78 results about "Lyapunov exponent" patented technology

The invention discloses a distribution line abnormity monitoring and early warning method and system, and relates to the technical field of distribution line intelligent monitoring. The method comprises the following steps: acquiring operation data acquired by multiple types of sensors, and performing time alignment, normalization and fusion processing to form a comprehensive data set; inputting the comprehensive data set into a generative denoising model based on noise and abnormal signal distribution separability learning, and outputting a clean data sequence through noise suppression and abnormal feature fidelity joint optimization; identifying an abnormal evolution trend with a nonlinear amplification characteristic by using a Lyapunov index and a Hurst index, and generating a risk assessment result; and constructing and dynamically adjusting a self-adaptive early warning threshold set according to a risk assessment result, and outputting abnormal early warning information when a risk index exceeds a threshold. The method realizes parallel noise suppression and abnormal feature fidelity, has dynamic identification and self-learning capabilities, and significantly improves the accuracy of distribution line anomaly detection and the stability of an early warning system.

The invention discloses an automatic debugging and fault diagnosis system, which relates to the field of automatic equipment maintenance and comprises a data acquisition and preprocessing module, a chaotic feature analysis module, a fault mode identification and prediction module, a debugging and diagnosis execution module and a system management and interaction module. The chaos phenomenon in equipment operation data is deeply analyzed through the chaos feature analysis module, whether the equipment operation state is in a chaos state or not and the chaos degree are accurately judged by using chaos feature parameters such as a Lyapunov index, fractal dimension and correlation dimension, and the fault mode identification and prediction module is combined, so that the fault detection accuracy is improved. The system can recognize a potential intermittent fault mode in advance, predict the fault occurrence time and probability and send out an early warning signal, the fault diagnosis method based on the chaos theory remarkably improves the accuracy and timeliness of fault diagnosis, workers are helped to take preventive maintenance measures in time, the non-planned downtime is shortened, and the fault diagnosis efficiency is improved. The production efficiency is improved.

The invention discloses an intelligent fault diagnosis system for a cold and heat source center, and belongs to the technical field of fault judgment of heating, ventilation and air conditioning equipment. The system collects an operation feature vector X (t) in real time, and a state signal database stores data; a signal processing module of the upper computer subsystem adopts empirical mode decomposition X (t) to generate an IMF sequence and a residual sequence rn (t), a characteristic analysis module judges chaotic characteristics by calculating the maximum Lyapunov index of each IMF, and a neural network modeling module selects a modeling path according to chaotic / non-chaotic marks to generate a predicted value. The result integration module fuses the multi-path prediction results, compares the multi-path prediction results with a preset threshold interval, and outputs normal, early warning or fault state marks; and the result display module dynamically visualizes the state curve and the maintenance suggestion. According to the system, real-time judgment and early warning of abnormal operation of the cold and heat source equipment are realized, and decision support is provided for preventive maintenance.

The invention discloses a lithium battery first-order RC-ECM fractional order chaotic system parameter identification method. The method comprises the following steps of defining a lithium battery first-order RC-ECM mathematical model by adopting Captop's fractional order differential; selecting a first-order RC-ECM model to describe the dynamic characteristics of the lithium battery in the working process, and establishing a first-order RC-ECM model fractional order mathematical model by adopting Captop's fractional order differential; a charge-controlled memristor, a resistor, an inductor and a first-order RC-ECM are adopted to establish a four-dimensional fractional order chaotic system; carrying out balance point analysis, dissipative and attractor existence analysis, Lyapunov exponential spectrum analysis and Poincare cross section analysis on a first-order RC-ECM fractional order chaotic system; a, B and D unknown parameters in a first-order RC-ECM fractional order chaotic system are identified by adopting a state observer, and then unknown parameter values in the first-order RC-ECM are further derived by utilizing a formula. Meanwhile, the influence of environment temperature, driving road conditions, load conditions and battery material conditions is avoided.

The invention discloses a grid-connected inverter fusion control method based on voltage and current source time sharing, and relates to the technical field of electric power automation, and the method comprises the steps: carrying out the two-phase rotating coordinate system conversion of power grid coupling data, and generating a voltage-current joint component; performing power error analysis on the voltage-current joint component, outputting a power grid state vector, performing modal decomposition on the power grid state vector, obtaining a dominant oscillation mode matrix, performing Lyapunov exponent calculation on the dominant oscillation mode matrix, forming a stability pre-judgment mark, and inputting the stability pre-judgment mark into a time-sharing fusion decision network model to obtain a time-sharing fusion decision network model. The characteristic coupling layer carries out cross physical quantity correlation, and the stability evaluation layer carries out stability boundary calculation and outputs a voltage-current source control signal. Through the intrinsic orthogonal decomposition algorithm and the time-sharing fusion decision network model, the response speed and the fusion control capability of the grid-connected inverter are improved.

The invention 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 the following steps: applying composite excitation formed by superposing multi-frequency harmonic signals, collecting eddy current response signals, and obtaining an initial time-frequency spectrum matrix through short-time Fourier transform; singular value decomposition is carried out on the matrix, and after main singular value components related to the lift-off effect are removed, a lift-off correction spectrum matrix is reconstructed; calculating the maximum Lyapunov exponent and the spectrum kurtosis value of the correction matrix as a first defect characteristic quantity and a second defect characteristic quantity respectively; and constructing a two-dimensional feature plane based on the two feature quantities, calculating an angle correction quantity by utilizing energy of a rejected component so as to adjust a defect judgment area, and finally judging the defect by combining a Manhattan distance and a corrected angle interval. According to the scheme, the lift-off effect interference can be effectively inhibited, the dynamic criterion is constructed, and high-precision and high-reliability detection of the weak defects in the copper wire is realized.

The invention provides an automatic test method and system for an intelligent power distribution automation terminal, and relates to the technical field of automatic test, and the method comprises the steps: obtaining the model information and environment information of a to-be-tested terminal; generating test data of the to-be-tested terminal in combination with the model information, the environment information and the Lyapunov index; inputting the test data into the to-be-tested terminal, and outputting operation data; analyzing the operation data by using a dynamic threshold value based on the potential energy field, and outputting abnormal data; fusing and classifying the abnormal data based on a pre-training model and the coupling weighting distance, and determining a fault type; and performing early warning according to the fault type. According to the analysis method based on the Lyapunov index and the dynamic threshold value, the test can comprehensively cover various functions and limit states of the equipment, so that potential anomalies and faults can be effectively found.

The invention discloses a water pump pressure pulsation diagnosis method based on variational mode decomposition and chaos theory, which belongs to the technical field of water conservancy equipment pressure pulsation fault diagnosis, and comprises the following steps: setting a pressure pulsation monitoring point, and collecting a pressure signal; decomposing the pressure signal at each monitoring point by adopting variational mode decomposition (VMD); performing spectrum analysis on each modal function of the pressure signal decomposed by the VMD, and extracting spectrum features; frequency components and pressure amplitude are extracted, and internal flow field state information is obtained; in combination with a chaos theory, obtaining different modal attractors and the maximum value lyapunov exponent lymax; and introducing the modal attractors obtained in the VMD and chaos theory analysis and the lymax into a weighted decision model, and obtaining pressure fluctuation information at different monitoring points in combination with influence weights. According to the water pump pressure pulsation diagnosis method based on the variational mode decomposition and chaos theory, the pressure pulsation occurring in the operation of the water pump is monitored, and damage possibly caused by abnormal pressure pulsation is prevented.

The invention relates to the technical field of flow field measurement, in particular to an ocean flow field measurement method and device, and provides the following scheme: the method comprises the following steps: collecting ocean environment parameters through a vertical sensor, determining an overlook angle of an inclination sensor based on an optimization algorithm, and generating a virtual Doppler measurement array through micro-amplitude scanning; inputting to a chaotic oscillator network to identify a Lyapunov exponent maximum direction, and determining a horizontal included angle of a second inclination sensor; furthermore, a frequency shift signal is collected, and the flow velocity and the flow direction are calculated by combining adjusting parameters, so that high-precision and high-adaptability flow field measurement is realized; the method has a dynamic response capability, and is suitable for direction structure identification and real-time flow direction calculation under complex sea conditions.

The application discloses a method for realizing a conservative charge-controlled memcapacitive hyperchaotic circuit with multiple coexisting attractors. First, a six-dimensional conservative memcapacitive circuit model is constructed by a charge-controlled memcapacitive device, and the memcapacitive characteristics are verified. Then, the balance point and dissipation are used to verify the conservativeness. By changing the system parameters and initial conditions, a larger Lyapunov exponent can be obtained, and it is found that the internal parameter e of the circuit can not only cause the circuit to exhibit complex transient chaotic phenomena, but also has large-scale range chaotic characteristics. The attractor basin, coexisting attractors and complexity reflect the extreme multistability and complex nonlinear dynamic behavior of the circuit. In addition, the initial offset boosting behavior is also found. The application combines the FPGA technology to design a digital conservative charge-controlled memcapacitive hyperchaotic circuit model, and the hardware simulation result is consistent with the numerical calculation result, thereby providing a new idea for the digital realization of a nonlinear storage system.

The invention provides a gait evaluation method and system based on a human body nonlinear system analysis technology, and the method comprises the steps: collecting a gait cycle six-channel high-precision time sequence signal outputted by a wearable inertial measurement unit, eliminating noise and gait difference through wavelet threshold denoising and Z-score standardization, extracting a chaotic feature vector composed of a Lyapunov index spectrum and a Kolmogorov entropy value, and carrying out the recognition of a gait signal, a wavelet neural network is adopted to realize nonlinear mapping of chaotic features and phase-space reconstruction parameters, and a self-adaptive feedback mechanism is introduced to dynamically optimize modeling parameters, so that the accuracy and personalized matching capability of gait pattern recognition and stability evaluation are effectively improved; quantitative characterization of the gait chaos level and real-time online model optimization can be achieved, and high-robustness support is provided for rehabilitation training and exercise aided decision making.

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.

The invention discloses a power grid new energy bearing capacity assessment and analysis method and device based on a security domain theory. The method comprises the following steps: acquiring power system fault information and generator time sequence operation data; establishing a motion equation based on the inertia center coordinate system, and identifying leading and lagging coherent generator groups; adjusting the output of a leading generator group generator, identifying a leading instability unit and a mode, searching a critical instability point, and determining a stability judgment reference parameter; constructing a multi-modal practical dynamic security domain by adopting a maximum Lyapunov index criterion sensitivity analysis algorithm and combining a stability criterion, a sensitivity matrix, a stability judgment formula and a constraint condition; and calculating the stability margin of the system and dynamically adjusting the proportion of the new energy, carrying out loop iteration until the stability margin meets a preset requirement, and outputting a new energy bearing capacity evaluation value. The method does not need to depend on a complex simulation model, achieves the advanced prediction of the transient stability and the quantification of the stability margin, improves the accuracy and practicability of the evaluation of the new energy bearing capacity, and assists the safe and stable operation of a power system.

The present application relates to the technical field of motor testing, and particularly relates to a kind of carbon brush health state online monitoring method and system of motor, the method comprises: according to the load condition and operating environment condition of target carbon brush in current operating period obtained in advance, determine target carbon brush target operating load environment cluster under current time;Filter out reference operating period;Determine the abnormal wear factor and potential abnormal wear feature vector of target carbon brush under current time;According to the Lyapunov exponent of vibration signal of target carbon brush in current operating period, and temperature change and current change of target carbon brush in current operating period, determine wear failure possible feature vector;Thus determine the health state of target carbon brush under current time.The present application considers the potential abnormal wear condition of target carbon brush under current time, realizes carbon brush health state monitoring, and improves the timeliness of carbon brush abnormal health state monitoring.

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.

The invention discloses a pathological speech feature processing method and system based on event driving, and the method comprises the steps: obtaining a pathological modal signal, carrying out the phase-space reconstruction through the pathological modal signal, obtaining a phase-space trajectory, and building a nonlinear dynamic system model based on the pathological modal signal and the phase-space trajectory; calculating a Lyapunov index of the system model to determine system characteristics; calculating a phase locking value between the pathological modal signal and an acoustic reference feature of a preset voice regulation and control system so as to couple the intensity; determining a target coding mode of each pathological modal signal based on the system characteristics and the coupling strength, and coding the pathological modal signals to generate a corresponding pulse event sequence; and all pulse event sequences are fused based on the phase locking value to generate a time event stream reflecting a pathological regulation mechanism, so that nonlinear pathological components in the pathological speech are effectively extracted, the pathological modality is coded into an event stream structure, and accurate and explainable pathological speech evaluation is supported.

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.

The application discloses a kind of hybrid energy storage auxiliary frequency modulation instruction prediction method and system of thermal power generating unit, belong to energy storage frequency modulation instruction prediction technical field, method: obtain original frequency modulation instruction sequence, carry out dynamic phase compensation;Phase reference value is calculated, and nonlinear correction is carried out;First correction signal is obtained;Information entropy gradient enhancement is carried out to sampling point;First correction signal is enhanced, and second correction signal is obtained;Chaotic time series reconstruction is carried out, and third correction signal is obtained;Chaotic time series reconstruction includes: constructing time delay embedding space to map to high-dimensional phase space, calculating local Lyapunov exponent, combining the time derivative of signal and chaotic adjustment factor to carry out dynamic reconstruction;Third correction signal is input to GRU neural network, and the output frequency modulation instruction prediction result is obtained.The application can predict the size of frequency modulation instruction in advance, let super-capacitor or battery act in advance, improve the yield value, and the degree of convenience and accuracy are high, with engineering applicability.

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 invention relates to the technical field of data processing, in particular to a load sensing optimization system and method, and the system comprises a quantization topology modeling module, a chaotic enhancement pressure generation control module and a causal anti-fact analysis module. The quantization topology modeling module maps micro-service nodes into quantum bits through the annealing algorithm unit and converts the quantum bits into Hamiltonian constraints, the chaos enhanced pressure generation control module receives topological graph recognition target nodes, the chaos sensing unit injects a chaos noise sequence, the instability prediction unit calculates a Lyapunov exponent to mark the chaos state, and the chaos sensing unit senses the chaos state. A hybrid decision engine serializes a quantum bit chain to generate a pressure generation path set, and a reinforcement learning agent optimizes path parameters and synchronizes across nodes through an over-distance parameter adjustment unit. The causal graph engine establishes a causal directed graph to execute anti-fact inquiry, and the vulnerability marking unit calculates quantum de-coherence risk entropy, outputs zero-knowledge proof and feeds back and updates topological constraints. And dynamic micro-service link tracking and cross-regional bottleneck accurate positioning are realized.

The application relates to encryption communication technology, and particularly discloses a construction, dynamic analysis and application method of a six-dimensional memristor hyperchaotic system. The method discovers that the new system has unique dynamic behaviors through a phase diagram, a bifurcation diagram and a Lyapunov exponent spectrum, generates symmetric coexistence cycles, chaos and hyperchaos double-wing attractors which depend on system parameters, shows extremely strong initial value sensitivity, has special symmetric extreme multistability and a memristor initial value enhancement behavior, generates symmetric four-wing attractors by adding a constant controller, shows more complexity and diversity, and finally, simulation is carried out through Multisim, the generated attractor is consistent with a phase diagram obtained through numerical simulation, and the feasibility of the new system is verified. The application has great advantages in encryption security and attack resistance.

The invention belongs to the technical field of intelligent monitoring, and discloses a voltage monitoring method for small power generation equipment, which comprises the following steps of: acquiring a voltage signal in real time, detecting a cable interference spectrum and extracting power density distribution by utilizing a coupling electromagnetic field integrated in a cable shielding layer metamaterial resonance unit, and outputting an interference frequency band center frequency; inputting the purified voltage signal into chaos analysis, generating a dynamic trajectory by adopting a sliding window phase space reconstruction method, and calculating a maximum Lyapunov index and a correlation dimension; and performing fusion calculation on the maximum Lyapunov exponent and the correlation dimension, dynamically generating a hierarchical control instruction, triggering a hierarchical control signal according to a dynamic chaotic intensity threshold, and transmitting the hierarchical control signal to a local terminal through a low-bandwidth communication protocol to generate a visual report. Through metamaterial dynamic tuning and chaos feature fusion, accurate suppression of an interference frequency band and efficient analysis of a nonlinear signal are realized, and the real-time performance of voltage monitoring is improved.

The invention discloses a construction and implementation method of a hyperbolic tangent fractional order memristor chaotic system. The method comprises the following steps: firstly, designing a fractional order magnetic control memristor model containing a hyperbolic tangent function, and constructing a fractional order memristor chaotic system based on the model; through high-resolution order parameter scanning and in combination with Lyapunov exponent spectrum, bifurcation diagram, phase diagram and complexity analysis, rich dynamic behaviors and high sensitivity of the system under order change are revealed; a difference equation model of the system is obtained through a frequency domain approximation and discretization method, digital modeling is completed on a DSP Builder platform, and hardware description language codes executable by an FPGA are automatically generated; finally, the real-time iterative operation of the system is realized on the FPGA development board, and the physical realizability of the chaotic attractor is verified through the oscilloscope. The invention provides a high-precision and low-power-consumption hardware implementation scheme for the fractional order memristor chaotic system, and is suitable for the information security fields of secret communication, image encryption and the like.

This invention discloses a robot navigation method suitable for large sparse environments, comprising the following steps: Step 1, establishing a system evolution equation based on the robot's state vector and kinematic equations, incorporating an environmental sparsity factor into the system evolution equations, and then introducing trajectory tracking weights and obstacle avoidance weights to construct a dynamic system model; Step 2, determining whether the dynamic system model is in a critical state between stability and chaos by solving the Jacobian matrix and Lyapunov exponent of the dynamic system model. When the maximum Lyapunov exponent is approximately equal to 0, the critical stability condition is reached; Step 3, based on the critical state, designing a path evaluation function to screen stable path points, and dynamically adjusting the obstacle avoidance weights through bifurcation parameter adjustment control equations to achieve adaptive switching between ordered tracking and chaotic obstacle avoidance, thus completing robot navigation in large sparse environments. This invention can ensure that robots can efficiently and stably complete navigation in large sparse environments.

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.

The application discloses a power supply management system based on distributed power grid energy storage equipment, and relates to the technical field of power supply management. The system collects and pre-processes light intensity and temperature data in a fixed time period, then reconstructs a phase space, obtains a maximum Lyapunov exponent through a small data amount method, compares the maximum Lyapunov exponent with a threshold value, selects an MPPT algorithm or an LSTM-MPPT algorithm to track the maximum output power of solar energy, and effectively improves the accuracy of maximum output power tracking. The maximum output power is sorted by time, and the slope k between the two points is obtained. Whether to start the standby energy storage equipment is determined by the size of k, and the charging and discharging conditions of the standby energy storage equipment are determined by the positive and negative of k. After starting the standby energy storage equipment, the loss of electric energy transmission between solar energy and the standby energy storage equipment is compared with the electric energy consumed by moving the standby energy storage equipment to solar energy, so as to select wire transmission or move the standby energy storage equipment, thereby avoiding electric energy loss.

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.