18 results about "Permutation entropy" patented technology

The invention provides a data-driven saline screw compressor unit dynamic modeling method, and belongs to the technical field of industrial intelligence and predictive maintenance. According to the method, adaptive deep denoising of original data is realized through a composite signal processing flow of fusing variational mode decomposition, permutation entropy criterion and wavelet packet transformation optimal threshold denoising; meanwhile, a sectional sampling strategy is introduced to enhance the diversity of training data. Then, a wavelet multi-scale energy entropy extraction layer is used for constructing a high-information-density feature vector; furthermore, a prediction model formed by multiple layers of stacked long and short-term memory network units is adopted, and the complex time sequence dependency relationship of the system is deeply captured. According to the method, pure and stable system dynamic representation can be extracted from high-noise industrial data, the dynamic characteristics of the system in the full working condition range are accurately described, and it is ensured that the prediction result is self-consistent physically and reliable in engineering, so that the prediction precision and generalization performance of the model are remarkably improved.

The invention relates to the technical field of fluid mechanical fault diagnosis, and discloses an emulsion pump fault intelligent diagnosis method and system based on multi-dimensional entropy feature fusion, and the method comprises the steps: constructing a multi-dimensional physical field state monitoring space, and synchronously collecting vibration acceleration, outlet pressure and flow time sequence signals; the method comprises the following steps: performing adaptive variational mode decomposition and effective sensitive component screening reconstruction on a vibration signal, calculating a normalized vibration quantile permutation entropy through phase-space reconstruction and quantile mapping, extracting a pressure fluctuation entropy and a flow pulsation variance, fusing the pressure fluctuation entropy and the flow pulsation variance into a multidimensional fault feature vector, inputting the multidimensional fault feature vector into a multi-classification support vector machine, and outputting an operation state label; executing hierarchical closed-loop control; according to the method, through quantile mapping and multi-physics field fusion, the problems of entropy value feature distortion and liquid-machine coupling weak fault feature masking caused by non-Gaussian impact noise are effectively solved, and the fault diagnosis robustness and accuracy of the emulsion pump under complex working conditions are improved.

The application relates to the technical field of intelligent monitoring and protection of power systems, and discloses a grounding fault diagnosis method based on intelligent time sequence feature extraction. In the feature extraction stage, a multi-scale convolutional neural network is adopted, combined with multi-scale permutation entropy, to perform coarse-grained processing on an original signal, calculate permutation entropy values under different scales, quantify the spatial distribution characteristics of signal complexity, and inhibit noise interference. After feature extraction, the features extracted by the multi-scale convolutional neural network and the multi-scale permutation entropy two channels are spliced through a feature fusion layer, and the fused features are input into a BiGRU module. In view of the problems that the traditional fault diagnosis method has insufficient multi-scale feature extraction, weak anti-noise ability and low time sequence modeling precision under complex working conditions, the application fuses multi-scale feature extraction and dynamic time sequence features, realizes high-precision diagnosis of grounding faults, and significantly improves the precision and robustness of grounding fault diagnosis.

The invention discloses a window optimization-based intra-day prospective dispatching method and device for a power grid, and relates to the technical field of power dispatching, and the method comprises the steps: obtaining a power grid data feature prediction model through employing historical power grid data features and an adaptive differential evolution algorithm; obtaining predicted values of intra-day power grid data features based on a power grid data feature prediction model, thereby generating a plurality of operation scenes; calculating corresponding net load power based on each operation scene, determining permutation entropy, sample entropy and fuzzy entropy based on all the net load power, and establishing a combined entropy calculation formula by using a multi-target equalization method; constructing a look-ahead window optimization model based on a combined entropy calculation formula, and solving the look-ahead window optimization model by adopting a carbon black stingless bee optimization algorithm to obtain a window optimization result; based on a window optimization result, establishing an intra-day look-ahead optimization scheduling model; and solving the intra-day look-ahead optimization scheduling model by adopting a state optimization algorithm to obtain a power grid intra-day look-ahead scheduling plan. The flexibility of the power grid dispatching plan is improved.

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 invention provides an equipment life prediction method and device, electronic equipment and a storage medium, and relates to the technical field of neural networks. Comprising the steps that a feature sequence of to-be-predicted equipment is collected based on an electrical life test result, and the feature sequence comprises feature parameter sets under continuous time steps; decomposing and reconstructing the feature sequence according to the feature sequence of the to-be-predicted equipment and a plurality of preset scale factors to obtain at least one feature subsequence of the to-be-predicted equipment under each preset scale factor; determining the permutation entropy of the to-be-predicted equipment under each preset scale factor according to the sequence permutation mode of each feature subsequence of the to-be-predicted equipment under each preset scale factor, and determining a multi-scale feature matrix corresponding to the to-be-predicted equipment according to the permutation entropy under each preset scale factor; and according to the multi-scale feature matrix corresponding to the to-be-predicted device, using a pre-trained life prediction model to predict life information of the to-be-predicted device. The method improves the accuracy of the life prediction result.

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.

The invention discloses a traveling wave model setting system and method based on waveform transient characteristics, and relates to the technical field of power system relay protection and fault localization. Secondary signals output by a traveling wave sensor are decomposed into a plurality of intrinsic mode components; screening effective intrinsic mode components; fitting the waveform of the effective intrinsic mode component, and obtaining a waveform distortion rate and a frequency abrupt change range in real time; adjusting the sampling frequency according to the waveform distortion rate and the frequency abrupt change range; and adopting a deep forest model to output the fault type and position. According to the method, the end effect and mode aliasing of empirical mode decomposition are effectively inhibited through complementary set empirical mode decomposition, the components containing effective traveling wave features can be adaptively reserved in combination with dynamic screening of the multi-sequence permutation entropy on the intrinsic mode components, the signal-to-noise ratio is remarkably improved, the end effect error is reduced, and the method has the advantages of being high in robustness and the like. The defect that effective components cannot be dynamically screened in the prior art is overcome.

The application discloses a kind of based on machine learning's rolling bearing fault diagnosis method, belong to mechanical equipment fault diagnosis technical field, comprising the following steps: step S1: VMD parameter optimization;Step S2: effective IMF component screening;Step S3: feature extraction and dimension reduction;Step S4: fault diagnosis.The application utilizes variational mode decomposition to reduce the noise in vibration signal, for the problem of variational mode decomposition parameter selection, adaptive grey wolf algorithm is used to optimize;The data after noise reduction is extracted using permutation entropy, and the dimension of fault feature is reduced using nonlinear principal component analysis algorithm, effectively reducing the influence of noise in vibration signal on fault diagnosis accuracy, which can significantly improve the accuracy of rolling bearing fault diagnosis.

The invention discloses an intelligent identification method for a failure mode of a spiral anchor foundation under a horizontal load, and relates to the technical field of geotechnical engineering and intelligent monitoring. The method comprises the following steps: acquiring load and displacement data, and calculating an arrangement entropy value of a stiffness time sequence window; based on the entropy value, utilizing a confidence coefficient accumulation mechanism to identify a steep drop type or slow deformation failure mode; and determining the ultimate bearing capacity by adaptively selecting a judgment criterion according to an identification result. The method is used for solving the problem that the bearing capacity is inaccurately judged due to the fact that brittleness sudden change and ductility progressive failure are difficult to distinguish by an existing single standard.

The invention discloses a GNSS deformation monitoring denoising method based on improved CEEMDAN and fuzzy permutation entropy, and relates to the technical field of mode decomposition denoising. Comprising the following steps: data acquisition: acquiring original GNSS deformation monitoring data which comprises position quantities in N, E and U directions, comes from a plurality of GNSS observation stations and comprises a plurality of interference noises; and CEEMDAN decomposition: performing complete ensemble empirical mode decomposition on the original GNSS deformation monitoring data to obtain a plurality of intrinsic mode function IMF components. According to the method, the CEEMDAN is improved to optimize the noise intensity coefficient and the number of decomposition times, so that the modal aliasing phenomenon of a traditional CEEMDAN method is effectively reduced, and IMF component redundancy is reduced; the complexity and the noise ratio of each IMF component are quantified by combining fuzzy permutation entropy, high and low frequency modality accurate classification is realized by matching with a dynamic threshold mechanism, and the problems that a traditional method lacks an effective noise recognition mechanism and depends on experience to set a classification standard are solved.

The application discloses a track fault diagnosis method and system, the method comprises the following steps: calculating the sample entropy, energy entropy, power spectrum and permutation entropy of the optimal solution, selecting the optimal entropy from the sample entropy, energy entropy, power spectrum and permutation entropy according to the preset optimal entropy weight selection strategy, and extracting typical features according to the optimal entropy; constructing an improved CNN-BiLSTM-SA neural network, and extracting sequence space features according to the improved CNN-BiLSTM-SA neural network; optimizing the hyperparameters of the improved CNN-BiLSTM-SA neural network according to the Cauchy-Euclidean clustering particle swarm optimization algorithm, and obtaining a track fault diagnosis model; fusing the optimal solution, typical features and sequence space features of the rail vibration signal to obtain fusion features, and inputting the fusion features into the track fault diagnosis model, so that the track fault diagnosis model outputs a track fault diagnosis result. The accuracy and efficiency of track fault category diagnosis can be improved.

This invention discloses a method for extracting features of lightning strike faults in transmission lines based on the Wild Dog optimization algorithm, which relates to the field of transmission line fault diagnosis technology. This invention utilizes the symplectic geometric mode decomposition method to decompose the fault traveling wave signal of the transmission line and calculate the symplectic geometric entropy of the effective symplectic geometric components. The Wild Dog optimization algorithm is introduced to optimize the initial parameters of the multi-scale permutation entropy to obtain the optimal multi-scale permutation entropy. Finally, the multi-scale permutation entropy and the symplectic geometric entropy are combined to construct a feature vector, which is then fed into a random forest classifier to obtain good classification results.

The application provides a rolling bearing fault feature enhancement method, adopts a WOA algorithm to initialize the value range of filter length and fault period, and determines a whale predation range; takes a one-dimensional vibration signal generated in the operation process of the rolling bearing as an input signal, randomly generates a set of filter length and fault period parameter combinations; inputs the parameter combinations and the one-dimensional vibration signal into a MOMEDA algorithm, calculates a fault feature enhancement signal, calculates the permutation entropy of the enhancement signal, updates the permutation entropy value, obtains the best parameter combination according to whether the permutation entropy is minimum, and calculates the corresponding fault feature enhancement signal. The application combines the characteristics of fast WOA optimization speed, sensitive permutation entropy to mutation signals and the ability of MOMEDA to enhance fault signals, and plays an important role in the discovery and prevention of early weak bearing faults.

The application discloses a secondary modulation signal recognition method and system based on a joint model deep learning, relates to electronic countermeasures, wireless communication and signal processing technology, and comprises the following steps: obtaining a sample secondary modulation signal and performing pretreatment; based on signal reconstruction and phase space statistical analysis permutation entropy, space-time characteristic samples are extracted from the sample secondary modulation signal after pretreatment; a lightweight deep learning model is adopted to jointly construct a data local space characteristic perception model and a time sequence backtracking control model, and the extracted space-time characteristic samples are used to train the joint model; space-time characteristics of a target secondary modulation signal are extracted, and a recognition result is output by using the trained joint model. The application can solve the problems of low recognition accuracy, large operation amount, and large number of prior samples required in the field of signal and information processing.