397 results about "Wavelet decomposition" patented technology

The invention discloses a single image super-resolution reconstruction method and system based on wavelet transform and cross-domain feature fusion. According to the method, firstly, a low-resolution RGB image is mapped to a high-dimensional feature space through a shallow feature extraction module; performing up-sampling and discrete wavelet decomposition on the features by using a wavelet feature mixing module to obtain multi-band features; low-frequency and high-frequency depth features are respectively extracted through a double-branch structure, cross-domain fusion is realized by means of a deformable cross attention mechanism, and the feature expression ability is enhanced in combination with residual connection; and finally, reconstructing a high-resolution image through convolution, up-sampling and regularization processing. In the training process, a pixel-level loss function is adopted to optimize network parameters, the multi-frequency-domain feature sensitivity is effectively improved, texture and structure information is balanced, the image contrast, definition and structural integrity are improved, and high-quality real-time super-resolution reconstruction can be achieved.

The invention discloses an assembly type building node stress monitoring and design feedback system based on BIM, and relates to the technical field of building engineering structure monitoring, a building information modeling model is constructed, component geometric information, a connection mode and a load path associated with nodes are extracted, and a node mechanical attribute initial parameter set is formed; in combination with node real-time stress monitoring data, Fourier transform and wavelet decomposition are carried out, and frequency domain characteristic parameters are extracted; constructing a finite element correction model, and simulating a stress response path under a multi-load combination; the predicted stress peak value is compared with the actually measured stress peak value, structural abnormal nodes are identified, parameter optimization is executed according to node construction information, the component size, the steel bar anchoring length or the concrete grade are automatically adjusted, an optimized parameter set is generated and written back into a building information modeling model, and closed-loop correction is formed; continuous monitoring, abnormity diagnosis and intelligent optimization of the node stress state can be achieved, and the safety and the intelligent level of assembly type building structure design are improved.

The invention discloses a gas pipeline operation risk dynamic evaluation system based on big data analysis, and relates to the technical field of gas pipeline safety monitoring, and the system comprises a multi-source data processing module, a feature extraction module, a local risk modeling module, a risk evolution prediction module and a dynamic early warning module. The multi-source data processing module is used for acquiring original multi-source time sequence data from a pressure sensor, a flow sensor, a temperature sensor, a geological monitoring device and a third-party construction disturbance data interface of a gas pipeline network, and performing time alignment, abnormity elimination, normalization and characteristic standardization processing on the data; a uniform input feature vector is formed; and the feature extraction module is used for decomposing the normalized time series data into a low-frequency trend component and a high-frequency residual signal by using discrete wavelet decomposition. The gas pipeline risk assessment system solves the problems of low gas pipeline risk assessment precision, insufficient risk propagation modeling and lack of dynamic early warning and emergency strategies in the prior art.

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 provides a structure health early warning method and system based on space-time correlation characteristics and digital twinning, and relates to the technical field of data processing. The method comprises the following steps: acquiring multi-source heterogeneous data of a target structure; performing dynamic sampling alignment and wavelet packet decomposition on the multi-source heterogeneous data to extract energy features to obtain synchronous data, and performing abnormal data filtering on the synchronous data to obtain cleaned fusion data; performing wavelet decomposition on a high-frequency vibration signal in the fused data to obtain a damage impact feature, performing time sequence processing on low-frequency temperature data in the fused data to obtain a temperature time feature, and performing dynamic graph convolutional network processing on strain data in the fused data to obtain a spatial correlation feature; constructing an input vector; calculating a damage degree index; and according to the damage degree indexes, early warning grades are divided, and corresponding control instructions are triggered for different early warning grades. By implementing the technical scheme provided by the invention, the accuracy of structural health early warning can be improved.

The invention relates to the technical field of ultrasonic signal processing, in particular to a wavelet and MAD adaptive threshold combined laser ultrasonic signal denoising method. The method comprises the following steps: firstly, preprocessing an acquired trigger channel signal and an ultrasonic channel signal, determining a signal starting point through differential positioning, and intercepting an effective signal segment; carrying out multilayer wavelet decomposition on the effective signal segment to obtain a wavelet coefficient of each layer; extracting a detail coefficient of the highest decomposition layer, and adaptively estimating a noise standard deviation based on a median absolute deviation criterion; calculating an adaptive threshold according to the noise standard deviation and the signal length, and processing each layer of wavelet coefficient by adopting a hard threshold function; and finally, carrying out wavelet inverse transformation reconstruction to obtain a denoised signal. According to the method, prior noise information is not needed, the noise level can be adaptively estimated, the optimal threshold value can be determined, the signal features are reserved while noise is effectively suppressed, and the signal-to-noise ratio is remarkably improved.

The invention discloses an ultra-low illumination-oriented SPAD active imaging data compression method, which comprises the following steps of: performing wavelet decomposition on histogram data of a single pixel in a time-frequency domain based on wavelet transform to obtain low-frequency data of each pixel after wavelet decomposition; by taking each pixel as a center, performing non-maximum suppression and data enhancement on the low-frequency data after the wavelet decomposition of the current pixel by using adjacent pixels to obtain processed compressed data; customizing different Gaussian kernel parameters for each pixel according to the possibility that each pixel is located at the boundary, and performing Gaussian filtering on the processed compressed data of each pixel to obtain filtered data; and performing depth estimation on the filtered data to obtain a final depth image. According to the method, the depth reconstruction performance of the laser pulse can be improved by utilizing the multi-resolution characteristic of wavelet transform, the space-time correlation of signal photons and the smoothness of Gaussian filtering.

The invention relates to the technical field of remote sensing image processing, and particularly discloses a low-light remote sensing image restoration method and system based on double-frequency domain processing, and the method comprises the steps: constructing an image restoration network which comprises a coding module, an intermediate enhancement module and a decoding module which are connected in sequence, the coding module and the decoding module are in jump connection; wherein the coding module, the intermediate enhancement module and the decoding module are each internally provided with a double-frequency-domain attention module, and each double-frequency-domain attention module comprises a Fourier attention sub-module used for global frequency domain feature modeling and a wavelet attention sub-module used for multi-scale detail feature extraction; by introducing the Fourier transform frequency domain processing technology, the global frequency characteristic analysis capability is provided, and efficient global modeling is realized. Secondly, introducing a wavelet decomposition frequency domain processing technology, decomposing the image into sub-bands with different scales and frequencies, and effectively separating a clear image and a degenerated component;

The invention provides a Rowland time delay signal prediction method and system, electronic equipment, a program product and a storage medium. The method comprises the following steps: acquiring an initial Rowland time delay signal; extracting a periodic term of the initial Rowland time delay signal, and inputting the periodic term to a constructed multi-periodic term and trend term model; calculating a residual signal between the observed value of the initial Rowland time delay signal and the output of the multicycle term and trend term model; wavelet decomposition and threshold denoising are carried out on the residual signals; fusing the output of the multi-cycle term and trend term model and the denoised residual signal through an adaptive weight mechanism to obtain a predicted Rowland time delay signal; the deterministic periodic term and the long-term trend drift of the Rowland time delay signal are captured through a multi-periodic term and trend term model, and non-stationary disturbance is processed by using wavelet denoising, so that the Rowland time delay prediction precision is remarkably improved, and the method is particularly suitable for a high-precision timing scene in a complex electromagnetic environment.

The invention relates to the technical field of power equipment monitoring, and discloses an abnormal discharge electric field feature extraction method. The method comprises the following steps: standardizing and normalizing data acquired from a multi-channel electric field sensor array of a transformer substation to generate a standardized data stream; performing wavelet decomposition, adaptive threshold noise reduction and reconstruction on the basis of the stream to obtain a denoised signal; time domain statistics and frequency domain energy spectrum analysis are carried out on the signals, features are fused, and a joint feature vector is constructed; training a convolutional neural network classifier based on the vector; and updating a dynamic threshold rule according to the classifier output probability, and finally realizing real-time early warning and model feedback through GPU parallel computing. According to the method, the signal-to-noise ratio and the feature expression capability of the abnormal discharge signal can be effectively improved, and the detection accuracy, the real-time performance and the self-adaptive capability of the system are enhanced.

The invention discloses a Raman distributed optical fiber temperature sensing signal denoising method and device, and relates to the technical field of optical fiber sensing, and the method comprises the steps: inputting a received original Raman signal into a trained wavelet guide dense residual network; carrying out channel feature enhancement and feature reuse enhancement based on wavelet decomposition on the original Raman signal in parallel by adopting an expansion convolutional dense network and a standard convolutional dense network, and correspondingly generating an expansion output feature and a standard output feature; after the expansion output features and the standard output features are spliced, the spliced features are input into a cascaded fusion convolution module and a channel attention module for feature processing, and original signal noise is determined; and subtracting the original signal noise from the original Raman signal to determine a de-noised Raman signal. Based on the scheme, the processing advantages of dense connection, residual learning and attention enhancement are fused under the guidance of the explicit frequency domain provided by wavelet decomposition, and the signal denoising reliability is improved.

The application relates to the technical field of target recognition, and specifically provides a small target recognition method for an unmanned aerial vehicle based on wavelet decomposition and a motion vector, which comprises the following steps: performing three-level wavelet decomposition on a small target image to obtain a third-layer low-frequency information region and a plurality of high-frequency detail information regions; performing image enhancement processing on the plurality of high-frequency detail information regions; performing image reconstruction on the plurality of high-frequency detail information regions after image enhancement and the third-layer low-frequency information region to obtain a reconstructed image; inputting the reconstructed image into a YOLOv5 improved model to recognize small targets in the reconstructed image; and performing false detection judgment on the recognized small targets. The application can effectively improve the recognition precision of small targets in a complex background by using wavelet transformation to enhance image detail information, and can further improve the precision of a target recognition algorithm by removing false detection caused by part of environmental noise through false detection judgment.

The invention relates to the technical field of sea wave height prediction, and discloses a multi-scale physical enhancement type irregular sea wave prediction method which comprises the following steps: (1) collecting historical data of sea waves, preprocessing the data, and dividing a training set, a verification set and a test set; (2) decomposing the wave data into a high-frequency component, a medium-frequency component and a low-frequency component through wavelet decomposition; (3) constructing a WaveFormer model; (4) the WaveFormer model is trained, and (5) the test set is sent to the WaveFormer model to be trained, and a wave height prediction value is obtained. According to the method, an original wave sequence is decomposed into a high-frequency component, an intermediate-frequency component and a low-frequency component by adopting two-layer stationary wavelet transformation, the model captures multi-scale characteristics of irregular waves explicitly, the reasonability of wave prediction is ensured, sequence-level prediction is realized on the premise of ensuring the prediction precision, the prediction efficiency is improved, and the prediction cost is reduced. And a data driving item is constructed based on a wave prediction result, so that the model prediction accuracy is further improved.

The invention belongs to the technical field of water quality parameter inversion, and particularly relates to a machine learning-based suspended matter concentration remote sensing inversion method and system, and the method comprises the steps: collecting a satellite remote sensing image and synchronous actual measurement suspended matter concentration data, and obtaining a space gridding water body remote sensing reflectivity matrix through radiometric calibration, atmospheric correction and water body mask. Reconstructing and denoising through wavelet decomposition, and normalizing and standardizing the spectrum to obtain a spectrum numerical sequence; multi-scale waveband combination and differential features are constructed based on the sequence, and sensitive features are screened out by using XGBoost. A physical constraint term is constructed in combination with sensitive characteristics and a water body radiation transmission rule, and an intermediate inversion result is obtained through numerical iteration. And performing deviation compensation on an intermediate result by using a deep learning residual error, and finally performing spatial smoothing, consistency verification and high-concentration saturation optimization to obtain a high-precision and spatially continuous suspended matter concentration spatial distribution result. According to the method, efficient feature mining and physical mechanism deep fusion are realized, and the explanatory and generalization ability of the model is greatly improved.

The application relates to a kind of multi-branch power distribution network double-end traveling wave fault location method and system, comprising: based on the deployment of multiple measuring points in the head and tail end and branch terminal of radial power distribution network, the line mode wave velocity is calculated by collecting topological information;Using the line mode and zero mode components extracted from the modulus decoupling of three-phase voltage data after fault;Select the strongest line mode component of global mutation as reference, generate wave head timing table through wavelet decomposition and mutation detection;Combined with topology, timing table and line mode wave velocity, corrected zero mode wave velocity, determine the fault interval and output fault location by using line mode and zero mode wave head time difference compensation correction.This application overcomes the positioning error caused by clock asynchronization and parameter disturbance through the wave head timing analysis and time difference compensation mechanism of multiple measuring points cooperation, realizes accurate ranging without the influence of clock error, significantly improves the ranging accuracy;At the same time, the point distribution requirement is simplified, and only branch single measuring point can realize accurate discrimination and positioning of fault interval, with high economy and deployment flexibility.

The invention relates to the technical field of Internet of Things security, in particular to a network attack early warning method based on an abnormal behavior map. Comprising; constructing a subject-behavior-resource three-layer heterogeneous graph, establishing a low-activity baseline model based on historical data, calculating weak association strength between nodes by adopting wavelet decomposition and a signal enhancement algorithm, and identifying a latent attack chain; the attack chain features are converted into multi-dimensional vectors, and an attack collaboration time window and intensity are predicted through a vector convergence angle and a distance change rate; the vulnerability of the graph topology is quantified, the edge weight is dynamically adjusted by using a gradient descent algorithm, and high-risk nodes are isolated; the convergence parameters are fed back to the baseline model and correlation strength calculation to form closed-loop optimization; and calculating a comprehensive threat index based on three-dimensional weighting of the latent threat intensity, the convergence urgency degree and the topology anti-attack capability, and realizing layered early warning. According to the method, the APT attack detection rate is remarkably improved, and the early warning response time is shortened.

The invention provides a building facility real-time monitoring and early warning method and system based on the Internet of Things, and relates to the technical field of building monitoring and early warning, and the method comprises the steps: obtaining real-time monitoring data through multiple sensors, and carrying out the sliding analysis and wavelet decomposition of the data, and obtaining a structural feature vector; calculating a spatial correlation weight by using a self-attention network, and clustering the spatial correlation weight; executing multi-scale decomposition to calculate an abnormal score; calculating a structure deviation degree by combining a variational encoder; and generating early warning information when the deviation degree of a plurality of continuous monitoring periods exceeds a threshold value. According to the invention, accurate positioning and early warning of building damage are realized, and the monitoring efficiency and safety are improved.

The invention discloses an ocean variable prediction method and system based on space-time coherence, and the method comprises the steps: carrying out the frequency spectrum transformation of an input tensor, obtaining a frequency spectrum transformation result, fusing the frequency spectrum transformation result with a Coriolis parameter, and obtaining an enhanced frequency domain feature; performing multi-scale wavelet decomposition on the enhanced frequency domain features to obtain multi-scale features; performing convolution processing on a depth variable in the input tensor to obtain a vertical mixing feature, and encoding different depth layers of the depth variable into depth embedding; fusing the vertical mixing feature, the depth embedding feature and the multi-scale feature to obtain a depth fusion feature; calculating a multi-scale evolution feature based on the constructed physical constraint item and the deep fusion feature; performing weighted fusion and fusion network processing on evolution characteristics of different scales in the multi-scale evolution characteristics to obtain space-time coherent characteristics; and predicting target ocean variables based on the space-time coherent features. The ocean variable prediction precision can be improved.

The invention provides a dynamic sequence recommendation mechanism for multi-scale wavelet transform of an intelligent maritime reconnaissance instrument, which belongs to the technical field of communication information services and comprises an embedded layer, a multi-scale wavelet decomposition layer, a wavelet neural network layer, a multi-view contrast learning module and a final recommendation layer. According to the method, the discrete wavelet transform is introduced to replace the traditional Fourier transform and discrete cosine transform, so that the dynamic interest change in the user behavior sequence is more accurately captured. According to the method, time and frequency localization analysis can be carried out on the signals at the same time, and the method is especially good at processing non-stable user behavior data containing mutation, so that long-term stable interests and short-term sudden interests of users are extracted and distinguished on different time scales. In addition, a wavelet neural network and an enhanced multi-view contrast learning mechanism are introduced, and the feature processing ability, generalization ability and recommendation precision of the model are further improved.

The invention relates to the technical field of image recognition, in particular to a sparse mask attention and wavelet transform-based power distribution equipment recognition method, which comprises the following steps of: synchronously acquiring a multi-view RGB image and a three-dimensional point cloud depth map of low-voltage power distribution equipment; displaying and separating multi-frequency features of the multi-view RGB image through a structured wavelet decomposition and reconstruction mechanism, and performing feature reconstruction through inverse wavelet transform to obtain an enhanced two-dimensional image; a ViT model is adopted to extract semantic features from the enhanced two-dimensional image, and the semantic features of the two-dimensional image are injected into the three-dimensional point cloud depth map to obtain a three-dimensional fusion feature map; and inputting the three-dimensional fusion features and the three-dimensional point cloud depth map into a pre-constructed three-dimensional instance segmentation model, and only executing attention calculation on the key points and local neighborhoods thereof by adopting a dynamic sparse mask attention mechanism to obtain a segmentation result of the low-voltage power distribution equipment. According to the method, high-precision target detection can be realized under sparse point cloud input, and resource allocation can be optimized and calculated.

The invention relates to a lightweight multi-scene disease and pest detection method and system based on RT-DETR. According to the scheme, firstly, a standardized image processing link is constructed, multi-band feature extraction is carried out on an input image by using a convolutional backbone network introduced with wavelet transform, an effective receptive field is expanded in a frequency domain through wavelet decomposition and an inverse reconstruction mechanism, and feature capture of a tiny insect pest target is enhanced while calculation redundancy is reduced. Furthermore, a bidirectional feature pyramid network including global and local double-branch collaborative modeling is adopted, cross-level dynamic interaction and gating fusion are performed on multi-scale features, and environmental noise interference such as veins and illumination under a complex farmland background is effectively inhibited. And finally, establishing a homography mapping model from a pixel plane to a geographic space according to camera calibration parameters, converting a visual detection result into a spatial distribution diagram layer with latitude and longitude information, and realizing dimension crossing of pest and disease damage monitoring from single-point identification to region-level risk assessment.

The invention discloses a paper defect detection method and system based on image texture analysis, and relates to the technical field of image processing, and the method comprises the steps: collecting a paper defect image; extracting color features and multi-channel texture features, and fusing the color features and the multi-channel texture features to form color-texture fusion features; carrying out wavelet decomposition, extracting low-frequency texture features and high-frequency texture features, and fusing the low-frequency texture features and the high-frequency texture features to obtain frequency domain-texture fusion features; shape features are extracted, and shape-texture fusion features are formed through fusion; splicing the three features to obtain a fused multi-source feature vector; and inputting the paper defect image and the fused multi-source feature vector into a multi-modal classification model to obtain a paper defect classification result. According to the method, the color features, the frequency domain features, the shape features and the texture features are fused, the recognition accuracy is improved, meanwhile, the classification model of MobileNetV3 and multi-feature fusion is adopted, the nonlinear relation of the multi-source features can be automatically learned, and the generalization ability is higher.

The invention relates to the technical field of radar signal processing, in particular to an urban environment low, small and slow target radar clutter suppression method, which comprises the following steps of: 1, acquiring and decomposing a signal; step 2, adaptive clutter cancellation; step 3, space-time combined treatment; step 4, constant false alarm rate detection; step 5, fine classification and identification; step 6, target tracking and early warning; according to the method, a cascade processing framework integrating multi-scale signal decomposition, adaptive filtering, space-time combined processing and intelligent identification is constructed, so that the defect that the performance of a traditional method is sharply reduced in a city strong non-uniform and non-stationary clutter environment is effectively overcome; and in combination with a recursive least square filter with dynamically adjustable parameters, the combined suppression capability of static ground clutter and dynamic traffic interference is remarkably improved, so that the detection of a'low, small and slow 'target under the condition of an extremely low signal-to-clutter ratio becomes possible.

The invention belongs to the technical field of geographic information processing, and discloses a CORS surveying and mapping information quality control method combined with space-time big data analysis. The method comprises the following steps: identifying an ionized layer abnormal disturbance region based on spatio-temporal multi-scale wavelet decomposition, accurately positioning a resolution attenuation boundary through a second derivative mutation feature, constructing a resolution defect compensation weight matrix, and generating an adaptive enhanced high-resolution delay prediction field by using tensor decomposition. Empirical mode decomposition is adopted to recognize a weak fluctuation mode, early capture of precision degradation threatening features is achieved, a time-varying quality threshold curved surface is dynamically constructed based on an evolution trend, and grading quality marking of CORS observation data is achieved. According to the invention, the misjudgment rate of quality evaluation is reduced, and the reliability of surveying and mapping information is improved.

The invention discloses a generative adversarial network image enhancement method combined with frequency domain information, relates to the technical field of computer vision, and solves the problems of spectrum leakage, ringing artifacts, splicing strips and structural dislocation easily caused by the existing frequency domain image enhancement method. The specific scheme is as follows: receiving a degraded image and extracting multi-scale features and boundary section features; the method comprises the following steps of: performing overlapping partitioning and periodic continuation expansion on the basis of features, performing spectral analysis on an expansion block to generate a parameterized window function and performing windowing, then obtaining a preliminary reconstructed image through complex field correction and overlapping perception mixed inverse transformation, and finally performing optimization reconstruction under a generative adversarial framework through a multi-scale wavelet decomposition and sub-band fusion decoder to obtain a reconstructed image. According to the method, the consistency of space geometric precision and texture details can be kept while artifacts and seams are inhibited, and the applicability and stability of image enhancement in a complex scene are improved.

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.

The invention relates to the technical field of photovoltaic cable fault positioning, in particular to a photovoltaic cable fault positioning method based on multi-source data fusion, which comprises the following steps of: deploying fault recording units at two ends of a combiner box and an inverter side of a photovoltaic string, acquiring a voltage waveform and a current waveform when a fault occurs, and performing band-pass filtering processing to obtain a fault recording unit; and a double-end synchronous transient traveling wave signal is obtained. In the photovoltaic cable fault positioning process, the purity and synchronism of input signals are guaranteed by collecting voltage waveforms and current traveling waveforms at the two ends and conducting filtering processing, wavelet decomposition is conducted on the basis of synchronous transient traveling wave signals, time points corresponding to modulus maximum points are extracted, energy values are calculated, and the fault positioning accuracy is improved. Double characterization of the time domain and the energy domain is achieved, and the time characteristic and the energy characteristic of traveling wave propagation can be synchronously analyzed.

The invention discloses a heart rate measurement method and system based on time-frequency fusion and dynamic gating attention, and the method comprises the steps: firstly carrying out the preprocessing of an acquired face video sequence, and obtaining a stable input image sequence; thirdly, extracting a three-dimensional feature map and time sequence features which change along with time through a time-frequency convolutional network; a time-frequency fusion module is constructed, complementary time-frequency features are obtained through time-domain convolution and frequency-domain wavelet decomposition, and feature adaptive fusion is realized by using a dynamic gating mechanism; and the local time relevance of the features is enhanced through a local sliding window attention module. And inputting the three-dimensional convolution features into a prediction module, performing regression to generate a remote photoelectric volume pulse wave signal, and performing frequency domain analysis on the signal to estimate the heart rate. According to the method, pulse related signals can be stably extracted under the conditions of illumination variation and slight head movement, and the method has high heart rate estimation precision and environment robustness and can be used for non-contact vital sign monitoring application.

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

The invention provides a power distribution automatic monitoring and early warning method and system based on voltage measurement, and belongs to the field of power distribution monitoring. Performing wavelet decomposition on a voltage signal of each node in the time-space matrix of the topological structure of the power distribution network to obtain a frequency band component of each layer and calculating an energy entropy of the frequency band component; regarding each layer of frequency band component as an independent individual, and integrating the lowermost layer of energy entropy from bottom to top to form an individual feature; analyzing correlation among individuals based on mutual information, screening individual pairs as independent states of nodes, and integrating all states to obtain node features; on the basis of node characteristics, the correlation between any two nodes is calculated by using an improved dynamic time warping distance to serve as a voltage fluctuation correlation coefficient; and constructing a dynamic fault propagation graph by taking the voltage fluctuation correlation coefficient as an edge, analyzing node features by using a pre-trained multi-head attention neural network to obtain a node risk coefficient, and calculating a total risk coefficient of a connected path to perform early warning, thereby realizing automatic monitoring and early warning of the power distribution network.