1077 results about "Wavelet decomposition" patented technology

The invention provides a coal mill fault diagnosis and prediction method and system based on big data analysis, and relates to the technical field of big data analysis, and the method comprises the steps: collecting a vibration signal of a coal mill bearing through a vibration sensor, carrying out the multi-scale analysis through wavelet decomposition, extracting features, constructing a deep convolutional neural network model, and carrying out the fault diagnosis and prediction of a coal mill bearing. A maximum mean value difference adaptive layer is introduced to carry out feature alignment on data of a source domain and a target domain, identification of fault types and degrees is realized, a health state evaluation index including a fault severity coefficient and a development trend coefficient is established, and the remaining service life is calculated and early warning is carried out. According to the invention, the accuracy and generalization ability of fault diagnosis are improved, accurate prediction and timely early warning of the bearing fault of the coal mill are realized, and the service life of equipment is prolonged.

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

The invention discloses a semantic alignment method for infrared image and microwave non-image information fusion. The method comprises the steps that infrared image and microwave non-image information fragments in the same scene are collected; carrying out preprocessing operation on the acquired infrared image and microwave non-image information; extracting spatial and semantic features of the infrared image, and extracting frequency domain features of microwave non-image information; embedding the infrared image features and the microwave non-image features into the same high-dimensional semantic space; calculating the similarity in semantic representation; and carrying out global optimal matching by adopting a Hungary algorithm. Respectively performing wavelet decomposition on the infrared image and the microwave features by adopting a wavelet transform fusion algorithm, and extracting low-frequency and high-frequency coefficients under different resolutions; by constructing a unified semantic representation space and an efficient feature fusion mechanism, the difference between the infrared image and the microwave data in the feature expression form and the semantic level is effectively solved, complementary information of two modes is fully mined, and the target recognition and environment perception ability in a complex environment is improved.

The invention discloses a cable joint health state assessment method and system based on multi-dimensional data fusion, and relates to the technical field of cable state assessment, and the method comprises the steps: collecting cable joint operation parameters, unifying a time sequence, and forming a two-dimensional matrix; performing spectral analysis on the operation parameters, and calculating energy characteristics of each sub-band by applying multi-stage wavelet decomposition; and constructing a Bayesian network structure, and calculating probability distribution according to the energy characteristics of the sub-bands. According to the method, spectral analysis is carried out through Fourier transform, the effect of identifying main frequency components and noise characteristics in signals is achieved, the effect of extracting energy characteristics of the signals in different frequency ranges is achieved by decomposing a two-dimensional matrix into a plurality of sub-bands by applying multi-stage wavelet decomposition, and the method has the advantages of being simple in structure and convenient to operate. By constructing a Bayesian network structure and defining feature nodes and state nodes, the health state evaluation of the cable can be dynamically updated according to data collected in real time.

The invention relates to an ore crushing equipment operation monitoring method. Comprising the following steps: establishing a physical field distribution equation including a vibration wave propagation path and sound wave attenuation characteristics in a crushing cavity in the ore crushing process; collecting multi-physical field data in real time by using a sensor, and converting the data into a characteristic field distribution diagram comprising a vibration intensity distribution diagram; calculating a real-time change rate of a working load through historical data and current data of equipment operation; the vibration intensity and the sound wave energy are combined, and three working conditions of a low-load area, a normal-load area and an overload area are defined in a partitioned mode; on the time scale, the signals are divided into short-time fluctuation and long-time trend, and two types of tracks are extracted through wavelet decomposition and a sliding window method; on the characteristic scale, analyzing the main frequency deviation of the vibration signal, the high-frequency energy peak value distribution of the sound wave signal and the gradient of temperature change; and combining a working condition partitioning result with a dynamic characteristic track, and establishing a working condition and characteristic linkage matrix.

The invention provides a real-time face blind restoration method and device based on identity constraint and frequency domain enhancement, and relates to the technical field of image processing, and the method comprises the steps: S1, generating an identity code through the fusion of CLIP and ArcFace, extracting spatial structure features in combination with ControlNet, and obtaining a high-frequency component through the decomposition of learnable wavelets; s2, an input image is mapped to a potential space, and identity, space and high-frequency features are fused to construct a condition vector; s3, performing iterative denoising based on the consistency model, and superposing lightweight wavelet residual correction to enhance high-frequency details; s4, according to the high-frequency energy dynamic distribution weight, reconstructing a multi-scale fusion image through inverse wavelet transform; according to the method, the problems of identity distortion, detail loss and low calculation efficiency of a low-quality face image in restoration are solved, and semantic consistency, detail fidelity and real-time performance of a restoration result are improved through identity-space-frequency domain ternary constraint, lightweight residual correction and degradation perception enhancement.

The invention relates to the technical field of cable fault detection, in particular to a cable channel pressure change monitoring system based on a sensing network, and the system comprises a pressure signal decomposition module which collects multi-point pressure readings based on pressure signal data collected by the sensing network, and carries out the step-by-step frequency division of signals through segmented filtering. According to the method, pressure data are collected at multiple points, segmented filtering and layered decomposition technologies are combined, the levels of signal analysis are refined, pressure characteristics of different frequencies are captured, monitoring comprehensiveness is ensured, energy entropy calculation is combined with a wavelet decomposition method, deep description of signal non-stationary characteristics is achieved, and the detection sensitivity of abnormal changes is improved. Recursive analysis is adopted in time sequence prediction, dynamic error window correction is combined, accurate prediction of future pressure changes is provided, sudden change detection is based on dynamic threshold and time point change rate judgment, abnormal conditions are quickly responded, trend correction is combined with offset data and measurement data, and the prediction precision and correction capacity of the pressure change trend are effectively improved.

The invention provides a sea level rise influence risk assessment method, medium and system, and belongs to the technical field of risk prediction and assessment, and the method comprises the steps: firstly, analyzing historical sea level data through wavelet decomposition and an ARIMA model, and predicting a future change trend; meanwhile, a digital elevation model is established by using Kriging interpolation, and dynamic change of the terrain is described by combining real-time monitoring data. Secondly, identifying key infrastructures and influence factors by applying an analytic hierarchy process and principal component analysis, and extracting key features through singular value decomposition; and based on a singular value decomposition result, constructing a sensitivity and vulnerability matrix, calculating the exposure degree in combination with Monte Carlo simulation, and comprehensively evaluating the regional vulnerability. Furthermore, asset loss is predicted by applying a random forest algorithm, economic loss is calculated, and an ecological vulnerability index matrix is established to evaluate ecological influence. And finally, realizing quantitative risk assessment by adopting fuzzy comprehensive evaluation of an entropy weight method and a risk prediction model of support vector regression.

The invention relates to the technical field of brain-computer interfaces, in particular to an electroencephalogram signal amplification method, device and equipment based on topological gradient field coding. The method comprises the following steps: acquiring a first data set corresponding to a scalp electroencephalogram of a testee through electroencephalogram acquisition equipment, and performing Hilbert transform to extract phase information so as to calculate a topological gradient field; key phase features meeting the energy concentration ratio feature and the duration feature are extracted from the topological gradient field, so that the key phase features with the energy concentration ratio threshold value larger than a preset value and the duration exceeding a time threshold value are screened out; inputting the key phase features to a pre-constructed generative adversarial network, and generating an enhanced phase field by a generator of the generative adversarial network through adversarial training; and combining the enhanced phase field with the low-frequency amplitude component, subjected to wavelet decomposition, of the first data set to construct a complex field, simulating spatio-temporal evolution of the complex field based on a nonlinear dynamic model, and generating a finally enhanced and amplified electroencephalogram signal. And efficient characterization of electroencephalogram phase information is realized.

The invention discloses a package management and control method fusing multivariable dynamic analysis, relates to the technical field of package dynamic management and control, and covers a complete process from data acquisition to protection strategy implementation. Firstly, multi-source sensors are arranged at packaging and transportation nodes, stress, acceleration and environmental parameters are collected in real time, and multi-dimensional original data covering the whole process is formed; then, carrying out time domain alignment and feature extraction on the data by utilizing denoising filtering, wavelet decomposition and environment coupling methods to obtain structured data of a unified coordinate system; and accurately identifying a packaging fatigue risk through a microcrack jump function and an accumulated impact measurement model, and generating a crack propagation trend and failure prediction. And finally, according to the risk change of the section, executing a sectional protection strategy, and dynamically adjusting and optimizing a protection scheme to ensure efficient management and control. The limitation of traditional packaging control is overcome through nonlinear modeling and real-time adjustment and optimization, and the method has a wide application prospect.

The invention provides a distribution line fault waveform denoising analysis method based on an improved wavelet threshold function and computer equipment, and relates to the technical field of power system fault detection and signal processing. The method comprises the following steps: S1, obtaining a fault waveform of a distribution line through actual measurement or analogue simulation, and converting the fault waveform into a fault signal; s2, through denoising effect comparison, selecting an applicable wavelet basis function and a decomposition layer number, and carrying out wavelet transformation to decompose a fault signal; s3, carrying out wavelet decomposition coefficient threshold processing on the fault signal by adopting a self-adaptive adjustment mechanism of a threshold and an improved threshold function; and S4, performing discrete wavelet inverse transformation on the wavelet decomposition coefficient subjected to threshold processing to reconstruct a signal so as to obtain a denoised fault signal. According to the method, through the improved threshold function and the multi-scale analysis technology, the fault waveform denoising effect can be remarkably improved, the accuracy and the real-time performance of fault detection are ensured, and therefore the reliability and the safety of a distribution line are improved.

The invention discloses a mechanical arm welding detection intelligent diagnosis method and system based on laser ultrasonic guided waves. According to the system, a high-precision multi-degree-of-freedom steering engine, an infrared distance measurement array, a temperature sensor, a laser vibration exciter and a laser vibration meter are integrated at the tail end of a mechanical arm, synchronous operation of welding and online detection is achieved by presetting a welding track, and the system collects an excitation signal, a reflection ultrasonic signal and temperature data through a high-speed data collection card; and the signal is preprocessed through a temperature matching model, phase compensation and baseline correction. Then, wavelet decomposition is adopted to extract time-frequency features, intelligent diagnosis is achieved through a pre-trained one-dimensional convolutional neural network, a dynamic threshold value is set based on the 3 sigma criterion, and recheck feedback is automatically triggered when detection data exceed the limit, the welding detection efficiency and precision are remarkably improved through the scheme, data closed-loop management and intelligent quality tracing are achieved, and the reliability of welding detection is improved. And the automatic production requirement of modern steel structure manufacturing is met.

The invention belongs to the technical field of computer data processing, and provides a biodiversity inversion method based on multi-source remote sensing image fusion. Comprising the steps of remote sensing image data acquisition, image preprocessing, image fusion processing, multispectral resolution image data generation, spectral feature extraction, final frequency feature extraction, feature integration and target ecological variable prediction. According to the invention, through wave basis adaptive selection and multi-scale wavelet decomposition, spectrum fidelity and space structure maintenance are considered, differential fusion of high and low frequency components under different scales is realized, and multi-source data complementarity and fusion image quality are improved; through spectral resolution refinement processing and multi-bandwidth scale simulation, the limitation of single resolution is broken through, and the capability of capturing complex spectral features of vegetation is enhanced; the spatial correlation is enhanced through spatial neighborhood feature fusion; and through an ecological variable inversion estimation model, multi-index synchronous prediction is realized, and the universality of the model is improved.

The invention relates to the technical field of crop growth monitoring, in particular to a crop growth monitoring method based on unmanned aerial vehicle remote sensing, which comprises the following steps of: acquiring a time sequence remote sensing image through an unmanned aerial vehicle and performing time phase processing to solve the problem of data inconsistency of a traditional method; a crop segmentation network based on wavelet transformation and edge guidance is constructed, high-frequency details and low-frequency semantic features are captured through wavelet decomposition, multi-scale dynamic interaction is achieved through cross-resolution feature fusion, and the problems of high-frequency detail loss and fuzzy segmentation are solved; based on a twin network, extracting dual-temporal global semantic features, and combining a difference compensation module to enhance the significance of the growth change, suppress noise interference and improve the weak change detection capability; and finally, fusing the segmentation mask and the difference characteristics through a multi-task framework, synchronously generating a pixel-level spatial distribution diagram and a time sequence thermodynamic diagram, realizing spatio-temporal conjoint analysis of the crop growth state, solving the problem of spatio-temporal information segmentation in a traditional method, and providing high-robustness monitoring decision support for precision agriculture.

The invention relates to the technical field of high-voltage switch cabinet partial discharge detection, and discloses a high-voltage switch cabinet partial discharge signal denoising method based on singular value decomposition and particle swarm optimization wavelet decomposition, and the method comprises the steps: firstly analyzing the characteristic parameters of a narrowband interference signal in a partial discharge signal, and extracting a partial discharge signal without narrowband interference; secondly, for the problem of white noise existing in the partial discharge signals, wavelet decomposition is carried out on the partial discharge signals after narrowband interference is eliminated, the partial discharge signals are converted into sub-band signals of different scales and frequencies, wavelet inverse transformation reconstruction is carried out after threshold processing, and denoised signals are obtained; and finally, optimizing the selection of threshold related parameters by using a particle swarm algorithm, solving the number of particles and a population optimal value by using a fitness function, and continuously iterating to finally obtain the optimal threshold related parameters. According to the method, the denoising effect is improved, and the method can be popularized to denoising of partial discharge signals of other devices in combination with performance characteristic parameters of different devices.

The invention discloses a bearing defect detection method and system based on machine vision and ultrasonic detection, and particularly relates to the technical field of industrial automatic detection, and the method comprises the steps: S1, a synchronous collection module: carrying out pulse triggering synchronous collection, and generating a time-space reference table; s2, a feature extraction module: performing image noise reduction segmentation and ultrasonic frequency domain decomposition, and outputting a defect feature vector; s3, a fusion identification module: performing cross-modal feature alignment fusion to generate a defect classification conclusion; s4, a size measurement module: performing contour fitting to calculate inner and outer diameters, and outputting a size deviation value; and S5, a comprehensive judgment module: carrying out threshold comparison logic judgment, and generating a multi-modal detection report. According to the method, a space-time reference is established through an encoder, images are segmented in a self-adaptive mode, features are extracted through wavelet decomposition ultrasound, feature weights are re-calibrated through a parallel network and an attention mechanism, composite defects are recognized through cross-modal fusion, comprehensive judgment is conducted in combination with dimensional deviation, and a multi-dimensional quality evaluation system is achieved.

The invention discloses a power load prediction method based on a DWT-CNN-LSTM model, and the method comprises the steps: carrying out the decomposition of collected original load data through employing a discrete wavelet decomposition method, obtaining a low-frequency load component and a high-frequency load component, carrying out the denoising of the high-frequency component on the basis, and not carrying out the special processing of the low-frequency component; inputting the load data of different components into a convolutional neural network, and extracting features of the load data of different components; and respectively substituting each load component after convolution into the LSTM model for training to obtain an LSTM load prediction model of each load component, and then carrying out sequence reconstruction on the load prediction value of each component to obtain the prediction value of the power load. According to the method, the problem that noise exists in signals can be effectively solved, and power load data can be accurately predicted.

The invention provides a hydro meteorology early warning method for an offshore oil and gas platform, and belongs to the technical field of offshore hydro meteorology. Extreme weather events are identified by adopting minimum probability abnormal event identification vectors to match abnormal characteristic parameters, and abnormal signal characteristic parameters are input into an ocean dynamics prediction model to calculate real-time sea condition parameters; calling a multi-temporal-spatial-scale early warning fusion matrix to combine with a wavelet decomposition technology and a recurrent neural network to realize multi-scale information integration, analyzing an environmental parameter change trend through a sea condition jump identification model and triggering an emergency response, dynamically adjusting system parameters according to a stability evaluation index vector, and optimizing prediction precision by adopting an early warning residual value compensation matrix. And finally, multi-level early warning information is generated and a real-time early warning notification is sent to an operator, so that the technical problem of insufficient early warning precision of an offshore oil and gas platform hydro meteorology early warning system in multi-spatio-temporal scale data fusion processing is solved.

The invention provides a multi-scale polarization data fusion method based on a 4K polarization imaging detector, and relates to the technical field of image processing, and the method comprises the steps: carrying out the adaptive edge detection of four original polarization images, generating a comprehensive edge image, and carrying out the definition scoring; performing color calibration on each original polarization image based on the ambient light intensity and the color temperature, and outputting a polarization image after color calibration and a color deviation score; combining definition scores, color deviation scores and global brightness information entropies of the four polarization images after color calibration to construct a multi-objective optimization function, and solving the multi-objective optimization function in a multi-exposure parameter space to obtain an optimal exposure parameter combination; applying the optimal exposure parameter combination to image capture of the next period, and calculating global quality reward values of four newly collected polarization images; when the global quality reward value meets a preset condition, wavelet decomposition and frequency division multi-scale fusion are carried out on the polarization images after color correction, a fused image is generated, and the quality of the polarization images is effectively improved.

The invention discloses a multi-scene positioning and intelligent warning system and method for industrial equipment, and the system comprises a track analysis module which obtains the radar signal data of the industrial equipment, carries out the denoising, extracts a signal intensity change feature, and generates a human body dynamic track when the matching degree of the feature and a preset metal reflection model exceeds a threshold value; the abnormity judgment module is used for acquiring the vibration sensing data of the key position, generating a vibration frequency spectrum by applying wavelet decomposition, and generating abnormal vibration alarm information if the vibration frequency spectrum exceeds a range; the intrusion alarm module is used for acquiring node signal sources, performing weighted fusion to obtain an initial positioning coordinate group, inputting the initial positioning coordinate group into a three-dimensional map model, obtaining a final coordinate result through multi-source fusion, and sending out intrusion alarm information if the result falls into a dangerous fence boundary; and the safety evaluation module is used for performing time alignment and deep fusion on the human body dynamic track, the abnormal vibration alarm information and the intrusion alarm information to generate a safety evaluation report. According to the invention, omnibearing safety monitoring and management of industrial equipment are realized.

The invention discloses an aircraft control surface oscillation fault monitoring method based on multi-scale wavelet transformation, and the method comprises the steps: carrying out the calculation through a PID (Proportion Integration Differentiation) control law through an autopilot according to an input flight path command, and obtaining a control surface control instruction deltadepth; a control surface deflection prediction value delta est is generated based on the nonlinear model and the control surface control instruction, the control surface deflection prediction value and an actual deflection value delta mea collected by the sensor module are subtracted to generate a residual signal Rn, and the residual signal is decomposed into a plurality of sub-band signals through wavelet decomposition; wavelet energy calculation is carried out on the sub-bands possibly containing the control surface oscillation fault signals, whether a fault occurs or not is judged according to the magnitude of an energy comparison threshold value, and the judgment threshold value is determined through Monte Carlo simulation. According to the method, instruction signals are separated by generating residual errors, wavelet transform processing is carried out on residual error signals, interference of noise and irrelevant frequency signals on monitoring performance is reduced, meanwhile, small-amplitude oscillation signals can be effectively monitored according to sub-band wavelet energy serving as a fault criterion, and the monitoring coverage rate of oscillation faults is remarkably increased.

The invention discloses a high-resolution hyperspectral image reconstruction method based on wavelet domain feature fusion and contrast learning, and belongs to the technical field of image fusion and super-resolution reconstruction. The method comprises the following steps: constructing a fusion network model comprising a wavelet transformation module, a cross-modal feature fusion module, a high-frequency contrast learning module and an image reconstruction module; performing end-to-end supervised training by using a training data set containing the low-resolution hyperspectral image and the high-resolution multispectral image; and after training is completed, inputting a test image pair to realize image reconstruction. According to the method, the detail retention capability is improved by combining wavelet decomposition and a directional fusion mechanism, the cross-modal high-frequency feature alignment capability is enhanced through comparative learning, a fusion image with high spatial resolution and high spectral consistency is finally generated, and the method is suitable for multi-modal image reconstruction tasks such as remote sensing, medical and natural images.

The invention relates to a multi-branch power distribution network double-end traveling wave fault positioning method and system, and the method comprises the steps: deploying a plurality of measurement points based on the head and tail ends of a radial power distribution network and branch terminals, collecting topological information, and calculating the linear mode wave velocity; using the three-phase voltage data after the fault to extract traveling wave line mode and zero mode components through modulus decoupling; a line mode component with the strongest global mutation is selected as a reference, and a wave head time sequence table is generated through wavelet decomposition and mutation detection; and correcting the zero-mode wave velocity by combining the topology, the time sequence table and the line-mode wave velocity, and determining a fault interval and outputting a fault position after compensation and correction by using the line-mode and zero-mode wave head time difference. According to the invention, through a wave head time sequence analysis and time difference compensation mechanism based on cooperation of multiple measuring points, positioning errors caused by clock asynchronization and parameter disturbance are effectively overcome, accurate distance measurement which is not influenced by clock errors is realized, and the distance measurement precision is significantly improved; and meanwhile, the point distribution requirement is simplified, accurate judgment and positioning of a fault interval can be realized only by a branch single measuring point, and high economy and deployment flexibility are both achieved.

The invention relates to the technical field of nondestructive testing, and discloses a crack detection device and method for precise automobile parts. The method comprises the following steps: carrying out ultrasonic multi-dimensional pre-scanning and acoustic impedance abnormal region identification on a to-be-detected automobile part, carrying out influence range calculation and sensitive threshold classification based on abnormal region distribution data, and obtaining stress-acoustic response time sequence data through partition grabbing force distribution and stress loading; three-dimensional stress field reconstruction and stress concentration area identification are realized; performing dual-frequency stress modulation excitation and multi-physics field synchronous detection on the stress concentration area to obtain a multi-physics field response signal, performing multi-parameter fusion calculation and multi-scale wavelet decomposition of acoustic components on the multi-physics field response signal, and performing feature dimension splicing to obtain crack feature classification data; and calculating and generating a crack detection result based on feature similarity matching and extension prediction. The precision and reliability of crack detection of the automobile parts are remarkably improved, and the damage risk in the detection process is avoided.

The invention discloses a lightweight super-resolution system and method of an adaptive wavelet attention network, and belongs to the technical field of image processing. The system is composed of a multistage wavelet attention module, a dynamic convolution kernel generation unit, a convolutional neural network and an output unit. The method comprises the following steps of: extracting features of a low-resolution image and performing Haar wavelet decomposition; calculating a cross-scale attention weight on each high-frequency sub-band and carrying out weighted fusion to highlight details; adaptively generating a dynamic convolution kernel of a Haar wavelet basis kernel weighted combination based on the fused features, and performing directional convolution enhancement on the features; high-resolution image reconstruction is realized through a lightweight residual network and pixel rearrangement; and during training, pixel domain mean square error and wavelet coefficient compensation loss joint optimization is adopted. The parameter quantity of the system model is smaller than 450KB, a 1080p video super-resolution task can be processed on mobile equipment in real time, and the texture recovery performance is improved by about 1.2 dB compared with that of an existing lightweight model.

The invention discloses a modulation signal identification method based on continuous wavelet transform and deep learning, and the method comprises the steps: firstly modulating a to-be-transmitted sequence at a transmitting end, obtaining a wireless continuous time signal at a receiving end, sampling the continuous time signal, carrying out the wavelet decomposition of the sampled data, and filtering out the high-frequency noise; carrying out reconstruction by using wavelet inverse transformation, and carrying out median filtering on the reconstructed signal to obtain a new data set; performing continuous wavelet transform on the new data set to obtain a time-frequency graph, and extracting feature information of a time domain and a frequency domain of the time-frequency graph to obtain a two-dimensional feature graph; constructing a convolutional neural network prediction algorithm model, and training the convolutional neural network prediction algorithm model by using the two-dimensional feature map extracted by continuous wavelet transform; and finally, classifying the modulation modes of the signals by using the trained convolutional neural network prediction algorithm model. According to the method, the modulation signal identification accuracy in a complex environment in reality is improved, the network parameter quantity is reduced, and the modulation signal identification efficiency is improved.

The invention relates to the technical field of power system communication, and discloses a fault diagnosis method and system for a substation communication network, and the method comprises the steps: obtaining multi-band signal data, and carrying out the fast Fourier transform to obtain spectrum distribution data; according to the spectrum distribution data, performing frequency band division to obtain an initial power ratio; performing adaptive filtering according to the initial power ratio and the multi-band signal data to obtain a fundamental wave amplitude fluctuation characteristic; performing wavelet decomposition according to the fundamental wave amplitude fluctuation characteristics to obtain abnormal signal time-frequency energy distribution; performing K-means clustering division according to the abnormal signal time-frequency energy distribution, and matching a harmonic fault database to obtain a fault type identifier; and according to the fault type identifier and the abnormal signal time-frequency energy distribution, ring network topology propagation time delay analysis is carried out, a fault position coordinate is positioned, and a fault diagnosis report is generated. According to the method, the network fault can be accurately diagnosed for multi-band complex signal interference.

The invention provides a pavement defect analysis and detection method and system, and relates to the technical field of defect identification. Through multi-source data fusion processing, a dynamic anchor frame adjustment mechanism, an improved feature extraction network and a multi-stage wavelet decomposition technology, the problem of missing detection of a traditional detection method under shadow interference is effectively overcome, the defect recognition precision in a complex scene is remarkably improved, refined quantitative evaluation of the pavement condition is achieved, and the method is suitable for large-scale popularization and application. The method has the technical advantages that the small-scale defect recognition precision in a complex scene is improved, the shadow interference resistance is enhanced, and multi-dimensional quantitative evaluation is realized.

The invention discloses a panchromatic sharpening method and system based on cross-resolution adversarial learning and a Mama network. The method comprises the following steps: acquiring a low-resolution multispectral image LRMS and a high-spatial-resolution panchromatic image PAN; extracting features of the low-resolution multispectral image LRMS to obtain a first feature map; extracting features of the high-spatial-resolution panchromatic image PAN to obtain a second feature map; performing wavelet decomposition and weighted fusion on the first feature map and the second feature map to obtain a multi-channel feature map, and performing dynamic fusion on the multi-channel feature map through a cross attention gating mechanism to obtain a fused feature map; and carrying out residual connection on the fused feature map and the low-resolution multispectral image, and then carrying out feature reconstruction to obtain a high-resolution multispectral remote sensing image. According to the method, the panchromatic sharpening performance of the remote sensing image can be improved, and the high-quality and high-resolution multispectral remote sensing image is obtained.

The invention relates to the technical field of data processing, and discloses a secondary equipment hidden danger mining method and system based on a wave recording file and monitoring data. The method comprises the steps of calculating a conditional probability and intervention probability difference identification causal relationship by analyzing a recording file in combination with equipment topology and protection logic to construct a correlation graph, and performing Fourier transform and wavelet decomposition on a sampling sequence to extract multi-scale features; small signal test excitation is injected, a system identification estimation transfer function is fused with monitoring data to form a health feature vector to judge a health state, and a correlation map and the health state are input into a map neural network to calculate weighted attention coefficients and aggregate neighbor information to predict a fault propagation path; and establishing a degradation model, correcting the failure rate, predicting the residual life and generating a graded early warning report. According to the method and the device, the transformation from passive post analysis to active predictive maintenance is realized, and the timeliness, accuracy and systematicness of hidden danger identification of the secondary equipment are improved.