165 results about "Continuous wavelet transform" patented technology

The invention relates to the technical field of intelligent manufacturing equipment fault diagnosis, and particularly discloses a Meta-Transform driven multi-working-condition equipment bearing fault diagnosis method and system. The method aims at bearing fatigue damage risks caused by dynamic adjustment of technological parameters of a numerical control machine tool in the aerospace manufacturing process and challenges such as feature distribution offset and fault sample scarcity caused by variable working conditions. The diagnosis system is constructed through three core modules. The method comprises the following steps: firstly, reconstructing an original bearing signal into a multi-scale time-frequency feature space by adopting continuous wavelet transform; then designing a causal Transform architecture with a strict lower triangle attention mask, and realizing feature extraction and classification according to a physical causal law of fault propagation; and finally, integrating the mechanisms into a model-independent element learning framework, and realizing cross-working-condition rapid self-adaption through a self-adaption gradient pruning strategy. The bearing fault diagnosis accuracy under the condition of few samples is improved, the interpretability and generalization ability of the model are enhanced, and the industrial application practicability of bearing fault diagnosis is improved.

The invention relates to the technical field of power equipment state monitoring, and particularly discloses a power cable electrical performance detection method and system, and the method comprises the steps: synchronously collecting a broadband electromagnetic signal, a mechanical vibration signal and a temperature signal at a cable monitoring point; calculating a wavelet coherence coefficient between the signals through continuous wavelet transform, and constructing a multi-modal coupling tensor fusing amplitude and cross-modal time-frequency correlation characteristics; performing time slicing and high-order singular value decomposition on the tensor to obtain a time-varying core tensor sequence, mapping the time-varying core tensor sequence into a high-dimensional manifold curve, and generating a system state fingerprint by calculating local curvature distribution and topology invariants of the curve; inputting the fingerprints into a pre-trained defect prediction model, and directly outputting defect inoculation probability and evolution stage judgment; according to the method, the limitation that early weak defect detection is not sensitive in a traditional method is broken through, and early warning and accurate diagnosis of cable insulation latent defects are achieved.

The invention relates to a flying dust monitoring data processing and classifying method based on multi-source sensing fusion, and the method specifically comprises the following steps: firstly, deploying multi-source flying dust monitoring sensor nodes in a target region to collect data, carrying out the marking, and generating a data set; performing continuous wavelet transform on the acquired data, extracting a wavelet energy spectrum and a Shannon entropy, and splicing to obtain an enhanced feature tensor; secondly, through a two-stage fusion and coding strategy, frequency band energy features are extracted through wavelet packet decomposition, multi-channel cross-correlation, statistical moment and ratio features are calculated to form time sequence mode coding features, and multi-source heterogeneous feature fusion is achieved in combination with a local time sequence feature matrix; then constructing a deep learning model containing a multi-scale time sequence feature extraction and dynamic fusion module, and inputting a fusion feature matrix for training; and finally, inputting the preprocessed new monitoring data into the trained model, and outputting a dust source and pollution level classification result. The dust monitoring data classification accuracy and the dust source identification precision can be effectively improved.

The invention discloses an intelligent pipe network leakage active prediction and early warning system based on multi-technology fusion, and the system comprises a multi-source heterogeneous data fusion collection module, a spatial-temporal feature depth extraction module, a degradation trend prediction and residual life evaluation module, and a multi-stage early warning and decision generation module. The multi-source heterogeneous data fusion acquisition module acquires and fuses ultrasonic guided wave signals, pressure flow time sequence data and environmental factor data; the spatial-temporal feature depth extraction module extracts spatial-temporal fusion features through continuous wavelet transform and a CNN-LSTM hybrid network; the degradation trend prediction and residual life evaluation module determines a degradation level, predicts residual life and quantifies a pipe explosion risk probability; the multi-stage early warning and decision generation module generates graded early warning signals and maintenance strategy suggestions, the technology crossing from post-event detection to pre-event prediction is realized, and the scientificity and refinement level of operation and maintenance management of a pipe network are effectively improved.

The invention discloses a frozen soil temperature feature recognition algorithm and a moisture and electric field state prediction model, and the method comprises the steps: synchronously collecting temperature, moisture, resistivity and meteorological data through arranging a multi-depth sensor array, and constructing a multi-source data set with time-space alignment; utilizing continuous wavelet transform and cross-modal coherence analysis, and combining a hidden semi-Markov model to extract a depth-periodic characteristic profile; a time domain fusion Transform model embedded with physical constraints is adopted to realize frozen soil multi-parameter physical consistency prediction and uncertainty quantification; and finally, outputting a prediction result through a visual interface and setting an automatic alarm. The problem of cycle identification caused by weak and non-stable deep frozen soil signals is solved, the prediction accuracy and the physical rationality are remarkably improved, an end-to-end solution from data quality control to early warning is formed, and the reliability and the practicability of frozen soil engineering monitoring are greatly enhanced.

The invention relates to a mechanical fault diagnosis method and system based on deep learning. The method comprises the following steps: converting a multi-source time domain signal into a time frequency image through continuous wavelet transform, extracting features by using a primary feature encoder, and extracting cross-source common features through adversarial training of a shared feature discriminator; therefore, a gated multi-scale encoder is guided to enhance common feature expression, and deep fusion of multi-source features is realized through a cross multi-head attention network. Global average pooling and maximum pooling are synchronously carried out on the fused features to give consideration to overall and local information, and a comprehensive feature vector is formed; and finally, by means of a double-branch diagnosis network, the training loss of the multi-class network is dynamically weighted according to the prior probability output by the binary network, so that multi-source information is effectively fused under the condition of data imbalance, and the accuracy and robustness of fault classification are remarkably improved.

The invention discloses a PVC film coating uniformity detection method based on spectral analysis, and relates to the technical field of optical detection, and the method comprises the following steps: S001, adopting a controllable incident illumination system with a multi-angle rotation function to carry out continuous angle scanning on the surface of a PVC film, collecting reflection spectrum intensity data in a corresponding wave band range according to different incident angles, and generating a spectrum data matrix containing interference characteristics; and S002, carrying out continuous wavelet transform processing on the spectral data matrix, carrying out multi-scale local decomposition on a spectral response signal, extracting and separating a periodic interference characteristic signal, and obtaining a purified spectral matrix after interference reduction. According to the method, through multi-angle spectrum collection and interference feature stripping, accurate identification of a coating thickness abnormal area and combination of strength indexes, spraying parameters are regulated and controlled, a detection-compensation-reinspection closed-loop mechanism is constructed, interference misjudgment is effectively avoided, and the accuracy and regulation and control efficiency of uniformity detection of the PVC film coating are improved.

The invention belongs to the technical field of power system monitoring and fault diagnosis, and particularly relates to a power fault signal denoising method and equipment. The method comprises the following steps: firstly segmenting a noisy signal, and mapping each sampling point into a superposed quantum state representing two possibilities of a fault and noise; secondly, generating attention distribution by analyzing local uncertainty, mutation degree and spectral characteristics of the signal, and guiding intelligent collapse of the quantum state according to the attention distribution to obtain a preliminary identity mark; then, multi-scale singularity features are extracted through continuous wavelet transform, and by verifying the physical propagation law of a modulus maximum chain, the preliminary marks are corrected and refined, real fault points are strengthened, and noise pseudo features are removed; and finally, reconstructing and fusing signals based on the refined state. The method has the adaptive focusing capability of data driving and the reliability of physical verification, and can extract weak fault transient characteristics in a high-fidelity manner under a strong noise background.

The invention discloses a seismic wave velocity model inversion method based on time-space coupling deep learning, and the method achieves a time-space coupling mechanism through a Fourier neural operator framework of a wavelet transform-attention mechanism, and supports the reconstruction of a spatial two-dimensional velocity model from a time sequence track of multiple seismic sources. Firstly, the local time-frequency representation of each seismic time-domain trajectory is extracted by adopting continuous wavelet transform, and then a wavelet time-frequency spectrogram is coded through a convolution feature extractor. Then, information from multiple sources is fused at each receiver point location through a multi-head attention mechanism, enhancing correlation between multiple sources. And finally, projecting the fused features into a two-dimensional Fourier neural operator to efficiently learn space mapping and reconstruct a bottom-layer velocity model based on sound velocity. According to the method, the time sequence characteristic and the spatial distribution characteristic of the seismic channel are fully utilized, the precision and robustness of an inversion result are improved, and a more reliable speed model is provided for geological structure interpretation and acoustic imaging.

The invention relates to the field of fan spindle state monitoring, and aims at synchronously acquiring sound and vibration signals through multiple channels, realizing nanosecond time alignment by adopting a precision time protocol, and performing denoising and normalization preprocessing on the signals to improve data integration and signal fidelity. Furthermore, short-time Fourier transform and continuous wavelet transform are combined to extract multi-scale time-frequency features, and a high-dimensional combined feature vector is generated in combination with cross-correlation analysis. And mapping the feature vectors to a low-dimensional manifold space through a local linear embedding algorithm, and constructing a dynamic mode reference template. Indexes such as curvature, track length and direction entropy are monitored in real time, whether the spindle has an abnormal evolution trend or not is judged through a self-adaptive curvature threshold, and abnormity judgment is achieved in combination with track backtracking verification. According to the scheme, the abnormal starting boundary of the spindle state can be caught in a refined mode, and the early warning and operation and maintenance response capacity of fan operation is effectively improved.

The invention discloses an electric power aerial patrol operation data mining method, and belongs to the technical field of intelligent patrol. Coordinate values and temperature data of target equipment are synchronously collected through an infrared thermal imager, a visible light camera and a vibration sensor carried by power equipment, infrared coordinates and visible light coordinates are projected to the same geographic coordinate system through affine transformation, space registration is achieved, continuous wavelet transformation is conducted on collected vibration signals, and the temperature of the target equipment is obtained. The method comprises the following steps: extracting vibration feature vectors including global vibration intensity, wavelet energy spectrum and wavelet entropy, calculating the weight of each feature through image quality, carrying out weighted fusion to calculate a state index representing the health state of equipment, judging the state index according to a dynamic threshold value, judging that the equipment is abnormal if the state index exceeds the threshold value, and judging that the equipment is abnormal if the state index does not exceed the threshold value. And a feedback mechanism is triggered to collect data again to monitor the trend, otherwise, the data is stored for subsequent analysis. According to the invention, the precision and reliability of abnormal state identification in electric power aerial patrol operation and the robustness to a complex field environment are significantly improved.

The invention discloses a bearing fault diagnosis method based on wavelet time-frequency coding and a convolution visual converter. The method comprises the following steps: collecting vibration signals of a bearing under different working conditions; performing continuous wavelet transform on the one-dimensional vibration signal to generate a two-dimensional time-frequency diagram with high time-frequency resolution; carrying out coding and image enhancement on the time-frequency graph through an enhanced color mapping mode; inputting the time-frequency graph into a convolution visual converter model, and realizing local and global combined extraction of vibration features through convolution projection, a self-attention mechanism and a multi-stage feature fusion structure; the features are classified to identify a state of health or type of fault of the bearing. According to the method, the diagnosis precision and robustness in a complex working condition and high-noise environment are effectively improved, and the method is suitable for state monitoring and early fault early warning of various types of rotary mechanical equipment.

The invention belongs to the related technical field of fault diagnosis, and discloses a fault diagnosis sample generation method, application and equipment based on a physical constraint adaptive migration network, and the method comprises the steps: (1) converting original vibration signals of reference equipment and monitoring equipment into a time sequence spectrogram through continuous wavelet transform; (2) processing the time sequence spectrogram by adopting a spectrum feature encoder of the physical constraint adaptive migration network to extract multi-scale spectrum features; wherein a loss function of the physical constraint adaptive migration network comprises content loss, style loss and frequency band energy loss; (3) processing the multi-scale spectrum features by adopting the adaptive style normalization module so as to fuse the fault content features of the reference equipment and the machine style features of the monitoring equipment; and (4) processing the fused features by adopting a feature reconstruction decoder so as to reconstruct and generate a synthetic sample. According to the invention, the problem of insufficient data authenticity and physical consistency is solved.

The invention discloses a power transmission line conductor galloping early warning method and system based on unmanned aerial vehicle edge identification, and relates to the technical field of conductor galloping early warning, and the method comprises the steps: planning a flight route of an unmanned aerial vehicle, deploying an edge calculation device and a neural network model group, enabling the unmanned aerial vehicle to fly along the flight route to collect image data and environment data, extracting a pixel mask of the wire and identifying a wire icing phenomenon through the neural network model group; generating a pixel-level displacement time sequence based on the pixel mask of the lead, converting the pixel-level displacement time sequence into a vibration displacement sequence, and extracting multi-scale vibration features from the vibration displacement sequence by using short-time Fourier transform and continuous wavelet transform to obtain a two-dimensional dual-channel vibration feature map; and outputting a galloping confidence score through a deep network based on the two-dimensional dual-channel vibration feature map. According to the method, high-precision and low-delay identification of conductor galloping is realized through multi-scale feature fusion and edge intelligent identification, and the false alarm rate of early warning is effectively reduced by combining propagation situation analysis and multi-source criterion graded early warning.

The invention discloses a multi-label interference identification method and device based on dual-domain asymmetric feature fusion, and the method comprises the steps: enabling a received interference signal to generate two types of time-frequency images through short-time Fourier transform and continuous wavelet transform, converting the two types of time-frequency images into RGB images, and inputting the RGB images into a dual-branch AsymResNet18FPN network to extract multi-scale features; cross-domain feature adaptive fusion is realized through channel splicing and attention weight generated by MLP; and finally, outputting an interference type combination by the multi-label classifier. According to the method, the complementarity of double-domain features is fully utilized, the direction perception and multi-scale modeling capability is enhanced, the problem of feature submerging under the low interference-to-signal ratio is effectively relieved, synchronous recognition of multiple composite interferences is supported, and the recognition precision and robustness in a complex electromagnetic environment are remarkably improved.

The invention provides a gearbox composite fault diagnosis method and system, and the method comprises the steps: collecting original vibration signals of a plurality of measurement points of a gearbox, and carrying out the data preprocessing, and obtaining a standard vibration signal; performing continuous wavelet transform on the standard vibration signal to form a time-frequency image tensor; extracting time domain and frequency domain statistical features of the standard vibration signal to form a statistical feature vector; constructing a fault diagnosis model based on a CNN-TabM network, inputting the time-frequency image tensor and the statistical feature vector, and outputting a predicted fault category; and evaluating the importance of each feature in the fusion feature vector based on a perturbation method, determining a measurement point corresponding to the key feature according to a predefined feature measurement point mapping relation, and outputting fault type and position information, the method can fully utilize multi-measurement-point space information, effectively solves the problems of signal aliasing and low signal-to-noise ratio, and improves the accuracy of fault detection. The multi-modal features are accurately fused, the fault position is accurately positioned, the fault is accurately diagnosed, and the maintenance efficiency of the equipment is improved.

The invention discloses an encrypted malicious traffic detection method and system and a computer readable storage medium, and belongs to the technical field of traffic detection. The encrypted malicious traffic detection method comprises the following steps: acquiring original network traffic data, and constructing a packet length sequence and a packet arrival time interval sequence to form an original time sequence matrix; and extracting a time-frequency energy distribution diagram and a frequency band energy sequence from the packet length sequence through continuous wavelet transform. A time domain time sequence convolution network based on an expansion causal convolution structure is adopted, and a time domain feature vector is extracted from an original time sequence matrix. And extracting frequency domain feature vectors from the time-frequency energy distribution diagram and the frequency band energy sequence by adopting a time-frequency attention and frequency band re-scaling attention module. And performing malicious traffic dichotomy and malicious traffic behavior type multi-classification processing on the time-frequency aggregation feature vector through a hierarchical classifier, and outputting a classification result. According to the scheme, the malicious traffic characterization capability of deep fusion of time-frequency features is realized, and the detection precision of the encrypted malicious traffic is improved.

The invention discloses a sleep staging method based on a piezoelectric sensing signal and related equipment, and the method comprises the steps: obtaining the piezoelectric sensing signal of a target object in a sleep period, and segmenting the piezoelectric sensing signal in the sleep period into a plurality of piezoelectric sensing signals according to a preset time length; performing signal amplitude alignment processing on the piezoelectric sensing signal of each segment to obtain an amplitude alignment signal of each segment; performing continuous wavelet transform on the amplitude alignment signal of each segment to generate a time-frequency graph corresponding to each segment; fusing the time-frequency graph of each segment with the time-frequency graph of the adjacent segment to obtain a context time-frequency graph; inputting the context time-frequency graph into a preset sleep staging model to obtain an initial sleep staging result of each segment; and correcting the initial sleep staging result of each segment to obtain a final sleep staging result of each segment. The method can effectively improve the accuracy of sleep staging, and can be widely applied to the technical field of sleep monitoring.

The invention discloses a drainage pipeline abnormal working condition identification method and system based on a detector attitude time-frequency image. The method comprises the following steps: acquiring a triaxial attitude angle signal of a drainage pipeline detector in a running process in a pipeline; wherein the three-axis attitude angle signal comprises a roll angle, a pitch angle and a yaw angle; carrying out abnormal attitude section extraction on the three-axis attitude angle signal; for each abnormal attitude section, performing continuous wavelet transform on the roll angle, the pitch angle and the yaw angle to obtain three-axis time-frequency images corresponding to the three-axis attitude angles, and combining the three-axis time-frequency images to form a multi-channel attitude time-frequency image; inputting the multi-channel attitude time-frequency image into a trained deep learning classification model, and outputting a working condition category corresponding to each abnormal attitude section; and generating a drainage pipeline fault detection report based on the drainage pipeline working condition category. The method can achieve the automatic recognition of typical abnormal working conditions such as blockage, siltation, leakage, mixed connection and disconnection, and is higher in recognition precision and robustness.

This invention provides a method and system for detecting impurities in flunarizine hydrochloride raw material. The detection method includes: acquiring the original chromatographic signal, performing empirical mode decomposition for denoising and baseline optimization to obtain a baseline-corrected and denoised chromatogram; determining the signal-to-noise ratio based on the energy relationship between noise and signal in preprocessing, determining the optimal analytical scale through a preset function, determining the number of chromatographic peak components by screening ridges through continuous wavelet transform, and extracting initial model parameters; establishing a mathematical model for the convolution of a standard Gaussian function and a modified Logistic distribution function for each component peak, using a trust-region reflection algorithm for nonlinear iterative fitting, with the objective function containing a least-squares residual term and a weighted regularization term; after the objective function converges, obtaining the optimal model parameters, and calculating the integral area of ​​each impurity component peak to determine its content.

The invention relates to the field of seismology, signal processing and artificial intelligence, namely to automated methods and systems for early detection of earthquakes based on the analysis of seismic data, and can be used to recognize and classify longitudinal waves (P-waves) preceding the main seismic shocks, using deep learning and computer vision methods. The aim of the present invention is to create an automated early earthquake detection system using the classification of seismic signal spectrograms generated by the complex Morlet wave CWT using the YOLO deep neural network architecture. The technical result is an increase in the accuracy and speed of early earthquake detection by analyzing the time-frequency characteristics of seismic signals and automatically localizing P-wave signatures using a neural network model. The device includes a seismic sensor, an analog-to-digital converter, and a microprocessor implementing time-frequency analysis and neural network detection algorithms. The seismic signal is recorded in real time, digitized, segmented into time intervals, and subjected to preliminary digital processing, including noise filtering and amplitude normalization. Each time interval is converted into a time-frequency representation using a continuous wavelet transform, generating a two-dimensional distribution of signal energy over time and frequency. Based on the obtained data, a spectrogram is generated and fed to the YOLO neural network detection model, which is capable of automatically detecting and localizing longitudinal P-wave signatures. Based on the neural network analysis, a determination is made regarding the presence of a P-wave and its arrival time is determined. If a predetermined threshold is exceeded, an early warning signal is generated. The system provides for data accumulation and the possibility of subsequent retraining of the neural network model.

The invention discloses an electronic device and a method for processing a motion signal. The method comprises the following steps: detecting by a radar to obtain a dynamic signal; performing continuous wavelet transform on the dynamic signal to obtain a wavelet magnitude map; segmenting the wavelet magnitude map to generate a plurality of samples; grouping the plurality of samples into a first cluster and a second cluster; sampling a motion signal from the dynamic signal according to the first cluster; and outputting the motion signal.

This invention discloses a photovoltaic system islanding detection method and system based on Santlet transform and Ridglet probabilistic neural network. The method includes: collecting historical voltage data of the photovoltaic inverter, preprocessing it, and then sequentially acquiring the data to be analyzed using a sliding window; performing time-frequency analysis using Santlet continuous wavelet transform to obtain the corresponding time-frequency spectrum; extracting multi-dimensional time-frequency feature vectors and inputting them into a trained Ridglet probabilistic neural network classification model to obtain the corresponding probability values; if the probability value is greater than a probability threshold, it is determined that the photovoltaic system has experienced islanding, and a trip signal is generated and sent to the grid-connected circuit breaker to disconnect it. This invention significantly improves the accuracy, speed, and reliability of islanding detection, effectively reduces the detection blind zone, and has good adaptability to complex power grid environments.

This invention discloses a lightweight neural network identification method for high-speed dynamic impact signals. In the process of penetrating multi-layered hard targets, the presence of signal adhesion in the overload signals from multiple dynamic impacts makes identification exceptionally difficult. To address this problem, a lightweight network identification method based on an attention mechanism is proposed. First, time-frequency feature analysis is performed on the overload signal equivalent to that from multiple impact test benches, and continuous wavelet transform is used to extract the time-frequency features as input to the neural network. A lightweight network architecture based on an attention mechanism is designed, eliminating redundant layers and adding residual connection structures and a lightweight attention mechanism, thus ensuring recognition accuracy while significantly reducing parameters.

This invention discloses a machine learning-based method for simulating and enhancing the vibration reduction effect of vibration isolation trenches, comprising the following steps: Step 1: Acquiring the time-domain vibration signal, geological parameters, and structural parameters of the vibration isolation trench from the disturbance source; Step 2: Performing continuous wavelet transform on the time-domain vibration signal using an improved Symlet wavelet function with edge window modulation to generate an energy spectrum; Step 3: Constructing a spectrum graph structure; Step 4: Constructing a frequency domain transfer graph neural network model and outputting frequency node features; Step 5: Introducing a time-varying spectrum absorption mechanism and outputting updated frequency node features; Step 6: Constructing a joint graph structure; Step 7: Introducing a frequency band residual shielding mechanism into the joint graph structure to iteratively evolve the structural parameters of the vibration isolation trench and obtain the optimal design parameters for the vibration isolation trench. This invention integrates the improved Symlet wavelet function and the frequency domain transfer graph neural network model to accurately simulate the vibration isolation response and intelligently optimize structural parameters.

The invention relates to a VMD-CWT-Transformer-based sound and vibration fusion fault diagnosis method for industrial rotating machinery, and belongs to the technical field of industrial mechanical equipment anomaly detection. According to the method, vibration and sound signals are collected synchronously, a signal intrinsic mode function is extracted by using VMD (variational mode decomposition), multi-scale time-frequency features are obtained through CWT (continuous wavelet transform), the multi-scale time-frequency features are input into a Transform network for sound-vibration fusion and depth feature coding, and the multi-scale time-frequency features are obtained. And finally, outputting a fault category, a health index or a residual life prediction result through the switchable multi-layer perceptron. According to the method, end-to-end intelligent diagnosis is realized, the problems of dependence on artificial features, poor generalization ability, scarcity of fault samples and the like of a traditional method are solved, and the method is suitable for rotating machine state monitoring and predictive maintenance under complex working conditions.

The application discloses a kind of power transmission line fault diagnosis methods based on fault data and text label multimodal fusion.The application is based on the adaptive window signal interception method of wavelet transform, detects traveling wave head mutation point by continuous wavelet transform, and accurately captures high-frequency transient characteristics by dynamically adjusting the interception window according to signal attenuation rate, effectively suppresses noise interference;The normalized signal is input into the Transformer architecture model together with the associated text data, the signal visual features are extracted by Vision Transformer, the text semantic features are extracted by BERT, and the cross-modal attention mechanism is used to realize feature deep fusion, and finally the fault type is output.The application ensures that the multi-modal fusion model maintains high generalization performance in complex environments across regions and seasons, accurately diagnoses the fault type, and ultimately supports second-level fault early warning, minute-level accurate positioning and natural disaster diagnosis decision, significantly reduces manual intervention and improves power grid operation efficiency.

The invention provides a water pump intelligent monitoring control system and an application method thereof. In the method, a control system firstly extracts a mechanical noise component corresponding to a real-time rotating speed from initial baseline data by utilizing the relevance between the rotating speed and mechanical noise, and then constructs a baseline noise template to obtain mechanical noise interference during normal operation of a water pump. And the control system subtracts the baseline noise template from the original acoustic vibration signal to obtain a residual signal containing impurity impact information. And then the control system carries out continuous wavelet transform on the residual signal, and a generated time-frequency energy distribution diagram can identify the time node and frequency characteristics of the impact event so as to realize positioning of the impact event. And finally, the control system calculates the impact event rate and the average impact energy value in a preset statistical time window, and quantifies the progressive accumulation process of blockage. According to the method, the technical problem that early blockage cannot be identified by fixed threshold alarm is relieved, and the timeliness of fault prediction is improved.

The invention discloses a deuterium spectrum phase correction method, terminal equipment and a computer readable storage medium. The deuterium spectrum phase correction method comprises the following steps: collecting plural spectrum data and generating a spectrum signal based on the plural spectrum data; performing continuous wavelet transform on the wave spectrum signal to obtain a real part time-frequency graph and an imaginary part time-frequency graph, and combining the real part time-frequency graph and the imaginary part time-frequency graph into a dual-channel tensor as input of a depth vision model; outputting a phase correction parameter of the spectrum signal through the depth vision model, wherein the phase correction parameter comprises an initial zero-order phase value and a first-order phase value; and generating a zero-order phase value according to the initial zero-order phase value, performing phase rotation correction on the complex spectrum data based on the zero-order phase value and the first-order phase value, and outputting a corrected spectrogram. The problem that the correction result is unstable when phase correction is carried out on the deuterium spectrum is solved, and the stability of phase correction of the deuterium spectrum is improved.

The invention discloses a submarine observation network photoelectric composite cable fault point positioning system fused with noise suppression, and the system comprises sensing broadband fault signal modules which are disposed at the M end and the N end of a base station high-voltage source of a submarine observation network and two joints of a photoelectric composite cable respectively, and are used for capturing broadband fault signals generated by a fault point to be positioned in real time; the photoelectric composite cable line noise suppression module is used for carrying out specific suppression on the broadband fault signal through a two-stage series noise suppression strategy to obtain a clean broadband fault signal after noise reduction; the broadband fault signal feature detection module adopts Morse wavelets with excellent time-frequency aggregation to perform continuous wavelet transform, and performs feature extraction on clean broadband fault signals in combination with TT transform to obtain moments when the broadband fault signals reach an M end and an N end respectively; and the fault position output module calculates the fault distance according to the moments of reaching the M end and the N end in combination with the total length and the propagation speed of the photoelectric composite cable of the submarine observation network to realize fault point positioning.