374 results about "Continuous wavelet" patented technology

A method for locating a high-impedance ground fault of a smart distribution network with topology change adaptation includes: acquiring a fault traveling wave sample within a specified time window after a fault occurs, and performing continuous wavelet transform on the fault traveling wave sample to obtain traveling wave full waveform feature information; establishing a graph structure of a power distribution network, obtaining a corresponding adjacency matrix, and obtaining node position and structure encoding information in the graph structure through graph random walk and graph Laplace transform; concatenating the node position, the structure encoding information and the traveling wave full waveform feature information to obtain a node feature, and inputting the node feature and an edge feature into the graph structure to establish a graph sample data set; constructing and training a Graph Transformer model; and calling the trained Graph Transformer model to locate a fault in to-be-detected sample data.

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 the technical field of power equipment monitoring, in particular to a transformer fault diagnosis method, system and device based on multi-modal deep learning and a storage medium. Obtaining the volume fraction of gas dissolved in oil of the transformer, the local discharge capacity, the sleeve dielectric loss factor, the vibration data and the infrared image data; continuous wavelet transform processing based on integrated gradient is carried out on the vibration data, and key fault frequency bands are dynamically screened to generate a time-frequency map; inputting the numeric data into a stack-type denoising auto-encoder network to extract depth features; respectively inputting the infrared image and the radio frequency map into a double-branch convolution encoder for early feature fusion, and extracting map depth features through a stack type convolution auto-encoder network; and based on the Dempster-Shafer evidence theory, carrying out conflict resolution and evidence synthesis on the fault probability distribution output by the two types of modes, and outputting a diagnosis result.

The invention relates to the technical field of belt conveyor carrier roller fault monitoring, and discloses a belt conveyor carrier roller fault distributed optical fiber monitoring system which comprises a double-light-source DAS system, optical fiber information acquisition, filtering preprocessing, time domain analysis, time-frequency domain analysis, phase analysis, multi-mode feature fusion, fault classification and fault position and grade output. By building a dual-light-source polarization diversity DAS system and combining an adaptive filtering preprocessing technology, high signal-to-noise ratio acquisition and noise reduction processing of vibration signals are realized, dual-light-source orthogonal polarization state transmission effectively suppresses polarization fading, wavelet packet denoising and variable-step LMS filtering collaboratively filter environmental noise and pulse interference, and the noise reduction performance of the vibration signals is improved. Meanwhile, the application of the dynamic weighted root-mean-square value and the continuous wavelet transform ensures the accurate capture of the time domain energy characteristic and the frequency domain time-varying characteristic, and lays a reliable foundation for the multi-modal characteristic fusion.

The invention belongs to the technical field of bearing fault diagnosis, and discloses a CNN-MFKAN-based bearing fault diagnosis method and system, and the method comprises the steps: obtaining a bearing fault signal, dividing data through a sliding window, and generating a two-dimensional time-frequency image through continuous wavelet transform, and storing the two-dimensional time-frequency image; constructing a bearing fault diagnosis model in combination with CNN and MFKAN; dividing the data into a training set, a verification set and a test set; inputting the training set into a bearing fault diagnosis model for training; optimizing the model parameters and judging whether convergence occurs or not, if not, returning to the training set, and if yes, completing training and storing the optimal model parameters; and calling the optimal model parameter to carry out bearing fault judgment to obtain a fault classification result. According to the bearing fault diagnosis model, the MFKAN module is innovatively designed, the extraction capability of the model for different frequencies and different scale features is effectively enhanced, and the recognition precision and robustness of bearing faults under complex working conditions are remarkably improved.

The invention discloses a plunger pump fault diagnosis method and device, a medium and equipment. The method comprises the following steps: acquiring a pressure signal and an original vibration signal of the plunger pump; acquiring data in different states; obtaining frequency domain information based on continuous wavelet transform; performing multi-source data fusion; acquiring a global dependency relationship between the input and the output based on an encoder; and the fault of the plunger pump is diagnosed. By introducing a multi-source data fusion method, time domain and frequency domain signals collected by different types of sensors are comprehensively processed, so that information of different data sources is effectively integrated, and the accuracy and comprehensiveness of fault diagnosis are improved; important features in fault signals are automatically recognized and weighted through the Transform model, the extraction precision of fault features is improved, the model can effectively learn the fault features in a few-sample environment by optimizing a network structure and training parameters in the training process, and the diagnosis precision is remarkably improved.

The invention discloses a fault diagnosis method and device, computer equipment and a computer readable storage medium, relates to the technical field of power systems and automation thereof, and solves the problem of fault misjudgment caused by important fault characterization of a method which is easy to lose when single modal information is used for fault diagnosis at present. The method comprises the following steps: converting a bus voltage signal into a two-dimensional time-frequency diagram based on a continuous wavelet transform model, and converting the bus voltage signal into a one-dimensional frequency spectrum sequence based on a fast Fourier transform model; performing feature extraction on the two-dimensional time-frequency graph based on a time-frequency image feature extraction model to obtain a first feature vector, and performing feature extraction on the one-dimensional frequency spectrum sequence based on a frequency spectrum sequence feature extraction model to obtain a second feature vector; performing feature fusion on the first feature vector and the second feature vector based on a feature fusion model, and obtaining a category probability vector by using a fault diagnosis model based on the fused feature vector; and determining the highest probability value in the category probability vector, and taking the corresponding fault type as a target fault type.

The invention relates to the technical field of wireless communication, and provides a wireless interference source automatic identification and classification system and method based on deep learning, and the method comprises the steps: 1, receiving an original wireless interference signal, and carrying out the denoising and filtering processing; signal energy is converted into a power spectrum and a time-frequency diagram through fast Fourier transform (FFT) and continuous wavelet transform (CWT); step 2, constructing a time-domain graph branch and a frequency-domain graph branch through a convolutional neural network CNN and a self-attention mechanism Transform architecture, and capturing frequency-domain features and time-frequency features of the interference signal at the same time; 3, fusing the frequency domain and time-frequency domain features through a multi-scale feature fusion network MT in combination with a convolutional neural network and a self-attention mechanism; and step 4, adopting a Softmax activation function to carry out classification identification on the fused features, and outputting the category of the wireless interference source. According to the invention, the wireless interference source can be efficiently, accurately and automatically identified and classified.

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 a soil heavy metal inversion method and system integrating satellite remote sensing and near-end sensing, and the method comprises the steps: collecting a soil sample, and measuring the soil heavy metal content and a soil visible light-near infrared spectrum; obtaining a time sequence multispectral image of a research area, calculating a spectral index, and selecting and screening bare soil pixels through a threshold value to obtain a bare soil image; performing spectrum correction on the bare soil image; obtaining a joint dictionary and a sparse coefficient through sparse representation and dictionary learning, and reconstructing a hyperspectral image of the bare soil image; converting the one-dimensional spectral data into a two-dimensional spectrogram by using continuous wavelet transform, extracting spectral features in combination with a 2D-CNN algorithm, and constructing a soil heavy metal inversion model; and using the trained inversion model to predict the soil heavy metal content of the research area based on the reconstructed hyperspectral image. According to the method, satellite remote sensing and near-end sensing are integrated to obtain a large-scale accurate soil heavy metal content distribution map, deep features are extracted in combination with a 2D-CNN algorithm, and the inversion model precision and model efficiency are improved.

The invention discloses a partial discharge mode identification method and device, and relates to the field of power equipment fault diagnosis, and the method comprises the steps: collecting a partial discharge signal of high-voltage electrical equipment; preprocessing the collected data to obtain a single pulse signal; the method comprises the following steps of: generating a time-frequency graph of a single pulse on the basis of a continuous wavelet transform (CWT) method, and carrying out spatial feature extraction on the generated time-frequency graph by using a multi-scale convolutional neural network (MCNN); the features extracted by the MCNN are input into a bidirectional long short-term memory neural network (BiLSTM), and the time sequence characteristics of the partial discharge features are obtained; an attention mechanism is used to focus key segments of a splicing sequence output by the BiLSTM, global features are extracted, and the accuracy of pattern recognition is improved. According to the invention, mode identification can be carried out according to the collected partial discharge signal, the partial discharge type is judged, related personnel are helped to make a targeted maintenance plan, and long-term reliable operation of electrical equipment is guaranteed.

The invention provides a method for carrying out corn quality seed selection by using a hyperspectral imaging technology, and belongs to the technical field of corn quality seed selection. Corn kernels are subjected to spectrum scanning in a wavelength range of 400nm to 2500nm, spectrum data are preprocessed through a continuous wavelet transform algorithm, baseline drift and noise interference are removed, and a high-quality seed selection result is obtained. And accurately extracting spectral characteristic peaks of protein, starch, grease and moisture. And establishing a characteristic peak distribution matrix to record peak intensity distribution, and calculating a characteristic peak intensity weight coefficient and a position offset. And constructing a characteristic peak quality incidence matrix, establishing a numerical mapping relationship between the spectral characteristics and the quality parameters, and obtaining a peak width parameter and a spectral noise level. The spectral quality fusion function is adopted to process the multi-dimensional characteristic parameters, and the comprehensive quality evaluation index and the single quality evaluation index are calculated, so that the technical problem that the multi-dimensional quality characteristics of the corn kernels cannot be accurately identified and accurately graded is solved.

The invention relates to the technical field of charging infrastructure management, and particularly discloses a charging pile appointment scheduling and path planning cooperation system and a charging pile appointment scheduling and path planning cooperation method. According to the method, continuous wavelet transform and fast Fourier transform are adopted to extract a charging rate fluctuation characteristic value and a power grid power supply stability characteristic value for quantitatively evaluating the service stability of a charging pile and the power supply reliability of a regional power grid, and the system fuses the characteristic values into a comprehensive characteristic vector as the input of a gradient boosting tree model so as to obtain a gradient boosting tree model; and training the prediction model to output a charging planning score so as to judge whether the user can smoothly complete the charging task, and if a prediction result does not meet a set threshold value, dynamically adjusting a reservation scheduling strategy and re-planning an optimal charging path.

The invention discloses an electro-hydrogen system safety domain dynamic regulation and control method based on digital twinning-reinforcement learning, and belongs to the field of power grid safety operation, and the method comprises the following steps: S1, constructing a multi-energy flow coupling model comprising new energy power generation, an electrolytic cell and a hydrogen storage tank; s2, decomposing a new energy original output sequence by adopting a method of combining continuous wavelet transform and an autoregressive moving average model to form time-frequency characteristics; s3, state space modeling considering space-time coupling; s4, generating a dynamic security domain based on digital twinning; s5, constructing a multi-target reward function, and solving by taking the dynamic security domain as a constraint; and S6, performing closed-loop verification and model evolution. By adopting the electric hydrogen system safety domain dynamic regulation and control method based on digital twinborn-reinforcement learning, safety domain dynamic adaptation and intelligent control of the electric hydrogen system under new energy fluctuation are realized, and the operation safety and economy of the system are remarkably 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 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 relates to the field of fault diagnosis methods, in particular to a fault diagnosis method based on multi-modal deep learning, and the method comprises the specific steps: S1, carrying out the processing of an original vibration signal of a bearing through continuous wavelet transform, and converting the original vibration signal into a time-frequency image; s2, constructing a multi-scale Mamba network model, and capturing a long-term dependency relationship in the time sequence data; s3, constructing a high-efficiency network model for extracting time-frequency features; s4, introducing a cross attention mechanism, and dynamically splicing the two modal features; and S5, verifying by using an MEF-Net model, and comparing the performance of the MEF-Net model with other reference models, thereby solving the problems that the integrity, accuracy and reliability of data are influenced due to the introduction of noise in the data acquisition process at the present stage, and the accuracy, accuracy and reliability of the data are influenced due to the inaccuracy, gradient disappearance, overfitting and the like of the data. Therefore, the problems of poor model training effect and low accuracy are solved.

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 relates to the technical field of acoustic emission damage detection, in particular to an acoustic emission source positioning method based on lightweight convolution and an attention mechanism, according to the method, a four-channel continuous wavelet image (CWT) is used as input, a DSConv module carries out integral solution on a standard convolution into channel-by-channel convolution and point-by-point convolution, and the network parameter quantity and the calculation overhead are effectively reduced; the attention module adaptively adjusts feature weights through channel and space attention combination, highlights key time frequency features and inhibits redundant information; the four-branch features integrate multi-channel complementary information through adaptive weighted fusion, so that the signal characterization capability is improved; gradient propagation is improved through residual connection, the deep feature learning efficiency is enhanced, and therefore light-weight and high-precision sound emission source positioning is achieved.

The invention relates to the technical field of mechanical fault diagnosis, and provides a fault diagnosis and classification method for a bearing of an aluminum alloy impeller die-casting ladling machine, which comprises the following steps: acquiring a vibration signal of the bearing of the ladling machine, carrying out continuous wavelet transform on the vibration signal, generating a time-frequency diagram, and carrying out adaptive grid segmentation on the time-frequency diagram. Grid granularity is adjusted according to the local change rate of the time-frequency graph, multi-scale nodes are generated, edge connection is generated for the multi-scale nodes based on a K-nearest neighbor algorithm, and a multi-scale graph structure is constructed; performing unsupervised feature extraction on the multi-scale image structure, including: performing data enhancement on the multi-scale image structure through edge deletion and feature mask to generate an enhanced view; a graph attention network encoder is used for encoding the enhanced view, graph-level embedding is generated, and graph-level embedding is optimized by comparing a loss function; and based on the optimized graph-level embedding, performing fault classification by using a classifier constructed by a graph attention network and a multi-layer perceptron, and outputting a fault category.

The invention provides a picoampere-level precision data acquisition method and system for a large model special for the corrosion industry, and relates to the technical field of corrosion, and the method comprises the steps: collecting electrochemical impedance spectroscopy data and corrosion environment parameters, obtaining a time-frequency domain characteristic pattern through continuous wavelet transform, determining corrosion characteristic indexes of a target frequency interval based on a self-adaptive segmentation algorithm, and obtaining a picoampere-level precision model. And constructing a causal reasoning decision tree, and updating the correlation strength of the corrosion knowledge graph by using a Bayesian network. The micro corrosion signal can be accurately captured, the corrosion mechanism evolution path can be predicted, and the corrosion monitoring precision and prediction accuracy can be improved.

The invention provides a vibration event classification method and system based on distributed optical fiber sensing, and relates to the technical field of structural health monitoring, and the method comprises the steps: collecting a vibration signal, and carrying out the de-noising processing, and obtaining a de-noised vibration signal; statistical features are extracted; performing continuous wavelet transform and short-time Fourier transform on the denoised vibration signals, and performing fusion based on an entropy weight mechanism to obtain a preliminary fusion feature map; image features are extracted by using Grubrum angle difference field coding; convolution is carried out on the preliminary fusion feature map to extract local features, and the local features are fused with the Grubrum angle difference field image features to obtain fused image features; performing depth separable convolution on the fused image features, and extracting image depth features; introducing multi-scale expansion convolution based on the features to obtain sequence features; and finally, performing multi-modal fusion on the statistical features, the image deep features and the sequence features to obtain final fusion features, and outputting vibration event categories through a full connection layer and a Softmax classifier.

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 technical field of audio signal processing and biomedical signal analysis, and still has a further optimized space for the recognition of anti-noise requirements, signal individual differences and complex pathological modes in a noise environment. The invention provides a double-path CNN heart sound classification method based on time-frequency and double-spectrum fusion features, and the method comprises the steps: carrying out the preprocessing of an original heart sound signal of a data set which is classified into a normal heart sound and an abnormal heart sound, and obtaining a to-be-recognized heart sound signal; based on dynamic continuous wavelet transform, adaptively selecting parameters to extract time-frequency characteristics, introducing bispectrum analysis, capturing nonlinear characteristics, generating a dual-channel characteristic pattern, and efficiently storing the dual-channel characteristic pattern in an HDF5 format; and based on a designed double-path convolutional neural network structure, respectively processing the extracted time-frequency and double-spectrum features, performing classification after fusion, and training a model in combination with category weighted loss and an optimization strategy to obtain a heart sound classification result. The heart sound recognition accuracy can be improved.

The invention relates to an unbalanced bearing data fault diagnosis method based on an improved condition Wasserstein generative adversarial network. The problem of bearing data imbalance under a small sample condition is effectively relieved. According to the technical scheme, the method comprises the following steps: firstly, processing a bearing vibration signal by adopting a sliding window overlapping sampling strategy, and converting the bearing vibration signal into a time-frequency image by applying continuous wavelet transform so as to enhance fault feature representation; secondly, an improved condition Wasserstein generative adversarial network is used for carrying out data enhancement, the network measures sample distribution difference through a Wasserstein distance, introduces gradient penalty and L1 loss, combines spectrum normalization and a self-attention mechanism, stabilizes a training process and prevents gradient explosion, and meanwhile, generates a specific category of high-quality samples through condition information; and finally, carrying out accurate diagnosis by adopting a convolutional neural network equipped with a global attention mechanism. According to the method, the common collapse and instability problems of the generative adversarial network can be avoided, and the precise diagnosis of the rolling bearing can still be realized under the conditions of small samples and unbalanced data.

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 relates to the technical field of computers, and discloses a power battery SOH prediction method based on big data. The method comprises the following steps: acquiring voltage, current and temperature time sequence data in a battery charging process; intercepting a constant current charging segment and generating a two-dimensional time sequence spectrogram through continuous wavelet transform; constructing a multi-task attention deep convolutional network, performing decoupling quantization on three attenuation mechanisms of lithium ion precipitation, active substance loss and internal resistance increase, and outputting quantization vectors of the attenuation mechanisms; and finally, calculating an SOH value through a comprehensive evaluation model with physical significance. According to the method, the unification of end-to-end self-learning, strong noise immunity and high interpretability is realized, and the SOH prediction precision and practicability are improved.

The invention discloses an elastomer layer carbon fiber composite material damage positioning method based on elliptic probability fusion, which comprises the following steps: taking different sensors as excitation sources, and collecting Lamb wave signals of a plurality of sensing paths; performing continuous wavelet transform by using a complex Morlet wavelet, and extracting actually measured flight time; introducing a layered wave velocity correction model to optimize an elliptical orbit method, and calculating theoretical flight time and damage probability; introducing a dynamic short-axis optimization model to optimize a probability weighting method, and calculating a damage probability; generating prior distribution by using results calculated by an elliptical orbit method and a probability weighting method; constructing a likelihood function based on the difference between the actually measured flight time and the theoretical flight time; the posterior distribution of the damage position is obtained through the Bayesian theorem in combination with the prior distribution and the likelihood function; sampling the posterior distribution by using an adaptive MCMC algorithm, and generating a probability cloud picture of the damage position; and outputting a final imaging result of the damage position, and positioning the damage position.

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.