173 results about "Sample entropy" patented technology

The invention relates to the technical field of crane line fault diagnosis, in particular to a crane line fault diagnosis system and method based on multi-source data fusion, which comprises a data acquisition and processing unit, a mechanical and electrical coupling characteristic unit and a characteristic fusion and fault quantification unit, the three-axis vibration acceleration and the three-phase current waveform of the track are obtained through the data collecting and processing unit, the mechanical and electrical coupling characteristic unit conducts three-dimensional vector synthesis and wavelet packet decomposition on vibration data, and a time-space incidence matrix of harmonic distortion and vibration is constructed. And the feature fusion and fault quantification unit outputs coupling factors by using a bidirectional long-short-term memory network and an attention mechanism, and outputs a fault probability value through a dynamic time warping matching algorithm after time-frequency domain analysis and sample entropy judgment, so that time-space correlation modeling and dynamic fault matching of multi-source data are realized. And the fault positioning precision and the diagnosis accuracy are improved.

The invention discloses an office building energy consumption prediction method and system, and relates to the technical field of energy consumption prediction, and the method comprises the steps: collecting the current office building energy consumption data; decomposing the load sequence into a plurality of modal components, calculating the sample entropy of each modal component, and recombining the modal components by using a K-means clustering algorithm according to the calculation result of the sample entropy; taking each mode component after recombination as a node, taking the time sequence similarity between the mode components as an edge between the nodes, and constructing a graph structure; inputting the graph structure into a GCN-Transform model, extracting spatial features of nodes in the graph structure, and introducing a self-attention mechanism to extract time sequence features; inputting the spatial-temporal characteristics into a full-connection layer to obtain an energy consumption predicted value in a future period of time; according to the method, space cooperation and time dependence can be considered at the same time in a complex and changeable energy consumption scene, so that a more accurate prediction result is provided.

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 discloses an electric vehicle charging load prediction method and system, and the method comprises the steps: collecting and obtaining the historical charging load time series data of an electric vehicle, and employing an ICEEMDAN model to carry out the decomposition of the charging load time series data, and obtaining a plurality of components; performing sample entropy calculation on the components, and performing signal classification by adopting K-medoids clustering according to a sample entropy result to obtain a high-frequency component F1, an intermediate-frequency component F2 and a low-frequency component F3; carrying out secondary decomposition on the high-frequency component F1 by adopting a characteristic mode decomposition (FMD) model to obtain a plurality of components; the MTS-Mixers model, the Crossform model and the DeepESN model are optimized by using an improved artemisinin AO optimization algorithm, and a high-frequency component F1, an intermediate-frequency component F2 and a low-frequency component F3 are predicted; the prediction results of the AO-MTSMixers model, the AO-Crossform model and the AO-DeepESN model are reconstructed, and the prediction result of the charging load is obtained; compared with a traditional method, the charging load prediction accuracy can be improved, and powerful support is provided for charging use and management of the electric vehicle.

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 discloses a spatio-temporal crime prediction method and system fusing spatio-temporal heterogeneous information. The method comprises the following steps: constructing a spatio-temporal data set; constructing a crime time sequence signal data set; periodically decomposing the time sequence signal into a plurality of intrinsic mode functions (IMF); the sample entropy is used as a fitness function to evaluate the advantages and disadvantages of decomposition results under different parameter combinations so as to determine the optimal input time window length; performing clustering processing on the crime data to identify urban crime hotspot areas, and generating crime spatial distribution data; performing feature analysis in time and space, and further completing contribution evaluation of key features to the crime situation; constructing a space-time prediction model, and segmenting a three-dimensional space-time data set into samples in a sliding window mode according to a time window determined in a self-adaptive mode to serve as input and output of the model; and completing training and tuning of the model. And a more accurate and efficient solution is provided for urban crime situation analysis.

The invention discloses a wind power plant wind speed prediction method, system and device and a storage medium, and the method comprises the steps: optimizing the white noise amplitude weight and noise addition times in adaptive noise ensemble empirical mode decomposition CEEMDAN through employing an improved energy valley optimization algorithm IEVO, and carrying out the CEEMDAN decomposition of the historical data of a wind power plant; the decomposed data is preprocessed by adopting sample entropy SE, so that the calculation complexity of the data is reduced; for the preprocessed data, introducing a random forest algorithm RF feature selection of a heuristic strategy for optimization, and eliminating redundant features; converting the optimized data from one-dimensional time sequence data into a two-dimensional image by using a GASF (Grubby Amplitude Shift Function), and extracting image features; the extracted image features are sent to a time sequence block converter Patch TST model for wind speed prediction, and a training algorithm GPTuner based on a gradient boosting decision tree is introduced into the Patch TST model to adjust model parameters; compared with a traditional method, the accuracy of wind speed prediction can be effectively improved, and stable operation of a wind power plant is guaranteed.

The invention relates to the field of state monitoring and fault diagnosis of power equipment, in particular to a transformer fault diagnosis method based on a chaos evolutionary optimization algorithm, which comprises the following steps of: 1, acquiring a magnetic flux leakage signal during operation of a transformer; 2, optimizing a parameter modal number K and a penalty factor alpha of variational modal decomposition by using a chaos evolutionary optimization algorithm; 3, performing variational mode decomposition on the magnetic flux leakage signal to obtain an intrinsic mode function component; 4, calculating the envelope entropy of the intrinsic mode function component, and obtaining an effective intrinsic mode function component through screening; 5, extracting the energy entropy and the sample entropy of the effective intrinsic mode function component to form a feature vector; and 6, inputting the feature vector into a pre-trained support vector machine classifier, and outputting a fault type diagnosis result of the transformer. According to the method, the CEO algorithm is combined with the ergodicity of chaotic mapping and the global search capability of the evolutionary algorithm, and the problems that VMD parameters K and alpha are sensitive and depend on experience, and a traditional optimization algorithm is prone to local optimum are effectively solved.

The invention provides a satellite network self-similar traffic prediction method based on double decomposition and a neural network, and relates to the technical field of satellite networks. The method comprises the following steps: firstly, performing first modal decomposition on an original self-similar traffic sequence of a satellite network by using a CEEMDAN algorithm, and decomposing the original self-similar traffic data sequence into a plurality of IMF components; dividing the IMF components into high-frequency components, intermediate-frequency components and low-frequency components by calculating the sample entropy of each IMF component after decomposition; performing second modal decomposition on the clustered high-frequency components by using a VMD decomposition model; then constructing a CNN-BiLSTM-based self-similar flow prediction model, and respectively inputting each IMF component subjected to two-time decomposition into the model for prediction; meanwhile, an improved artificial rabbit optimization algorithm is adopted to optimize hyper-parameters of the self-similar flow prediction model, and an optimal self-similar flow prediction model is obtained. The method can effectively improve prediction precision and reduce prediction errors.

The invention discloses a method for predicting the charging load of an electric vehicle, and the method comprises the steps: collecting the historical charging load data of the electric vehicle, and carrying out the one-time decomposition of the time series data of the charging load through employing an ICEEMDAN model, and obtaining 10 components; performing sample entropy calculation on components obtained by primary decomposition, performing signal classification by adopting K-medoids clustering according to a sample entropy result to obtain a high-frequency component, an intermediate-frequency component and a low-frequency component respectively, optimizing parameters in an MTS-Mixers model, a Crossform model and a DeepESN model by using an improved AO optimization algorithm, and performing signal classification by adopting K-medoids clustering to obtain a high-frequency component, an intermediate-frequency component and a low-frequency component; and predicting the high-frequency component, the intermediate-frequency component and the low-frequency component by using an MTS-Mixers model, a Crossform model and a DeepESN model, and finally reconstructing prediction results of the three models to obtain a prediction result of the charging load.

The invention discloses an orbit fault diagnosis method and system, and the method comprises the steps: calculating a sample entropy, an energy entropy, a power spectrum and a permutation entropy of an optimal solution # imgabs0 #, selecting an optimal entropy from the sample entropy, the energy entropy, the power spectrum and the permutation entropy according to a preset optimal entropy weight selection strategy, and extracting typical features according to the optimal entropy; an improved CNN-BiLSTM-SA neural network is constructed, and sequence space features are extracted according to the improved CNN-BiLSTM-SA neural network; hyper-parameters of the CNN-BiLSTM-SA neural network are optimized and improved according to a Cauchy-Euclidean clustering particle swarm optimization algorithm, and an orbit fault diagnosis model is obtained; the optimal solution # imgabs1 # of the steel rail vibration signals, the typical features and the sequence space features are fused to obtain fused features, the fused features are input into a rail fault diagnosis model, and the rail fault diagnosis model outputs and obtains a rail fault diagnosis result. The accuracy and efficiency of track fault category diagnosis can be improved.

The invention discloses an ultra-short-term wind power prediction method based on scene real-time switching. The method comprises the following steps: firstly, extracting a multi-dimensional feature vector capable of representing a wind speed fluctuation degree from a historical wind speed time sequence; and then clustering the multi-dimensional feature vectors by adopting a K-means clustering algorithm, and dividing the wind power operation state into a plurality of scenes. Then distinguishing a strong fluctuation scene and a weak fluctuation scene according to the sample entropy of each scene, wherein the strong fluctuation scene is predicted by adopting a CNN-BIGRU model; and the weak fluctuation scene adopts a least square support vector machine (LSSVM) model. And finally, according to the real-time wind speed data, identifying fluctuation characteristics of a previous time sequence segment of a to-be-predicted moment, calculating an Euclidean distance with a scene clustering center, judging a scene to which the to-be-predicted moment belongs, selecting a corresponding prediction model, and dynamically generating a wind power prediction result. According to the method, adaptive switching of different scenes can be realized in an environment with frequent and complex wind speed fluctuation, and the accuracy and stability of ultra-short-term wind power prediction are effectively improved.

The invention relates to the technical field of fault diagnosis, and discloses a fault diagnosis method and system applied to an automobile air conditioner compressor. The method comprises the following steps: firstly, introducing auxiliary noise to decompose a vibration signal, calculating a decomposition result by using a sample entropy method to obtain a sample entropy, and identifying and removing noise; secondly, variational mode decomposition is carried out on the vibration signals after noise elimination, an improved crayfish optimization algorithm is used for optimizing a decomposition scale and a penalty factor in the variational mode decomposition process, and a final IMF component signal set is obtained; mapping the final IMF component signal set to polar coordinates through a symmetric polar coordinate transformation method to form a fault diagnosis image set; and finally, training to obtain a convolutional neural network fault diagnosis model, identifying a fault diagnosis image set, and outputting a fault diagnosis result of the automobile air conditioner compressor. The vibration signal of the automobile air conditioner compressor is processed and analyzed, the purpose of fault diagnosis is achieved, and the method is objective and accurate.

The invention discloses a landslide displacement double-layer fusion prediction method and model, and the method comprises the steps: carrying out the decomposition of an original displacement time sequence through employing an ICEEMDAN algorithm, and obtaining a plurality of IMF components; performing feature engineering on each IMF component, representing a displacement trend by adopting a trend slope and a window mean value, representing mutation early warning by adopting kurtosis and a frequency spectrum entropy, representing a period rule by adopting a main frequency and a zero-crossing rate, representing system stability by adopting a sample entropy and a standard deviation, and constructing a three-dimensional feature space fusing a time domain and a frequency domain; data standardization is carried out on the extracted features, the interference effect of dimensions on the model is eliminated, and it is ensured that all feature dimensions are within a unified calculation scale range; a CNN-BiLSTM model is constructed for each IMF component; a CPO algorithm is used to optimize the CNN-BiLSTM model; according to the method, the data acquisition difficulty during model training and use can be reduced, and the usability of the model in actual deployment is enhanced; and the prediction precision and the accuracy of landslide displacement prediction are improved.

The invention discloses a wind power plant power prediction method and system in extreme weather and a medium, and belongs to the technical field of renewable energy power generation prediction. Comprising the following steps: acquiring historical weather characteristic data and corresponding wind power plant power data, and screening out weather characteristics with high correlation with the wind power plant power data through a maximum mutual information coefficient; processing the power data of the wind power plant by adopting a CEEMDAN method and a VDM method in combination with a sample entropy analysis and wavelet packet threshold denoising method to obtain a noise-suppressed and feature-enhanced wind power plant power sequence; based on weather features and a wind power plant power sequence with noise suppression and feature enhancement, a wind power plant power prediction model is constructed according to the BiLSTM network after hyper-parameter combination optimization to predict the power of the wind power plant in extreme weather; performing interval shrinkage reconstruction on the wind power plant power in the future extreme weather through time sequence similarity to obtain a final prediction result of the future wind power plant power; according to the invention, the wind power plant power prediction precision in extreme weather is 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 provides a monitoring method and system based on intrinsic multi-scale sample entropy. The monitoring method comprises the following steps: acquiring original data; preprocessing the data to obtain a data time sequence to be analyzed; a step of applying the EEMD to the data time sequence and decomposing the data time sequence into E-IMFs; and an iMSE calculation step, wherein a plurality of iMSEs are obtained. At least a portion of the plurality of iMSEs is used as an output of the monitoring method.

The invention discloses a distribution hidden danger assessment and prediction method based on a natural time domain and a fuzzy rough set, and belongs to the technical field of operation and maintenance of power grid equipment. According to the method, based on collected multi-source monitoring signals of leakage current, induction current, temperature and the like, adaptive signal preprocessing is carried out by adopting a method of combining empirical mode decomposition (EMD) and sample entropy, effective mode components are effectively extracted, and noise interference is suppressed; natural time domain analysis is introduced on the basis of a traditional time domain, an event sequence is constructed, dynamic features are extracted, and a hidden danger feature data set with time sequence evolution information is formed; further performing unsupervised attribute reduction on the high-dimensional features by using a fuzzy rough set theory, removing redundant information, and retaining key discrimination features; and finally, identification and trend prediction of wiring hidden danger types are realized through a support vector machine (SVM) classifier. According to the method, the accuracy and robustness of hidden danger identification are improved, and effective technical support is provided for intelligent operation and maintenance of power distribution.

The invention provides a sea wave height prediction method and system based on hybrid quadratic decomposition, and relates to the technical field of sea wave height prediction, and the method comprises the steps: obtaining to-be-predicted original wave height data; performing primary decomposition on the original wave height data to obtain an intrinsic mode function and a residual term; calculating a sample entropy of the intrinsic mode function; according to the size of the sample entropy, classifying the intrinsic mode function to obtain a reconstructed mode component; performing secondary decomposition on the high-frequency components through a VMD algorithm to generate sub-sequence components; establishing a periodic sensing time deep learning model; and inputting the subsequence component, the intermediate-frequency component and the low-frequency component into a periodic sensing time deep learning model, and outputting a sea wave height prediction result.

The invention discloses an intelligent bearing health monitoring method based on MSE-SPP-KAResnet, and relates to the technical field of bearing health monitoring. Through a spatial pyramid pooling layer, a KA optimization strategy and a multi-scale sample entropy module, the precision problem of a traditional fault diagnosis method under a multi-channel and unbalanced data set is effectively solved; compared with the prior art, the method has the advantages that pooling processing and feature fusion are carried out on multi-channel features under three spatial scales of global, medium scale and local through the spatial pyramid pooling layer, and the feature extraction capability of multi-channel heterogeneous data is improved; a KA optimization residual module is utilized to enhance the gradient flow and nonlinear mapping capability, and the recognition precision of minority class samples is effectively improved; the introduced multi-scale sample entropy module improves the robustness of signal complexity and uncertainty, and finally ensures the high efficiency and accuracy of bearing fault diagnosis in an unbalanced data environment.

The invention provides a method for predicting the concentration of gas dissolved in transformer oil based on mixed decomposition and Bi TCN-Bi GRU, and is applied to the field of prediction of the concentration of gas dissolved in oil. The method comprises the following steps: firstly, carrying out preliminary decomposition on a gas concentration sequence by utilizing improved self-adaptive noise complete set empirical mode decomposition; secondly, reconstructing a sub-sequence in combination with a sample entropy, and performing secondary decomposition on a high-entropy sub-sequence by using variational mode decomposition; and finally, constructing a Bi TCN-B GRU prediction model, simultaneously capturing forward and reverse features of time series data by using bidirectional convolution and a bidirectional gating mechanism, predicting subsequences of primary decomposition and secondary decomposition, and reconstructing a gas concentration sequence. Compared with the prior art, the method is higher in prediction precision and better in robustness and generalization.

The invention relates to the technical field of brain fatigue detection, provides a robust feature extraction and brain fatigue detection method and system based on electroencephalogram signals, and aims to solve the problems of difficulty in cross-individual robust feature extraction and lack of quantitative evaluation means in the prior art. The method comprises the following steps: constructing time-frequency-space multi-scale fusion features based on electroencephalogram signals, and performing feature screening of preset times on the fusion features based on contribution values of the fusion features to brain fatigue detection results to obtain a candidate feature group; and determining the target frequency of the single feature based on the selection times and the preset times of the single feature in the candidate feature group, and determining a robust feature set from the candidate feature group based on the target frequency. The robust feature set comprises # imgabs0 # and # imgabs1 # sub-band energy, total band energy, # imgabs2 # band energy ratio, sample entropy and approximate entropy, and left and right brain symmetric distribution exists in the feature set. And the robust feature set is input into the brain fatigue detection model to obtain a detection result, so that the brain fatigue recognition precision is remarkably improved.

The invention discloses an air duct vibration on-line monitoring system based on multiple sensors and a fault prediction method, and belongs to the technical field of air duct state monitoring and fault prediction.The method specifically comprises the steps that air duct vibration acceleration and noise signals are collected in real time through the sensors, and a time-aligned signal sequence is formed through analog-to-digital conversion and preprocessing; extracting a time-frequency domain feature from the vibration signal sequence, extracting a sound pressure level and a harmonic distortion degree from the noise signal sequence, and generating an air duct feature vector through feature fusion; constructing a Bayesian probability model based on the vector, calculating a fault occurrence probability, judging existence and generating a confidence score; calculating the sliding sample entropy of the vibration signal sequence, and marking an entropy value abnormal interval; and a union set of a fault interval diagnosed by the Bayesian probability model and an entropy abnormal interval is obtained, after time sequence trend analysis, early warning information is generated and pushed to an operation and maintenance platform when a joint judgment condition is met, and online updating of a normal vibration entropy model is triggered, so that the fault detection and early warning precision is improved.

The invention discloses a power load prediction method, system and device, and a storage medium. The method comprises the steps of obtaining and cleaning power load historical data and environment feature data; the method comprises the following steps of: decomposing a plurality of intrinsic mode components by using generalized dynamic mode decomposition (GDMD), and dividing a training set and a test set; a GCN-GRELM fusion prediction model is constructed, and an improved triangular topology aggregation optimizer ITTAO is adopted to optimize parameters; calculating an adaptive weight based on the energy ratio of the subsequences and the sample entropy to obtain a preliminary prediction result; constructing an error data set by using the training set error sequence and the environment characteristic data, correcting a predicted value by combining an unscented Kalman filter (UKF) correction model, and outputting a final load prediction result; according to the method, the processing capability of nonlinear and non-stationary data is improved through GDMD decomposition and adaptive weight allocation, the generalization capability is enhanced in combination with a GCN-GRELM hybrid model and an ITTAO optimizer, and the calculation efficiency and prediction precision are improved by using GRELM efficient calculation and UKF dynamic correction.

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 invention relates to the field of signal processing, and discloses a noise reduction method and system based on millimeter wave radar signals, and the method comprises the following steps: collecting millimeter wave radar signals to be denoised; the method comprises the following steps: constructing a fitness function of a ratio of a mean value to a variance of a multi-scale Kolmogorov entropy based on a millimeter wave radar signal; optimizing a decomposition mode number K and a penalty factor alpha of variational mode decomposition by using a starfish optimization search algorithm; variational mode decomposition is carried out on the millimeter wave radar signal; the sample entropy of each intrinsic mode component is calculated, the mode position with the maximum sample entropy break variable is determined, the intrinsic mode components with the sample entropy smaller than the position serve as signal modes, and the rest serve as noise modes; carrying out wavelet threshold denoising on the noise mode; and performing signal reconstruction by using the signal mode and the de-noised noise mode, and outputting a de-noised radar signal. The millimeter-wave radar gas leakage signal noise reduction method solves the problem that the noise reduction effect of millimeter-wave radar gas leakage signals is poor in the prior art, and has the advantage of being capable of improving the signal-to-noise ratio of the signals.

The invention relates to a landslide deep deformation monitoring data noise reduction method based on an HBP-VMD combined improvement wavelet threshold. The method comprises the steps of collecting a landslide deep deformation original signal; the sample entropy is used as a fitness function, a badger optimization algorithm is adopted to optimize VMD decomposition parameters, and an optimal combination parameter combination is obtained; substituting the optimal combination parameter into the VMD, and performing VMD decomposition on the original signal to obtain K intrinsic mode components IMF of different frequencies; calculating a variance contribution rate and a correlation coefficient corresponding to each obtained IMF component, and dividing the IMF components into an effective component, a noisy component and a noise component; retaining the obtained effective component, abandoning the noise component, and carrying out noise reduction processing on the noisy component by using an improved wavelet soft threshold; and reconstructing the IMF component after noise reduction and the effective IMF component, and finally realizing signal noise reduction. According to the method, the deformation monitoring signal of the deep part of the landslide can be efficiently stripped from the noisy signal, and the waveform is clearer than that before noise reduction; the SNR of the signal after noise reduction is the highest, the SMES is the lowest, and the excellent noise reduction effect is achieved.

The invention relates to an electroencephalogram signal preprocessing method based on particle swarm optimization (PSO), ensemble empirical mode decomposition (EEMD) and independent component analysis (ICA), and aims to improve the quality of electroencephalogram signals and enhance analyzability of the signals. The method comprises the following steps: firstly, carrying out EEMD (Ensemble Empirical Mode Decomposition) on an original electroencephalogram signal, generating a plurality of noise auxiliary signals by adding white noise with different intensities, carrying out EMD on each noise auxiliary signal, and then averaging intrinsic mode functions (IMF) of all the noise auxiliary signals to obtain a final IMF, thereby reducing the problem of mode aliasing and improving the reliability of the electroencephalogram signal. Useful components and noise components are preliminarily separated out; secondly, optimizing EEMD parameters by using a PSO algorithm; a particle swarm is initialized, each particle represents a possible parameter combination (such as Gaussian white noise standard deviation and noise adding times), a fitness function is defined, a quality index of a signal is taken as a target, positions and speeds of the particles are iteratively updated, the parameter combination is optimized, and finally optimal parameters are applied to perform EEMD decomposition, so that an optimized IMF is obtained. Therefore, the complexity of manual parameter adjustment is avoided, and the decomposition accuracy and stability are improved. Then, the sample entropy of each IMF is calculated, a sample entropy threshold value is set, the IMFs with the sample entropy values higher than the threshold value are screened out, IMF components with low information content are effectively removed, effective components with high information content are reserved, and the analyzability of the signals is further improved. And finally, combining the screened effective IMF component with the original electroencephalogram signal to generate a virtual multi-channel signal, performing Fast ICA (Independent Component Analysis), separating out independent electroencephalogram signal components, further removing noise, and improving the purity and the signal-to-noise ratio of the signal. Through the steps, the quality of the electroencephalogram signals can be remarkably improved, and a solid foundation is provided for subsequent signal analysis and application.

The embodiment of the invention provides a multi-mode collaborative gas pipe network hidden danger identification method and system, and relates to the technical field of gas pipe networks. The identification method comprises the following steps: acquiring data of a pressure sensor, a temperature sensor and a gas concentration sensor at key nodes of the gas pipe network; performing wavelet packet decomposition on the data to obtain sub-band signals of the signals; performing feature extraction on the sub-band signals to obtain an energy entropy, a sample entropy and a permutation entropy; normalizing the energy entropy, the sample entropy and the permutation entropy to obtain a corresponding normalized entropy value; inputting the corresponding normalized entropy value into the intelligent prediction model, and outputting a probability distribution diagram of the pipe network hidden danger position; and leakage point positioning and corrosion degree grading identification are carried out according to the probability distribution diagram. According to the method, data of pressure, temperature and gas concentration sensors in the gas pipe network are effectively integrated, through wavelet packet decomposition and deep feature learning, pipe network leakage, corrosion and other hidden dangers are accurately recognized, and stable operation of a gas supply system is ensured.

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.