19 results about "Multiscale entropy" patented technology

The invention relates to a bridge structure vibration monitoring and analysis method based on artificial intelligence, and the method specifically comprises the following steps: setting an acceleration sensor network at a key part of a bridge, collecting and marking a vibration signal sample, and forming a data set; a high-resolution time-frequency matrix of sample adaptive optimal kernel time-frequency distribution is calculated, a damage sensitive resonance frequency band is positioned according to spectrum kurtosis, and the time-frequency matrix is enhanced by the resonance frequency band subjected to adaptive gain enhancement; dividing a matrix frequency axis multi-scale binary tree, calculating and normalizing sub-band average energy, quantifying energy distribution uniformity through information entropy, and splicing multi-scale entropy values into feature vectors; then constructing and training a bridge detection data model; and finally, preprocessing new data, inputting the preprocessed new data into the trained model, and automatically analyzing to obtain a bridge health monitoring result. Through time-frequency optimization, multi-scale entropy and deep learning technologies, the problems of large noise, difficult feature extraction and low automation and accuracy of traditional monitoring can be solved.

The invention discloses an anomaly detection method and device based on the receiving power of a low-orbit satellite-borne GNSS receiver, and the method comprises the steps: obtaining a source receiving power sequence of the low-orbit satellite-borne GNSS receiver, and carrying out the enhancement processing based on Doppler frequency shift compensation and ionospheric noise filtering, and then generating a to-be-detected receiving power sequence; performing feature extraction on the to-be-detected receiving power sequence and then generating a receiving power feature vector based on a multi-scale entropy feature and a chaotic feature; and comparing the received power feature vector with a pre-constructed dynamic anomaly threshold to generate an anomaly detection result. According to the method, time sequence mismatching caused by frequency shift is overcome, energy distortion caused by ionosphere disturbance is also remarkably weakened, so that the accuracy and sensitivity of a subsequent nonlinear feature extraction process are guaranteed, and particularly, the robustness and effectiveness of GNSS receiving power anomaly detection can be remarkably improved in a complex dynamic space environment.

The invention relates to the field of railway fault diagnosis, in particular to a railway switch fault diagnosis method, device and equipment, and the method comprises the following steps: S1, collecting an original vibration signal of a switch, and decomposing the original vibration signal of the switch through variational mode decomposition to form a mode signal; s2, extracting multi-scale entropy features from the decomposed modal signal, wherein the multi-scale entropy features are respectively a multi-scale wavelet coherent weighted permutation entropy, a multi-scale weighted diversity entropy and a multi-scale Mel spectrogram fusion entropy; s3, the multi-scale entropy features are respectively used for training a support vector machine classifier, so that a plurality of independent diagnosis models are constructed, and a plurality of diagnosis results are obtained; and S4, integrating a plurality of diagnosis results through a decision fusion strategy of hard voting to obtain the fault type of the switch.

The invention provides a calibration method, device and equipment for a digital electric energy meter with a self-calibration function, and the method comprises the steps: obtaining a to-be-detected pulse electric energy signal and a standard electric energy pulse signal which are inputted to the digital electric energy meter in real time, and carrying out the preprocessing of the signals, and generating an error sequence; performing multi-scale coarse graining processing on the error sequence to generate a coarse graining sequence based on different scale factors; calculating a sample entropy value of each coarse graining sequence, generating entropy spectrum characteristics, and identifying frequency components based on an error source in the entropy spectrum characteristics; and realizing calibration of the digital electric energy meter based on the frequency components and the corresponding calibration weights. Through a multi-scale entropy analysis technology, multi-scale quantization of error signal complexity is realized, and the limitation that a traditional single time window comparison method cannot distinguish frequency characteristics is overcome.

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 invention relates to the field of control, and discloses a gas turbine TCA cooling water temperature accurate regulation and control method, which comprises the steps of carrying out multi-scale analysis on a gas turbine TCA system temperature time sequence, generating an eigenmode component set, constructing a multi-scale entropy impedance model of temperature dynamic characteristics according to the eigenmode component set, accurately describing the temperature dynamic characteristics, and calculating the temperature of the gas turbine TCA system. The model is combined with system control parameters, a temperature dynamic response relation is established, a multi-step prospective temperature prediction sequence is generated, and the future temperature change trend can be mastered in advance. And carrying out system impedance matching analysis based on the sequence to obtain an impedance matching analysis result, designing a variable impedance adaptive prediction strategy according to the analysis result, dynamically adjusting the system impedance characteristic, and generating a variable impedance prediction instruction set adaptive to the current working condition. According to the invention, through multi-scale analysis and prospective prediction, accurate prediction of the cooling water temperature of the gas turbine TCA is realized.

The invention relates to the technical field of equipment monitoring and fault diagnosis, and discloses a deep feature and manual feature fused coal mine equipment abnormity monitoring method, which comprises the following steps: carrying out standardization processing on a vibration acceleration signal to obtain a standardized signal; performing Hilbert-Huang transform decomposition and multi-scale entropy calculation on the standardized signal; inputting the standardized signal into a one-dimensional convolutional neural network and a bidirectional long-short term memory network which are connected in series; performing weighting processing on the depth hidden feature sequence by using the attention weight; and inputting the comprehensive feature vector into a multi-layer perceptron, and generating a monitoring result representing the abnormal state of the coal mine equipment through full connection layer and activation function processing of the multi-layer perceptron. According to the method, the attention mechanism guided by the local entropy is fused with the depth features, fault impact is automatically focused under strong noise, advantage complementation of the mechanism and the data is realized, and the robustness and accuracy of abnormal monitoring of the coal mine equipment are improved.

This invention relates to the field of bioinformatics, and particularly to a method for analyzing the effects of traditional Chinese medicine compound on H1N1 influenza based on host entropy change trajectories. This invention combines multi-omics data obtained from experiments and converts it into multi-scale entropy. Then, it forms a global entropy trajectory from the time-series multi-scale entropy. Finally, it constructs a correlation phenotypic matrix by combining the entropy change characteristics of the global entropy trajectory with the original dynamic indicators. This makes the entropy value not only an abstract mathematical indicator but also a measurement standard with clear biological significance. Therefore, it is possible to systematically and comprehensively analyze the impact of Yupingfeng powder on the host infected with H1N1 influenza, providing strong support for revealing the viral infection mechanism and the strategy of using Yupingfeng powder for treatment.

The invention relates to the field of industrial control systems, and discloses a geothermal well system technology integrated intelligent control and cooperative operation system, which comprises a data acquisition and preprocessing module, a data processing module and a control module, the multi-scale entropy potential barrier modeling module is used for calculating an operation mode entropy representing the operation uncertainty of the geothermal well system and a mode transition potential barrier representing the conversion difficulty between modes; the dynamic constraint manifold generation module is used for generating a virtual constraint manifold for defining a safe and efficient operation interval of the control variable; the active detection and adaptive learning module is used for adaptively adjusting parameters of the multi-scale entropy barrier modeling module and the dynamic constraint manifold generation module; and the cooperative control execution module is used for generating a control instruction issued to an actuator. According to the method, the operation modal entropy and the modal transition barrier are introduced, and the dynamic virtual constraint manifold is generated, so that the system is controlled to change from passive response to active risk avoidance, and the operation safety of the system is improved.

The application provides an industrial control protocol covert channel detection method and system, the method comprises the following steps: obtaining multi-source industrial control network communication data, obtaining conversation flow, command flow and field level change sequence through protocol identification and hierarchical analysis; dividing three behavior windows of fixed time length, conversation length and logical transaction, extracting communication events to form multi-granularity behavior sequence; calculating information entropy, transition entropy or structure entropy and normalizing to construct dynamic baseline entropy image; detecting entropy variation drift based on adaptive sliding threshold, marking abnormal interval; performing multi-dimensional feature clustering on entropy variation events and combining logical verification to output suspected covert channel instances; generating a traceability report and feeding back optimized detection model parameters. Through multi-scale entropy analysis and closed-loop optimization, the application realizes high-precision, low-false alarm and adaptive detection of the covert channel.

The invention provides a laparoscope real-time navigation method based on augmented reality, and relates to the technical field of myoelectricity sensing, the laparoscope real-time navigation method comprises the following steps: using an impedance mapping model to output a neurodynamics coupling index by fusing an antagonistic muscle synergy contraction index, a neural rhythm multi-scale entropy, a power spectrum gravity center shift rate and an action mapping manifold degree; based on the index, through hyperbolic tangent mapping and square attenuation logic, a dynamic drift compensation gain signal and a visual information rendering density factor are generated, and adaptive adjustment of pose locking rigidity, color saturation and transparency is realized. According to the method, a visual noise reduction and physiological feedforward compensation mechanism driven by cognitive resources is constructed, model drift is effectively inhibited, and cognitive loads are reduced; in combination with index change rate-based rendering frequency adjustment and physiological signal failure switching logic, the real-time precision and robustness of the navigation system in a complex operation scene are enhanced.

The invention discloses a bearing fault diagnosis method integrating generative screening and adaptive feature enhancement. The method comprises the steps of S1, collecting original data and expanding a data scale by using a generative adversarial network; s2, extracting a high-quality sample by adopting a correlation screening mechanism; s3, noise is suppressed and key features are strengthened through a feature denoising module; and S4, realizing fault identification by using the bidirectional convolutional network. According to the method, the WGAN-OP and the SCC are combined to realize high-quality data expansion, and the distribution consistency and availability of the generated samples are improved. Through a feature denoising and enhancement integrated module (AFME), in combination with modal decomposition and a multi-scale entropy screening strategy, the signal-to-noise ratio is remarkably improved, key features are highlighted, an adaptive bidirectional time sequence convolutional network (ABiTCN) is constructed, multi-direction dependent capture and feature dynamic fusion are realized, and the diagnosis stability and generalization ability of the model under complex working conditions can be improved.

The application discloses a kind of storage chip compatibility test method, equipment and medium, it is related to chip compatibility test technical field, including, the multiple-source heterogeneous data generated when the running preset inducing test load of the storage chip to be measured is collected, and fusion is carried out, generates multidimensional time series data set;Based on multidimensional time series data set calculation multiscale entropy feature vector, from the compatibility fault feature knowledge base pre-constructed, obtain reference fault mode feature vector, combine multiscale entropy feature vector and reference fault mode feature vector to generate chip health state feature vector;Migration meta-learning model is constructed, and chip health state feature vector is input into migration meta-learning model, and risk probability score and fault root cause classification result are generated by forward inference calculation.The application generates multidimensional time series data set by collecting multiple-source heterogeneous data, realizes the collaborative collection and integration of the multiple physical parameters of storage chip.

This disclosure provides a method and system for predicting frequency regulation commands for supercapacitor energy storage coupled with thermal power units, belonging to the field of power system frequency regulation technology. The method includes: performing topology analysis on the supercapacitor energy storage, thermal power, and coupled control subsystems to determine the collaborative division of labor mechanism and analyze the multi-scale characteristics of the commands; hierarchically collecting system parameters and constructing a standardized matrix; preprocessing the raw command data; decomposing the command sequence based on multi-scale characteristics using an improved variational mode decomposition model that introduces slack variables to obtain multiple intrinsic mode function subsequences; calculating the similarity of the subsequences based on multi-scale entropy and dynamic time warping, and generating a comprehensive component by weighted fusion according to energy ratio; inputting each comprehensive component into an improved adaptive feature-gated cyclic unit network for parallel prediction; and destandardizing and superimposing the prediction results to obtain the final predicted value, and performing multi-dimensional evaluation. The embodiments of this disclosure achieve high-precision and highly engineering-adaptable frequency regulation command prediction.

The embodiment of the disclosure provides a kind of super-capacity energy storage coupling thermal power unit frequency modulation instruction prediction method and system, belong to power system frequency modulation technical field.The method comprises: the topology analysis of super-capacity energy storage, thermal power and coupling control subsystem, determine collaborative division mechanism, and analyze instruction multi-scale characteristics;Layered acquisition system parameters and construct standardization matrix;The original instruction data is preprocessed;Based on multi-scale characteristics, the improved variational mode decomposition model is used to decompose instruction sequence by introducing slack variable, to obtain multiple intrinsic mode function subsequences;Based on multi-scale entropy and dynamic time warping, the similarity of subsequence is calculated, and the integrated component is generated by energy proportion weighted fusion;Each integrated component is input into the improved adaptive feature gated recurrent unit network for parallel prediction;The final prediction value is obtained by anti-standardization superposition to the prediction result, and multidimensional evaluation is carried out.The embodiment of the disclosure realizes high-precision, high-engineering adaptability frequency modulation instruction prediction.

The application relates to a bridge structure vibration monitoring and analyzing method based on artificial intelligence, and specifically as follows: an acceleration sensor network is arranged at key positions of a bridge, vibration signal samples are collected and labeled, and a data set is formed; a high-resolution time-frequency matrix of a sample adaptive optimal kernel time-frequency distribution is calculated, a damage sensitive resonance frequency band is positioned according to a spectral kurtosis, and a resonance frequency band enhanced time-frequency matrix is enhanced through adaptive gain enhancement; then, a matrix frequency axis multi-scale binary tree is divided, average energy of a sub-band is calculated and normalized, energy distribution uniformity is quantified through information entropy, and multi-scale entropy values are spliced into a feature vector; then, a bridge detection data model is constructed and trained; finally, new data is input into the trained model after pre-processing, and bridge health monitoring results are automatically analyzed. Through time-frequency optimization, multi-scale entropy and deep learning technology, the application can overcome the problems of large noise, difficult feature extraction, low automation and low accuracy in traditional monitoring.

The invention discloses a storage chip compatibility test method and device and a medium, and relates to the technical field of chip compatibility test.The method comprises the steps that multi-source heterogeneous data generated when a to-be-tested storage chip runs a preset induction test load is collected and fused, and a multi-dimensional time sequence data set is generated; calculating a multi-scale entropy feature vector based on the multi-dimensional time sequence data set, obtaining a reference fault mode feature vector from a pre-constructed compatibility fault feature knowledge base, and combining the multi-scale entropy feature vector with the reference fault mode feature vector to generate a chip health state feature vector; and constructing a migration element learning model, inputting the chip health state feature vector into the migration element learning model, and generating a risk probability score and a fault root cause classification result through forward reasoning calculation. According to the method, the multi-dimensional time sequence data set is generated by collecting the multi-source heterogeneous data, and collaborative collection and integration of various physical parameters of the storage chip are achieved.

The application provides a multiscale entropy gated DWTformer meteorological data time series prediction method and device, and belongs to the technical field of meteorological time series prediction. Based on the Transformer network, a multiscale entropy gated discrete wavelet time series decomposition module is designed to realize adaptive decomposition of periodic terms and trend terms to describe data change trends at different time scales, and a Wasserstein self-attention mechanism and an exponential smoothing prediction module are introduced to extract features at different frequency scales, fully excavate the time series dependence between production data, and effectively solve the problem of meteorological data prediction with nonlinear and non-stationary characteristics. The improved Transformer time series prediction model DWTformer proposed by the application performs multiscale time series feature extraction and prediction on meteorological data, better excavates the potential time series dependence of meteorological data, improves the prediction accuracy, and the experimental results show that the multiscale entropy gated DWTformer meteorological data time series prediction method has higher prediction accuracy than existing methods.

The invention relates to the technical field of power electronics, and discloses a bridge connection capacitance balance type PCS converter comprising a data acquisition module used for acquiring PCS system multi-mode sensing data and calculating multi-scale entropy features; the feature extraction module is used for constructing an initial time sequence causal graph model; the structure optimization module is used for optimizing a causal graph structure by adopting a mixed structure learning algorithm; the anti-fact reasoning module is used for establishing a structural causal model and a situation simulator and realizing anti-fact query and analysis; the causal evaluation module is used for calculating an average causal effect and a comprehensive causal influence index and quantifying the influence of each factor on the service life of the capacitor; the health assessment module is used for constructing a system health index in combination with nonlinear conversion and a time decay effect; the maintenance decision module is used for generating an optimal maintenance strategy and evaluating cost effectiveness under a multi-constraint condition according to the health index; according to the method, a deep causal mechanism of capacitance degradation is disclosed, correlation and causality are effectively distinguished, and accurate maintenance decision support is provided.