62 results about "Spectral entropy" patented technology

The invention provides a wind turbine generator voiceprint fault recognition method, and relates to the technical field of wind turbine generator state monitoring and fault diagnosis, and the method comprises the steps: carrying out the noise reduction of an original audio signal through variational mode decomposition, screening a target mode of which the frequency, energy and kurtosis accord with features, and reconstructing the signal; extracting a Mel frequency cepstrum coefficient and a sensing noise robust coefficient, and generating multi-dimensional voiceprint data in combination with statistical characteristics such as a frequency spectrum gravity center, a spectrum entropy, energy, kurtosis and a zero-crossing rate; constructing a support set based on the prototype network, realizing small sample fault classification by calculating the Euclidean distance between the feature vector and the prototype vector, and outputting a preliminary result; judging whether the voiceprint is abnormal according to a preset threshold value, if so, storing the voiceprint into a dynamic abnormal voiceprint knowledge base; frequently occurring abnormal samples are manually labeled and added into a support set, the prototype network is retrained to update the model, and continuous optimization of the fault recognition capability is achieved.

The invention relates to the technical field of artificial intelligence and data processing, and discloses an energy storage equipment operation state monitoring method based on Internet of Things acquisition, which comprises the following steps: constructing a vibration data sample; performing state labeling on the vibration data sample to form a labeling database; performing sliding mean filtering and linear interpolation processing on the vibration data sample; carrying out adaptive spectrum noise suppression by adopting a dual-threshold wavelet packet noise reduction function; carrying out frequency band division by adopting a weighted multi-scale spectral entropy feature enhancement method; constructing a deep neural network model for classifying the running state of the energy storage equipment, and training the deep neural network model by using a labeling database; and inputting preprocessed vibration data acquired in real time into the trained deep neural network model, and performing state identification and fault alarm according to a category probability prediction vector output by the model. The running state of the energy storage equipment can be accurately monitored in real time, potential faults can be found in time, and measures can be taken.

The invention relates to the field of online analysis of chemical components of materials, and discloses an online monitoring process for components of a lead frame electroplating solution, which comprises the following steps: conventionally monitoring the spectrum of the electroplating solution to calculate the concentration; calculating a spectral entropy index based on the deviation degree of the real-time spectrum and a pre-established statistical baseline model, and comparing the spectral entropy index with a preset failure trigger threshold; a dynamic calibration step is started only when the spectral entropy index exceeds the preset failure trigger threshold, the dynamic calibration step compares a physical true difference spectrum with a model virtual difference spectrum by injecting a standard pulse to generate a model error vector, and the model error vector is utilized to correct the chemometrics model, so that the stoichiometry accuracy is improved. According to the method, on-demand calibration is realized, and the consumption of the standard substance is reduced while the real-time accuracy of the model is guaranteed.

The invention discloses a Lidar point cloud registration optimization method based on geometric feature reliability, and the method comprises the steps: carrying out the preprocessing and initial alignment of a laser radar scanning point cloud, extracting the features of a maximum principal curvature and a minimum principal curvature based on the local curvature of the point cloud, dividing a point region into two types of geometric features of angular points and plane points according to the threshold value of the maximum principal curvature, and carrying out the registration of the angular points and the plane points. On the basis, fitting quality factors including fitting errors, local curvatures and spectral entropies of the linear features and the plane features are calculated respectively, then the three factors are fused into a unified reliability weight based on a Bayesian probability model, and finally the feature reliability weight is introduced into an optimization objective function of iterative nearest point registration to execute weighted ICP registration. And outputting positioning and attitude determination results. According to the method, the reliability of geometric features is quantitatively evaluated, and a weighted optimization framework is constructed, so that the point cloud registration precision and robustness are remarkably improved, and the problem that a traditional ICP algorithm is sensitive to unreliable features in feature degradation or high-dynamic scenes is effectively solved.

The invention relates to the technical field of power monitoring, in particular to an online monitoring method and system for a power control cabinet. The method comprises the following steps: acquiring a time domain signal, tracking a fundamental wave frequency, adjusting a sampling window, and performing Fourier transform to obtain a harmonic amplitude and a phase; harmonic waves are divided into harmonic wave correlation groups according to association rules, harmonic wave phase consistency coefficients are calculated to weight the total energy in the groups, and weighted harmonic wave energy is obtained; total inter-harmonic energy is calculated, and an inter-harmonic structure factor is obtained based on the spectral entropy. And acquiring a real-time load rate and a change rate, and calculating a synchronous change coefficient and a load factor. And summing the weighted harmonic energy and the corrected inter-harmonic energy to obtain total distortion energy, and obtaining a comprehensive distortion index according to the total distortion energy. According to the scheme, the structured inter-harmonics possibly caused by the early failure of the equipment can be found earlier, and the requirements of early warning and predictive maintenance are met.

The invention provides a regional electromagnetic intelligent evaluation method and system based on spectral entropy weighting, and the method comprises the following steps: collecting electromagnetic spectrum data and the motion state of a patrol vehicle, and constructing a virtual aperture data matrix through the motion information; space covariance analysis and subspace projection are carried out on the matrix, multipath scattering interference is separated and eliminated, and a pure field intensity sequence is obtained through reconstruction. By calculating the normalized spectrum entropy of the Doppler power spectrum of the pure field intensity sequence, an inverse proportion weight coefficient representing the signal confidence is generated; and weighting the weight to a pure field intensity sequence, and mapping the pure field intensity sequence to a geographic space. And dynamically adjusting the density of the evaluation grid according to the field intensity gradient, and finally carrying out weighted centroid calculation based on inverse proportion weight to obtain a regional electromagnetic environment evaluation result. According to the method, the technical problem that a regional evaluation result is not accurate due to the fact that multipath components cannot be separated and single-point data quality cannot be evaluated in traditional mobile patrol is solved.

The invention discloses a multi-modal data processing method and system based on an attention mechanism, and relates to the technical field of deep learning, and the method comprises the steps: collecting a multi-modal data set, carrying out the multi-scale time sequence calibration through dynamic time warping, and obtaining a time sequence alignment data stream; performing cross-modal semantic association on the time sequence alignment data stream to form a multi-modal feature vector; performing sparse processing on the multi-modal feature vector by using a multi-head self-attention mechanism to generate potential sparse representation; and carrying out coarse graining analysis and fluctuation mode capture on the potential sparse representation, generating a feature sequence length and a variance descriptor, and carrying out spectral entropy calculation to obtain a data complexity score. According to the method, cross-modal semantic association is performed by using canonical correlation analysis, and meanwhile, differential processing is performed on samples with different complexities through the hierarchical adaptive processing model, so that dynamic matching of computing resources is realized, and the resource utilization rate of multi-modal data processing is remarkably improved.

The invention relates to a node entanglement perception and heterogeneous agent collaborative reinforcement learning defense method oriented to an electric power information physical system, and belongs to the technical field of electric power information physical systems. According to the method, firstly, a simulation model in which a power system layer and a communication system layer are tightly coupled is constructed, a node entanglement degree is introduced to serve as a core index for quantifying network toughness, and key fragile nodes are accurately identified by analyzing the sensitivity of network spectrum entropy to node disturbance. On the basis, a heterogeneous multi-agent reinforcement learning framework composed of a communication agent and an electric power agent is designed and is responsible for route optimization and network reconstruction respectively. By constructing a collaborative objective function fusing a voltage recovery reward and a node entanglement perception promotion reward, the intelligent agent is guided to actively enhance the overall robustness of the system network while recovering the power supply quality. And a hierarchical cooperative training mechanism is adopted, so that a heterogeneous agent learns a cooperative decision strategy, and an information physical cooperative defense mechanism under a cross-domain fault is realized.

The invention provides an insurance accident scene video evidence chain generation and credible evidence storage method and system, and the system comprises an edge multi-mode connector suite, a unified data bus, a multi-task neural network engine and a target system adaptation layer. Real-time videos are collected through the industrial vision module; the data bus provides event-driven time sequence-document-streaming hybrid transmission; the neural network engine introduces a spectral entropy modulation activation function of a production line load rate and semantic confidence, performs multi-modal coding on equipment data and visual features, and outputs semantic mapping, load prediction and control instructions; and the adaptation layer pushes the instruction to an MES platform, a WMS platform and a quality detection SaaS platform to form a millisecond-level closed loop. According to the technical scheme, private protocol zero code access, semantic unification, self-adaptive scheduling and defect detection are achieved, the data compression rate and the defect recall rate are greatly increased, and traceable safety is achieved.

The invention relates to the technical field of power equipment state monitoring and fault diagnosis, and discloses a fusion type high-voltage switch cabinet insulation state quantitative monitoring method and system, and the method comprises the steps: firstly obtaining a partial discharge ultrahigh frequency and sound wave signal, and calculating a generalized Renyi spectrum entropy and an entropy gradient index; carrying out nonlinear correction on the medium sound wave propagation velocity by using the entropy gradient index, establishing an equivalent sound velocity model in a medium degradation state, and obtaining a standardized equivalent time difference; then, constructing a two-dimensional phase plane state space taking the standardized equivalent time difference and the generalized Renyi spectral entropy as dimensions, and analyzing a differential evolution trajectory of a state vector on a time sequence; and finally, constructing a comprehensive state loss function, mapping to generate an insulation health degree index, and carrying out life prediction. According to the invention, through medium degradation wave velocity constitutive mapping and phase plane conjoint analysis, monitoring errors caused by medium aging are solved, and dynamic accurate quantitative evaluation of the insulation state is realized.

The present application relates to the field of on-line analysis of material chemical composition, and discloses an on-line monitoring process for the composition of a lead frame electroplating solution, comprising: calculating the concentration of the electroplating solution by conventional monitoring of the spectrum; calculating a spectrum entropy index based on the degree of deviation of the real-time spectrum from a pre-established statistical baseline model, and comparing the spectrum entropy index with a pre-set failure trigger threshold; starting a dynamic calibration step only when the spectrum entropy index exceeds the pre-set failure trigger threshold, the dynamic calibration step generating a model error vector by comparing the physically real differential spectrum with the model virtual differential spectrum through injection of a standard pulse, and correcting the chemometrics model using the model error vector, so that the present application realizes on-demand calibration, ensures real-time accuracy of the model, and reduces the consumption of the standard.

The invention discloses an enterprise energy consumption management method based on data fluctuation load analysis. The method comprises the following steps: collecting multi-dimensional real-time operation data of a power enterprise production system; in a sliding time window, extracting fluctuation characteristics such as standard deviation, kurtosis and spectral entropy from the time sequence data of the key parameters, and converting the fluctuation characteristics into fluctuation characteristic vectors; constructing a data fluctuation load model, and learning a non-linear relationship between fluctuation feature vectors and system energy efficiency and carbon emission indexes; on the basis of a model prediction result, a fluctuation load index (FLBI) is calculated and used for quantitatively evaluating the health bearing capacity of the system for operation fluctuation; and finally, according to the FLBI value and the change trend thereof, proactive sub-health early warning, root cause diagnosis and accurate optimization control suggestions are generated. According to the invention, the information is used as core information for analyzing the dynamic health degree of the system, and the transformation from post-event alarm to pre-event early warning is realized.

This invention relates to the fields of artificial intelligence and data processing technology, and discloses a method for monitoring the operating status of energy storage devices based on IoT data acquisition. The method includes: constructing vibration data samples; labeling the vibration data samples to form a labeled database; performing moving average filtering and linear interpolation on the vibration data samples; using a dual-threshold wavelet packet denoising function for adaptive spectral noise suppression; using a weighted multi-scale spectral entropy feature enhancement method for frequency band division; constructing a deep neural network model for classifying the operating status of energy storage devices, and training the deep neural network model using the labeled database; inputting the preprocessed vibration data acquired in real time into the trained deep neural network model, and performing status identification and fault alarm based on the class probability prediction vector output by the model. This invention can monitor the operating status of energy storage devices in real time and accurately, promptly detect potential faults, and take appropriate measures.

The invention relates to the technical field of non-contact vital sign monitoring, and discloses a millimeter wave radar vital sign monitoring method and system based on VMD, and the method comprises the steps: obtaining sample data containing vital signs; performing multi-feature fusion analysis to obtain a frequency band energy ratio, a frequency spectrum entropy, signal-to-noise ratio estimation and a peak value concentration ratio of the vital signs; judging whether the frequency band energy ratio, the frequency spectrum entropy, the signal-to-noise ratio estimation and the peak value concentration ratio of the vital signs meet preset conditions or not so as to match corresponding penalty factors and decomposition layers in a grading manner; decomposing the micro-motion signal by adopting a VMD algorithm to obtain a plurality of sample components; performing multi-feature weighted scoring on the sample components; screening the sample component by adopting a bidirectional interference suppression strategy to obtain a target signal; and based on the target signal, reconstructing to obtain a vital sign signal, and outputting a monitoring result. The method has the effect of improving the monitoring precision and timeliness of the vital signs in the real-time processing scene.

The invention discloses a target orientation estimation method, system and equipment based on a single-vector hydrophone, and belongs to the technical field of underwater acoustic signal processing. The method comprises the following steps: acquiring a sound pressure signal in a sound pressure channel of the single-vector hydrophone and a particle vibration velocity signal in a three-dimensional vibration velocity channel; constructing a matrix based on the sound pressure signal and the mass point vibration velocity signal; calculating weights, wherein the weights comprise sound energy flow weights and spectral entropy weights; introducing an adaptive coefficient to fuse the sound energy flow weight and the spectral entropy weight to obtain a joint weight; weighting the covariance matrix through the joint weight to obtain a feature R, and performing decomposition and dimension reduction on the feature R to obtain a dimension-reduced covariance matrix; and constructing an MVDR beam former based on the dimensionality-reduced covariance matrix, and carrying out azimuth spectrum scanning so as to carry out target azimuth estimation. According to the method, the energy information and the spectrum structure information of the vector hydrophone are fused, and the noise is suppressed in combination with the dimension reduction technology, so that high-precision target orientation estimation in a complex environment is realized.

The invention relates to the technical field of electric power detection, in particular to an electricity consumption abnormity real-time detection method and system based on an intelligent electric energy meter, and the method comprises the following steps: S1, collecting the electricity consumption parameter data of the intelligent electric energy meter in real time; s2, calculating a benchmark reference value of load fluctuation based on the current data in the preset statistical period; and S3, carrying out time-frequency analysis on the current data, dynamically reconstructing frequency band energy distribution of the current data, and generating a spectral entropy feature representing the chaos degree of the energy distribution. The method comprises the following steps: dynamically adjusting the number of frequency bands (compressing high-frequency noise when a load is stable and expanding low-frequency resolution when the load is abnormal) of wavelet packet decomposition according to a fundamental wave energy ratio, then introducing a cross-frequency-band energy transfer matrix to quantify an energy coupling relation between the frequency bands, and correcting the energy weight of each frequency band according to the energy coupling relation; and finally, calculating a self-adaptive and anti-aliasing reconstructed spectrum entropy. The problem of characteristic fuzziness caused by characteristic similarity (frequency band aliasing) of normal load fluctuation and electricity larceny behaviors in frequency spectrums is effectively solved.

The present application relates to the technical field of underwater acoustic signal processing, and particularly relates to a method for detecting underwater target echo in a strong reverberation background, comprising the following steps: performing band-pass filtering on received sonar echo signals to obtain filtered signals; performing sliding window segmentation on the filtered signals to form a plurality of short-time signal segments; extracting the envelope of the short-time signal segments and performing normalization processing on the envelope; performing differential processing on the normalized envelope to obtain a differential envelope signal; performing frequency spectrum transformation and frequency weighting processing on the differential envelope signal to obtain a weighted envelope spectrum; performing feature extraction on the weighted envelope spectrum, calculating the spectral entropy and spectral variance, and constructing a spectral variance-entropy ratio; and determining whether a target echo exists. The method can reduce the false alarm rate while maintaining a high detection probability, and avoids the performance degradation of existing matched filtering methods in a strong reverberation condition.

The application discloses a kind of adaptive control parameter optimization methods for multi-field industrial control system, belong to industrial control and intelligent control technical field;By collecting the multi-source heterogeneous time series data in multi-field industrial control system, a unified system state representation is constructed;The non-invasive virtual state evolution of multiple candidate control parameters is deduced, and the corresponding system response trajectory is generated;Introduce the preferred parameter verification mechanism based on physical residual and parameter risk potential, and screen out the parameter combination that violates physical constraints or has high risk;Based on spectral entropy and spectral geometric center, the frequency domain topology gating analysis is carried out, the stability and safety of the control parameters are constrained in frequency domain, and the application of the control parameters is triggered only when the frequency domain stability condition is met. Realize the safe, robust adaptive optimization of multi-field industrial control system control parameters without interfering with the existing production process, improve the reliability and engineering practicability of complex industrial control system parameter setting.

The invention provides a method for evaluating a signal quality index SQI in anesthesia equipment, which belongs to the field of anesthesia equipment monitoring and comprises the following steps: S1, performing data preprocessing on electroencephalogram data acquired in a database; s2, performing EEG signal quality judgment on the preprocessed EEG data, including obtaining impedance characteristics, signal spectrum entropy, total power in bandwidth and electrotome interference; s3, iteratively calculating a signal quality index SQI; and S4, when the impedance characteristic weight is less than 90, correcting the signal quality index SQI, reducing the impedance characteristic weight to 0.3, and increasing the electrotome interference weight to 0.6. According to the method, the SQI index oscillation caused by sudden change of a certain type of features can be limited by using two independent features in combination with a real-time correction incremental algorithm, so that the purpose of accurately evaluating the signal quality is achieved, and the influence of disturbance on index data is avoided.

The invention discloses a quality detection method for an impact-resistant material forming structure, and particularly relates to the technical field of material quality testing, and the quality detection method comprises the following steps: carrying out nondestructive testing and inherent frequency identification by cutting out a sample; in a drop hammer impact test, a spectrum entropy value of a force signal is analyzed in real time, when the entropy value is too low and energy is concentrated on inherent frequency, it is judged that a resonance risk exists, and then an actuator integrated in an impact hammer is triggered to generate additional perturbation to avoid resonance; comprehensively evaluating the anti-impact quality grade and the spectrum stability of the structure in combination with defect, frequency, modulation triggering, energy absorption and morphology multi-source data; according to the invention, through real-time time-frequency analysis and dynamic modulation, the resonance risk in impact can be diagnosed and suppressed online, and full-scale integrated characterization from defects, dynamic characteristics to impact response is realized. Based on a multi-source information fusion and comprehensive quality evaluation method, a more scientific and conservative quality admission basis is provided for high-reliability application.

The application provides an alarm method, comprising: filtering a vibration signal to obtain a first noise signal; judging whether the maximum amplitude of the first noise signal is greater than a preset amplitude threshold, if not, starting to execute step a again, if yes, recording the first noise signal and obtaining a vibration signal near the first noise signal as a second noise signal; dividing the second noise signal into multiple frames of noise signals, performing Fourier transform on each frame of the multiple frames of noise signals to obtain multiple energy spectra, and performing frequency segment correction on the multiple energy spectra to obtain multiple corrected energy spectra; calculating the proportion of the energy of multiple frequency components in each corrected energy spectrum to the entire spectrum to obtain multiple probability values of the multiple frequency components, and calculating the corresponding spectral entropy of each frame through the multiple probability values; obtaining a suspected spectral entropy curve, calculating the average Euclidean distance between the suspected spectral entropy curve and multiple standard spectral entropy curves, and alarming if the maximum value of the average Euclidean distance is less than a preset distance threshold.

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.

This invention discloses a method for identifying server operational anomalies, relating to the field of electrical fault diagnosis technology. It addresses the problems of difficulty in detecting dynamic performance degradation of power supplies and high false alarm rates due to environmental noise. First, instantaneous voltage and current waveforms are synchronously acquired during load step transients. The transient response window is defined based on the current ramp-up slope, and the voltage drop amplitude is extracted. Then, the response window is aligned with the command trigger moment in the time domain, the lag time is calculated, and correlation analysis is performed with the voltage drop amplitude to accurately determine dynamic adjustment anomalies in the power supply circuit. If the dynamic adjustment is normal, the fundamental frequency of the instantaneous current waveform is removed, and a time-frequency decomposition algorithm is applied to calculate the energy spectral density and spectral entropy values ​​of each independent frequency band, constructing a frequency domain feature vector. Finally, the spectral correlation of adjacent nodes on the same busbar is used to separate common-mode interference from the external power grid, and internal hardware physical anomalies are determined only based on the remaining differential-mode components, achieving accurate identification of server power supply health and hardware faults.

This invention belongs to the field of bearing fault monitoring technology, specifically relating to a precision bearing full-lifecycle fault early warning method based on multi-source vibration signal decoupling. The method includes: acquiring vibration signals from three channels of the bearing; calculating the impact activity level for the current time window based on the local energy of all micro-segments of the spatial vector mode sequence and the number of times the spatial vector mode in the spatial vector mode sequence is greater than the mean of the spatial vector mode sequence; calculating the autocorrelation coefficient and skewness coefficient of the spatial vector mode sequence and obtaining the background noise coupling strength; obtaining the envelope spectral entropy of the spatial vector mode sequence and weighting it using the impact activity level and the background noise coupling strength to obtain a fault characteristic index; and outputting a graded early warning based on the fitting slope of the fault characteristic index for multiple consecutive time windows and an early warning threshold. This invention overcomes the masking of weak features by strong background noise, significantly reduces early missed and false alarms, and achieves high-precision monitoring throughout the entire lifecycle.

This invention relates to the field of underwater acoustic signal processing technology, and in particular to a method for enhancing the line spectrum of moving targets based on an elite selection genetic algorithm. Based on the Doppler-warping transform, the method utilizes an elite selection genetic algorithm to autonomously optimize and estimate the target's motion parameters. Using the entropy of the transformed target signal's spectral function as the cost function, the optimal motion parameters are searched by comparing the spectral entropy values ​​under different parameters. Then, the optimal parameters are used to perform a Doppler-warping transform on the original signal, refocusing the line spectrum energy dispersed by the Doppler effect near the original frequency, thus achieving autonomous enhancement of the moving target's line spectrum. This invention effectively solves the problems of the inability to accumulate moving target line spectrum energy over long periods and the autonomous search of Doppler-warping transform parameters, improving the probability and accuracy of line spectrum detection. It can be applied to data backtracking and review processing in underwater moving target detection, and has good engineering practical value.

The invention relates to the technical field of underwater acoustic signal processing, in particular to an underwater target echo detection method under a strong reverberation background, which comprises the following steps of: carrying out band-pass filtering processing on a received sonar echo signal to obtain a filtering signal; performing sliding window segmentation processing on the filtering signal to form a plurality of short-time signal segments; extracting an envelope of the short-time signal segment, and carrying out normalization processing on the envelope; performing differential processing on the normalized envelope to obtain a differential envelope signal; performing frequency spectrum transformation and frequency weighting processing on the differential envelope signal to obtain a weighted envelope spectrum; performing feature extraction on the weighted envelope spectrum, calculating a spectrum entropy and a spectrum variance, and constructing a spectrum variance entropy ratio; and judging whether target echoes exist or not. According to the method, the false alarm rate can be reduced, meanwhile, the high detection probability is kept, and the defect that the performance of an existing matched filtering method is seriously reduced under the strong reverberation condition is overcome.

The invention discloses a network traffic abnormal behavior identification method based on deep learning, and relates to the technical field of network security, and the method comprises the steps: constructing a traffic state matrix through traffic sample features, extracting a high-frequency traffic slice, and calculating a maximum Lyapunov index to generate a self-adaptive numerical integration step length; dynamically generating a channel mask vector by combining the power spectrum entropy of the frequency domain transformation; according to the method, an anomaly perception model containing flow inertia and a frequency domain analysis branch is constructed, an inertia branch executes Euler discretization operation by utilizing integral step length to capture cross-scale time domain dynamic characteristics, and a frequency domain branch accurately filters mimicry noise through mask vector gating and an activation energy sorting mechanism; and jointly training the model by using the total loss function fusing manifold regularization and elastic filtering, and outputting a judgment result. According to the method, numerical divergence under chaotic burst traffic is effectively overcome, and the recognition robustness in the face of unknown bandwidth attacks and feature cheating is remarkably improved.

The invention provides a novel mixed deep learning ship motion prediction method and system, and relates to the technical field of ship motion prediction. The method comprises the following steps: decomposing ship motion data by adopting a CEEMDAN method to obtain intrinsic mode functions (IMF) of different time scales; performing spectrum entropy analysis and sliding window optimization on the IMF to obtain a hybrid deep learning model training sliding window length; carrying out joint modeling by adopting Transform-BiLSTM (Bidirectional Long Short Term Memory), so as to obtain a predicted value of each IMF (Inertial Measurement Function) mode; and the predicted values of all the IMF modes are summed to reconstruct a final motion predicted value. According to the method, the precision and robustness of ship motion prediction are remarkably improved, and powerful support is provided for safe navigation of the ship.

This invention provides a method, apparatus, and storage medium for detecting voids in the inner layer of concrete. The method includes: acquiring echo signals from the area of ​​concrete to be inspected; calculating the quadratic penalty factor α and the number of decomposition layers k of a variational mode decomposition algorithm based on the echo signals and a sparrow search algorithm, with the objective function being the minimum fitness function value, to obtain a parameter combination (k, α); wherein the fitness function is constructed based on power spectral entropy, error severity index, and center frequency evaluation index; decomposing the echo signals using the variational mode decomposition algorithm according to the parameter combination (k, α), obtaining k echo signal modal components; calculating the autocorrelation function of each echo signal modal component in the k echo signal modal components; and determining the detection result of voids in the area of ​​concrete to be inspected based on the autocorrelation function of each echo signal modal component. This invention offers high detection accuracy and speed, meeting the needs of concrete defect detection.

The invention relates to the technical field of drilling operation, in particular to a PDC drill bit remaining life prediction method based on torsion time sequence data, and the method comprises the steps: collecting the torsion data of a drill bit, and constructing the torsion time sequence data; using a variational mode decomposition algorithm containing a frequency band bandwidth constraint term and a time domain sparsity constraint term to extract a wear mode component; calculating the logarithmic energy feature of the wear mode component and the multi-scale spectral entropy, and carrying out weighted fusion to obtain a composite degradation index; and calculating the ratio of the composite degradation index to the accumulated operation time, multiplying by a preset wear acceleration factor to obtain an instantaneous degradation rate, and combining with a preset failure threshold to obtain the residual life. According to the method, the wear mode component is effectively extracted by introducing the time domain sparsity constraint term, and the nonlinear prediction model is constructed in combination with the wear acceleration factor, so that the accuracy of predicting the residual life of the PDC drill bit is improved.