68 results about "Sample entropy" patented technology

The invention discloses an intelligent power prediction method considering dynamic load change, and relates to the technical field of power grid load prediction, and the method comprises the steps: collecting original load data, carrying out the preprocessing, constructing a VMD constraint optimization model, carrying out the four-stage improvement of optimization parameters through employing an improved dung beetle optimization algorithm, so as to generate IMF components, reconstructing the IMF component by calculating a sample entropy to obtain a low-frequency component and a high-frequency component; establishing a Kalman filtering state space model based on the low-frequency component, and decomposing the low-frequency component into a residual component and a pseudo trend component through a Kalman filtering recursive algorithm; external features are obtained, the high-frequency component, the residual component and the pseudo trend component are aligned and spliced with the external features, multi-component collaborative prediction is carried out through a local-global interactive attention mechanism, and a final load prediction result is obtained; and generating a power demand visualization chart based on the final load prediction result. And reliable decision support is provided for power dispatching and energy management.

The invention relates to the technical field of machine learning, and discloses a load prediction method based on dual feature processing and error correction. The method comprises the following steps: acquiring multi-source time sequence data; decomposing the historical load data into a plurality of modal components by adopting a variational modal decomposition algorithm; classifying each modal component into different frequency levels according to the size of the sample entropy; performing phase-space reconstruction according to the modal component of each frequency level and the corresponding external influence factor data, and generating a multivariable phase-space data set of each frequency level; respectively inputting the multivariable phase space data set of each frequency level into the corresponding load prediction sub-model, generating prediction output results, and superposing the prediction output results; constructing a residual sequence based on the historical load data and the initial load prediction result; inputting the residual error sequence into a residual error prediction model to obtain a load residual error prediction value; and compensating the initial load prediction result through the load residual prediction value. According to the scheme, the load prediction accuracy can be improved.

The invention relates to the technical field of load prediction, and provides a power load prediction method and system based on time sequence decomposition and an attention mechanism, and the method comprises the steps: carrying out the adaptive time sequence decomposition of an obtained original load sequence, calculating the sample entropy of each decomposed component, and carrying out the clustering; constructing a group of encoder and decoder networks for each piece of clustered data, performing parallel encoding to extract features, performing serial decoding reconstruction on the features from low frequency to high frequency, and outputting prediction data from low frequency to high frequency step by step; the weight is initialized based on the sample entropy, the trained weight is obtained through optimization in the encoder and decoder network training process, and the predicted value of the power load is obtained through weighted fusion. According to the method, adaptive time sequence decomposition, a weight mechanism guided by sample entropy and an attention-enhanced encoder-decoder structure are introduced, so that multi-component collaborative modeling and cross-scale dynamic prediction are realized, and the prediction accuracy and stability in complex load data and small sample scenes are effectively improved.

The invention discloses a self-adaptive optimization method and system for online decoding of a motor imagery brain-computer interface, and the method comprises the steps: carrying out the real-time self-adaption of an electroencephalogram data stream of a target user through a teacher-student model framework on the premise that the privacy protection of source domain training data does not need to be accessed; performing batch weight normalization during testing, decoupling normalization statistic updating and parameter optimization by stopping gradient operation, and stabilizing feature representation; a dynamic category specific entropy threshold mechanism is combined with online category frequency and batch confidence to adaptively screen a high-confidence sample for each category; a dynamic online reweighting strategy is designed, and weights are distributed according to the sample entropy and the category frequency to balance the optimization process; and decoupling contrast learning based on a fixed prototype is introduced, and feature space distribution is optimized. According to the method, the problems of statistic drift, poor fixed threshold adaptability, category imbalance sensitivity, insufficient feature optimization and the like are solved, and the adaptability, the stability and the robustness of cross-user motor imagery brain-computer interface online decoding are improved.

The application provides a multi-parameter fusion real-time monitoring and early warning method and system for anesthesia depth, relates to the technical field of medical monitoring, and comprises the following steps: acquiring physiological parameter data, performing multi-scale decomposition and calculating sample entropy values, extracting baseline features and fluctuation features to form an anesthesia feature vector; an initial anesthesia depth index is output by using a deep learning model; a cause-effect correlation matrix is constructed by calculating time-varying transfer entropy based on intrinsic mode function decomposition, dominant cause-effect patterns are extracted, and the anesthesia depth index is corrected; and real-time monitoring and early warning reports are generated. The application can improve anesthesia depth evaluation accuracy and reduce anesthesia risks.

The present application provides a method for bearing fault diagnosis across working conditions by using a minimum entropy optimized prototype contrast network, and relates to the technical field of intelligent fault diagnosis. In the pre-training stage, an auxiliary domain discriminator is constructed to assist DA with the discriminant information of the classifier, the classification difficulty is evaluated by sample entropy, and the performance degradation in the DA process is inhibited. In the training stage, the learning vector quantization method is adopted to find the prototype. Through intra-domain prototype contrast learning, the sample features are closely gathered around the same prototype in the feature space, while being separated from the different prototypes. Then, the intra-class consistency of the features is enhanced, and the inter-class distinguishability is improved, so as to realize the precise alignment in the feature space. In addition, the cross-domain instance-prototype learning aligns the semantic structure in the shared embedding space, alleviates the negative transfer problem through the fine-grained alignment strategy, and improves the generalization ability of the model. Through the pseudo-label generation and the weighted loss function, the generalization performance of the model in the cross-working-condition small sample scene is improved.

The invention provides a feature fault diagnosis method for time domain statistics of a main shaft bearing of an aero-engine, which comprises the following steps of: S1, determining a rotating speed working condition of the aero-engine, determining main parameters and feature frequency of the main shaft bearing, and obtaining a bearing vibration signal through a vibration sensor; s2, optimizing the interception length of the data by adopting sample entropy; s3, calculating the short-time kurtosis time of each rotating speed working condition according to the data interception length, and obtaining a history curve of the short-time kurtosis time; and S4, analyzing the rotating speed working condition and the bearing dangerous working condition of the bearing early-stage fault. According to the method, the fault of the main shaft bearing of the aero-engine is diagnosed through the time domain characteristic statistical index of the short-time kurtosis, the working state of the main shaft bearing under each rotating speed working condition is obtained, and the early fault symptom of the bearing can be found; and the interception length of the data is optimized through the sample entropy, so that the method is suitable for complex and changeable rotating speed working conditions of the aero-engine.

This invention belongs to the field of wireless communication technology, and particularly relates to a method for predictability assessment of electromagnetic communication data. It includes the following steps: Step 1: Collect electromagnetic communication data from multiple receivers and transmitters over a period of time, and calculate the intrinsic predictability of the parameter time series at each point based on true entropy, sample entropy, and multi-scale entropy; Step 2: Select the time scale with the minimum entropy value, preprocess the electromagnetic communication data time series, and construct a spatiotemporal similarity graph to transform the electromagnetic communication data into graph data; Step 3: Locally construct a spatiotemporal graph convolutional network model with node-level attention, perform local prediction, and calculate the prediction accuracy; Step 4: Perform predictability assessment on the electromagnetic communication data. This invention proposes for the first time a comprehensive assessment framework that can both calculate intrinsic predictability and guide the establishment of prediction models, enabling a comprehensive evaluation of the predictability of electromagnetic communication data.

The application discloses a rolling bearing degradation trend prediction method based on linear regression and TCN, characterized in that: firstly, the full life cycle vibration data of the rolling bearing is collected through an acceleration sensor, the sample entropy of the data is calculated and obtained as the performance degradation index of the bearing, the sample entropy is preprocessed smoothly, then the SPT point of the bearing is determined according to the linear regression model and mu+3delta, the influence of different window lengths on the determination of the SPT is analyzed, finally the preprocessed sample entropy value is input into the trained TCN network for degradation trend prediction. The application can effectively and timely determine the SPT point of the bearing, the TCN can well fit the performance degradation trend of the bearing, and the problem that the equipment cannot be safely operated due to the too late discovery of the bearing fault is avoided; compared with the method of adopting the recurrent neural network prediction, the accuracy of the method for the equipment health management is obviously improved, and a new idea is provided for the degradation trend prediction method of the bearing.

The invention relates to the technical field of intelligent driving, in particular to a risk early warning method for a heart rate scatter diagram in the driving process. The real-time heart rate scatter diagram is calculated and analyzed in real time through the improved box counting method to obtain the fractal dimension, the dimension and entropy combined feature space is constructed through the improved sample entropy algorithm and the fractal dimension, the primary early warning information is generated through primary early warning judgment, the problems that a traditional method cannot recognize instantaneous risks and early warning is delayed are solved, and the early warning accuracy is improved. The real-time performance and the accuracy of risk identification are improved; the method comprises the following steps: acquiring a heart rate signal, analyzing the heart rate signal through wavelet packet decomposition to obtain a time-frequency feature vector, acquiring a steering wheel grip parameter, carrying out fusion analysis on the steering wheel grip parameter and the time-frequency feature vector to obtain early warning verification information, judging and outputting a risk early warning level through a risk level, and carrying out adaptability early warning through an individual heart rate baseline. The false report and missing report rate is reduced, and an accurate decision basis is provided for an intelligent driving system.

The application discloses a load prediction method and system based on big data, and relates to the technical field of power grid load prediction.The method comprises the following steps: based on a least squares support vector machine, pre-processing load historical data; through decomposing the load historical data with reduced complexity, obtaining a zero-crossing rate and sample entropy, and determining the multi-frequency components of the load data; through a preset hybrid algorithm and in combination with the multi-frequency components of the load data, training a multi-factor weighted combination analysis model; based on the multi-factor weighted combination analysis model and according to an improved grey wolf algorithm, determining the weight of the load prediction result of each prediction factor module; and weighting and combining the load prediction results of each prediction factor module to obtain a final load prediction result.The application improves the robustness of the model, dynamically updates the model output result, optimizes the weight proportion among the factor modules, and improves the adaptability of load prediction in a complex environment.

The invention relates to the technical field of milling cutter state monitoring, and provides a self-adaptive denoising method suitable for a milling cutter state monitoring signal, and the method comprises the steps: decomposing an original complex signal into a plurality of IMF components in a milling process through employing a CEEMDAN algorithm, calculating the sample entropy of each IMF component, and carrying out the classification and recombination, obtaining a reconstructed signal and a high-complexity component; a sample entropy is used as a fitness function for measuring signal complexity, and an RIME algorithm is guided to automatically find a parameter combination capable of enabling the VMD decomposition effect to be optimal; performing VMD secondary decomposition on the high-complexity component by using the optimized parameter combination, calculating PCC of the decomposed IMF component and the original signal, and selecting an effective IMF component according to a correlation coefficient weight; and carrying out superposition reconstruction on the screened effective IMF components to generate a de-noised signal with a high signal-to-noise ratio, and taking the de-noised signal as a feature input signal for milling cutter state monitoring. The optimal denoising state is automatically adjusted according to the tool working condition, and the working condition adaptability of the system is improved.

The invention belongs to the technical field of aviation cable fault positioning, and particularly relates to a cable fault positioning method based on time-frequency domain analysis, which comprises the following steps: firstly, collecting a cable fault signal, adopting a GWO-VMD algorithm, taking a minimum sample entropy as a fitness function, and optimizing to find an optimal penalty factor and decomposition layer number, so as to obtain a fault positioning result; removing a high noise component obtained by VMD decomposition according to the current sample entropy, and obtaining a reflected signal after noise reduction; performing cross term suppression on the reflected signal after noise reduction by using smooth pseudo Wigner distribution to obtain discrete SPWVD; and performing blind area elimination on the discrete SPWVD based on a blind area elimination algorithm of hypothesis verification to obtain a fault distance. A grey wolf optimization algorithm is applied to a variational mode decomposition algorithm, and parameters of decomposition layers and penalty factors of the VMD are optimized, so that the problem of interference of noise on reflection signal extraction is solved; secondly, the smooth pseudo Wigner distribution is adopted to solve the problem of cross term interference among a plurality of linear components in the traditional WVD distribution, and the positioning precision is improved.

The application discloses a kind of millimeter wave sign detection methods based on swarm intelligence and improved wavelet threshold value, wherein the method includes: obtaining the original vital sign signal collected by millimeter wave radar;Construct the multi-objective fitness function of fusion sample entropy, pearson correlation coefficient and kurtosis;The multi-objective fitness function is optimized using the improved Harris eagle optimization algorithm, and the optimal parameter combination of CEEMDAN decomposition algorithm is adaptively solved;Based on the optimal parameter combination, the original vital sign signal is decomposed by CEEMDAN, and a plurality of intrinsic mode function components are obtained.The application constructs the cascade processing framework of "parameter optimization-signal separation-noise suppression", solves the problem that CEEMDAN parameter depends on artificial experience, respiratory and heartbeat signal band aliasing and the problem of insufficient performance of traditional wavelet threshold denoising.

The invention discloses an electroencephalogram signal reconstruction method and system based on artifact removal. The method comprises the steps that multi-channel electroencephalogram signals are obtained and subjected to filtering preprocessing; blind source separation is carried out through independent component analysis, and statistical independent signal source components and a hybrid matrix are obtained through decomposition; constructing an equivalent current dipole model to calculate a space artifact index, evaluating signal complexity in combination with a time sequence sample entropy, and judging and identifying an artifact component and a neural activity component through weighted fusion and a self-adaptive threshold value; neural activity components are reserved, artifact components are zeroed, and pure electroencephalogram signals are reconstructed through mixed matrix inverse transformation. According to the method, accurate identification and effective removal of the electroencephalogram artifacts are realized, and high-quality data are provided for electroencephalogram analysis.

The application discloses a street lamp line ground fault intelligent diagnosis system and method, belongs to the technical field of power equipment operation monitoring, and comprises a data acquisition and preprocessing unit, a feature extraction unit and a fault diagnosis unit. The data acquisition and preprocessing unit collects original residual current data of a street lamp line through a residual current transformer and an oscilloscope. The application adopts a parrot optimization algorithm to adaptively determine optimal decomposition parameters of VMD, overcomes the limitations of artificial parameter setting, ensures the quality of data decomposition, lays a foundation for extracting high-quality fault features, solves the problems of lack of reliability of existing fault protection, great influence of human factors, and low accuracy of existing street lamp ground fault diagnosis systems and methods, calculates sample entropy values, constructs a multi-dimensional feature vector set representing line states, inputs the multi-dimensional feature vector set into a fault diagnosis unit, and the fault diagnosis unit trains a kernel extreme learning machine classifier by using the feature vector set.

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 invention discloses a sparse sample entropy criterion-based physical characteristic parameter estimation method, which comprises the following steps of: determining a pneumatic system fault factor and a physical constraint thereof, and converting the physical constraint into an integral form; solving an integral form based on a Lagrange direct construction method to obtain a probability density function, and ensuring that the probability density function has physical significance; screening target distribution conforming to physical constraints from a preset general probability density function library, and extracting functions conforming to basic distribution shape requirements in the target distribution to form a function group; dividing the function group into a single-parameter group and a double-parameter group based on the parameter scale, respectively calculating to obtain corresponding entropy information, sorting differential entropy results, and taking a maximum value to obtain most unbiased probability density distribution conforming to the physical constraint. According to the method, high-confidence parameter estimation under the condition that the sample size is scarce is realized, the limitation of data shortage is broken through, and the probability density function can be stably generated only by utilizing physical characteristics such as extreme values and peak positions.

The application discloses a kind of adaptive optimization method and system for motor imagination brain-computer interface online decoding, the method is under the privacy protection premise without accessing source domain training data, by teacher-student model framework, the electroencephalogram data flow of target user is carried out real-time adaptation;Adopt test time batch renormalization, decouple normalization statistic update and parameter optimization by gradient operation stop, stabilize feature representation;Dynamic class-specific entropy threshold mechanism, combined with online class frequency and batch confidence, high-confidence samples are adaptively screened for each class;Design dynamic online reweighting strategy, distribute weight according to sample entropy and class frequency to balance optimization process;Introduce decoupled contrast learning based on fixed prototype, optimize feature space distribution.The application solves the problems of statistic drift, poor adaptability of fixed threshold, class imbalance sensitivity and insufficient feature optimization, and improves the adaptability, stability and robustness of cross-user motor imagination brain-computer interface online decoding.

The application relates to the technical field of water quality monitoring, and specifically discloses a water body sample reservation environment multi-parameter monitoring system, which realizes adaptive filtering by using empirical mode decomposition and sample entropy analysis to generate an optimized time sequence by real-time collection of multi-dimensional parameters such as temperature, pH, dissolved oxygen, turbidity and conductivity; identifies pollution source characteristic components by dynamic principal component analysis and blind source separation; constructs a causal correlation network by using dynamic time warping and transfer entropy, and forms a multi-parameter collaborative response spectrum by combining with graph convolution topological feature extraction; realizes pollution type identification based on the spectrum by multi-scale morphological analysis and density clustering, generates an early warning report containing pollution level evaluation and trend prediction by using confidence weighted decision fusion; solves the technical problem of evidence distortion caused by sample state drift in the traditional sample reservation process, and realizes full-chain water quality monitoring from data collection to intelligent diagnosis.

The application provides a building energy consumption prediction method based on adaptive decomposition and intelligent reconstruction, and relates to the technical fields of building energy management and artificial intelligence, which comprises the following steps: firstly, through adaptive decomposition parameter optimization based on energy entropy, the optimal modal number and penalty factor of VMD or CEEMDAN are automatically determined, and the original energy consumption sequence is decomposed into several intrinsic modal components; then, according to the sample entropy and average period of each component, the components are divided into high-frequency noise, detail components and trend components, and a differentiated PSO-XGBoost strategy is used for modeling; then, the component prediction results are weighted and fused through a dynamic weighted intelligent reconstruction mechanism based on error feedback; finally, through an incremental learning and federal migration collaborative framework, the online updating of the model and the multi-building knowledge migration are realized. The application realizes the automatic determination of the decomposition parameters and the differentiated modeling of the components, and significantly improves the prediction accuracy and robustness.

The application is a power prediction method based on empirical Fourier decomposition and sample entropy aggregation, belonging to the technical field of power system prediction. The method firstly performs empirical Fourier decomposition on the power time series to obtain multiple oscillation components and residual components; then calculates the sample entropy of each component and aggregates them into a small number of aggregated components according to the entropy value similarity; then trains three types of base models of support vector regression, extreme gradient boosting and long short-term memory network for each aggregated component and residual component in parallel; further, the outputs of each base model are used as stacked features to train a ridge regression meta-model for fusion; finally, the rolling prediction mode is adopted to predict each component, and the final power prediction value is obtained by adding the prediction results of the aggregated components and the residual components. The application is suitable for power system dispatching and transaction decision support.

The invention relates to the technical field of power system measurement and data processing, discloses a distributed load prediction method based on current data of an electric energy meter, and aims to solve the problems of mixed distributed load components, low prediction precision and poor dynamic adaptability in the prior art. The method comprises the following steps: acquiring a high-sampling-rate three-phase current original waveform and synchronously recording a voltage phase angle and a timestamp; performing Kalman filtering noise reduction, sliding window effective value extraction and normalization processing on the current data; adaptively decomposing the current signal into a plurality of intrinsic mode function components by adopting an improved variational mode decomposition algorithm, and extracting sample entropy and energy proportion characteristics; constructing a high-dimensional load characteristic fingerprint database containing a time domain, a frequency domain and a non-stationary characteristic; unsupervised load type division is realized through density-based noise application spatial clustering; according to the technical scheme, the precision, the robustness and the real-time response capability of load power prediction are remarkably improved.

The application discloses a wind pipe vibration online monitoring system and a fault prediction method based on multiple sensors, and belongs to the technical field of wind pipe state monitoring and fault prediction, and specifically comprises the following steps: collecting wind pipe vibration acceleration and noise signals in real time through sensors, forming time-aligned signal sequences through analog-digital conversion and pretreatment; extracting time-frequency domain features from the vibration signal sequences, extracting sound pressure levels and harmonic distortion degrees from the noise signal sequences, and generating wind pipe feature vectors through feature fusion; constructing a Bayesian probability model based on the vectors, calculating a fault occurrence probability, determining existence, and generating a confidence score; calculating the sliding sample entropy of the vibration signal sequences, and marking the entropy abnormal interval; taking the union of the fault interval diagnosed by the Bayesian probability model and the entropy abnormal interval, performing time series trend analysis, generating early warning information when the joint determination condition is met, and pushing the early warning information to an operation and maintenance platform, triggering online update of the normal vibration entropy value model, and improving the fault detection and early warning precision.

The invention provides a charging module state identification method and device based on sample entropy, and belongs to the technical field of fault identification. The method comprises the following steps: acquiring a plurality of working performance parameter sequences of a charging module; wherein each working performance parameter sequence is a value of one working performance parameter at a plurality of moments; aiming at each working performance parameter sequence, normalizing each working performance parameter in the working performance parameter sequence to obtain a plurality of parameter normalized sequences of the charging module; performing empirical mode decomposition on each parameter normalized sequence to obtain a plurality of parameter component sequences of the charging module; respectively calculating a sample entropy of each parameter component sequence to obtain a plurality of parameter sample entropies of the charging module, and combining the parameter sample entropies as a fault feature vector of the charging module; and inputting the fault feature vector into a trained state recognition model to obtain a fault state of the charging module. According to the invention, robustness and accuracy of state identification of the charging module can be improved.

The invention discloses a rotating machine fault feature optimization extraction method based on variational mode extraction and comprehensive detection indexes, and the method comprises the following steps: S1, decomposing an input vibration signal through employing a variational mode extraction method, and calculating a corresponding sample entropy value; s2, constructing a fitness function of a particle swarm optimization algorithm by using the comprehensive detection index; s3, parameters of the variational modal extraction method are optimized in combination with the comprehensive detection index and a particle swarm optimization algorithm; s4, using the variational mode under the optimal parameter to extract and decompose the input vibration signal; s5, calculating the sample entropy of each modal component to obtain a fault feature set; and S6, sending the fault feature set into a probabilistic neural network for fault classification. According to the method, a solution is provided for parameter selection of variational mode extraction, so that the optimal fault feature of the rotating machine is obtained, the signal decomposition efficiency and the fault classification precision are improved, and intelligentization of fault diagnosis of the rotating machine is promoted.

This invention discloses a method and apparatus for identifying stator winding grounding faults in hydro-generators, relating to the field of hydro-generator fault identification technology. The method includes acquiring the zero-sequence current signal during a single-phase grounding fault in the hydro-generator; using the minimization of envelope entropy as the fitness function, the IDBO algorithm is used to adaptively optimize the number of modes and penalty factor in variational mode decomposition (VMD) to obtain the optimal parameter combination; wherein the IDBO algorithm is obtained by fusing Logistic-Tent chaotic mapping, exponential decay convergence factor, and random perturbation mechanism; based on the zero-sequence current signal and the optimal parameter combination, signal decomposition processing is performed to obtain multiple intrinsic mode functions (IMF) components; the three components with the highest correlation coefficients are selected as effective fault features, their sample entropy is calculated, and the sample entropies are combined into a feature vector representing the fault state; the feature vector is used as an input layer variable and imported into a support vector machine for fault identification.

The invention discloses a pilot attention assessment system and method based on multi-mode physiological signal fusion. The system collects EEG, EOG and simulator data, and microsecond-level synchronization is achieved through a precise time protocol; iCA, 0.5-45 Hz filtering and baseline correction are carried out on the EEG, frequency band energy, sample entropy and LZ complexity are extracted, fixation / glancing segmentation and DBSCAN clustering are carried out on the EOG to form a region of interest, and stability features are calculated. And the double-flow time sequence network introduces cross attention between the second fusion layer and the fourth fusion layer to realize cross-modal dynamic registration, and outputs five-level attention and the operation risk in the next three seconds. ATI indexes are constructed, adaptive weighting is carried out according to flight phases, and a gaze thermodynamic diagram is generated by combining kernel density estimation and used for prompting and training feedback. According to the scheme, the state of the pilot is accurately evaluated in real time, and the method has good robustness and generalization ability.

The application discloses a landslide displacement double-layer fusion prediction method and model, uses an ICEEMDAN algorithm to decompose an original displacement time sequence, obtains a plurality of IMF components, carries out feature engineering on the IMF components, adopts a trend slope and a window mean value to represent a displacement trend, adopts kurtosis and spectral entropy to represent a mutation early warning, adopts a main frequency and a zero-crossing rate to represent a periodical law, adopts sample entropy and a standard deviation to represent system stability, constructs a three-dimensional feature space fusing time domain and frequency domain, carries out data standardization on the extracted features, eliminates the interference effect of dimensions on the model, and ensures that all feature dimensions are in a unified calculation scale range, constructs a CNN-BiLSTM model for each IMF component, and uses a CPO algorithm to optimize the CNN-BiLSTM model, so that the data acquisition difficulty during model training and use can be reduced, the usability of the model in actual deployment can be enhanced, the prediction precision is improved, and the accuracy of landslide displacement prediction is improved.

The invention relates to the technical field of wind driven generator fault diagnosis, in particular to a bearing current damage identification method and device based on multi-mode manifold maintenance, and the method comprises the steps: collecting fault operation data of damage and fault-free normal bearing operation data; performing noise reduction on the acquired data, then decomposing the acquired data into a plurality of modal signals in a manifold space, calculating a high-dimensional sample entropy of each manifold subspace modal signal, and obtaining multi-modal manifold sample entropies of normal, fault and current-damaged bearings; on the basis of the sample entropy corresponding signals, state exclusive analytic signals of all bearings are constructed through Hilbert transformation, instantaneous amplitude, phase, frequency and sample entropy statistical features are extracted, and an initial feature set is constructed; and carrying out dimensionality reduction by adopting a locality preserving projection method to obtain a low-dimensional mainstream feature set, inputting the low-dimensional mainstream feature set into a classifier of a neural network model, and comparing current damage with other state feature differences to realize accurate recognition. According to the invention, the accuracy of bearing current damage identification is improved.