6 results about "Permutation entropy" patented technology

The application discloses a modeling method and device for a vehicle-network interaction system and a nonvolatile storage medium. The method comprises the following steps: acquiring time series signals in the vehicle-network interaction system; performing modal decomposition on the time series signals to obtain first-layer intrinsic modal components; calculating target permutation entropy of the first-layer intrinsic modal components; in the case that the target permutation entropy of the first-layer intrinsic modal components is greater than a preset entropy threshold value, determining that the first-layer intrinsic modal components are abnormal modal components; repeating the above steps to obtain an abnormal component set and normal modal components; performing denoising processing on the abnormal component set, and performing weighted aggregation on the processed abnormal component set and the normal modal components to obtain a reconstructed signal; and performing federated learning on multiple subjects based on the reconstructed signal to obtain a target global model. The application solves the technical problem that the current modal decomposition technology is prone to modal aliasing and affects the charging and discharging rule analysis in the vehicle-network interaction.

The present application relates to a kind of electromagnetic reversing valve life prediction method based on flow signal, belong to hydraulic component life prediction field.The present application utilizes improved lumped average modal empirical decomposition method, by adding positive and negative pairs of noise to reduce the degree of modal aliasing in modal decomposition, using permutation entropy to detect abnormal component, realize the accurate adaptive modal decomposition of nonlinear measured signal;Application kernel principal component method, introduce nonlinear function as kernel function, based on the principle of mapping, convert original space into high-dimensional space, form new data set, using principal component analysis for data dimension reduction in high-dimensional data space, form feature vector, get performance degradation fusion index;Through cubic exponential smoothing processing;Finally based on the trained adaptive neural network model, establish the life prediction model of electromagnetic reversing valve, calculate the life of electromagnetic reversing valve.This method can effectively predict the pressure drop trend and life of electromagnetic reversing valve.

This invention discloses a multi-objective prediction method for power system sources and loads based on Laguerre polynomial theory, relating to the field of power system prediction and intelligent dispatching technology. The method includes the following steps: using Spearman Rank Correlation Coefficient (SRCC) to analyze the correlation of characteristic influencing factors of wind power, photovoltaic power, and power load; and using Robust Local Mean Decomposition (RLMD) to decompose the time series of wind power, photovoltaic power, and power load into high-frequency and low-frequency components to reduce their fluctuations; using Weighted Permutation Entropy (WPE) to analyze the complexity of the subsequences after RLMD decomposition, merging subsequences with similar complexity to reduce the model's prediction complexity; and constructing a hybrid Laguerre neural network prediction model using Laguerre polynomials. This invention is the first to simultaneously consider both accuracy and stability objectives in source and load prediction, selecting a compromise solution in the Pareto front using the MORUN algorithm, making the prediction results more applicable to power system dispatching scenarios with high robustness requirements.

The application discloses a sleep timing data brain-computer interface adjustment method based on artificial intelligence, and comprises the following steps: collecting and preprocessing multidimensional physiological signals to generate a standardized data set; performing permutation entropy calculation on the standardized data set to obtain a permutation entropy characteristic sequence and construct a timing alignment matrix; performing weighted average on the permutation entropy characteristic sequence according to the timing alignment matrix to obtain alignment characteristics; arranging the alignment characteristics in time sequence to obtain a characteristic matrix, inputting the characteristic matrix into a fusion analysis model to output a final sleep state label and an adjustment demand signal, combining a preset adjustment rule library and individual adaptation characteristics of a subject to generate an adjustment instruction adapted to the current sleep state. Compared with the prior art, the application extracts features through permutation entropy and timing alignment cooperation, provides feature input containing signal complexity information and timing correlation information for sleep state analysis, realizes sleep state recognition and adjustment demand judgment in combination with a fusion analysis model, and generates an adjustment instruction.

This invention discloses a method and system for monitoring encrypted protocols, belonging to the field of cyberspace security technology. The method includes: parsing encrypted session data and simultaneously extracting protocol semantic feature vectors and randomness original data sequences; determining variational mode decomposition parameters through fast Fourier transform, adaptively decomposing the random sequence to obtain multiple intrinsic mode function components, and calculating the permutation entropy of each component to construct a multi-scale entropy spectrum; fusing the protocol semantic feature vector and the entropy spectrum and inputting them into a pre-trained correlation model to output a multi-dimensional risk feature vector; matching a dynamic evaluation strategy according to the protocol type, calculating a comprehensive risk score by combining a risk threshold vector and a weight vector, and outputting a graded compliance conclusion and key risk description based on the score. This invention achieves deep correlation analysis of the randomness and semantics of encrypted protocols from multiple scales and dimensions, overcoming the limitations of traditional methods' single-scale detection and static judgment.