72 results about "Dynamical system" patented technology

A hardware emulator and an emulation system including the hardware emulator are provided. The hardware emulator includes an artificial neural network-based reconstruction model configured to reconstruct dynamics of a dynamical system based on input data and a memristor-based circuit configured to emulate state space representation of the dynamical system based on the reconstruction model.

The invention discloses a fault analysis method, device and equipment for a rotary drilling rig and a medium, and belongs to the technical field of engineering machinery technologies. The fault analysis method for the rotary drilling rig comprises the following steps: acquiring equipment parameters of the rotary drilling rig, and respectively establishing corresponding mathematical models for a plurality of functional systems of the rotary drilling rig according to the equipment parameters; wherein the functional system comprises a mechanical system, a hydraulic system, a control system and a power system; establishing a model library by utilizing the mathematical model; the simulation models corresponding to all the function systems are connected based on the model library, and a digital prototype of the rotary drilling rig is obtained; the digital prototype is controlled to operate according to multiple fault working conditions, and the component energy loss of the digital prototype under each fault working condition is obtained; and establishing a mapping relationship between the energy loss of the component and the fault type of the fault working condition, and performing fault analysis on the rotary drilling rig according to the mapping relationship. The fault analysis precision and efficiency of the rotary drilling rig can be improved.

The invention discloses a time sequence prediction method based on cumulative causality and application, and belongs to the technical field of time sequence prediction.The method comprises the steps that firstly, a structural causality model is established for a power system containing observable reason variables, unobservable time-varying reason variables and to-be-predicted target variables; constructing an initial value and a time variation value of an encoder network characterization unobservable reason variable; and establishing a dynamic convolutional neural network prediction model by using the cumulative causal effect of a time-varying reason variable and a time-varying mechanism of convolutional expression, and outputting a result through a mask layer and accumulation. The prediction method based on the accumulative causal effect and the application are suitable for time sequence prediction problems such as metal component aging deformation prediction and the like, and have good potentials of stripping false correlation and improving time sequence prediction precision and stability.

The invention discloses a thermal power generating unit coordination control data and mechanism hybrid modeling method and device, computer equipment and a computer readable storage medium, and relates to the technical field of thermal energy engineering and power system modeling and control. Establishing a high-precision dynamic mechanism model of the working medium flow and heat transfer process of the boiler-steam turbine system; then, constructing an error correction model fusing the long-short-term memory network, the convolutional neural network and residual connection; and finally, fusing the mechanism model and the error correction model through parallel computing to form a dynamic hybrid model with physical interpretability and high precision. Through the hybrid modeling strategy, the prediction accuracy of key parameters such as the main steam pressure, the separator outlet steam enthalpy value and the unit load in the wide load range, especially under the dry-state operation working condition is remarkably improved, and a reliable model basis is provided for designing an advanced unit coordination control system.

Systems, methods, software, and devices are disclosed herein related to trajectory computation by way of a memory-based learning (MBL) controller. An MBL controller in various embodiments stores a set of trajectories in memory. The trajectories connect various initial states of a dynamical system with a target state. In addition to the memory, the controller further includes a processor that collects a current state of the dynamical system and determines, using memory-based learning (MBL) on training instances derived from the set of trajectories, a control policy that defines a trajectory connecting the current state of the dynamical system with the target state. The processor controls the dynamical system according to the control policy.

The invention relates to the technical field of intelligent design of optical lenses, and discloses a multi-series superposed complex periodic structure arrangement method and system.The method comprises the steps of normalized coordinate transformation, multiple sine wave superposition, an iterative function system, a Julia set algorithm, an LSystem grammar system, parameter adjustment, moire fringe fusion and the like; and a complex periodic structure with high complexity, adaptability resistance and natural morphological characteristics is generated. Advanced technologies such as logarithm uniform distribution wave number generation, a chaos game algorithm, linear mapping complex number conversion, a turn graph interpreter, multi-scale entropy complexity quantification and an Otsu automatic threshold method are adopted, and accurate parameterization control and manufacturing adaptation are achieved. The system comprises a coordinate transformation module, a wave interference calculation module, a fractal generation module, a complex power system module, an LSystem processing module, a parameter control module and a contour surface extraction module. According to the invention, a systematic solution can be provided for complex structure design of optical devices such as myopia prevention and control lenses.

The application discloses a target three-dimensional temperature field prediction method based on a generated diffusion model. The method steps are as follows: a simplified geometric model of the target is established, and key parameters such as target material, power system and meteorology are determined; an outer boundary heat balance equation of the target is established, and the surface temperature field under different working conditions is calculated as basic data; a multi-modal feature extraction module is established to perform deep feature extraction and fusion on three-dimensional coordinates, node temperatures and key parameters; a generated diffusion model is established to train the fused features, learn the mapping from feature noise to real temperature features; according to input parameters and three-dimensional shape constraints, the predicted node temperature value is output through a reverse denoising process, and the diffusion loss is calculated, and finally a complete three-dimensional temperature field is generated. The application effectively solves the problem of low efficiency of traditional simulation and realizes fast and accurate prediction of the target temperature field.

This application discloses a multivariate time series prediction method and apparatus, relating to the field of data prediction technology. The method includes: constructing a network model based on a graph convolutional neural network according to the topology of a target nonlinear dynamic system; the network model includes an adaptive graph module, an encoder, a spatial neural differential equation module, a temporal neural differential equation module, and a decoder connected in sequence; training the network model using a historical dataset of the target nonlinear dynamic system to obtain a multivariate time series prediction model; introducing state feedback through the spatial neural differential equation module to reveal the evolution pattern of the spatiotemporal time series in the spatial dimension, and introducing neural differential equations based on nonlinear state transition theory to simulate the state evolution at the temporal level, thereby improving the effectiveness of data prediction by fusing spatial features while suppressing feature oversmoothing.

The application provides a consistency tracking method and system of a nonlinear ship group power system, relates to the technical field of cooperative control, and performs coordinate transformation on a dynamic equation of the nonlinear ship group power system, takes a second-order strict feedback equation with time-varying input time delay obtained as a dynamic equation of a follower, determines a dynamic equation of a leader, and determines a communication relationship between the leader and the follower; a backstepping method is used to design a consistency tracking controller of the nonlinear ship group power system; and the nonlinear ship group power system is controlled based on the consistency tracking controller; the application defines an error coordinate conversion containing a compensation system based on a newly designed Lyapunov-Krasovskii function, designs an adaptive controller, processes unknown non-differentiable time-varying input delay, quantizes the controller by using a lag quantizer, optimizes the controller structure by using a command filter, and improves the stability and accuracy of the consistency tracking control.

The invention relates to a stochastic dynamics system energy prediction method and device, electronic equipment and a medium. The method comprises the following steps: setting a displacement variable of the stochastic dynamics system about time, and establishing an energy function of the stochastic dynamics system according to the displacement variable; constructing a multi-scale data set according to the energy function; the initial model is trained through the multi-scale data set, and a feature extraction model is obtained after training is completed; the feature extraction model is used for extracting dynamic features of the stochastic dynamic system; the dynamic characteristics at least comprise an energy predicted value at the current moment; constructing a drift term according to the drift network and the dynamic characteristics, and constructing a diffusion term according to the diffusion network and the dynamic characteristics; and calculating an energy predicted value of the next moment based on the energy predicted value of the current moment, the drift term and the diffusion term. According to the method, based on the improved discretization incremental energy prediction algorithm, the discretization scheme conforming to the physical law is adopted to improve the energy prediction numerical stability and prediction precision.

A closed-loop selective attention system for resolving conflicts in multi-source or multi-speaker environments, including a plurality of internal attention models, each outputting a probability distribution over candidate sources and an associated confidence score, a fuser detecting conflicts when two or more of said attention models output high-confidence predictions that disagree, a selective sampling policy querying one or more external agents, wherein each external agent possesses a knowledge base, a reliability model, and a communication protocol, a trust and reliability module assigning and updating dynamic trust scores for internal and external agents based on past performance, an efficiency optimizer minimizing communication overhead and decision delay by balancing token usage cost and latency cost, and a dynamical system formulator ensuring convergence of the conflict resolution process under bounded trust, decaying step size, and limited sampling.

The invention discloses a full-automatic slurry mixing control system and method for oil field well cementation equipment, and belongs to the technical field of oil field well cementation or ocean engineering, and the method comprises the following steps: S1, collecting slurry state data, power system operation data and external environment data through a multi-dimensional state sensing module to generate a real-time working condition data set; s2, based on the real-time working condition data set, adopting a preset slurry rheological state differential model to predict dynamic evolution of slurry viscosity so as to generate future slurry viscosity; s3, according to the future slurry viscosity and the preset slurry safety temperature upper limit, a preset lowest energy consumption trajectory planning model is adopted, and the optimal flow control trajectory of the power system is solved; s4, according to the real-time working condition data set, a preset optimal activator injection decision model is adopted, and the optimal activator dosage is worked out, and the accuracy and reliability of future slurry viscosity prediction are greatly improved.

Provided is a process, including: obtaining, with a computer system, access to a specification indicating which regions of an embedding space are designated as anomalous relative to vectors in the embedding space characterizing past behavior of a first instance of a dynamical system; receiving, with the computer system, multi-channel input indicative of a state of a second instance of the dynamical system; and classifying, with the computer system, whether the state of the second instance of the dynamical system is anomalous by: encoding the multi-channel input into a vector in the embedding space; causing the specification to be applied to the vector; obtaining a result of applying the specification to the vector; and classifying whether the state of the second instance of the dynamical system is anomalous based on the result; and storing the classification in memory.

The invention discloses a flow thermal coupling calculation method in the starting process of a partial air inlet type turbine. The flow thermal coupling calculation method comprises the following steps: S1, establishing a three-dimensional model in which a fluid domain and a solid domain of an underwater turbine are conjugated; s2, performing grid division on the three-dimensional model; s3, establishing a numerical simulation calculation method, setting an initial condition of a fluid domain and a simulation calculation time step length, and forming a numerical simulation model; s4, establishing a combustion chamber model and an aircraft power system dynamic model by adopting a user-defined function; and S5, embedding the self-defined function into the model in the S3 to obtain the output torque of the turbine under a time step, substituting the output torque into the model in the S4 to obtain turbine parameters, updating boundary conditions, and carrying out calculation of a next time step until simulation time is met, so as to obtain temperature distribution of the turbine disc in the starting process. According to the flow-heat coupling calculation method, the problem of inaccurate flow-heat coupling simulation caused by quick change coupling of a plurality of physical quantities in the starting process of the underwater part air inlet type turbine is solved.

This invention discloses a method for controlling the speed lag during the water entry transient of a vehicle's propulsion system, comprising the following steps: Step 1, constructing a cross-medium aerial propulsion system and performing aerial and underwater thermodynamic design to obtain the cross-medium system configuration; Step 2, establishing a dynamic mathematical model of the cross-medium propulsion system based on the cross-medium system configuration; Step 3, designing a control algorithm based on the dynamic mathematical model of the cross-medium propulsion system to adjust the fuel pump displacement during the water entry process, achieving closed-loop control of the turbine speed, and obtaining an optimized propulsion system; Step 4, simulating the optimized propulsion system to obtain the control response characteristics of the cross-medium propulsion system during the water entry process. The speed lag control method for the water entry transient of a vehicle's propulsion system disclosed in this invention solves the problems of existing PID control being prone to instability under engineering delays and failing to provide a safety margin for the determination of response time for sensors and actuators.

The invention provides a structural damage positioning method and system based on wavelet-PCA, and relates to the technical field of structural damage positioning. The method comprises the following steps: acquiring a structure vibration response signal through a sensor to obtain an original signal data set; de-noising the signal by adopting wavelet transform, and extracting a principal component characteristic matrix through principal component analysis; performing phase-space reconstruction on the principal component time sequence to generate a high-dimensional power system track; calculating quantization factors such as recursion rate, certainty and average diagonal length based on recursion plot analysis, and forming a recursion quantization feature matrix; generating a spatial correlation distribution diagram by analyzing the correlation between the recursion characteristics of different sensor positions; and realizing damage positioning according to the correlation abnormal region. The early weak damage can be effectively detected, the defect that a traditional linear analysis method is insensitive to nonlinear characteristics is overcome, and the method has the advantages of being high in anti-interference performance and accurate and reliable in positioning.

The invention belongs to the technical field of power system control, and particularly relates to a low-communication-cost neural network controller of a nonlinear power system and a design method of the low-communication-cost neural network controller. According to the method, the event-driven regulation and control algorithm is designed for the nonlinear power system by using a machine learning method, so that optimal triggering is realized while calm control is performed, and effective control under limited communication resources is realized. An event-driven mechanism is considered, that is, whether the control strategy is updated or not is determined according to the real-time state of the target system, so that the communication cost generated by updating the control signal is reduced; the optimal triggering problem in event-driven regulation is considered, that is, the event triggering frequency in specified time is minimized; according to the invention, two schemes of a direct method and an indirect method are designed to realize optimal trigger control under an event-driven mechanism; and a strict stability and optimality guarantee is provided for a control strategy obtained by neural network training by using an approximate projection method. Finally, the superiority of the technical scheme is verified by taking an industrial heat exchanger as an example.

The invention relates to the technical field of power system control of a wing-in-ground-effect ship, and discloses a hybrid wing-in-ground-effect ship energy optimization method and system based on model prediction, and the method comprises the following steps: collecting the multi-dimensional operation state information of a hybrid wing-in-ground-effect ship in real time, the state information comprises flight state parameters, environment parameters and hybrid power system parameters; a hybrid power prediction model fusing the ground effect characteristics is constructed, the prediction model comprises a ground effect environment sub-model, an energy flow sub-model and an energy consumption prediction sub-model, and the ground effect environment sub-model is used for representing the incidence relation between the ground effect and the environment parameters. A hybrid power prediction model fusing ground effect characteristics is constructed, a multi-objective optimization function is established in combination with multi-dimensional operation state information, dynamic iteration control is realized by adopting a rolling time domain optimization algorithm, and the energy requirements of the wing-in-ground-effect ship in different flight stages and under different environment conditions can be accurately adapted.

An approach for generating time-series dynamic-system training data. The approach may comprise providing a plurality of dynamic systems to a dynamic system dictionary. Where the dynamical system dictionary may comprise a library of functions. The approach may further comprise classifying each of the plurality of dynamic systems. Where classifying may comprise, generating a hierarchical dynamic system data, based on constraint learning, with an encoder and noise generator. The approach may further comprise training a diffusion decoder to generate a time-series segment, based on the classified plurality of dynamical systems. Further, the approach may comprise providing a first time series data segment and generating time-series dynamical training data based on the diffusion decoder using the first time-series data segment as input.

The invention discloses a beam damage identification method based on a Koopman auto-encoder neural network, and the method comprises the steps: collecting the dynamic response data of a structure under the impact load effect based on the finite element simulation analysis of a cantilever beam; then, the collected data is preprocessed, and a data set used for training a Koopman auto-encoder neural network is constructed; training a Koopman auto-encoder neural network by using the data set so as to learn a nonlinear evolution law of the beam structure power system and obtain a linear lifting form of the beam structure power system; and based on the trained network, calculating Koopman modals of a health state and a to-be-detected state, and constructing a damage index by comparing the difference between the health state and the to-be-detected state, thereby realizing accurate identification and positioning of the beam structure damage. According to the method, the characteristic that a nonlinear system is globally linearized according to the Koopman theory is utilized, the defect that a traditional method is sensitive to structural nonlinearity and environmental noise is overcome, and the method has the advantages of being high in recognition precision, high in anti-noise capacity and the like and is suitable for beam structure health monitoring under complex working conditions.

The invention provides a Fokker-Planck equation solving method based on a physical information neural network, and belongs to the technical field of structure disaster prevention and reduction and machine learning, and the method comprises the steps: 1, building a one-dimensional FP equation changing with time according to an actual engineering structure, and constructing physical information; 2, acquiring a training data set according to the motion equation computational domain; 3, introducing the constructed physical information into a neural network, and training the neural network by using the data set; 4, effectively approaching the exact solution of the FP equation by using the trained neural network; and 5, introducing a robustness evaluation mechanism to ensure the stability and reliability of the model under different conditions. By introducing physical constraints, the method constructs a universal calculation framework suitable for various FP equation related problems, and provides a new solution for probability evolution modeling of a random power system. By means of the framework, an equation can be solved to obtain a probability density function of engineering structure dynamic response, and the probability density function is further used for structure reliability analysis and failure probability evaluation.

A method of monitoring an elevator car in an elevator shaft includes acquiring position data indicative of a position of the elevator car, acquiring motion data indicative of a motion of the elevator car, and determining, from a dynamical system model, an estimated position of the elevator car. The dynamical system model describes a motion of the elevator car based on input variables that include the position data and the motion data. The method further includes determining an offset value indicative of a motion data offset, wherein the offset value is generated such that the dynamical system model fits the position of the elevator car indicated by the position data, determining a sensor reliability parameter based on the offset value, and providing output data including the sensor reliability parameter.

The application discloses a nonlinear multi-scale dynamic system control mechanism inference method based on Koopman operator theory, which is based on the Koopman operator theory and establishes a reduced-order mechanism model of a dynamic system from observed time series data. First, the observed time series data is dynamically sampled based on the adaptive sampling strategy designed by the application, the sampling rate is adjusted according to the data change, and the information of different time scales is effectively separated. Then, cubic B-spline interpolation is applied to locally smooth and numerically differentiate the discrete sampling points. On this basis, the nonlinear modeling capability of the Koopman operator theory is used to innovatively construct a Block-Hankel matrix to capture the multi-element dynamic characteristics of the system; and the kernel singular value decomposition technology is used to extract the main dynamic mode, so that the system identification is extended to a nonlinear dynamic system. Finally, the SINDy algorithm is used to identify the reduced-order model, so as to realize accurate modeling of a complex nonlinear multi-scale system. On the basis of the adaptive sampling and multi-scale data processing capability, the application combines the advantages of Koopman embedding and sparse regression, has strong robustness and universality, and has theoretical and practical significance for mechanism modeling of nonlinear and multi-scale systems.

The invention, which belongs to the technical field of image processing and artificial intelligence, discloses a multi-level image intelligent damage assessment and prediction system for highway infrastructures, comprising a multi-scale feature level extraction module, a damage type intelligent identification module, a time sequence damage evolution prediction module and an adaptive maintenance decision module. Multi-scale geometric invariance features are extracted through topological manifold learning, robust damage identification is realized based on a Lie group transformation theory, a damage evolution prediction model is established by using a power system theory, a maintenance decision is optimized by using a game theory framework, and a closed-loop adaptive optimization system is formed. According to the invention, comprehensive assessment, accurate prediction and intelligent decision-making of damage are realized, and the intelligent level and preventive maintenance capability of health monitoring of highway infrastructures are significantly improved.

The invention discloses a fixed-time anti-interference control system and method for an eddy-current hybrid power system, and the method comprises the steps: obtaining a disturbance affine form nonlinear mathematical model of a turbine engine of the eddy-current hybrid power system through system identification, and designing a fixed-time disturbance observer to estimate the lumped interference of the system in real time; a feed-forward signal measurer is designed to measure and calculate a feed-forward quantity in real time, and a fixed-time anti-interference feed-forward composite controller is constructed based on a nonlinear backstepping method and a dynamic inverse method, so that accurate control of the rotating speed of a turbine engine in the eddy-current hybrid power system is realized. The system designs a virtual control quantity through a nonlinear backstepping method, and a controller adopts a non-smooth instruction filter and an interference and feed-forward compensation mechanism to realize effective compensation for the uncertainty of the system. The method has high control precision, good anti-interference capability and closed-loop stability, and is suitable for rotating speed control of the turbine engine in the eddy-current hybrid power system under complex working conditions.

A method for predicting rare trajectories (θ) of a dynamic system (1) is presented, comprising the steps of: - specifying a reward function (r) for quantum-assisted reinforcement learning of the rare trajectories (θ), - optimizing parameters (θ) of a parameterized quantum circuit (2) configured to determine a strategy (θ) of quantum-assisted reinforcement learning based on the specified reward function (r), - generating the rare trajectories (θ) based on the strategy (θ) determined by the optimized parameterized quantum circuit (2). Furthermore, a quantum computer is presented.

This invention discloses a multi-level image-based intelligent damage assessment and prediction system for highway infrastructure, belonging to the fields of image processing and artificial intelligence technology. The system includes a multi-scale feature extraction module, a damage type intelligent identification module, a temporal damage evolution prediction module, and an adaptive maintenance decision module. It extracts multi-scale geometrically invariant features through topological manifold learning, achieves robust damage identification based on Lie group transformation theory, establishes a damage evolution prediction model using dynamical system theory, optimizes maintenance decisions using a game theory framework, and forms a closed-loop adaptive optimization system. This invention achieves comprehensive damage assessment, accurate prediction, and intelligent decision-making, significantly improving the intelligent level of highway infrastructure health monitoring and preventive maintenance capabilities.

The embodiments are directed to an inferential sensing system, methods and computer program product of an estimator for estimating parameters of complex nonlinear time-varying systems from scarce system output measurements. The estimator comprises a two-step process to accurately estimate the time-varying parameters of the time-varying system based on the input and output sample of the time-varying system. First, multiple filters in the high frequency processing loop, operating independently and concurrently process the input and output samples of the time-varying system to generate a hypersurface comprising time series objects. Each filter is restricted to adapt only a subset of the modeled time-varying parameters. The hypersurface comprising the time series objects is aggregated over several iterations of the high frequency processing loop. Second, the hypersurface is passed through a neural network in the low frequency processing loop to infer estimates of the time-varying system parameters.

The application discloses a piezoelectric driving optimal trajectory tracking control method based on an adaptive step length, and comprises the following steps: a piezoelectric ceramic driver is mathematically modeled by using an electromechanical coupling nonlinear dynamic system model to obtain a piezoelectric ceramic driver model; the piezoelectric ceramic driver model is converted into a linear controllable canonical subsystem; an expected displacement x * (0) at time t=0 is taken as an initial actual tracking displacement x(0); a feedback control u is obtained by using the linear controllable canonical subsystem; the initial actual tracking displacement x(0) is substituted into the linear controllable canonical subsystem, and an actual tracking displacement is obtained by using a Runge-Kutta-Felberg method with an adaptive step length; a performance index function of the linear controllable canonical subsystem is determined, the feedback control u and the actual tracking displacement are substituted into the performance index function, and an optimal performance index of the piezoelectric ceramic driver model is obtained. In the calculation process, the piezoelectric ceramic driver can be improved in calculation efficiency while maintaining high precision.

The invention discloses a piezoelectric ceramic driver control method based on a multi-grid method, and belongs to the technical field of precise driving control. Aiming at the problems of low positioning precision caused by inherent nonlinear characteristics of hysteresis, creep and the like of a piezoelectric ceramic driver, many parameters of a traditional complex model and large calculation amount, the method comprises the following steps: firstly, establishing an electromechanical coupling nonlinear power system model, and accurately converting an original system into a linear system through a feedback linearization technology; then constructing an optimal control problem, deducing a regular equation set by using a Pontryagin minimum principle, and performing time discretization by using an implicit Euler format to ensure numerical stability; and finally, introducing a multi-grid algorithm to efficiently solve the large-scale discrete system. According to the method, the calculation complexity is remarkably reduced while the model precision is reserved, the convergence speed is increased by more than 8 times compared with a traditional iteration method, the robustness to parameter changes is high, a precondition device does not need to be adjusted, the positioning error can be controlled within the range, and the method is suitable for the high-end manufacturing fields such as precise positioning and optical focusing.