24 results about "Lyapunov exponent" patented technology

The invention belongs to the field of power system monitoring and fault diagnosis, and particularly relates to a cable branch box health early warning method based on fusion of insulation resistance and leakage current. The method comprises the following steps: acquiring insulation resistance and leakage current signals which are synchronously aligned by using a sensing monitoring unit, and extracting deterministic characteristics through nonlinear preprocessing; performing phase-space reconstruction on the leakage current signal, and calculating the maximum Lyapunov exponent, correlation dimension and other dynamic characteristic indexes so as to quantify the chaotic evolution degree responded by the insulating medium; and constructing an evaluation model based on fusion of insulation resistance and dynamic indexes, calculating a comprehensive health index, comparing the comprehensive health index with a threshold value, and determining an early warning level. According to the method, the qualitative change omen at the initial stage of insulation degradation can be sensitively captured by fusing the macroscopic electrical parameters and the microdynamics evolution characteristics, the advancement and the anti-interference capability of early warning are improved, reliable support is provided for refined operation and maintenance of the cable branch box, and the power supply reliability of a power system is guaranteed.

This invention discloses a comprehensive wastewater treatment management system and method, belonging to the field of wastewater treatment technology. The method acquires historical water quality parameter sequences and real-time process parameters for each monitoring node; performs phase space reconstruction on the historical water quality parameter sequences to obtain the attractor trajectory matrix of each node; calculates the maximum Lyapunov exponent difference between nodes and constructs a dynamic correlation matrix; uses the dynamic correlation matrix to drive virtual particles to randomly walk in the node space, with the movement probability determined by the local entropy of the current node's attractor trajectory matrix; inputs the walking trajectory sequence into a deep neural network based on a residual structure to output the water quality exceedance risk index for each node at future times; and adjusts the operating parameters of the wastewater treatment equipment based on the risk index. This application can dynamically capture the nonlinear correlation characteristics between nodes, improving the accuracy and timeliness of risk prediction for wastewater treatment systems.

This invention relates to the field of flow measurement and industrial process control technology, specifically to a gas ultrasonic flow meter and its measurement method; it includes a measurement pipe section, signal acquisition, phase space reconstruction, symplectic geometric analysis, and adaptive solution modules; the system acquires the original acoustic time-series signal and maps it into a high-dimensional state trajectory matrix; its core is to use symplectic geometric transformation to extract dynamic invariants such as the maximum Lyapunov exponent, determine the flow field distribution correction coefficient, and compensate the basic flow velocity to generate a standard flow rate; this invention transforms a complex inverse fluid mechanics problem into a geometric topology feature extraction problem, eliminating the need for massive model training, significantly reducing computational power consumption, and solving the computational deadlock problem.

This invention provides a method for identifying transient processes in traction networks based on phase space reconstruction and neural networks. The method includes: acquiring transient current signals; calculating the maximum Lyapunov exponent of the transient current signals; if the exponent is greater than zero, proceeding to the next step; for transient current signals that meet the phase space analysis conditions, adaptively determining the optimal delay time using the average mutual information method and adaptively determining the optimal embedding dimension using the pseudo-nearest neighbor method; reconstructing the phase space of the transient current signals to obtain a reconstructed phase space vector; extracting dynamic attribute features reflecting the dynamic characteristics of the transient process and constructing a joint feature vector; inputting the joint feature vector into a pre-established and trained radial basis function neural network classification model, and outputting the category identification result of the transient process through the model. This invention can solve the problems of poor identification ability, difficulty in fully revealing the deep dynamic characteristics of transient signals, and limited adaptability to complex scenarios in existing technologies.

The application provides a MES system operation data flow asynchronous processing method, and belongs to the technical field of MES systems.The application collects material flow data by deploying multilevel sensors and distributes globally unique batch identifiers, calculates Lyapunov exponents, correlation dimensions and Kolmogorov entropy to identify and isolate abnormalities by using a phase space reconstruction anomaly detection module, calculates an entropy increase index for normal data and optimizes resource allocation by a double-layer game scheduling model, dynamically adjusts a back pressure threshold according to a resource allocation scheme and starts a fuse degradation strategy, performs cross-batch correlation analysis and corrects timing through a delay compensation mechanism, and adaptively adjusts a processing mode according to risk assessment parameters, thereby solving the technical problem of system stability decline caused by abnormal data propagation and pollution of downstream processing results in the asynchronous processing of batching operation data flow.

This invention provides a data-driven quality control method for the entire manufacturing process of ultra-high voltage (UHV) equipment, comprising the following steps: using a hybrid algorithm combining attention mechanism and mutual information entropy to screen coupling factors, constructing a multi-stage bidirectional coupling matrix to quantify coupling strength, and introducing the Lyapunov exponent to determine the critical state of deviation amplification; constructing a multi-objective optimization model based on the coupling matrix and Lyapunov exponent, deriving the correction amount of key process parameters for each process, and conducting dynamic intervention throughout the entire process by combining adjustments to the testing process and adaptation of terminal testing accuracy; establishing a manufacturing and maintenance coupling mapping library, mining implicit mapping paths through graph neural networks, adapting to different equipment types using transfer learning, and updating model parameters through bi-cycle iterative updates. This invention can capture the nonlinear amplification risk of small deviations, avoids relying on manual experience to adjust process parameters, and clarifies the correspondence between manufacturing parameter deviations and maintenance failures, thereby achieving traceability of quality defects.

This invention discloses an event-triggered accelerated adaptive control method for a MEMS gyroscope coupled network system, comprising the following steps: S1, establishing a MEMS gyroscope coupled network model based on a series-parallel coupling mechanism; S2, performing dynamic analysis of the coupled network using phase diagrams, time history diagrams, and Lyapunov exponents; S3, designing an equivalent analog circuit based on Kirchhoff's laws; S4, building a semi-hardware experimental platform using a double-layer PCB circuit board, and empirically verifying the nonlinear chaotic oscillations in the coupled network system through experimental measurements using an oscilloscope and a power signal generator; S5, designing an accelerated adaptive controller.

This invention relates to the field of healthcare informatics technology, specifically to a dynamic prediction system for the risk of delayed chest closure after surgery in children with congenital heart disease. It collects high-frequency vital signs signals during surgery, imaging report text, and clinical structured data; extracts the maximum Lyapunov exponent, correlation dimension, and approximate entropy as chaotic features; quantifies the ambiguous semantics in the imaging report into membership values; uses Dempster-Shafer evidence theory to fuse multimodal features and encode uncertainty; constructs a dynamic ensemble learning model, detects concept drift using ADWIN and updates it online, dynamically weights it based on Shapley values; uses multi-objective reinforcement learning to dynamically optimize the risk threshold; and generates counterfactual explanations to provide individualized intervention suggestions. This invention achieves accurate and dynamic early warning of the need for delayed chest closure.

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

The application discloses a mapping method based on double discrete memristor, and relates to the technical field of memristor, two new double-memristor hyperchaotic mapping models of isomorphic sine-sine discrete memristor SSDM mapping and heterogeneous sine-cosine discrete memristor SCDM mapping are constructed by using two periodic discrete memristors; the mapping has a plane fixed point, and stability distribution is divided according to the second characteristic root in the parameter and initial value plane; bifurcation behavior depending on coupling strength, coexistence extreme multistability controlled by the initial value of the memristor are studied by using a bifurcation diagram, Lyapunov exponent, phase diagram and local attraction basin; under the control of the memristor, coexistence infinite heterogeneous attractors are self-replicated along a straight line and a plane; numerical results show that the SSDM and SCDM mappings can not only generate highly complex hyperchaotic attractors, but also exhibit extreme multistability behavior controlled by the initial condition of the memristor distributed on a straight line and a plane.

A method for measuring an air-coupled ultrasonic stress by extracting a Lyapunov exponent peak of a low signal-to-noise ratio air-coupled ultrasonic signal through a moving window. In view that the ch

This invention relates to the fields of data processing and artificial intelligence, and particularly to a data analysis and optimization method based on artificial intelligence. The method includes the following steps: S1, reconstructing heterogeneous time-series data collected by edge devices into high-dimensional phase space trajectories; S2, calculating the local Lyapunov exponent of the trajectory and mapping it to a joint space to obtain comprehensive state points; S3, calculating the local dynamic information entropy of the state points, and constructing simple complexes using points greater than a threshold as topological landmarks; S4, performing continuous homology calculation on the complexes to extract Betti numbers and generate a set of topological barcodes; and S5, calculating the duration integral of the barcode set and controlling communication transmission based on its deviation from the reference integral. In this invention, by calculating the local Lyapunov exponent of the phase space trajectory and using it as a mapping basis, various types of sensor data can be directly and equivalently calculated in a unified joint space.

This invention discloses a method for frequency and voltage coordinated support in a new energy power grid, comprising: collecting electrical quantity signals from multiple generating units to construct multi-unit power angle trajectory data; treating this data as a space curve in differential geometry and calculating its local curvature and torsional rate to establish a three-dimensional dynamic coupling model that can quantify instantaneous nonlinear coupling characteristics, forming a three-dimensional coupling state matrix; using this matrix and combining it with Lyapunov exponents to dynamically evaluate the support capacity of the generating units, obtaining a dynamic support capacity matrix; based on the dynamic support capacity matrix, modeling the coordinated control problem as a non-cooperative game model and solving for Nash equilibrium to decouple conflicts in multi-unit coordinated strategies and determine the coordinated control weight matrix; and performing multi-objective coordinated optimization based on this weight matrix to generate a coordinated support control command sequence. This invention can accurately quantify transient coupling, achieve dynamic adaptation of control strategies, and improve the frequency and voltage stability of high-proportion new energy power grids.

This invention provides a method and apparatus for evaluating the hunting stability of wheelsets in turnout areas based on rail profiles, relating to the field of vehicle dynamics technology. The method includes: calculating the wheel-rail geometric characteristics of different wheelsets for a set of rail profiles; solving for the equivalent taper of displacement based on the equivalent theory of hunting motion wavelength and wheel-rail geometric characteristics, correcting the contact equilibrium point through a gravity-restored stiffness coefficient, and obtaining the equivalent taper function; smoothly and differentiably fitting the equivalent taper function, and constructing a nonlinear dynamic model with degrees of freedom using a smooth exponential function; numerically extending the nonlinear dynamic model based on train speed and identifying Hopf bifurcations, and determining the bifurcation type using Lyapunov exponents to obtain the critical hunting speed; constraining the profile selection and stability evaluation of the rail profile set based on the critical hunting speed and the critical speed, and obtaining the hunting stability evaluation result. This invention solves the problem of potential safety hazards in turnout operation.

The present application relates to the technical field of flow measurement and industrial process control, in particular to a kind of gas ultrasonic gas flowmeter and its measuring method;It contains measurement pipe section, signal acquisition, phase space reconstruction, symplectic geometry analysis and adaptive solution module;System through the acquisition original sound wave time sequence signal, mapping is high-dimensional state trajectory matrix;Its core is to use symplectic geometry transformation to extract maximum Lyapunov exponent and other dynamic invariants, determine flow field distribution correction coefficient, and compensate the basic flow rate to generate standard flow;The present application converts complex fluid mechanics inverse problem into geometric topology feature extraction problem, without mass model training, significantly reduces the operation power consumption and solves the computing deadlock problem.

The application is specifically an artificial intelligence-based system maintenance task intelligent scheduling system, and relates to the technical field of artificial intelligence, nonlinear dynamics and computer system operation and maintenance, comprising: a univariate time series data acquisition and denoising module; a phase space reconstruction module; a chaotic feature quantitative calculation module; a dynamics state discrimination and early warning module; and an entropy-driven task blocking and scheduling execution module.In the application, Takens embedding theorem is used to realize univariate micro time series signal acquisition, and the global dynamics state of the system can be completely characterized by only CPU interrupt response delay and memory bus bandwidth occupancy, thereby reducing data transmission, storage and calculation overhead; and the maximum Lyapunov exponent is calculated to realize accurate quantification of steady state, bifurcation and chaotic state.

A method and system are disclosed for optimizing the charging process of a lithium-ion battery by utilizing a determined parameter (e.g., the Lyapunov exponent) as an indicator of cell degradation (e.g., anodic overpotential). The system and method achieve intelligent charging and may involve an integrated controller that adjusts the charging current in real-time (or near real-time, or otherwise) based on the cell degradation indicator calculated by probing the battery. This method offers various potential benefits, which can be tuned to include extended battery life, optimized charging timing, and improved safety.

This invention relates to the field of power robot operation style recognition technology, and discloses a method, system, device, and storage medium for power robot operation style recognition. The method includes acquiring multimodal data of the power robot, performing cross-modal reasoning to obtain the task; obtaining a standard motion trajectory based on the task; inputting acceleration data into a feature extraction model to obtain aggression features; performing wavelet packet decomposition and spectral energy entropy calculation on the mechanical data to obtain refinement features; calculating the Lyapunov exponent and root mean square deviation of the trajectory based on the standard motion trajectory and trajectory data to obtain stability features; inputting the aggression, refinement, and stability features into a style scoring model to obtain a three-dimensional style score; and obtaining the operation style of the power robot based on the three-dimensional style score. This invention can accurately identify operation styles, reduce human-machine interaction conflicts through real-time matching of operation styles, and thus improve the execution efficiency of power tasks.

The present application relates to the technical field of defect detection, in particular to a copper wire internal defect detection method and system combined with eddy current. The method comprises: applying a composite excitation superimposed by multi-frequency harmonic signals and collecting eddy current response signals, obtaining an initial time-frequency spectrum matrix through short-time Fourier transform; performing singular value decomposition on the matrix, reconstructing the lift-off correction spectrum matrix after eliminating the main singular value component related to the lift-off effect; calculating the maximum Lyapunov exponent and spectral kurtosis value of the correction matrix as the first and second defect characteristic quantities respectively; constructing a two-dimensional feature plane based on the two characteristic quantities, and calculating the angle correction amount using the energy of the eliminated component to adjust the defect judgment region, and finally combining the Manhattan distance and the adjusted angle interval to judge the defect. That is, the scheme of the present application can effectively suppress the interference of the lift-off effect, construct a dynamic criterion, and realize high-precision and high-reliability detection of the internal weak defects of the copper wire.

Aspects of the present disclosure relate to methods that can be used for control of a battery, such as through charge management, the methods including obtaining a value indicative of at least one of a dynamic state of a balance, a periodic behavior, a quasi-periodic behavior, a chaotic behavior, and a random behavior of a battery, the value can relate to an electrodynamic parameter such as a Lyapunov Exponent, a Correlation Dimension, a Sample Entropy, and a Hurst Exponent, etc., the value being obtained from voltage measurements or current measurements of the battery; and operating the battery to maintain the battery within one of the dynamic states based on the value.

The present application relates to the technical field of physiological signal processing, and particularly relates to an early risk prediction method based on multi-modal physiological signal chaotic feature fusion. Through acquisition of multi-modal physiological signal time series data and phase space reconstruction to form a chaotic attractor, identification of an invariant manifold and a saddle point and tracking of a heteroclinic orbit to construct a cross-modal critical state correlation atlas, calculation of a geodesic distance between a current physiological state point and a critical transition path based on the atlas to generate a potential energy function distribution, deduction of an optimal evolution trajectory and a bifurcation point position under multi-modal collaborative constraints to generate an early risk evaluation result, identification of orbit drift using an actual observation sequence and backtracking of a Lyapunov exponent spectrum to rescale the invariant manifold topological dimension and the saddle point position to synchronously correct the correlation atlas to form a dynamically self-consistent closed loop. The method can accurately predict early cardiac risk and realize dynamic self-consistency, thereby improving evaluation accuracy.

This invention relates to the field of photovoltaic power generation operation monitoring and electrical control technology, and particularly to a method and system for improving the power generation efficiency of photovoltaic arrays. The method includes: simultaneously acquiring non-stationary wind speed sequences, photothermal parameters, high-frequency DC current, and DC voltage sequences; constructing a three-dimensional time-frequency energy spectrum by performing empirical mode decomposition and Hilbert transform on the high-frequency DC current; targeting and eliminating flutter eigenmodes in the energy spectrum to reconstruct a pure electrical sequence by combining the wind speed calculation with the wind load disturbance frequency band; substituting the pure electrical sequence and voltage sequence into a fractional-order impedance differential equation containing constant-phase elements to establish an anomalous dynamic mapping relationship; solving this mapping to obtain the fractional order, and outputting hidden crack distortion parameters when the value falls below a preset benchmark; calculating the maximum Lyapunov exponent of the thermodynamic system based on this, and using the negative value range as a constraint to solve for the safety clamping voltage and issue control commands. This invention achieves precise decoupling of wind load disturbances and can actively avoid the risk of module thermal runaway.

This invention discloses a method for assessing the resilience of lake ecological networks based on an adaptive food web model. The method includes: acquiring time-series data of organisms in the target lake over the years to obtain preprocessed time-series data; extracting time-series area data of submerged plants in the target lake; constructing an adaptive food web model subjected to combined disturbances of water level amplitude and human fishing based on the preprocessed time-series data and the area time-series data; estimating the model parameters of the adaptive food web model using the Markov chain Monte Carlo method to obtain the optimal values ​​of the model parameters; constructing a time-varying Jacobian matrix based on the adaptive food web model with the optimal model parameter values; calculating the maximum Lyapunov exponent under the interaction of different water level amplitude coefficients and human fishing intensities to identify the water level amplitude threshold and human fishing intensity threshold that lead to steady-state abrupt changes, providing a scientific basis for water level regulation and fisheries management optimization.

The application discloses a kind of photovoltaic power multiscale intelligent prediction method and system based on multivariate space-time meteorological elements, belong to new energy power prediction technical field.The method includes: based on Hausdorff dimension adaptive coding realizes multiscale fractal space-time representation;Wavelet neural operator is used in wavelet domain to learn function space mapping to reduce computational complexity;Lyapunov exponent guided chaos attention mechanism is introduced to enhance extreme event modeling;Physical information neural network embedding energy conservation and other physical constraints are constructed;Dynamic causal graph neural network modeling time-varying interaction between meteorological elements is established;Hierarchical Bayesian ensemble is used to integrate multiscale prediction results and quantify uncertainty.The application realizes full-scale accurate prediction from second level to seasonal level, significantly improves the prediction robustness, computational efficiency and physical consistency under extreme weather.