel price, and the system comprises a user behavior fine-grained tracking module, a dynamic pricing decision engine module, a cross-channel cooperative control module, and a compliance auditing and feedback module. Through a real-time data stream fusion technology, multivariate signals such as competition dynamic signals, user behavior signals and external environment signals are integrated, and second-level strategy response is realized in combination with the online training capability of a reinforcement learning model; through dynamic state space modeling, variables such as demand popularity, user sensitivity and external risk are coded into six-dimensional vectors, and the limitation of a fixed formula is broken through in combination with the nonlinear mapping capability of a deep Q network; through three measures of dynamic modeling, elastic constraint and cross-chain cooperation, the problems of response lag, high compliance risk and split user experience of a traditional pricing technology are solved.

The invention relates to the technical field of power distribution network evaluation, and discloses a power distribution network bearing capacity evaluation system based on dynamic correction. The system comprises a dynamic data acquisition module, a multi-dimensional state space construction module, a security domain analysis module, a partition coupling degree calculation module and a bearing capacity evaluation engine module. The dynamic data acquisition module acquires a power injection quantity sequence, a voltage deviation ratio sequence and uncontrollable parameter fluctuation data of each partition node of the power distribution network; a multi-dimensional state space construction module performs dimension raising mapping on the sequence to generate a linearized power flow state space model containing a power-voltage Jacobian matrix; the security domain analysis module corrects the boundary of the model according to uncontrollable parameter fluctuation and generates a dynamic security operation constraint set; the partition coupling degree calculation module quantifies an electrical independence index by means of a spectrum radius; and the bearing capacity evaluation engine constructs a chance constraint optimization model, outputs the photovoltaic maximum accessible capacity of each partition and a safety guarantee supply control strategy set, and improves the evaluation accuracy and practicability.

The invention discloses a target detection method based on Mama feature fusion, and relates to the technical field of image target detection. According to the method, the innovative implementation of the VSSA module is utilized, a selective scanning mechanism of the state space model is applied to 2D visual data processing, the long-distance dependency relationship in the image is effectively captured through state space modeling in four directions, the limitation of a traditional state space model in the two-dimensional visual data processing process is solved through the multi-direction processing strategy, and the processing precision of the 2D visual data is improved. The model can comprehensively perceive spatial dependency relationships in different directions in an image, the VSSA adopts learnable state space parameters to dynamically model a feature sequence, the ability of the network to understand a complex space structure is enhanced, the method is particularly suitable for processing scenes needing long-distance context information, and in addition, the method is combined with MTMHSA, so that the complexity of the network is reduced. And the fusion capability of different levels of features in target detection is further enhanced. Through the innovation, the model can better understand the target in the image, and the positioning and classification precision of the target is improved.

The invention discloses an epilepsy prediction method based on a self-adaptive space-time attention and dynamic fusion network, and relates to a brain signal preprocessing module, a context attention coding layer, a long-distance content dependence network, a self-adaptive time sequence state space modeling module and an epilepsy prediction module. The brain signal preprocessing module obtains corresponding time domain information, frequency domain information and an adjacent matrix; an adjacent matrix is regarded as a graph structure, the weight of each edge of the graph structure is input into a context attention coding layer, spatial features between channels are extracted, the weight is dynamically adjusted through aggregation of neighbor node information and attention, and a node feature matrix is output; inputting the node feature matrix into a long-distance content-dependent network to output global spatial features and corresponding time sequences; obtaining space-time dynamic fusion features through a self-adaptive time sequence state space modeling module; and inputting the space-time dynamic fusion features into an epilepsy prediction module and then outputting an epilepsy prediction result According to the invention, accurate prediction can be realized.

The invention relates to the field of mechanical equipment health management, in particular to a large sliding bearing fault detection and evaluation method, device and system. Comprising the following steps: collecting multi-source sensing data, and constructing a comprehensive data set; constructing a state space model based on a sliding bearing physical mechanism; the multi-source sensing data and the state space model are fused through Bayesian filtering, and hidden state parameter posterior distribution is dynamically estimated; generating a virtual fault sample by using a generative adversarial network in combination with a physical rule base; designing a Bayesian space-time sequence diagnosis model based on an attention mechanism, and generating fusion health state features; processing and fusing the health state features by using a degradation process model, and predicting the remaining service life of the bearing; and based on the health state, the fault probability and the remaining service life, setting multi-stage early warning threshold values, and triggering intelligent early warning. According to the method, the defect that a single model is insufficient in adaptability and generalization ability under complex working conditions is overcome, and the accuracy and reliability of fault detection are remarkably improved.

The invention discloses a power system load dynamic optimization method based on reinforcement learning. The method comprises the following steps: S1, collecting power system data to construct a state space; s2, constructing a hierarchical reinforcement learning model based on the state space, and dividing a high-level decision and a low-level execution task; s3, training a high-level decision model, and outputting a scheduling task target category instruction in a high-level state; s4, training a low-layer execution model, and outputting a control action in combination with a current node state and a high-layer instruction; s5, introducing an evolutionary mechanism to generate a strategy population and optimizing a low-layer execution model; s6, fusing an evolutionary mechanism and a strategy gradient to synchronously optimize individuals with excellent performance; s7, deploying the trained model to a power dispatching system; s8, performing model parameter fine tuning based on scheduling feedback; and S9, continuously applying the fine-tuned model to load scheduling control. According to the invention, power load accurate scheduling and strategy efficient adaptive optimization are realized, and system responsiveness and operation stability are improved.

The invention relates to the technical field of heat dissipation control, and discloses a heat dissipation fan multi-order filtering control method and device, and the method comprises the steps: collecting the operation parameters of a multi-fan heat dissipation system, and carrying out the construction of a dynamic state space model, and obtaining a system operation state equation and a temperature observation equation; inputting the system operation state equation and the temperature observation equation into an expectation maximization sliding window iteration extended Kalman filter to obtain a system state estimation value and a prediction error covariance matrix; performing three-stage series design on the cut-off frequency of the low-pass filter, the passband range of the band-pass filter and the coefficient of the adaptive filter to obtain a system multi-order filtering control model; based on a system multi-order filtering control model, an airflow coupling influence coefficient matrix among multiple fans is calculated, the total energy consumption, the noise level and the heat dissipation efficiency are subjected to optimization solution, a fan control optimization parameter set is obtained, the heat dissipation efficiency is guaranteed while the energy consumption is reduced, and dynamic balance among all control units is achieved.

The invention relates to the technical field of power grid operation and maintenance, and discloses an AI-based reactive compensation optimization method and system, and the method comprises the following steps: integrating the four-dimensional characteristics of traffic flow, power grid load, user preference and equipment health degree, constructing a space-time correlation graph model, achieving the adaptive evolution of a topological structure through dynamic graph convolution and an attention mechanism, and obtaining a reactive compensation optimization model. The method comprises the following steps: fusing spatial-temporal features of a graph neural network with a BiGRU time sequence for modeling, injecting an equipment health degree attenuation factor, generating a charging demand prediction result with uncertainty quantification, expanding an intelligent agent state space, improving a reward function, and embedding equipment health degree constraints in a strategy network and near-end optimization. By sensing the degradation state of the equipment in real time and intelligently adjusting the reactive compensation strategy, the service life of the old equipment is prolonged while the voltage stability is guaranteed, the deviation accumulation effect is effectively inhibited through feedforward-feedback composite control and dynamic rescheduling, and accurate matching of a scheduling instruction and actual output is ensured.

The invention discloses an efficient approaching rainfall forecasting method, and belongs to the technical field of meteorology. The method specifically comprises the following steps: firstly, acquiring radar echo data of a public meteorological platform, constructing an echo sequence data set, and dividing the echo sequence data set into a training set and a test set; then, on the basis of the state space model and the convolution model, an efficient double-branch fusion approach rainfall forecasting model is constructed; the model is composed of an encoder, a middle layer and a decoder. The middle layer adopts a state space module and a convolution module to form a double-branch architecture to realize multi-frequency feature extraction, and dynamic weight distribution of multi-scale features is realized through a feature fusion module. Thirdly, training an approaching rainfall forecasting model by using the training set; and finally, inputting radar data observed in real time to obtain a forecast result at a future moment. According to the invention, characteristics of different scales and frequencies in echo data can be effectively captured; while the parameter quantity and the calculation quantity of the model are reduced, the performance of forecasting the short temporary rainfall can be greatly improved.

The invention relates to the technical field of clean environment control, in particular to a dynamic pipe network and model predictive control clean room energy-saving pressure control method and system. Obtaining current pipe network impedance according to the pipe network impedance curve; calculating the current valve impedance according to the valve opening; calculating the total pressure drop of the current pipe network; constructing a fan dynamic model based on a fan similarity law to calculate the air volume at the next moment; a clean room pressure difference dynamic model is constructed, and the clean room pressure difference at the next moment is calculated; constructing a state space model and a target function of a model prediction controller; and when the pressure difference of the clean room is unstable, predicting the change trend of the pressure difference of the clean room in the next time period, and reversely solving the optimal fan frequency and valve opening of the discretization state space model by using the model prediction controller so as to achieve the lowest pipe network impedance and obtain the optimal fan frequency. The control precision and robustness of the pressure difference of the clean room are remarkably improved, and meanwhile energy consumption is reduced.

The invention discloses a physical information neural network-driven ocean island typhoon scene generation method and system. The method comprises the steps of collecting multi-source heterogeneous meteorological data and performing space-time alignment preprocessing; constructing a coarse-scale space-time probability prediction model, capturing space correlation of meteorological elements by using a graph topology learning network, efficiently processing long-time-sequence dependence of typhoon evolution by integrating a state space model with linear complexity, and generating a probabilistic typhoon scene with coarse resolution through a multivariable joint distribution probability model; further constructing a physical downscaling model, taking a coarse-scale prediction result as condition input, and performing physical consistency downscaling on a coarse-scale scene by embedding an atmospheric fluid mechanics equation in a loss function as a physical hard constraint; and finally, outputting a high-resolution typhoon scene with probability reliability and physical authenticity.

The invention discloses a simulation optimization method for a digital twin factory. The simulation optimization method comprises the steps of obtaining workshop equipment data and performing classified storage; generating a task priority and resource configuration rule based on the basic operation data of the equipment; constructing a three-dimensional space model by using a 3D modeling tool, and creating a digital twinborn simulation factory model in combination with real-time production data; constructing a dynamic planning state space and a decision variable, and determining the resource use time, the resource allocation position and the specific quantity of each task; according to the resource allocation scheme and the task chain dependency relationship, evaluating a risk value of a task node in combination with historical equipment fault data; and based on the influence range report of the production fault chain, evaluating a task execution sequence and adjusting resource allocation and task time arrangement. According to the method, the influence of faults on a production plan and resource waste are reduced, the stability and adaptability of a factory system are remarkably enhanced, and the reliability and fine management level of overall production are improved.

The invention belongs to the related technical field of artificial intelligence, and discloses a robot time sequence imitation learning method and system based on a Mama coding complete history, and the method comprises the steps: receiving a multi-modal observation sequence in a task execution process of a robot, the multi-modal observation sequence comprising observation data of at least one sensor; processing the multi-modal observation sequence by using a sequence processing module based on a state space model, and updating a time sequence output of complete historical information of one code up to the current time step at each time step; and predicting the next step or a series of future actions of the robot based on the time sequence output of the current time step so as to control the robot to simulate. The time sequence processing module based on the state space model is utilized to process and encode the complete observation history in the task execution process of the robot, so that a non-Markov decision-making imitation learning method is realized, and the learning efficiency and the execution success rate of the robot in a complex and state-dependent long time sequence operation task are improved.

The invention relates to the technical field of artificial intelligence computer vision, in particular to an event camera pedestrian detection method based on a space-time state space model, and the method comprises the steps: collecting a pedestrian detection data set based on an event camera, obtaining original event data, carrying out the preprocessing, determining an event tensor, and obtaining an event frame sequence; modeling is carried out through combination of a state space and an event tensor, an event-driven recursive space-time state space module is defined as a core unit, and an isomorphic deep neural network architecture is constructed; training the isomorphic deep neural network architecture by adopting the training set, and verifying through the verification set; inputting the test set into the verified isomorphic deep neural network architecture for detection, and generating a pedestrian detection result; the collaborative optimization of sparse adaptation-dynamic capture-noise suppression is realized in a unified framework, the essential characteristics of the event camera triggered based on brightness change are theoretically fit, and higher robustness and generalization ability are shown in an actual complex scene.

The invention provides a heat exchanger intelligent design method based on deep reinforcement learning, and relates to the technical field of data processing. Establishing a flow model for simulating the flow path, speed distribution and pressure change of the fluid in the heat exchanger; according to the geometrical shape of the heat exchanger, the physical property of a heat exchanger material and fluid flowing in the heat exchanger, a heat exchange model is established by combining the thermodynamic principle, and the heat exchange efficiency under different fluid flowing conditions is calculated; the flow model and the heat exchange model are coupled, so that a coupling performance evaluation model for evaluating the overall performance of the heat exchanger under the specific working condition is obtained; and determining a state space, an action space and a reward function according to the flow heat exchange intensity coupling performance evaluation model. The design efficiency is improved.

The invention discloses an iterative learning control optimization method for a direct current motor with non-repetitive disturbance, and relates to the technical field of direct current motor control, and the method comprises the steps: lifting a nominal model of a nonlinear system to a high-dimensional linear space through a deep Koopman operator, and obtaining the description of a high-dimensional state space model of the nonlinear system; converting the state space model into a linear repetition process model, and obtaining a linear matrix inequality condition of system convergence in a high-dimensional space by using a two-dimensional iterative learning control method; further, the intelligent agent is trained in a high-dimensional state space model iterative learning control system with model mismatch and a non-repetitive uncertainty nonlinear system, and finally the intelligent agent is applied to an actual system. The control law gain obtained by the method can better adapt to an original nonlinear system, a deep reinforcement learning algorithm is designed to compensate non-repetitive disturbance, and zero-error tracking of expected output is basically achieved.

The invention relates to the technical field of intelligent mechanical arm control, and discloses a mechanical arm track optimization method and system based on deep learning and a fuzzy algorithm, and the method comprises the steps: building a kinematic model of a mechanical arm, determining the working space of the mechanical arm, carrying out the high-density random sampling, and generating a three-dimensional point cloud picture of a reachable region at the tail end of the mechanical arm; constructing a path planning model, designing a state space and an action space, and constructing a reward function; time-impact double-target optimization is carried out on the tail end path point sequence, a smooth joint trajectory is constructed, and balance between the shortest trajectory execution time and the minimum joint impact is achieved on the premise that speed and acceleration constraints are met; and tracking control is carried out on the trajectory, external disturbance and unmodeled dynamics are estimated and compensated in real time, a parameter adaptive law is designed, and the trajectory tracking precision of the system in a complex environment is improved. The autonomy, the accuracy and the anti-interference capability of the hot-line work mechanical arm in a complex environment are improved.

The invention discloses an engine residual service life prediction method and system based on space-time state selection, and belongs to the field of equipment residual service life prediction. According to the method, multiple technologies such as a time sequence-space double-branch coding and hierarchical fusion mechanism, a selective state space model, a cross-layer residual jump connection and parameter sharing mechanism and an efficient self-attention mechanism are integrated, and an STSSFormer network model is constructed to predict the residual service life of the aero-engine; the system mainly comprises a data selection and preprocessing module, an input sample construction module, a model training module and a residual service life prediction module. According to the method, the feature expression and fusion capability is comprehensively enhanced, and the method has remarkable advantages in the aspects of prediction accuracy of the remaining service life, model robustness and engineering practicability.

The invention relates to the technical field of vibration isolation control, discloses an air floating support quasi-zero stiffness active vibration reduction method based on model predictive control, and aims to solve the problems of high vibration isolation requirements of precision equipment in a low-frequency vibration environment, response lag of a traditional vibration isolation technology and insufficient control precision under uncertainty. The method comprises the following steps: step 1, carrying out system modeling on the vibration damping device to obtain a state space model of the vibration damping device, and introducing a Kalman filter into the state space model to predict state information in real time; step 2, constructing an optimization problem and a constraint condition of the MPC controller; step 3, carrying out robustness and stability analysis on the MPC controller; and step 4, fusing sensor data through the MPC controller after robustness analysis, solving an optimization problem through an optimization algorithm, calculating an optimal control input # imgabs0 # in real time, driving a voice coil motor, and suppressing low-frequency vibration.

The invention relates to the technical field of aerocar mode switching, and discloses a distributed driving aerocar self-adaptive fault-tolerant control method for actuator multimode failure, which comprises the following steps: constructing a unified six-degree-of-freedom dual-mode state space model; residual signals are generated based on extended Kalman filtering and a sliding-mode observer, and fault types and positions are positioned in real time through a lightweight classifier; the method comprises the following steps: extracting residual time-frequency features, identifying hard faults by using a lightweight convolutional neural network, quantifying soft fault degrees through an incremental support vector machine, fusing multi-source information based on a Bayesian network to output fault types, levels and confidence coefficients, and introducing an incremental learning mechanism to realize self-evolution of a diagnosis model; a virtual control instruction is generated by adopting hierarchical sliding mode control, thrust and torque distribution of remaining actuators is optimized based on a dynamic quadratic programming algorithm, control parameters are adjusted online in combination with a Lyapunov adaptive law, aerodynamic interference and model uncertainty are inhibited, attitude stability and trajectory tracking in air-ground mode switching are guaranteed, and the method has the advantages of being high in reliability and high in reliability. And the fault-tolerant performance and the operation safety of the hovercar in the air-ground mode switching process are obviously enhanced.

The invention discloses a course resource recommendation method and device based on an improved state space model, equipment and a medium, and relates to the technical field of learning resource recommendation, and the method comprises the steps: collecting user course resource data, obtaining and preprocessing an interaction sequence, mapping the user interaction sequence to a low-dimensional vector space, and inputting the low-dimensional vector space into the improved state space model; the target interest course is matched with the candidate course resource set, the recommendation score is calculated, and the recommendation list is generated, so that the calculation complexity is reduced, the user interest is accurately captured, and the efficiency and accuracy of course resource recommendation are improved.

The invention relates to the technical field of 5G edge collaborative power supply driving, and discloses an intelligent 5G edge collaborative power supply driving system, which comprises a state sensing module used for collecting multi-source data of each power supply device in the power supply driving system in real time through a multi-source sensor; the load prediction module is used for predicting a future load by using a load prediction algorithm and outputting a load prediction value; the thermal path analysis module is used for constructing a thermal path diagram, predicting a thermal diffusion trend and adjusting power output of the power supply driving system; the power dispatching optimization module is used for adjusting the power of the power supply based on the optimization objective function; and the remote cooperative control module is used for the cloud platform to generate an optimization strategy according to the real-time states of load prediction, temperature prediction and power supply scheduling. According to the method, the integrity and the time relevance of the equipment operation state expression are improved by constructing the continuous time state evolution model in a modeling mode of combining the multi-source state perception and the state space model.

The invention discloses a high-resolution remote sensing image semantic segmentation method based on multidirectional parallel selective scanning, and belongs to the technical field of high-resolution remote sensing image processing. According to the method, a multidirectional parallel selective scanning model is provided, direction perception modeling is carried out through 8-direction serialization scanning in combination with a state space model SSM, and the multidirectional long-distance dependence capture capability is enhanced while the linear calculation complexity is kept. A pyramid encoder-decoder structure is constructed, multi-level feature extraction is realized through a four-stage OSSBlock module, and local details and global semantics are dynamically fused in cooperation with SE attention jump connection of a decoder. A selective scanning mechanism is adopted to replace self-attention, and linear complexity calculation is realized through a state space parameter matrix; and designing a mixed loss function, and improving the small target segmentation precision in combination with the class balance of Dice Loss and the difficult sample mining capability of Focal Loss.

The invention discloses a multi-energy system optimization scheduling method based on a carbon flow theory and deep reinforcement learning. The method comprises the following steps: firstly, carrying out mathematical description on a multi-energy system dynamic optimization problem based on a carbon emission flow theory, and establishing an objective function and constraint conditions of multi-energy system optimization scheduling; then, the scheduling decision problem is expressed as a Markov decision process framework, a reinforcement learning environment is created, and a state space, an action space and a reward function of the multi-energy system are defined; meanwhile, in the depth deterministic strategy gradient algorithm, the training efficiency is improved by introducing an experience replay mechanism based on proportion priority, the overfitting risk in the network training process is reduced by adopting an L2 regularization measure, and the generalization ability of the network in the optimization scheduling decision of the multi-energy system is improved. The method can respond to random fluctuation of a source side and a load side in real time, outputs a coordinated scheduling strategy, ensures low-carbon operation of a multi-energy system, and overcomes the defect that a traditional energy scheduling system cannot quickly adapt to changes.

The invention provides a transient overvoltage risk assessment method, device and equipment for an AC / DC system containing high-proportion new energy, and a storage medium. Relates to the technical field of power systems and automation thereof. The method comprises the following steps: modeling a power system into a discrete nonlinear power system based on phase change measurement unit data, constructing a state space, introducing a Koopman operator to determine an observation function, and establishing a dynamic time sequence track prediction model based on the observation function to output a predicted dynamic time sequence track; an Informer model is constructed, in response to an input predicted dynamic time sequence track, feature weights are distributed through a ProbSparse self-attention mechanism, hierarchical feature down-sampling is performed by using a distillation mechanism, and a predicted transient voltage amplitude is output; and quantifying the transient overvoltage risk level according to the predicted transient voltage amplitude. According to the method, the problems of low isolated time section feature prediction precision and poor long-sequence dynamic evolution feature evaluation timeliness in the prior art are solved.

The invention discloses a fault-tolerant control method for the rotating speed of a driving motor of a deep-sea robot. The fault-tolerant control method comprises the following steps: S1, establishing a discrete state space model based on electrical and mechanical equations of the motor; s2, determining a fault characteristic parameter to describe the loss probability of the control signal when the control signal is attacked and interfered; s3, calculating a feedback control matrix according to the fault characteristic parameters; s4, calculating a feedforward control matrix according to the fault characteristic parameters; s5, constructing a controller based on the feedback control matrix and the feedforward control matrix, calculating a control signal online and transmitting the control signal to an execution mechanism; and S6, the execution mechanism receives the control signal, and if the signal is lost, the stored control signal is adopted to replace the control signal, and control is executed in real time. According to the invention, fault-tolerant control of the motor rotating speed can be realized under the condition that the control signal is randomly lost, and the motor rotating speed can stably track a predetermined trajectory.