78 results about "Wavelet basis functions" patented technology

The invention relates to the technical field of laser spectrum detection, in particular to an LIBS (Laser-induced Breakdown Spectroscopy) spectrum noise reduction method, system and equipment based on adaptive threshold wavelet transform and a storage medium. Acquiring an original spectral signal of the laser-induced breakdown spectroscopy; performing five-layer multi-layer wavelet decomposition on the original spectral signal by adopting a db4 wavelet basis function to obtain a high-frequency coefficient and a low-frequency coefficient of each layer; calculating a noise intensity standard deviation based on the detail coefficient of the highest decomposition layer; dynamically determining the optimal value of the regulation factor through a double-layer optimization strategy combining a grid search method and a golden section iterative optimization method; constructing an adaptive threshold value based on the noise intensity standard deviation and the adjustment factor; carrying out threshold value processing on the high-frequency coefficient by adopting a self-adaptive threshold value; and performing wavelet reconstruction on the processed high-frequency coefficient and low-frequency coefficient, and outputting a denoised spectral signal. While the LIBS spectral signal-to-noise ratio is remarkably improved, the spectral feature form is completely reserved, and reliable technical support is provided for laser-induced breakdown spectroscopy detection in a complex industrial environment.

The invention discloses an ancient textile image restoration system based on artificial intelligence. The system comprises a multi-source image acquisition module, a damaged area detection module, a pattern generation module, a color restoration module, a texture synthesis module and a multi-scale fusion module. The system introduces a wavelet guidance-frequency domain attention mechanism and a rotation invariant Haar wavelet basis function to realize accurate identification and classification of a damaged area; a saliency-guided wavelet decomposition control and self-adaptive threshold denoising method is combined, so that the perception capability of slant textures and edge details is improved; the texture synthesis module constructs a hierarchical modeling strategy fusing Gram style loss, Wasserstein style loss and a total variation regular term, and realizes generation of high-quality textures with unified styles and smooth edges; the system can be widely applied to cultural relic digital repair and display scenes.

The invention relates to a GIS partial discharge optical detection method and device, and a medium. The method comprises the following steps: collecting a GIS partial discharge optical signal through a light guide rod, and carrying out the preprocessing of the GIS partial discharge optical signal; self-adaptive wavelet packet decomposition is carried out on the preprocessed signals, noise signals are separated through a self-adaptive threshold value adjustment algorithm, multi-scale time-frequency characteristics are extracted from the signals after noise separation, and according to the self-adaptive wavelet packet decomposition, a wavelet basis function is selected in a self-adaptive mode according to statistical indexes of the extracted signals so as to carry out wavelet packet decomposition; and constructing a lightweight convolutional neural network model, classifying the extracted multi-scale time-frequency features, and identifying a partial discharge signal. Compared with the prior art, the method has the advantages that the anti-interference capability is high, weak partial discharge signals can be effectively extracted, and the real-time requirement is met.

The invention provides a multivariate time sequence prediction method and system and a medium, an input time sequence is adaptively decomposed into an approximate coefficient sequence and a detail coefficient sequence by adopting an adaptive discrete wavelet transform and inverse transform mode, and compared with the previous wavelet transform depending on a predefined wavelet basis function, the multivariate time sequence prediction method and system have the advantages that the time sequence prediction efficiency is improved; the mode is more flexible, and filtering kernels of discrete wavelet transform and inverse transform can be updated in a data driving mode in the training process according to the characteristics of the time sequence, so that the filtering kernels are more suitable for the current sequence. Wavelet transformation realized by carrying out convolution operation on a time dimension lacks extraction and modeling of correlation between global information and different variables in a multivariate time sequence; therefore, a coefficient mixing module and a wavelet domain attention channel enhancement module are provided to carry out supplementary modeling on an approximation coefficient and detail coefficient sequence obtained after wavelet transformation. The method can meet the prediction requirements of the time series in actual production such as power load.

The invention discloses a natural gas pipeline leakage detection method based on novel wavelet basis transformation and singular value decomposition in a two-dimensional convolutional neural network. Firstly, a sound signal collected by a sound wave sensor is converted into a digital signal; secondly, in the data preprocessing stage, singular value decomposition is carried out on the digital signals to effectively eliminate background noise interference, and then batch normalization is carried out on the processed data; then, converting the one-dimensional time sequence signal into a two-dimensional time-frequency image by adopting a self-defined Morlet wavelet basis function; and finally, based on the time-frequency images, constructing and training a 2D-CNN model for fault classification, and presenting a diagnosis result through a confusion matrix and a comparison graph. According to the method, 97.55% of fault recognition accuracy is obtained in a public data set, and compared with other competitive methods, the method shows more excellent noise robustness and classification performance, and has higher accuracy and wide application prospects in pipeline leakage diagnosis in a complex noise environment.

The invention discloses a photovoltaic access power distribution network fault diagnosis method based on fault identification, and particularly relates to the technical field of power grid fault diagnosis, and the method comprises the steps: synchronously obtaining multi-point electrical quantity monitoring data, photovoltaic system inverter operation data and environment data of a power distribution network side, and carrying out the abnormality elimination and delay compensation preprocessing; based on the switching frequency of the photovoltaic inverter and the line parameters of the power distribution network, adaptively selecting a wavelet basis function and a decomposition scale, and extracting a fusion fault feature vector; inputting the feature vectors into a two-channel hybrid neural network model for intelligent diagnosis, and outputting fault section positioning, type classification, severity level and occurrence time information; and finally generating a fault processing instruction according to the diagnosis result. According to the method, the problems of low diagnosis precision, slow response and the like caused by single data source, poor feature extraction self-adaption and simple model structure of the existing method are effectively solved, the accuracy and reliability of complex fault recognition are remarkably improved, and closed-loop automation of fault diagnosis and processing is realized.

The invention discloses a separation overload and impact signal identification method and system based on wavelet transform, and belongs to the technical field of mechanical engineering and signal processing.The method comprises the steps that S1, separation data are collected and preprocessed, and the separation data comprise separation acceleration data; s2, decomposing and separating the acceleration data through discrete wavelet transform to obtain a detail coefficient and an approximation coefficient; s3, reconstructing a high-frequency impact signal and a low-frequency overload signal based on the detail coefficient and the approximation coefficient, and obtaining an impact response positive spectrum and a negative spectrum; s4, setting different wavelet basis functions and decomposition layers, repeating the step S2 and the step S3, and separating impact and overload data through multi-objective optimization; and S5, according to the optimized separation impact and overload data, an environment test condition or a product structure strength design index is formulated. According to the method, the problems that complex trend terms cannot be accurately identified and validity criteria are lacked in the prior art are solved, and the signal separation precision and the engineering application reliability are remarkably improved.

The invention discloses a partial discharge signal feature extraction method and system, and the method comprises the following steps: collecting a current signal on a grounding loop cable of detected electrical equipment, and carrying out the preprocessing of the current signal; converting the preprocessed signal into a two-dimensional matrix, and performing singular value decomposition on the two-dimensional matrix to obtain a principal component; matrix reconstruction is carried out based on the principal component to generate a de-noising matrix, and the de-noising matrix is restored to a time domain signal sequence; performing discrete wavelet transform on the time domain signal sequence to obtain a multi-level approximation coefficient and a detail coefficient; the discrete wavelet transform selects a wavelet basis function from the candidate wavelet basis set based on a preset adaptive wavelet basis selection mechanism, and adaptively determines the number of wavelet decomposition layers based on the signal dominant frequency characteristics; and inputting the approximation coefficient and the detail coefficient into an inverse wavelet transform module to obtain a final partial discharge signal. According to the method, efficient, accurate and universal partial discharge signal feature extraction can be realized.

The invention provides a fault identification method for a photovoltaic energy storage power distribution system, and relates to the technical field of photovoltaic system fault identification, and the method comprises the steps: S1, setting a fault threshold standard; s2, collecting real-time voltage and current signals in the photovoltaic energy storage power distribution system by using a signal source; s3, selecting a proper wavelet basis function; s4, performing convolution operation on the collected signals and the selected wavelet basis function to obtain wavelet coefficients of the signals at different scales and positions; s5, analyzing the characteristics of the wavelet coefficient; and S6, according to the extracted features, in combination with a preset fault threshold, judging whether the system has a fault or not. Real-time voltage / current signals are obtained by means of a signal source to provide data support in the subsequent discrete wavelet transform process, the signals are decomposed into approximate coefficients and detail coefficients by means of wavelet transform, and the fault state existing in the power distribution process of the photovoltaic energy storage system is determined according to the high-frequency and low-frequency conditions of the approximate coefficients and the detail coefficients. And early detection of potential fault hidden dangers is facilitated.

The invention provides an electrical equipment operation state real-time statistical analysis method and system, and belongs to the field of electrical equipment operation state monitoring, and the method comprises the steps: obtaining multi-dimensional time sequence sensor data during the operation of electrical equipment, selecting a proper wavelet basis function based on Shannon information entropy to carry out wavelet packet decomposition, and extracting time-frequency domain features; constructing a dynamic relation graph which takes a sensor as a node and takes a Granger causal relationship as a weight, and learning node space features by using a graph convolutional network in combination with time-frequency features; the node features are input into a gating circulation unit, and electrical equipment state sequence codes fused with space-time dependence are generated; and carrying out Viterbi decoding by using the state transition cost matrix, and reasoning an optimal electrical equipment operation state path. According to the method, the uncertainty of a prediction result can be quantified, clear confidence evaluation is provided for a final analysis conclusion, and the reliability of decision making is improved.

The invention provides a bidirectional LSTM photovoltaic power generation prediction method based on wavelet decomposition and double attention, and belongs to the technical field of power generation prediction. Selecting an optimal wavelet basis function by using a particle swarm optimization algorithm to carry out adaptive noise complete set empirical mode decomposition on the power sequence to obtain a multi-layer intrinsic mode function component, and carrying out adaptive denoising and power signal reconstruction according to a multi-scale permutation entropy and a Bayesian risk minimization criterion in combination with meteorological conditions; key feature variables are extracted through a maximum information coefficient, a bidirectional long-short-term memory network prediction framework is established, a feature attention mechanism and a double-path time attention structure are introduced, and when power mutation or irradiance mutation is detected, a sparse attention weight rapid reconstruction mechanism is triggered to complete prediction. The technical problem that the prediction precision is reduced when the photovoltaic power generation power changes suddenly under the cloudy weather condition is solved.

The invention discloses an overhead transmission line fault detection and classification method based on discrete wavelet transform and a time sequence convolutional network, and relates to the technical field of electronics. The method comprises the following steps: acquiring original signals of three-phase current and grounding current of the overhead transmission line, and carrying out normalization processing and moving average filtering preprocessing; discrete wavelet transform (DWT) of a Daubechies wavelet basis function Db4 is adopted to perform multi-scale decomposition on the preprocessed signal, a high-frequency detail coefficient is extracted, and a multi-channel time sequence input vector is constructed; and inputting the input vector into a time sequence convolutional neural network (TCN). According to the method, fault features are efficiently extracted through DWT, time sequence data are accurately modeled through TCN, the problems that a traditional method is low in accuracy, slow in response and weak in anti-interference capacity in a complex fault scene are solved, the fault classification accuracy is 99.9%, the parallel computing capacity is high, training is stable, the method can adapt to different fault conditions, and the method is suitable for large-scale popularization and application. And the operation reliability and the intelligent protection level of the power system are effectively improved.

The present application relates to the technical field of composite material structure monitoring, in particular to a composite material structure damage identification method based on acoustic emission wavelet packet energy characteristics, which comprises: S1, obtaining acoustic emission signal data of the composite material and performing pretreatment; S2, using a wavelet base function and a discrete wavelet inverse transform to perform wavelet packet decomposition on the denoised acoustic emission signal data of the composite material; S3, obtaining the processed acoustic emission signal data of the composite material, and extracting the characteristic energy of each acoustic emission signal data; S4, using a clustering method to perform clustering analysis and completing the composite material structure damage identification. The present application uses acoustic emission signals as the basis for analysis, uses wavelet transform and k-means clustering to accurately realize the structure damage identification and judgment of the composite material, can realize dynamic and continuous monitoring, is not affected by the complexity of the structure, has wide applicability, and is suitable for various composite materials.

The present application relates to the technical field of data analysis, and more particularly, to a solid-state battery performance test method and system based on data analysis, comprising: obtaining voltage time series data of a solid-state battery, and removing a direct current component to obtain a fluctuating voltage sequence; based on a ratio of a second-order difference to a first-order difference of the fluctuating voltage sequence and a signal-to-noise ratio, calculating a local transient response index at each time, which is used to represent a local steepness of voltage fluctuation. The present application aims at the problem that solid-state battery micro-short circuit waveforms are diverse and easily covered by noise, and through constructing a local transient response index, the steepness of voltage fluctuation is quantified in real time, and a small deformation wavelet basis function highly matched with the current signal form is dynamically generated, and a feature value calculation method with a form proximity penalty term is introduced, so that precise matching and enhancement of weak short circuit signals are realized, non-fault interference is effectively eliminated, and the signal-to-noise ratio and accuracy of detection are significantly improved.

The invention relates to a vegetation leaf dry matter content remote sensing high-precision inversion method. The method comprises the following steps: S1, constructing a vegetation leaf sample data set; s11, constructing a blade actual measurement data set; s12, generating an analog data set and / or an analog data set added with noise; s13, dividing the actual measurement data set into an actual measurement training set and an actual measurement verification set; s2, dry matter weak information features are extracted through continuous wavelet transform; s21, carrying out multi-scale analysis calculation on dry matter weak information by using a continuous wavelet transform method; s22, performing wavelet basis function transformation on the original reflection spectrum of each leaf sample to obtain wavelet coefficient characteristics; s23, carrying out correlation analysis calculation on the wavelet coefficient characteristics and the LMA; s24, a threshold value is set, and wavelet coefficient characteristics with sensitivity to LMA spectrum weak information are screened out; and S3, constructing an LMA inversion model based on wavelet coefficient coupling machine learning. The method is high in inversion precision and strong in noise robustness.

The present application relates to the technical field of electric vehicle reducer fatigue analysis, and particularly relates to a method for preparing a frequency domain of a gear fatigue load spectrum of an electric vehicle reducer based on CCWOA, comprising: S1: constructing a bending stress load spectrum of a reducer gear of an electric vehicle; S2: generating an optimal wavelet base function based on discrete wavelet parameter optimization of an energy leakage criterion; S3: performing threshold optimization based on a CCWOA algorithm to obtain an optimal threshold; the CCWOA algorithm introduces a Logistic-Tent chaotic mapping mechanism and a cosine iteration strategy in the WOA algorithm; S4: performing discrete wavelet transform on the bending stress load spectrum of the reducer gear of the electric vehicle based on the optimal wavelet base function and performing load spectrum frequency domain coding through the optimal threshold; and S5: realizing fatigue analysis of the electric vehicle reducer based on a gear fatigue acceleration load spectrum of the electric vehicle reducer. The present application realizes high-precision compression of the reducer gear load spectrum and equivalent retention of fatigue damage, and provides a reliable scheme for fatigue analysis of the electric vehicle reducer.

This invention relates to a wavelet threshold-based method and system for denoising cold test vibrations, comprising the following steps: constructing a vibration parameter database based on the acquired diesel engine cold test vibration signal; performing wavelet decomposition on the vibration parameters in the database according to the selected wavelet basis function and decomposition level to obtain wavelet coefficients; performing wavelet reconstruction on the vibration parameters based on a threshold function to obtain denoised cold test vibration parameters, specifically: shrinking the wavelet coefficients within the threshold range to zero, while keeping the wavelet coefficients outside the threshold unchanged; and processing the acquired diesel engine cold test vibration signal using the denoised cold test vibration parameters to obtain a denoised cold test vibration signal. The threshold function is used to solve the oscillation and distortion problems existing in the denoising process, thereby improving the denoising effect of the cold test vibration signal.

The application discloses a kind of lithium battery energy storage station based on edge computing data preprocessing method.It includes the following steps: obtaining wavelet base function is discretized, and discrete wavelet function is obtained;Discrete wavelet transform is carried out to arbitrary input acoustic signal, and approximate value and noise value are decomposed to discrete wavelet;Threshold size is determined according to sample estimation;Wavelet coefficient is reorganized according to threshold, and the inverse transform of wavelet energy spectrum after wavelet coefficient processing is carried out, reconstructs time signal;Denoising evaluation index function is established, for the one that fails to meet, reconstructed signal is returned again decomposition, and second threshold determination is carried out again, and reconstruction is carried out again, if the signal after twice reconstruction still cannot meet, it will be discarded;The reconstructed signal after wavelet denoising and meeting the requirement signal-to-noise ratio evaluation index is extracted by principal component analysis PCA eigenvalue.The beneficial effects of the application are: it can realize denoising dimension reduction processing to obtain eigenvalue.

The invention belongs to the technical field of rock mechanics and geological engineering, and aims to solve the problems of insufficient precision, low preparation efficiency and high cost of natural joint morphology simulation at present. The preparation method of the rock standard component for the natural joint test comprises the following steps: collecting a to-be-tested rock block, and cutting to obtain a rock test block for later use; constructing point cloud data based on a wavelet basis function and a random phase; generating specification grid points from points in the point cloud data, and calculating a height value corresponding to each grid point to obtain a grid file; checking whether the simulated natural rough joint surface has statistical characteristics of natural joint fluctuation morphology or not based on normal distribution; and importing the grid file into a rock carving machine to generate a carving path, and carrying out rough carving and fine carving to obtain the rock standard part for the test. According to the invention, the multi-scale roughness characteristic and anisotropy of the natural joint can be efficiently and accurately simulated, and a high-quality standardized test piece is provided for a rock mechanical test.

The application discloses a circuit breaker feature extraction method and device based on a wavelet threshold, equipment and a medium, and relates to the field of power data analysis.The method comprises the following steps: determining a wavelet base function according to the data characteristics of time-series current data of an intelligent circuit breaker to be analyzed; performing wavelet threshold denoising on the time-series current data under a plurality of preset decomposition layers respectively according to the wavelet base function, so as to obtain a plurality of groups of denoised current data; wherein, the wavelet threshold denoising is performed based on a wavelet threshold function with an adjustable threshold processing amplitude adjustment factor; determining the correlation degrees of the time-series current data and each group of denoised current data respectively according to a preset correlation degree screening function, and screening a plurality of groups of denoised current data according to the correlation degrees to obtain optimal denoised data; and extracting error features of the intelligent circuit breaker according to the time-series current data and the optimal denoised data. Through the implementation of the application, the accuracy of feature extraction of the data of the intelligent circuit breaker can be improved.

The application discloses a signal denoising method and device based on DE optimization of wavelet parameters. The method comprises the following steps: selecting an initial wavelet base function, performing wavelet decomposition on a noisy signal according to an initial decomposition layer number and initial layer thresholds at each decomposition scale to obtain detail coefficients at each decomposition scale and approximation coefficients at a maximum decomposition scale; taking the initial wavelet base function, the initial decomposition layer number and the initial layer thresholds at each decomposition scale as position parameters of an initial population in a DE algorithm, and obtaining optimal wavelet base functions, optimal decomposition layer numbers and optimal layer thresholds at each decomposition scale based on the DE algorithm; performing denoising processing on each detail coefficient according to the optimal wavelet base functions, the optimal decomposition layer numbers and the optimal layer thresholds at each decomposition scale to obtain target detail coefficients, and combining the target detail coefficients and the approximation coefficients to perform signal reconstruction to obtain a target signal. The application can optimize the decomposition layer number and the layer threshold by using the DE algorithm, and improve the denoising effect.

The invention relates to a wavelet neural network dynamic construction method, which comprises the following steps of: setting a nonlinear system, and processing an unknown nonlinear function in the nonlinear system by adopting a self-adaptive scaling wavelet network approximation framework; processing the non-linear function, namely determining an initial wavelet space for approaching the non-linear function by utilizing an initial wavelet frequency estimator of the adaptive scaling wavelet network approaching framework; on the basis of a scaling parameter self-adaptive adjustment mechanism, scaling parameters are dynamically updated on line so as to adjust the frequency bandwidth of the wavelet basis function and enable the frequency bandwidth to be matched with the spectrum distribution of the nonlinear function; and based on a wavelet basis function increasing and cutting mechanism of the adaptive scaling wavelet network approximation framework, increasing and cutting a wavelet basis function on the basis of the initial wavelet space so as to further approach the nonlinear function.

The present application relates to the technical field of data denoising, and particularly relates to a transient electromagnetic denoising method and system based on feature extraction. The present application first obtains the noise level of each decomposition level in combination with the first noise degree and the second noise degree of the decomposition level in all high-frequency component signals and all low-frequency component signals corresponding to the optimal wavelet base function; adjusts the initial wavelet threshold according to the noise level of each decomposition level to obtain the adjusted wavelet threshold of each decomposition level; and performs denoising according to the high-frequency component signal, the low-frequency component signal and the adjusted wavelet threshold corresponding to each decomposition level to obtain the denoised transient electromagnetic signal. The present application improves the denoising effect by constructing the adjusted wavelet threshold suitable for the noise level of each decomposition level, so that the denoised transient electromagnetic signal accurately reflects the geological information.

The invention relates to the technical field of bearing detection, and discloses a rolling bearing fault identification method and system, and the method comprises the steps: carrying out the waveform noise filtering reconstruction processing of vibration waveform data through employing a sym8 wavelet basis function; a data sample is obtained in an overlapping sampling mode at an interval that the bearing rotates by one circle; the method comprises the following steps: acquiring six types of samples of a bearing normal state, an inner ring light fault, an inner ring heavy fault, an outer ring light fault, an outer ring heavy fault and a rolling body fault, selecting a db1 wavelet basis function for each fault data sample, performing four-layer discrete wavelet packet decomposition, and calibrating data labels for the six types of samples; and fault data samples of the six types of calibration data labels are sent to a ResNet34 neural network for training, and the data samples are input into a trained ResNet34 neural network model for fault classification and identification. According to the invention, the fault identification accuracy and generalization capability are improved.

The invention belongs to the technical field of signal processing, and particularly relates to a wavelet basis function automatic selection method based on multi-index comprehensive evaluation and application thereof, and the method comprises the steps: 1, inputting a time sequence signal, and carrying out the data preprocessing and standardization; step 2, initializing a wavelet basis function candidate library; step 3, constructing a WNN multi-index parallel verification network; step 4, wavelet decomposition and signal reconstruction are carried out on a plurality of primary functions in the candidate library, and multi-index parallel performance evaluation is carried out through a WNN multi-index parallel verification network; 5, performing normalization and weight configuration on multiple indexes to calculate a comprehensive evaluation score; and step 6, selecting an optimal wavelet basis function based on the comprehensive evaluation score, and applying the optimal wavelet basis function to the data processed in the step S1 to perform final decomposition and reconstruction of the signal. According to the method, the aspects of selection precision, robustness, comprehensive performance and the like are obviously improved, and meanwhile, the calculation efficiency is obviously optimized.

The application discloses a curtain wall system connecting node stress state recognition method based on deep learning, and the method comprises the following steps: collecting original stress time series data and synchronous environment temperature data of a curtain wall connecting node to form a sample set; based on dynamic time clustering, the original stress time series data of each sample is segmented and divided, and an adaptive feature mapping function combining segmented information and a wavelet base function is used for feature mapping, and then dimension reduction is performed through principal component analysis to obtain a dimension reduction feature vector; a deep learning recognition network is constructed, and a double supervision loss function containing a weighted time series focal loss and an attention consistency regularization term is used to train the deep learning recognition network; and the obtained enhanced feature sequence and external physical features are input into the trained deep learning recognition network to output a stress state category of a node to be recognized. The application realizes automatic and engineering deployable transformation from original multi-source monitoring data to a clear state grade.

The present application belongs to the technical field of rock mechanics and geological engineering, and aims to solve the problems of insufficient simulation accuracy, low preparation efficiency and high cost of natural joint topography. A preparation method of rock standard parts for natural joint test is provided, which comprises the following steps: collecting rock blocks to be tested, cutting to obtain rock test blocks for standby; constructing point cloud data based on wavelet basis function and random phase; generating standard grid points in the point cloud data, calculating the height value corresponding to each grid point, and obtaining a grid file; testing whether the simulated natural rough joint surface has the statistical characteristics of natural joint fluctuation topography based on normal distribution test; importing the grid file into a rock carving machine to generate a carving path, performing rough carving and fine carving, and obtaining a rock standard part for test. The present application can efficiently and accurately simulate the multi-scale roughness and anisotropy of natural joints, and provide high-quality standardized test pieces for rock mechanics tests.

This invention discloses a method, device, electronic equipment, and storage medium for leak detection of a blood pressure simulator, comprising: inflating the target instrument to a preset pressure, acquiring the pressure signal of the target instrument under static conditions at preset time intervals to obtain a pressure decay signal sequence; mapping the pressure signal in the pressure decay signal sequence to quantum states through quantum phase encoding to obtain a pressure signal quantum sequence; performing multi-scale decomposition of the pressure signal quantum sequence using hybrid wavelet basis functions and calculating the decomposition coefficients corresponding to each decomposition scale; calculating the quantum Shannon entropy corresponding to each decomposition scale based on the decomposition coefficients; solving for the optimal decomposition scale using a quantum annealing algorithm based on the quantum Shannon entropy corresponding to all decomposition scales; calculating the leakage rate based on the pressure decay rate and the entropy change rate of the quantum Shannon entropy corresponding to the optimal decomposition scale; and obtaining the leakage result based on the leakage rate. This method can improve the sensitivity and accuracy of leak detection.

The invention provides an easily-confused emotion feature extraction method based on adaptive wavelet transform, and aims to improve the recognition performance of easily-confused emotions. According to the method, learnable parameters beta, gamma and a are introduced into a wavelet basis function, the time-frequency form of the wavelet basis function is dynamically adjusted, multi-scale time-frequency decomposition is carried out on input voice signals, two-dimensional tensor feature representation is obtained, and extracted features reserve time local changes of the voice signals and differences of different frequency bands in emotion distinguishing. Meanwhile, a projection gradient descent method is adopted to carry out constraint optimization on parameters, the stability and energy convergence of a primary function are ensured, and a gradient return updating mechanism is utilized to enable wavelet parameters to be adaptively optimized in the training process. According to the method, emotional characteristics with higher discrimination and robustness can be extracted, and the accuracy and generalization ability of emotion recognition are improved.

A method for aircraft decision recognition based on multi-wavelet dynamic modeling and spiking neural networks is proposed. This method acquires multi-source sensor data, flight trajectory data, and corresponding decision command data. Preprocessing is performed to improve the consistency of the multi-source sensor data. A multi-source time-varying dynamic model for aircraft trajectory prediction is constructed using time-varying autoregressive theory of exogenous inputs. Multi-wavelet basis function expansion is performed on the parameters of the multi-source time-varying dynamic model to obtain the multi-wavelet time-varying dynamic model. A sparse multi-wavelet time-varying optimized model is obtained. A model parameter estimation and identification method is designed, and the model is reconstructed based on the identified parameters. Time-domain and frequency-domain information is extracted from the identified model parameters and used as input to the subsequent intelligent decision model. A control command generation and intelligent decision model is constructed, with key flight state information as input and decision command data as output. After training, the control command generation and intelligent decision model is obtained.