43 results about "Dynamical system" patented technology

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.

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 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.

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.

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 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.

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 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.

The invention relates to a dynamic nonlinear system having a plurality of degrees of freedom. The system has at least one potential element, and eigenmodes of the system are produced by means of the potential element. A potential is produced by means of the at least one potential element, and the potential causes an acceleration tangential to the basic trajectories of the system, a basic trajectory being a trajectory of the potential-free system.

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 proposes a hybrid time-scale based weakly nonlinear dynamic system equation discovery method. The method first constructs the dynamic system state variables and phase space trajectory according to the bridge wind-induced vibration response, and organizes the training data based on the phase space structure; then a dynamic symbolic network is established to learn the local dynamic law and overall evolution law of the system in the time recursion process; finally, combined with sparse constraints and parameter screening, the explicit control equation of the target dynamic system is obtained. The method can effectively combine the phase space structure information, multi-time scale characteristics and the explainable expression ability of the symbolic network, and improve the stability and explainability of the bridge wind-induced vibration dynamic equation identification.

The invention relates to the technical field of nonlinear power system uncertainty analysis, in particular to a method for predicting dynamic response of a pumped storage wind power interconnection grid-connected system under the influence of uncertain parameters. Comprising the following steps: S1, establishing a mathematical model reflecting the dynamic characteristics of the pumped storage wind power interconnection grid-connected system; s2, selecting each parameter in the mathematical model constructed in the step S1 as an uncertain parameter; s3, calculating the amplitude distribution of the dynamic response of the system state variable under the influence of the uncertain parameters based on a Fourier amplitude sensitivity test expansion method; s4, calculating uncertainty according to the amplitude obtained in the step S3; s5, predicting the dynamic response of the system based on the calculated uncertainty; and an important theoretical basis is provided for uncertainty analysis and performance optimization of the system.

The invention provides an intelligent gridding region division method and system based on multi-dimensional data analysis, and the method comprises the steps: firstly reconstructing the multi-dimensional dynamic data of each key node in a target power grid into a phase space of a multi-dimensional nonlinear power system; and a dynamic topology hypergraph of the target power grid is constructed based on the dynamic coupling relationship of the node state trajectory. A Hough-Laplacian operator is defined on the dynamic topology hypergraph, and an energy flow field in a target power grid is decomposed into a circulation component and a gradient flow component. Respectively delineating an energy closed-loop oscillation area and a risk potential energy conduction area based on the spatial distribution characteristics of the circulation component and the gradient flow component, and defining the areas as risk grids; and other nodes are clustered to form a stable grid. And finally, combining all the risk grids and the stable grids to complete combination segmentation of the target power grid. The final gridding result can accurately reflect the dynamic response mode and the risk propagation path of the power grid under real disturbance.

The invention provides a power system parameter tolerance determination method and device, a storage medium and equipment, and relates to the field of power system engineering, and the method comprises the steps: obtaining an initial preset range of the power parameter tolerance; according to the initial preset range of the dynamic parameter tolerance, a simulation model of the power system is constructed, the simulation model represents the relation between the dynamic parameter tolerance and crankshaft performance parameters, and the crankshaft performance parameters comprise the maximum contact pressure of a bearing bush; and optimizing the dynamic parameter tolerance according to the initial preset range of the dynamic parameter tolerance and the simulation model to obtain a target preset range of the dynamic parameter tolerance, so that the maximum contact pressure of the bearing bush is smaller than the preset pressure. According to the method, various methods such as parametric modeling, multi-physics field coupling simulation, tolerance contribution quantitative analysis, dynamic judgment and closed-loop optimization are integrated, the parameter tolerance of the power system is accurately determined, the local contact pressure of the bearing bush is reduced, the reliability of the power system is improved, and the service life of the power system is prolonged.

The embodiment of the invention discloses a verification method and a verification system for a ship dynamic positioning control system. The method comprises the following steps: generating a real-time state signal of a ship by a power system, a measuring system and a propeller system; the first DP control system receives the real-time state signal and generates a first DP control instruction according to the real-time state signal; the second DP control system receives the real-time state signal and generates a second DP control instruction according to the real-time state signal; wherein in the verification mode, the propeller system receives the second DP control instruction and verifies whether the function operation of the second DP control system is normal or not according to the second DP control instruction. According to the scheme, two DP control systems can coexist on one ship, the DP control system to be verified is verified, and the cost and risk of real ship verification are greatly reduced.

The application discloses a rotary drilling machine fault analysis method, device, equipment and medium, and belongs to the technical field of engineering machinery. The rotary drilling machine fault analysis method comprises the following steps: obtaining equipment parameters of the rotary drilling machine, and establishing corresponding mathematical models for multiple function systems of the rotary drilling machine according to the equipment parameters; wherein the function systems comprise a mechanical system, a hydraulic system, a control system and a power system; a model library is established by using the mathematical models; a simulation model corresponding to each function system is connected based on the model library, and a digital prototype of the rotary drilling machine is obtained; the digital prototype is controlled to operate under multiple fault conditions, and component energy loss of the digital prototype under each fault condition is obtained; a mapping relationship between the component energy loss and a fault type of the fault condition is established, and fault analysis is performed on the rotary drilling machine according to the mapping relationship. The application can improve the fault analysis precision and efficiency of the rotary drilling machine.

A neuromorphic computing system is disclosed in which inputs are embedded onto a continuous manifold realized by a dynamical substrate of interconnected processing elements that update their states in response to events. The substrate converges to attractor states that encode input-dependent representations, from which geometric properties, including metric tensor components and curvature, are derived from physical characteristics such as spike timing, synaptic weights, and conduction delays. Trajectories on the manifold emerge through evolution of the substrate state and follow geodesic-like paths arising from competitive propagation dynamics. Perturbation-based methods estimate curvature by measuring divergence of nearby trajectories. Parameters of the substrate are adaptively modified via activity-dependent plasticity rules, enabling experience-driven reshaping of the manifold geometry. The substrate may comprise spiking neural networks, memristive arrays, photonic processors, or analog dynamical systems.

This application provides a method and related apparatus for predicting solution field data of a dynamic system, belonging to the field of artificial intelligence. The method includes: calling a context feature learning network to generate initial environmental context features based on prior solution field observation data of the target dynamic system in the environment to be predicted; using these initial environmental context features as the current environmental context features, calling a partial differential equation surrogate model, and generating predicted solution field observation data based on a reference initial state and reference observable conditions, using the current environmental context features as conditions; updating the environmental context features based on the predicted solution field observation data, prior solution field observation data, current environmental context features, and initial environmental context features, and using the updated context features as the new current environmental context features, until a preset number of iterations; calling the surrogate model, and generating target solution field data based on the target initial state and target observable conditions, using the adapted environmental context features of the final iteration as conditions. This can improve the prediction accuracy of unseen operating conditions.

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 provides a condensate water supply system model inversion method and device based on measured data fusion, and belongs to the technical field of ship power systems.The method comprises the steps that actual measurement data of a condensate water supply system are obtained, and the actual measurement data comprise the pressure, temperature and flow of the condensate water supply system; constructing a condensate water supply system simulation model, and based on actual measurement data, performing inversion optimization on each sensitive model parameter of the condensate water supply system simulation model by adopting a particle filter algorithm to obtain each corresponding target model parameter so as to perform inversion to obtain an optimal condensate water supply system simulation model; wherein the sensitive model parameters comprise a system heat transfer coefficient and a resistance coefficient. According to the invention, real-time accurate inversion of the condensate water supply system model can be realized.

The application discloses a series hybrid electric vehicle energy management method based on event-triggered multi-target MPC, and belongs to the technical field of hybrid electric vehicle energy management and motion control. Firstly, a power system and a dynamics system of the vehicle are modeled, and the obtained mathematical model is used as a prediction model of multi-target MPC. Then, according to a control target, a suitable optimization target function and system constraints are selected to construct a multi-target MPC constraint optimization problem. The system state at the current time is sampled, a finite time domain multi-target MPC constraint optimization problem is constructed and solved, and the optimal control sequence predicted at the current time is obtained, and the first s optimal controls are applied to the vehicle system. Finally, an event-triggered mechanism is designed, the new system state is obtained at a new triggering time, and the multi-target MPC constraint optimization problem is updated, and the rolling iteration is performed until the control process is finished. The application guarantees the stability of the power battery pack and the engine power of the series hybrid electric vehicle, and the calculation efficiency of the optimization problem is higher.