24 results about "Sample entropy" patented technology

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 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 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 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 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.

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 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 thrust bearing tile temperature prediction method based on SGMD decomposition and SE reconstruction combined with AVOABiLSTM / Informer comprises the following steps: collecting original signals of thrust bearing guide tile temperature and temperature-related influence factor data; preprocessing the original data; applying SGMD to the obtained temperature sequence to obtain a plurality of initial single-component sequences; calculating sample entropy SE of each initial single-component obtained; based on a preset similarity threshold, superimposing and reconstructing similar single components to obtain a plurality of reconstructed modal components; dividing the reconstructed modal components into high-complexity components and low-complexity components according to the sample entropy values of the reconstructed modal components; inputting the high-complexity components into BiLSTM for training and prediction, and inputting the low-complexity components into an Informer model for training and prediction; using AVOA to optimize key hyperparameters of the BiLSTM and the Informer model; and fusing the obtained component prediction results in a point-by-point superposition manner to obtain the final guide tile temperature prediction value. The method can obtain high-precision guide tile temperature prediction results.

This invention relates to the field of tourist volume interval prediction technology, specifically a multi-objective tourist volume interval prediction method and system based on frequency mixing. The prediction method uses weekly tourist volume intervals as the prediction objective, and daily search index, AQI, and rainfall as high-frequency influencing variables. A standardized variable matrix is ​​constructed through frequency mixing data alignment. The matrix is ​​then reconstructed into low, medium, and high-frequency feature subsequences using wavelet packet decomposition combined with sample entropy. A multi-type candidate model library is then built. The optimal sub-model for each subsequence is selected based on adaptive weighted interval evaluation indicators and 5-fold cross-validation. Finally, a sparrow optimization algorithm is used to search for the optimal weights by minimizing the interval mean absolute percentage error and root mean square error as dual objectives. The prediction results of the optimal sub-models are weighted and fused to obtain the final value, thereby effectively preserving high-frequency data features, achieving multi-scale feature decoupling of the sequence, and significantly improving the accuracy and reliability of tourist volume interval prediction.

The application discloses a wind power prediction method and system based on frequency domain adaptive super parameter optimization, which comprises the following steps: firstly, collecting historical wind power and meteorological data; secondly, proposing a brand-new frequency domain coverage overlap coefficient as a fitness function, combining the grey wolf optimization algorithm to adaptively optimize the super parameter of the variational mode decomposition, and using the optimized parameter to decompose the power sequence; thirdly, calculating the sample entropy of each component and reconstructing it into three categories of randomness, fluctuation and trend according to the entropy value to reduce the complexity; then, screening the strongly correlated meteorological features for each component based on the Pearson correlation coefficient, and inputting them into a prediction model for prediction; finally, superimposing the predicted values of each component to obtain the final power prediction result. The application solves the problem of artificial setting of the VMD super parameter, realizes the balance between the decomposition quality and the prediction efficiency, and significantly improves the accuracy and practicability of the ultra-short-term wind power prediction.

The application discloses a seasonal frozen region railway roadbed settlement prediction method based on an improved echo state network. First, a multi-sensor monitoring platform is built, railway roadbed settlement, air humidity and multi-depth soil humidity time series data are collected, and correlation analysis and feature screening are performed. Then, the VMD (Variational Mode Decomposition) and sample entropy reconstruction method are used to adaptively denoise and enhance the multi-scale features of the screened input feature sequence. Then, an improved echo state network (IESN) model is constructed. The model introduces a small-world network topology to generate a reserve pool connection weight matrix, and adds a configurable delay mechanism to enhance the ability to capture complex time series dynamic characteristics. Finally, an improved ivy algorithm (IIVYA) is proposed, which is a fusion of multi-elite reverse learning, Cauchy mutation and stable climbing strategy, which is used for global and efficient optimization of the hyperparameters of the IESN model to obtain the final prediction model.

The present application belongs to the field of wind power generation technology, aiming at the problem of voltage instability of wind farm grid-connected point caused by wind power uncertainty and grid-connected fault, a reactive power distribution strategy based on sample entropy improved symplectic geometry modal decomposition is proposed. The strategy first decomposes the total reference reactive power of the grid-connected point through symplectic geometry modal decomposition to obtain symplectic geometry components, calculates the sample entropy and similarity threshold of each component, and reconstructs the high, medium and low frequency components. Then, according to the difference in response time of the rotor side converter of the doubly-fed wind power plant and the static synchronous compensator, combined with the coordination of the shunt capacitor, the reference power of the three is distributed according to the frequency band, and the reactive power fluctuation is coordinated and suppressed. Simulation verification shows that this method avoids the defects of traditional algorithms, maintains voltage stability while greatly reducing the configuration capacity of reactive power compensation devices, and has effectiveness and economy.

The application discloses a wind power prediction method and system based on VMD and entropy condition flexible network, which first acquires wind power data and meteorological characteristic data, obtains intrinsic mode function components through variational mode decomposition and calculates sample entropy; then inputs the sample entropy into an entropy encoder to generate an entropy embedding vector, splices the meteorological characteristic data and the mode components to form time sequence input features; further inputs the time sequence input features and the entropy embedding vector into an entropy condition flexible time convolution network, modulates channel attention, inflation rate mixing coefficients and residual jump coefficients through the entropy embedding vector, and outputs flexible time sequence features; then inputs the flexible time sequence features and the entropy embedding vector into an entropy condition flexible bidirectional long short-term memory network, embeds the entropy embedding vector in the gating calculation to modulate memory strength, and forms bidirectional time sequence representation; finally outputs a prediction value through a full connection layer.

This invention provides a method and system for controlling construction waste crushing based on load feedback. The method includes: acquiring the stator current signal of the crusher's main shaft motor; extracting the active current component representing torque; constructing a time sliding window sequence; calculating the algebraic difference between the current active current component and a preset target value to obtain the original load deviation; constructing a weighted function to correct the load deviation amplitude; obtaining the load change rate by taking the first derivative with respect to time; building a fuzzy logic controller, using a Gaussian membership function in the fuzzification stage to establish a mapping between sample entropy and the width of the Gaussian distribution, and setting the membership function width in real time; adjusting the fuzzy subset of PID parameters according to fuzzy rules; using the peak-to-mean ratio of the sliding window sequence as an impact factor to weight and correct the centroid method defuzzification results; superimposing the adjustment increment onto the PID reference parameters; and combining the corrected load deviation to calculate the control command and adjust the output frequency of the feed motor inverter. This invention enables multi-dimensional adjustment of the crusher.

This invention discloses a method and system for load forecasting in steel enterprises based on rolling variational mode decomposition, belonging to the field of power load forecasting technology. The method includes: collecting load and external variable data; using multiple methods to jointly identify and correct outliers; filling missing values ​​with cubic spline interpolation; adaptively determining the window length based on the autocorrelation function; optimizing the variational mode decomposition parameters using a particle swarm optimization algorithm; performing rolling decomposition on the load sequence to obtain intrinsic mode function components; calculating the energy proportion and sample entropy of each component; constructing a weighted screening index to retain key components; using an ARIMA model to predict low-frequency trend components and an attention-time convolutional network to predict mid-to-high-frequency fluctuation components; constructing a weight learning module, inputting a feature vector containing multi-dimensional features, generating fusion weights for each component, and weighted summing to obtain the final predicted value. This invention avoids future information leakage and achieves frequency-domain differentiated modeling and adaptive weight fusion.

The application provides a kind of integrated energy system multi-element load prediction method, system, equipment and medium, belong to energy prediction technical field.The method comprises: obtaining historical load and meteorological data, based on uniform information coefficient screening strong correlation characteristics;Adopt the joint method of complete set empirical mode decomposition combined with sample entropy and wavelet threshold denoising to denoise load sequence;The processed data is input into parallel graph convolution network and bidirectional long short term memory network model to extract and fuse spatio-temporal depth features;The feature is input into the multi-task learning framework based on attention mechanism, dynamically adjusts the loss weight of each load prediction task to jointly learn;Finally, the predicted values of electric, cold and heat loads are output synchronously.The application can deeply mine the spatio-temporal correlation and coupling characteristics between loads, dynamically model complex relationships, and effectively suppress data noise, thereby improving the accuracy, generalization ability and robustness of multi-element load joint prediction.

This invention relates to the field of smart power plants and discloses an alarm logic optimization method for thermal control in smart power plants, used for intelligent early warning of dynamic health status in thermal control. The method includes: first, preprocessing the time series of key process parameters to generate a dimensionless data sequence; then, reconstructing the phase space to construct a multi-dimensional phase space point set; next, analyzing the complexity at multiple time scales and calculating the sample entropy value; obtaining an anomaly index by comparing the sample entropy value with a benchmark entropy value; and triggering early warning, alarm, or normal signals in a graded manner based on the index and the original measured values. This invention can also generate control command sequences to adjust the equipment operating status, select characteristic entropy values ​​by analyzing the response characteristics of sample entropy values, and call benchmark entropy values ​​according to operating conditions, thereby achieving accurate and reliable alarms.

This invention, based on mode decomposition denoising and GAT-BiLSTM, belongs to the field of renewable energy prediction technology. It aims to address the low accuracy issues in wind power prediction caused by high-frequency noise interference and insufficient mining of the spatiotemporal correlation of multidimensional meteorological factors. The method first collects wind power and meteorological data such as wind speed and direction. After preprocessing using the isolated forest algorithm, the wind power sequence is decomposed using ICEEMDAN. High-frequency noise modes are located by combining sample entropy, and the signal is reconstructed after filtering with a Savitzky-Golay filter. Then, a GAT-BiLSTM model is constructed. GAT is used to mine the feature space correlation, and BiLSTM is used to extract bidirectional time dependence, thus achieving wind power prediction. This invention achieves accurate noise reduction of wind power signals, deeply integrates the spatiotemporal correlation of wind power and meteorology, significantly improves prediction accuracy and robustness, and provides reliable decision support for the economic dispatch of integrated energy systems.

This invention provides a mechanism-based data-driven hydrological runoff prediction method and system. The method first collects historical rainfall-runoff, temperature, wind speed, humidity, water level, and flow data. It then processes the data using a dynamic noise amplitude adaptive decomposition method, dividing it into high, low, and mid-frequency components based on sample entropy. The high-frequency components are then further refined and key features are extracted. A multi-scale network model integrating physical information is constructed to improve the evapotranspiration, runoff generation, water source distribution, and confluence links of the Xin'anjiang hydrological model, achieving mechanism- and data-driven runoff prediction. Combining water level prediction with a peak constraint penalty mechanism, a block-based adaptive optimization algorithm is used to jointly optimize the model and decomposition parameters. This invention can suppress mode aliasing, significantly improve decomposition accuracy, quantify complexity through entropy values, achieve data-driven partitioning of low, mid, and high frequencies, avoid the subjectivity of human thresholds, and improve the physical interpretability of subsequent modeling.

This invention relates to an intelligent energy data analysis and prediction method based on a large language model, comprising: natural language parsing and instruction generation; energy data retrieval and preprocessing; multi-dimensional feature extraction of standard time series, and calculation of feature vectors reflecting time series characteristics; matching the optimal model or combined model from a pre-set model library based on the feature vectors, performing parallel computation and fusing the results to obtain prediction results; generating graphical reports; and self-learning iteration. This invention transforms complex energy analysis needs into automated instruction generation through a large language model, eliminating the technical barriers between users and professional databases; it introduces quantitative diagnostic indicators such as sample entropy, significantly improving the matching degree between the prediction model and data features; and it employs a multi-model fusion mechanism based on error weighting, which significantly improves prediction accuracy and robustness in complex energy fluctuation scenarios compared to a single prediction model.

This invention relates to the field of wind power new energy technology, and in particular to a method and system for quantitatively evaluating the volatility and intermittency of wind power. It introduces a sparrow search algorithm to iteratively optimize key parameters of variational mode decomposition, overcoming the shortcomings of traditional methods that rely on human experience for parameter selection, are highly subjective, and are prone to getting trapped in local optima, thus significantly reducing mode aliasing. By constructing a dual-threshold mode division rule based on center frequency and sample entropy, it achieves accurate identification of noise-dominant components. Through targeted denoising and superposition reconstruction, it effectively filters out noise interference while preserving the true fluctuation details of wind power to the greatest extent. Based on the reconstructed signal, it calculates the volatility index and wind power ramp duty cycle separately, achieving independent quantification of wind power volatility and intermittency characteristics, solving the problems of ambiguous definitions and mixed indicators in existing technologies, and providing accurate quantitative basis for power system dispatch.

The application provides an industrial enterprise energy consumption monitoring method and system, and belongs to the technical field of industrial intelligence and energy monitoring management, comprising the following steps: synchronously collecting multi-modal industrial data such as energy flow and production flow and performing physical semantic labeling; based on physical principles, device parameter derivatives and theoretical boundary features, combining correlation indexes and multi-scale sample entropy to calculate energy metabolism entropy; a dynamic benchmark prediction model is constructed by fusing physical constraints; the abnormality is determined by the real-time and benchmark EME value deviation and the adaptive threshold value, the abnormality is analyzed and located, and a self-explaining diagnosis report is generated. The application solves the problems of the prior art, such as shallow perception, black diagnosis and slow reaction, improves the robustness and diagnosis explainability of the model, realizes energy consumption disorder degree prediction and accurate abnormality diagnosis, and provides support for energy optimization and equipment operation and maintenance.