191 results about "Soft thresholding" patented technology

The invention discloses a pediatric abnormal breathing detection method and system based on a physiological signal, and relates to the technical field of medical information processing, and the method comprises the steps: collecting a multi-modal physiological signal, employing wavelet decomposition and a soft threshold function to eliminate noise, and outputting a preprocessing signal group in combination with morphological opening and closing operation; calculating a time domain feature, a frequency domain feature and a nonlinear feature of the preprocessed signal group, organizing the features into a three-layer pyramid structure, analyzing time sequence similarity by adopting an attention mechanism, and outputting a fusion feature sequence; and carrying out preliminary anomaly detection on the fused feature sequence, carrying out classification based on an age grouping threshold, extracting context feature vectors of windows before and after an abnormal point, and carrying out secondary discrimination to realize accurate identification and graded early warning of the respiratory anomaly. The respiration monitoring system with high reliability, high adaptability and intelligent learning ability is constructed, and comprehensive technical support is provided for early recognition, real-time early warning and intelligent diagnosis of children respiratory system diseases.

The invention discloses a short-term load prediction method and system based on improved GWO-VMD-LSTM, and the method comprises the steps: carrying out the parameter optimization of a mode decomposition number and a penalty factor which need to be determined before VMD feature extraction, carrying out the variational mode decomposition of a load feature time sequence signal, decomposing the signal into a series of intrinsic mode function components, and carrying out the VMD-LSTM. An improved wavelet packet soft threshold function is used, a lugish-greed mechanism is provided to improve the function, denoising processing is performed on IMF component signals, feature coefficients and modal kurtosis values of all components are extracted, and the denoising effect is evaluated; a network structure of a long short-term neural network is improved, a parameter adaptive adjustment optimization algorithm is provided, the learning rate of the LSTM is dynamically optimized, and data is input into the network for training and prediction. The accuracy of short-term load prediction and the precision of classification can be improved, and short-term accurate prediction of the power load is facilitated.

The invention relates to a remote sensing image change detection system and method based on multi-modal deep learning. According to the system, the detection precision is improved by constructing a twin network fusing a CNN, a Transform and an attention mechanism; a lightweight backbone network is constructed by adopting multi-size depth separable convolution, and noise is adaptively suppressed and feature expression is enhanced in combination with a soft thresholding channel space attention mechanism; harr wavelet transform downsampling is innovatively introduced to reserve high-frequency details, and traditional pooling operation is replaced to reduce information loss; the twinborn branch semantic deviation is relieved through cross-channel feature exchange, and a Transform codec is used for modeling a long-range dependency relationship; in the decoding stage, edge feature recovery is enhanced by adopting feature splicing and a progressive up-sampling strategy; according to the system, model parameters are reduced, meanwhile, the precision and the anti-interference capability of remote sensing building change detection are remarkably improved, and the system has the advantages of high efficiency and detail keeping.

The invention discloses a rotary machine fault diagnosis method based on residual contraction convolution and an attention mechanism, and belongs to the field of rotary machine fault diagnosis. According to the method, firstly, a soft threshold filtering activation function is designed based on Meta-ACON, a soft threshold filtering module is developed around the activation function, and the module can adaptively learn a filtering threshold and a forward dynamic weighting coefficient, so that irrelevant features are effectively filtered, and the expression ability of effective features is enhanced. And then, in combination with multi-channel and multi-scale residual convolution, a multi-channel residual shrinkage convolution unit is constructed to realize multi-scale and multi-channel effective feature extraction. Besides, according to the method, a high-order statistical modeling idea is utilized, a GSoP attention mechanism is introduced behind a multi-channel residual shrinkage convolution unit, and the ability of extracting discriminative features is enhanced through second-order statistical information of a high-level channel feature map. Test results show that the method has good fault identification capability and generalization capability.

The invention discloses a laboratory detection data processing method and system based on a machine learning technology, and relates to the technical field of data processing. According to the method, the signal is decomposed through discrete wavelet transform, the noise standard deviation is calculated based on the median of the highest frequency coefficient, the high signal-to-noise ratio data is reconstructed through the dynamic threshold and the soft threshold function, and the signal quality is improved; a sliding window is used for extracting time sequence signal statistics and FFT frequency domain features, spatial features are extracted in combination with an SIFT algorithm, high-correlation features are reserved through mutual information screening, and redundancy is reduced; constructing a graph convolutional network anomaly detection model and an XGBoost-LightGBM weighted regression model, and eliminating pollution data through an anomaly probability threshold to obtain a normal regression predicted value; predicting a compensation amount according to the environmental parameters by using an LSTM network, and obtaining calibration laboratory data according to the normal regression predicted value and the predicted compensation amount; according to the method, the problems of noise sensitivity, feature splitting, model isolation and environment drifting of multi-source data are solved, and the laboratory analysis precision and robustness are remarkably improved.

A cooling capacity prediction method and system for heating, ventilation and air conditioning and a related device. The method comprises: by means of a denoising algorithm, decomposing an original signal into multiple intrinsic modes and a residual, the multiple intrinsic modes constituting an intrinsic mode function; using the Pearson correlation coefficient to classify the intrinsic modes into a first intrinsic mode dominated by an effective signal and a second intrinsic mode dominated by noise; decomposing the second intrinsic mode by means of wavelet soft thresholding denoising to obtain a third intrinsic mode; reconstructing the first intrinsic mode and the third intrinsic mode to finally obtain denoising data; taking the denoising data as an input of a preset neural network to finally obtain a cooling capacity prediction model, and using the cooling capacity prediction model to predict the cooling capacity of heating, ventilation and air conditioning to obtain a prediction result of the cooling capacity of the heating, ventilation and air conditioning. Compared with the prior art, the present invention solves the problems of nonlinearity, coupling, and long-term dependency in a cooling capacity prediction process, and increases the speed and accuracy of the cooling capacity prediction model.

The invention relates to a sleep respiration detection method, a sleep respiration detection system and a sleep respiration detection cloud platform for apnea syndrome, and the sleep respiration detection system for apnea syndrome is used for collecting a respiration signal caused by thoracic movement of a wearer, and processing and uploading the respiration signal through a microcontroller. According to the sleep respiration detection method, original signals are denoised based on wavelet transform and a soft threshold function, and an apnea event is judged through the combination of a dynamic amplitude threshold and a time threshold. The sleep respiration detection cloud platform adopts an SVM and LSTM-TCN fusion model to identify respiration abnormity, calculates risk scores in combination with blood oxygen decline, event duration and frequency, and triggers early warning in a grading manner. The system supports federated learning and model issuing, and realizes personalized recognition and cloud edge collaboration. The sleep apnea monitoring system realizes continuous monitoring and intelligent identification of sleep apnea, and is suitable for home health management and remote medical treatment.

The invention relates to the technical field of ultrasonic detection and noise reduction, and provides an external transmission hose defect ultrasonic detection method based on reciprocity and a hybrid neural network, and the method comprises the steps: carrying out the filtering processing of an initial signal, and obtaining a first signal; decomposing the first signal into an approximation coefficient and a first detail coefficient; performing soft threshold processing on the first detail coefficient to obtain a second detail coefficient, and reconstructing the second detail coefficient and the first approximation coefficient to obtain a second signal; performing normalization processing on the second signal to obtain a third signal; inputting the third signal into a convolutional neural network model to obtain a spatial feature vector; inputting the spatial feature vector into a recurrent neural network model, and extracting a time feature vector; fusing the spatial feature vector and the time feature vector to obtain a noise reduction signal; the convolutional neural network model and the recurrent neural network model are trained and then integrated with an ultrasonic detection system, defect characteristic signals are output, and the online detection precision and detection efficiency of the output hose are improved.

The invention relates to the field of image processing, and particularly discloses a rural highway pavement disease intelligent identification and positioning system comprising an image standardization module used for obtaining a standardized grayscale image; the wavelet decomposition module is used for obtaining a low-frequency sub-band and a plurality of high-frequency sub-bands; the high-frequency processing module is used for carrying out soft threshold processing on the high-frequency coefficient and retaining high-variance region features; the inverse transformation module is used for reconstructing the de-noised image; the edge enhancement module is used for highlighting crack and pit slot target contour information; the binarization module is used for segmenting a foreground disease candidate region; and the edge filling module is used for separating the disease from the background. According to the method, the core contradiction between background impurity removal and disease feature retention in rural highway tiny disease recognition is effectively solved, a traditional denoising algorithm either smooths tiny disease features or cannot thoroughly remove background impurities, and the system achieves the balance of the background impurity removal and the disease feature retention through cooperation of multiple modules.

The invention relates to the technical field of test and measurement, and discloses a series fault arc detection method, which comprises the following steps: collecting current data of fault arc waveforms and normal arc waveforms of a plurality of different loads; performing wavelet transformation on the preprocessed current data, dynamically extracting detail coefficients of a target layer number, calculating a standard deviation, a normalized energy ratio and an energy entropy based on the wavelet coefficients, splicing peak-to-peak values to form a four-dimensional feature vector, adding a normal or fault label, and converting the feature vector into a three-dimensional tensor; the residual shrinkage module is used for constructing a deep residual shrinkage network model based on a one-dimensional convolutional neural network and introducing an attention threshold generation and soft thresholding mechanism; training the model and monitoring the performance by adopting an early stop method; and inputting label-free sample data to the model, and outputting a detection result. The problems that in the prior art, deep features cannot be reflected, the training cost is high, and overfitting is prone to occurring are solved, and the purposes of improving the accuracy, being high in stability and efficient in detection are achieved.

The invention provides a feature extraction method for an electroacoustic background interference signal of operation power transformation equipment, which belongs to the technical field of power transformation equipment, and comprises the following steps: acquiring an electroacoustic signal, preprocessing the electroacoustic signal, and generating a controlled interference signal at the same time; and performing time-frequency analysis on the preprocessed electroacoustic signal and the controlled interference signal, and optimizing the interference signal parameter to enable the interference signal parameter to be highly similar to the electroacoustic signal. And constructing a wavelet basis function library based on the optimized controlled interference signal, and performing wavelet packet decomposition on the electroacoustic signal to obtain a plurality of frequency band sub-signals. A self-adaptive threshold model is established by using a controlled interference signal, and soft threshold denoising processing is performed on sub-signals. Time-frequency features of the denoised sub-signals and the controlled interference signals are extracted, a feature mapping relation is established, and an initial feature set is obtained; and performing nonlinear dimensionality reduction on the initial feature set by adopting principal component analysis to obtain a dimensionality-reduced feature set. And an improved support vector machine model is adopted to evaluate the importance of dimension reduction features, and an optimal feature set is selected as an electroacoustic background interference signal feature.

The invention provides a multi-light-source scattering water turbidity detection method, system, equipment and medium, and relates to the technical field of turbidity detection.The method comprises the steps that the influence of wavelength combinations on pollutant recognition performance is evaluated according to a preset fitness function, and an optimal wavelength combination is obtained; aiming at the optimal wavelength combination, adopting a reward function to learn a light source switching and irradiation strategy, and determining a light source control strategy; controlling a multi-wavelength light source to irradiate the water sample by adopting the light source control strategy to obtain scattered spectrum data; performing wavelet multi-scale decomposition and soft threshold filtering on the scattering spectrum data to obtain preprocessed scattering spectrum data; and establishing a quantitative relation model by adopting the preprocessed scattering spectrum data, and determining the pollutant concentration. Through wavelength combination optimization and intelligent learning of a light source control strategy, the accuracy and efficiency of water quality pollutant detection are remarkably improved, and an intelligent solution is provided for water quality monitoring.

The invention provides a bearing signal expansion method and system based on a chaotic particle swarm algorithm and a generative adversarial network, and the method comprises the following steps: firstly, carrying out the preprocessing of a collected bearing acceleration signal, and carrying out the denoising through employing the discrete wavelet transform (DWT) in combination with a Bayesian soft threshold method; secondly, determining a proper signal sample length according to a Nyquist sampling theorem, slicing the signal by adopting a sliding window strategy, inputting the sliced data into a chaos particle swarm algorithm for optimization, and searching an optimized vector which is closest to the structural similarity of the sliced data; and finally, inputting slice data of a real sample into a discriminator, and superposing the optimized feature vector with random disturbance to serve as initial input of a generator. In the training process, the parameters of the generator and the discriminator are mutually confronted and updated until a preset training round is reached. And the small sample problem and the class imbalance problem in bearing fault diagnosis are effectively relieved.

The invention relates to the technical field of marketing operation based on big data analysis, and discloses a marketing operation method based on big data analysis. Preprocessing the user-commodity interaction data to construct an initial interaction matrix; constructing a co-occurrence counting matrix; automatically calculating a sparse penalty coefficient and a convergence threshold; setting an optimization objective function and a soft threshold function; initializing a projection matrix and iteratively solving a sparse projection matrix through a coordinate descent algorithm; constructing a commodity sparse co-occurrence adjacency set; and generating a personalized recommendation result in combination with user historical behaviors. The analysis quality is improved through data preprocessing and denoising, commodity potential correlation is mined through co-occurrence modeling, adaptability is enhanced through automatic parameter adjustment, a projection matrix optimization result is sparse and interpretable, the structure sensing capacity of recommendation is improved through an adjacent structure, and finally high-precision and high-correlation recommendation is achieved. The structure definition, the calculation efficiency and the model generalization ability are excellent, and an efficient and stable personalized marketing recommendation system is constructed.

The invention relates to the technical field of signal processing, and discloses a flip chip vibration signal denoising method and system, and the method comprises the steps: obtaining a vibration signal responded by a to-be-detected flip chip under the action of external ultrasonic excitation, carrying out the segmentation processing of the vibration signal, constructing an initial sparse dictionary according to the segmented vibration signal, and iteratively updating the sparse dictionary; in the iteration updating process, sparseness parameters are adjusted in real time along with the number of iterations, and soft threshold denoising is conducted on residual terms; introducing a feature projection matrix to perform joint modeling on each section of vibration signal, and constructing a joint optimization model according to the updated sparse dictionary and the corresponding sparse coefficient; alternately optimizing the sparse coefficient and the feature projection matrix in the joint optimization model to obtain an optimal solution of the sparse coefficient; and combining the updated sparse dictionary and the optimal solution of the sparse coefficient to reconstruct the vibration signal to obtain a denoised vibration signal. According to the method, transient features can be effectively extracted, expression of key features is enhanced, noise is suppressed, and denoising robustness and reconstruction precision are improved.

The invention discloses a broadband signal parameter identification method based on wavelet noise reduction and an improved VMD-Prony algorithm, and the method comprises the following steps: carrying out the noise reduction preprocessing of a broadband signal based on a soft threshold wavelet noise reduction method, so as to reduce the interference of the noise to a measurement process, and building an evaluation index according to a Pearson's correlation coefficient and a signal-to-noise ratio for evaluating the noise reduction effect; parameters of a mutual information entropy and an energy entropy optimization VMD algorithm are introduced, the noise-reduced signal is decomposed by using the parameter optimization VMD algorithm, a plurality of modes with center frequencies are decomposed, and the complexity of broadband signal processing is reduced; performing parameter estimation on each mode by using a Prony algorithm, selecting a proper model order p according to the characteristics of the modes, performing numerical calculation by constructing a plurality of functions, obtaining frequency, amplitude and phase information of each mode, and realizing parameter identification of the broadband signal; according to the invention, the problems of noise interference and insufficient identification precision of the broadband oscillation signal in the parameter identification process are solved.

The invention discloses a multi-target angle tracking method in combination with an exponential weight and Kalman filtering. According to the method, an FAPI algorithm is adopted as an initial framework for improved design, firstly, a novel exponential weight factor self-adaptive soft threshold amplitude limiting method is superimposed on a signal subspace tracking cost function, and soft threshold amplitude limiting is carried out on abnormal measured values based on signal energy residual errors at adjacent moments; useful information of the signal is effectively and reliably reserved while alpha stable distribution noise is suppressed, so that a relatively steady signal subspace tracking result is obtained; secondly, a TLS-ESPRIT algorithm is combined to obtain a real-time estimation result of a multi-radiation source target angle; and finally, taking the real-time estimation result of the multi-radiation source target angle as a measurement value of Kalman filtering, and further smoothing the real-time estimation result by using the Kalman filtering characteristics to improve the accuracy of the angle estimation result in the scene of rapid angle change. And finally, accurate tracking of the maneuvering multi-radiation source target angle under the complex electromagnetic environment noise is realized.

The invention relates to the field of data anomaly detection, and particularly discloses a multi-stage anomaly detection system based on multivariable time sequence data, which comprises an input data acquisition module used for acquiring time sequence multivariable data; the MTS-Mixer spatio-temporal feature extraction module is used for extracting multi-level time sequence correlation features through a feature mixing technology and generating low-rank compression prediction features; the time sequence anomaly detection module is used for realizing anomaly positioning and classification by adopting alternate stacking of an Angle-Attention layer and a feed-forward network, expanding anomaly differences in combination with prior constraints and adversarial learning and fusing reconstruction errors and association differences to generate anomaly scores; and the attention mechanism soft thresholding module is used for suppressing noise, enhancing key signals and outputting high signal-to-noise ratio representation. According to the method, multiple technologies are integrated, and real-time anomaly detection and interpretable positioning of the high-dimensional time series data are realized through multi-stage collaborative optimization.

The invention discloses a low signal-to-noise ratio seismic detection model based on a deep residual shrinkage network, which introduces an attention mechanism on the basis of designing a soft threshold function according to different noises in seismic records collected by different stations or detectors, and constructs the deep residual shrinkage network. The method is used for detecting seismic events in low-signal-to-noise-ratio seismic records. In the designed network, a soft threshold function is inserted into a deep network structure as a nonlinear transformation layer so as to effectively filter the influence of noise-related features. Meanwhile, an attention mechanism is introduced into the network, a self-adaptive slope module is added, the optimal threshold value of seismic signal denoising is automatically obtained, and optimal processing of different signals and different degrees of noise is achieved. Through training, the deep residual shrinkage network can adaptively determine the de-noising threshold, so that each seismic signal has its own threshold set, the purpose of accurately detecting seismic events under strong background noise is achieved, and the accuracy of seismic event detection is improved.

The invention discloses an ocean electromagnetic signal denoising method based on wavelet transform, which comprises the following steps of: performing wavelet decomposition on a noisy ocean electromagnetic signal to obtain a wavelet coefficient of each layer; carrying out threshold calculation on the high-frequency wavelet coefficient and obtaining soft and hard threshold functions for processing the wavelet coefficient; and performing self-adaptive threshold processing on the high-frequency wavelet coefficient, namely quantifying the high-frequency wavelet coefficient by adopting a self-adaptive threshold function which dynamically adjusts the characteristics of a soft threshold and a hard threshold by adjusting a parameter k belonging to [0, 1], and when k is equal to 0, the self-adaptive threshold function is equivalent to the soft threshold function, when k is equal to 1, the self-adaptive threshold function is equivalent to the hard threshold function, and when k is equal to 0, the self-adaptive threshold function is equivalent to the hard threshold function. To eliminate discontinuity and constant deviation at the threshold; 4, performing inverse wavelet transform on the processed wavelet coefficient, and reconstructing to obtain a de-noised ocean electromagnetic signal; not only are the signal-to-noise ratio and definition of the signals significantly improved, but also a more reliable and accurate data basis is provided for real-time monitoring, accurate analysis and subsequent scientific research of the marine electromagnetic signals.

The invention relates to the technical field of medical data processing, in particular to a radiology department image data analysis system, which comprises a self-matching texture recognition module, an image data analysis module, a search window size and shape adjustment module, a window area structure fitting degree calculation module, a self-matching texture recognition module and a self-matching texture recognition module, wherein the self-matching texture recognition module collects image data, analyzes texture types in images, adjusts the search window size and shape and calculates the window area structure fitting degree according to texture features; and aggregating the data of the same-type regions. According to the method, more refined data analysis is realized by adjusting the size and shape of the search window and accurately calculating the structural fitting degree of the region according to the texture features, the objectivity of data analysis is optimized by gathering the data of the similar region and updating the weight of the fitting degree, the texture features and edges are effectively processed by applying an iterative soft threshold algorithm, and the accuracy of data analysis is improved. Compared with the prior art, the method has the advantages that data complexity is reduced, processing speed is increased, in addition, topological structure analysis strengthens intuitive understanding of a complex pathological structure, global mode features are analyzed through a graph network, diagnosis accuracy is enhanced, and data support is provided for disease prediction.

The invention discloses a hydroelectric generating set vibration signal noise reduction method based on SW-EEMD (Single Wave-Ensemble Empirical Mode Decomposition) and Hansel-SVD (Hanker-Singular Value Decomposition). The method comprises the following steps: processing an original noisy signal by adopting an SW-EEMD method; screening effective IMF components through correlation coefficients; respectively constructing Hankel matrixes for the screened IMF components and residual components; respectively carrying out singular value decomposition on the constructed Hankel matrix; carrying out soft threshold processing on the obtained singular value; the method aims at restraining the end effect of EEMD, noise in the vibration signals can be effectively filtered out, signal features are enhanced, more real and effective signal components can be obtained easily, and more reliable data are provided for state operation and maintenance of the hydroelectric generating set.

The invention discloses a signal processing method and device for a wind measurement laser radar, and the method comprises the steps: carrying out the windowing interception of an original laser echo signal, and obtaining an intercepted signal; performing dual-tree complex wavelet decomposition transformation on the intercepted signal to obtain each level of approximation wavelet and each level of detail wavelet; performing correlation operation and soft threshold filtering on each level of detail wavelet to obtain a corrected detail wavelet; combining the highest-level approximation wavelet with the corrected detail wavelet to obtain a preliminary noise reduction signal; performing Bayesian maximum posteriori estimation filtering on the preliminary noise reduction signal to obtain a final noise reduction signal; performing dual-tree complex wavelet inverse transformation on the final noise reduction signal to obtain a noise reduction time domain signal; performing fast Fourier transform on the denoised time domain signal to obtain a frequency domain signal; and performing frequency spectrum correction on the frequency domain signal, and then extracting to obtain Doppler frequency. According to the invention, noise filtering of the signals of the wind measurement laser radar is realized, and the Doppler frequency extraction accuracy is improved.

The invention relates to the technical field of image recognition, and discloses a target detection method and device and electronic equipment. The method comprises the steps of obtaining a to-be-detected image; performing wavelet decomposition on the to-be-detected image to obtain a high-frequency component and a low-frequency component; performing soft threshold processing on the high-frequency component and performing enhancement processing on the low-frequency component to obtain to-be-identified data; and inputting the to-be-identified data into a pre-trained target detection model to obtain a detection result. According to the method and the device, the adaptability to environment change is higher, the robustness is better, and thus the method and the device have higher recognition accuracy and reliability.

The invention discloses a blind separation and time-frequency noise reduction combined comprehensive pulsar time establishing method. The method comprises the following steps: selecting a pulsar meeting a preset requirement; processing the timing residual error of the selected pulsar; establishing a mixed signal model, iteratively solving an anti-mixing matrix through an approximate negentropy contrast function, and separating independent components from the multi-dimensional features; carrying out Pearson correlation analysis on each separated independent component and reference time; and performing multi-scale decomposition on the selected common-mode clock difference signal by adopting a plurality of wavelet bases, and performing Bayesian soft threshold processing to obtain the final comprehensive pulsar time. According to the method, the long-term stability of the comprehensive pulsar is improved, the common problems of aliasing and modal mixing in a high-noise environment in the prior art are solved, and the extracted signal is more stable.

The invention provides a rolling bearing fault signal noise reduction and feature extraction method based on adaptive semi-soft threshold set noise reconstruction empirical mode decomposition (ASTENEEMD) and (Sort-time Fourier Transform, STFT), and belongs to the field of intelligent diagnosis of rolling bearing faults. The rolling bearing fault signal noise reduction and feature extraction method based on the adaptive semi-soft threshold set noise reconstruction empirical mode decomposition (ASTENEEMD) and the Sort-time Fourier Transform (STFT) is characterized in that the rolling bearing fault signal noise reduction and feature extraction method based on the adaptive semi-soft threshold set noise reconstruction empirical mode decomposition (Sort-time Fourier Transform (STFT) is provided. According to the method, firstly, noise reduction of an original vibration signal of the rolling bearing is achieved through self-adaptive semi-soft threshold set noise reconstruction empirical mode decomposition, then, the noise reduction signal is converted into a time-frequency image through short-time Fourier transform, and time-frequency feature extraction of a non-linear and non-stable vibration signal is achieved. According to the method, the rolling bearing fault signal quality is enhanced, the noise reduction effect can be remarkably improved, the fault features in the signal are clearer, rich time-frequency features can be extracted, and reliable input is provided for subsequent fault diagnosis by using a deep learning model.

The present invention provides a method and device for real-time monitoring of the working state of a gearbox, relating to the technical field of gearbox testing. In the present invention, all characteristic information in time domain and frequency domain and other scale spaces is integrated through wavelet transform. Then, during the training process of the artificial intelligence model, methods such as attention mechanism and soft thresholding are adopted to screen the characteristics of the time domain information and frequency domain information, realize noise reduction, obtain key characteristics related to the fault type, and train an artificial intelligence model based on the key characteristics and fault type. By using the thus obtained artificial intelligence model to conduct real-time monitoring on the gearbox, the noise in the vibration signal can be effectively reduced, the influence of the noise on the gearbox fault prediction can be weakened, and the accuracy and reliability of the shape-integrated gearbox fault diagnosis can be improved.

The invention provides an ultrasonic thickness measuring method and equipment. The method comprises the following steps: transmitting an ultrasonic signal to a test piece by using ultrasonic thickness measurement equipment to obtain an echo signal, carrying out soft threshold-based wavelet filtering processing and Wiener deconvolution filtering processing on the echo signal, then calculating an autoregression coefficient based on a high signal-to-noise ratio signal, reconstructing an original signal through an autoregression spectrum extrapolation model, and finally obtaining the thickness of the test piece. And finally, calculating the thickness of the measured piece according to the time difference of two signals in the reconstructed signals. According to the signal processing method provided by the invention, the thickness of the thin film can be clearly and accurately measured by using the transducer with relatively low frequency, the cost of an ultrasonic thickness measurement system is effectively reduced, and the requirement for measuring the thickness of a thin film material in an industrial environment is met.

The invention discloses an acoustic signal partial discharge detection system of a distribution network transformer, which is characterized by comprising a multi-sensor acquisition unit, a synchronous clock control unit, a signal preprocessing unit, an edge calculation processing unit and a communication and storage unit, the edge calculation processing unit integrates a cross-band denoising module, a multi-scale feature extraction module, a lightweight neural network diagnosis module and a diagnosis result fusion module, wherein the cross-band denoising module is used for performing joint denoising on audible sound and ultrasonic frequency band signals. According to the invention, a set of partial discharge detection scheme for a distribution network transformer scene is established, and the scheme flow comprises denoising, feature extraction, neural network training and diagnosis. According to the scheme, audible audio frequency band and ultrasonic frequency band data are covered, and interference generated by mechanical vibration of the transformer in the ultrasonic frequency band is removed through a soft threshold value of wavelet transformation and a pulse detection denoising algorithm.

The invention discloses a buoy-based water body apparent spectrum real-time acquisition method, device and equipment, a medium and a product, and relates to the field of marine environment intelligent monitoring, and the method comprises the following steps: preprocessing original marine spectrum data to generate high-quality marine spectrum data with time-space alignment; reconstructing a three-channel spectral data matrix through multi-scale discrete wavelet decomposition and soft threshold quantization compression, inputting the three-channel spectral data matrix into a dynamic weight model, and identifying a spectral abnormal mode; obtaining an abnormal feature mark set, calculating spectral shape parameters, and generating a shape parameter sequence; and fusing the abnormal feature mark set and the shape parameter sequence to generate a spectral morphological feature curve, integrating the three-channel spectral data matrix, the abnormal mode and the shape parameter sequence, and outputting a real-time water body apparent spectrum acquisition report. Through deep fusion of the dynamic weight model and the multi-dimensional morphological features, the accuracy and reliability of spectrum anomaly detection in a complex marine environment are remarkably improved.