1383 results about "Wavelet" patented technology

The invention relates to a water quality real-time detection system which comprises the following modules: a multi-source sensing module which is based on a multi-parameter sensing array, adopts a self-adaptive sampling strategy, realizes sensor time sequence synchronization through a state estimation algorithm, completes water body multi-dimensional parameter acquisition in combination with a micro-fluidic chip, generates a multi-modal sensing data set, and transmits the multi-modal sensing data set to a data processing module; the multi-source sensing module comprises a multi-source sensing sub-module, a signal conditioning sub-module, a time sequence synchronization sub-module and an anomaly capture sub-module. The method has the advantages that through the synergistic effect of the adaptive sampling strategy and the state estimation algorithm, the multi-sensor time sequence synchronization precision is remarkably improved, the phase deviation problem caused by traditional fixed frequency sampling is effectively eliminated, the sliding window polynomial fitting is combined with the wavelet threshold de-noising technology, and the multi-sensor time sequence synchronization precision is improved. High-frequency noise interference is greatly suppressed on the premise that effective components of the signals are reserved, and meanwhile, the abnormal value detection accuracy is improved through a dynamic threshold mechanism.

According to the cooling tower early fault early warning method based on vibration state monitoring, vibration signals and working condition labels of key parts of the cooling tower are synchronously collected through multiple channels, and data quality is improved through preprocessing operation such as band-pass filtering and normalization; time-frequency features are extracted in a multi-scale mode through self-adaptive variational mode decomposition and wavelet packet transformation, signal complexity is quantized through energy entropy, and weak fault detection capacity is enhanced; the obtained features are input into a deep belief network after being subjected to principal component analysis dimensionality reduction, and automatic classification and recognition of the equipment operation state are achieved; dynamic early warning grade adaptation is carried out according to an identification result in combination with a working condition label, the environmental adaptability and stability of early warning are effectively improved, the method further has the functions of early warning sample recording and periodic model iterative optimization, and the fault identification precision and robustness in a complex noise environment are remarkably improved.

The invention relates to the technical field of high-voltage circuit breaker fault diagnosis, and discloses a multi-feature optimization fusion high-voltage circuit breaker fault diagnosis method. The method comprises the following steps: adaptively optimizing variational mode decomposition parameters by adopting a particle swarm optimization algorithm, and accurately decomposing an original vibration signal; performing noise dominant and fault feature dominant classification on the intrinsic mode function based on permutation entropy; aiming at the two types of modes, respectively taking signal-to-noise ratio maximization and kurtosis maximization as targets, and implementing differential wavelet threshold denoising; after reconstructing the signal, extracting an energy entropy, a singular value entropy and a power spectrum entropy to form a multi-dimensional feature vector; and inputting the data into a support vector machine classifier subjected to particle swarm optimization hyper-parameter for state diagnosis. According to the invention, through full-chain collaborative optimization, the accuracy and robustness of fault diagnosis in a strong noise environment are significantly improved.

The invention relates to the technical field of mode recognition and data recognition, and discloses an intelligent visual detection method for non-standard precision part surface microdefects, which comprises the following steps: acquiring surface gray level image data of a to-be-detected part, physically abandoning low-frequency components through discrete wavelet transform, and reserving high-frequency detail components to construct a frequency domain input tensor; constructing a double-flow reconstruction model containing a space domain coding network and a frequency domain coding network, and minimizing the distribution difference of the same feature between double-domain characterization through potential feature space consistency constraint joint optimization; the method comprises the following steps of: calculating a spatial domain residual image and a frequency domain residual image, combining a texture topological residual image extracted by structural tensor characteristic decomposition, and generating a comprehensive abnormal response image through weighted fusion to judge the defect, and effectively inhibiting macroscopic geometric contour interference through frequency domain decoupling and a topological check mechanism on the premise of not needing a standard geometric template. And sensitive perception and accurate identification of weak texture defects on the surface of the non-standard part are realized.

The invention provides a self-adaptive bearing fault diagnosis method based on multi-base wavelet fusion. The objective of the invention is to solve the problems of noise reduction, insufficient feature extraction and low diagnosis precision under noise conditions. A Kaisixi University bearing public data set is used as original data, and Gaussian noise with different SNRs is superposed to simulate various noise intensities. And uniformly carrying out length alignment, down-sampling, equal-length segmentation, division and normalization preprocessing. Then, wavelet bases such as sym4, db4, coif5 and the like are adopted for parallel multi-scale decomposition and reconstruction; and adaptively determining the number of decomposition layers and a threshold strategy according to the noise level, and generating a de-noising branch. And performing weighted fusion on the denoising results of the branches, and performing iterative denoising on the residual error. Signals subjected to noise reduction processing are sent to a double-branch convolution-cycle-attention network, a convolution layer extracts features, an LSTM and a self-attention module capture time sequence changes, and accurate recognition of various bearing faults is achieved. The training adopts a segmented attenuation learning rate and an early stop strategy, and the robustness and generalization ability of different SNR working conditions are improved.

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 construction emergency early warning method and system, and the method comprises the following steps: collecting the three-dimensional coordinates of a constructor, combining a sliding time window with a wavelet packet energy entropy and other indexes, and generating a multi-scale movement disorder index through principal component analysis and fusion; continuously unstable persons are recognized according to the disorder index, after the trajectory of the persons is segmented, a weighted graph is constructed in combination with hidden Markov and building information model environment parameters, and the cognitive mismatch degree is calculated; mapping the cognitive mismatch degree to a space grid, calculating a local Moran index, fusing a density gradient and a mechanical operation sequence resonance result, and constructing a propagation weight matrix; constructing a heterogeneous graph based on weight matrix guidance, calculating risk influence propagation potential energy, combining historical disorder sequence analysis and relative entropy, and coupling to obtain a group-level instability pre-judgment value; and outputting a comprehensive critical level for the pre-judgment value over-limit individuals through motion trend prediction, spatial intersection and shortest path algorithms. The safety during building construction operation is improved.

The invention provides a dense overlapping target detection method based on a wavelet enhancement sparse hybrid expert model. The method comprises the following steps: firstly, extracting multi-layer features through a backbone network to capture multi-scale spatial representation; secondly, discrete wavelet transform is introduced to each level of features, spatial features are decomposed into a frequency domain, collaborative modeling of frequency domain and spatial domain features is realized, the reservation capability of detail and texture information is improved, a lightweight dynamic hypergraph aggregation module is introduced into the deepest layer of features, a hyperedge structure is adaptively learned, and the feature fusion is realized; modeling a high-order incidence relation in a local area in an explicit manner; and thirdly, in the decoding process, candidate queries are screened and reweighted through an IoU perception query selection mechanism, and a dynamic routing mechanism of sparse hybrid experts is introduced, so that query self-adaptive specialized representation learning is realized, and the target detection precision and reliability in a complex scene are effectively improved.

The invention relates to a helicopter rotor crack fault identification method and device based on video semantic segmentation. The method comprises the steps that video collection and denoising and stability enhancement processing are carried out through an unmanned aerial vehicle; through designing a global-local feature interaction double-branch network, parallel computing and bidirectional fusion are carried out on context global feature extraction branches and detail mining branches, so that rotor wing crack feature extraction is realized; designing a hierarchical crack feature reconstruction decoder to realize cross-scale fusion and spatial precise alignment of crack features; and designing a multi-frequency-domain boundary sensing enhanced training head, and realizing multi-scale feature extraction and dynamic weight distribution of the crack edge through the synergistic effect of a multi-stage wavelet frequency domain decomposition module and a self-adaptive boundary weighting supervision module. Through the innovative modules, high-precision and robust crack detection is realized aiming at the problems of view limitation, global-local feature fusion difficulty, detail loss caused by down-sampling, edge blur, class imbalance and the like in helicopter rotor crack detection.

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 relates to the technical field of communication network fault diagnosis, and discloses a communication information processing method and device. The method comprises the steps that a voiceprint sensor collects an original signal, and a quantum coding time-frequency matrix is generated through time-frequency conversion and quantum coding; the matrix and real-time circuit diagram data are fused, and topology-fault joint features are generated through feature alignment and wavelet-convolution joint extraction; analyzing the features to reconstruct a node relation graph, and generating real-time updated circuit diagram data through graph optimization; loading an incremental parameter adapter initialization graph neural network based on the data, and executing fault propagation deduction to output fault coordinates and confidence data; generating an incremental parameter adapter according to confidence data federal optimization; and analyzing the fault coordinate matching topology library based on the adapter, and associating the maintenance knowledge base to output a visual repair suggestion. According to the method, fault dynamic deduction is realized, the cross-line generalization ability is enhanced by an incremental learning mechanism, bandwidth occupation is reduced by edge-cloud cooperation, and the fault positioning efficiency and accuracy are remarkably improved.

The invention relates to an epilepsy prediction method based on adaptive sparse attention and a hierarchical graph convolution network, and the method comprises the steps: carrying out the time domain convolution, spectrum transformation and Haar wavelet down-sampling of an electroencephalogram signal, respectively generating time domain, spectral domain and fidelity down-sampling features, and fusing the features into a low-level feature set; on the basis of a sparse attention mechanism, constructing and applying a multi-level sparse mask to adaptively screen and weight-aggregate key discriminative features in the feature set to obtain screened features; on the basis of the feature, by constructing a local channel graph and a global frequency band graph and respectively executing graph convolution, capturing local spatial correlation of each channel in a single frequency band and global cross-frequency-band spatial dependence among different frequency bands, and fusing the local spatial correlation and the global cross-frequency-band spatial dependence into an embedded feature; and inputting the embedded features into a classifier to obtain a state probability, and triggering an alarm based on the state probability. Therefore, the problems of key information loss, insufficient time-space spectrum dependent modeling and feature redundancy are solved, and the accuracy, stability and real-time performance of epilepsy prediction are improved.

The invention relates to the technical field of system monitoring, and discloses an information system full-link monitoring method based on high-frequency index acquisition optimization, which comprises the following steps: monitoring the running state of an information management system in real time, dynamically adjusting the sampling frequency by means of a customized service key identification component and a comprehensive load prediction model, and performing real-time monitoring on the sampling frequency. Transmitting the target data to the edge computing node; a lightweight monitoring agent is deployed at an edge node, and a wavelet signal decomposition algorithm is adopted to extract features and distinguish data types; constructing an information management business knowledge graph and an entity-relation-business rule base, associating abnormal features, and generating an abnormal root cause report in combination with a time sequence prediction model and a knowledge constraint large language model; based on report and information service priorities, monitoring resources are dynamically allocated in the edge-cloud collaborative architecture, and related model parameters, service association rules and constraint weights are optimized according to operation and maintenance feedback. According to the invention, targeted monitoring requirements in the business peak period and efficient utilization of system resources can be met at the same time.

The invention discloses a cross-working-condition fault diagnosis method based on knowledge embedding and multi-scale attention, belongs to the technical field of cross-working-condition fault diagnosis, designs a domain knowledge embedding signal processing method based on wavelet packet transformation, envelope spectrum analysis and statistical feature analysis, and constructs a knowledge feature matrix and a statistical feature matrix. Fault information which is not easily influenced by working condition changes is highlighted, and the dependence of a deep learning model on a target domain sample is effectively reduced; a multi-scale attention mechanism is designed to extract depth fault features in an input matrix, and compared with an original multi-head attention mechanism, multi-scale is introduced, so that the parameter quantity of a model is reduced, and the feature extraction capability is more flexible; by embedding domain knowledge highlighting domain invariant fault information into a signal processing end, the method can show excellent variable working condition fault diagnosis performance when a target domain sample is completely lacked.

The invention provides an unmanned aerial vehicle visible light-infrared image cooperative enhancement network and method based on dual-branch cooperation and frequency adaptive fusion, and relates to the technical field of unmanned aerial vehicle multi-modal image enhancement and super-resolution restoration. According to the method, visible light image enhancement and infrared image super-resolution reconstruction are respectively carried out by adopting a heterogeneous double-branch architecture; a frequency adaptive fusion module is embedded in a visible light branch, and spectrum decomposition and feature refining of degradation sensing are realized through a learnable frequency mask; in the infrared branch, a long-range dependency relationship is captured through a residual Transform module; bidirectional cross-modal guidance is realized through a multi-modal feature interaction module, the module integrates wavelet convolution transformation, frequency perception fusion and a cross-modal Transform mechanism, and feature alignment and semantic complementation of space-frequency double domains are realized. According to the method, the visual quality, the detail recovery capability and the cross-modal collaborative robustness of the visible light and infrared images of the unmanned aerial vehicle under complex illumination, weather and degradation conditions can be effectively improved.

The invention discloses a patient improvement effect analysis method for controlling spinal cord electrical stimulation through an implantable brain-computer interface, and relates to the technical field of medical rehabilitation, and the method comprises the steps: multi-dimensional collaborative data collection: implanting electrodes in a target brain region and below a spinal cord injury segment, installing a detection element at an exoskeleton key part, and carrying out multi-dimensional collaborative data collection; a sensor is attached to a lower limb preset muscle group, electroencephalogram signals, SCS stimulation parameters, EXS motion data and neuromuscular response data are synchronously collected, and time correlation marks are embedded; according to the method, the reliability of motion intention decoding is remarkably improved by adopting a mode of combining multi-source signal preprocessing and a multi-mode intention recognition model, and in the signal preprocessing stage, the self-adaptive filtering algorithm combining Kalman filtering and wavelet threshold denoising is applied, so that the motion intention decoding efficiency is improved. SCS electrical stimulation interference, EXS motor noise and physiological noise in the electroencephalogram signals are effectively removed.

The invention discloses a blade bolt state monitoring system and method based on an acoustic emission technology in the technical field of state monitoring of wind power generation equipment. The method comprises the following steps: acquiring acoustic emission signal data of a blade bolt connection area acquired by an acoustic emission sensor array; sequentially carrying out preprocessing and feature extraction processing on the basis of the acoustic emission signal data, and then calculating a wavelet packet energy entropy value; judging whether a triggering condition of multi-sensor data fusion weighting judgment is met or not based on the wavelet packet energy entropy value; when any triggering condition is met, it is judged that the state of the bolt is abnormal, and after comprehensive state recognition is conducted through multi-sensor data fusion weighted judgment and modal acoustic emission wave velocity correction positioning, early warning information including the damage type, the severity degree and position information is output; when the triggering condition is not met, it is judged that the bolt state is normal, and circulating monitoring continues. According to the invention, accurate evaluation and early warning of the blade bolt state are realized through signal acquisition, processing, analysis and early warning.

The invention relates to the technical field of electric power system control, and discloses an electric power system arc harmonic collaborative suppression and intelligent closed-loop control system, which comprises the steps of: acquiring sensor data and uploading the sensor data to an edge computing unit by deploying a sensor; wavelet threshold denoising and electromagnetic interference compensation are adopted; the method comprises the following steps: constructing a coupling model containing arc plasma dynamic resistance, optimizing VMD parameters through PSO, extracting a joint feature vector, filling an arc and harmonic dynamic coupling analysis gap, calculating a total harmonic distortion rate and arc extinction time in combination with a power grid damping ratio, solving an optimal SVG trigger angle, and solving the problem of lack of power grid stability evaluation; correcting the opening time of the circuit breaker, calculating the error of the total harmonic distortion rate and the error of the arc extinction time, and adjusting the SVG trigger angle; calculating the precision of the evaluation model, calculating the full life cycle through the aging coefficient, analyzing and judging whether an early warning signal is generated or not, and triggering equipment replacement early warning based on the generated early warning signal.

The invention belongs to the technical field of remote sensing image target detection, and discloses a multi-source remote sensing image detection method based on wavelet and dynamic hyperbolic normalization. The method comprises the following steps: firstly, inputting a multi-source remote sensing image, and carrying out cross-scale feature extraction through a CSWFF module; then, cross-modal feature fusion is realized through a CM-AFGPF module; then characteristic calibration and enhancement are carried out through a DyHPSA module; and finally, target positioning and classification are completed through a detection head, and a detection result is generated. Through the CSWFF module, the DyHPSA module and the CM-AFGPF module, a unified detection framework is constructed, the limitation of single-modal detection is broken through, the robustness and precision of multi-source remote sensing image target detection are remarkably improved, and efficient cross-modal feature fusion, multi-scale feature reservation and complex background noise suppression are achieved.

The invention discloses a building safety intelligent monitoring, early warning, prevention and control method, and the method comprises the steps: collecting a physical state parameter and an environment disturbance parameter of a building structure body in real time through a distributed monitoring node group disposed at a key part of the building structure body, and forming an original monitoring data flow; performing space-time alignment and noise reduction processing on the original monitoring data stream by using an adaptive weighted fusion algorithm to generate a standardized structure response data set; extracting a multi-dimensional time-frequency domain feature vector representing the health state of the structure from the standardized structure response data set based on a wavelet packet transformation and principal component analysis combination method; and inputting the multi-dimensional time-frequency domain feature vector into a pre-trained twin neural network, and outputting abnormal region positioning information and an abnormal degree quantitative index. According to the method, the building mechanics mechanism and the artificial intelligence technology are deeply fused, a full-closed-loop intelligent prevention and control system from accurate risk identification to active regulation and control is constructed, and the reliability and timeliness of building safety monitoring in a complex environment are remarkably improved.

The invention relates to the technical field of hydraulic valves, and discloses a digital hydraulic valve intelligent diagnosis system which comprises a data acquisition module, a data preprocessing and label generation module, a feature fusion module, a classification diagnosis and decision fusion module and a service life prediction and suggestion generation module. Data such as pressure, vibration, displacement, flow and pollution degree of the valve are comprehensively captured through deployment of a multi-source sensor array at key positions of the digital hydraulic valve and a self-adaptive acquisition strategy, early fault feature omission is avoided, then through preprocessing means such as soft-hard hybrid wavelet threshold denoising and multi-sensor time alignment, the data precision is effectively improved, and the accuracy of the data is improved. Then, through dynamic-static layered feature fusion and an attention weighting mechanism based on GRU, different feature advantages under steady-state and fault working conditions are fully combined, the fault feature distinction degree is greatly enhanced, and the problem that similar faults are likely to be confused is solved.

The invention discloses a traffic flow prediction method and system based on TEDDGN, and belongs to the technical field of traffic flow prediction, and the method comprises the steps: carrying out the convolution operation of a traffic time sequence and a wavelet function through employing discrete wavelet transform, achieving the trend-event decoupling of traffic flow data, projecting a signal to different scale spaces, and carrying out the prediction of the traffic flow. Obtaining independent trend components and event components; processing the obtained trend component and event component by adopting a multi-scale time learner; spatial feature extraction is carried out on the processed data, modeling is carried out through adaptive graph convolution and dynamic graph convolution in spatial feature extraction, and the adaptive graph convolution and the dynamic graph convolution are dynamically fused and output through a learnable gating coefficient; inputting the output spatio-temporal characteristics into an output layer to form a TEDDGN prediction model; a TEDDGN prediction model is trained; and performing traffic flow prediction by using the trained TEDDGN prediction model. According to the invention, the accuracy of traffic flow prediction is realized.

The invention discloses a rotor acoustic anomaly detection method, system and device based on an auto-encoder and wavelet packet energy entropy and a medium, and belongs to the technical field of hydroelectric generating sets, and the method comprises the steps: collecting an original acoustic signal, carrying out the noise reduction of the original acoustic signal, and carrying out the multi-channel data fusion to obtain an integrated acoustic signal; inputting the integrated acoustic signal into a wavelet packet for transformation processing to obtain wavelet packet coefficients under different scales; calculating energy values of different frequency band signals based on wavelet packet coefficients to form an energy entropy feature vector; constructing an implicit feature model of the hydroelectric generating set rotor in a normal state according to the energy entropy feature vector; and carrying out feature reconstruction on the energy entropy feature vector, calculating a reconstruction error, carrying out dynamic statistical analysis, and carrying out real-time monitoring and abnormity judgment on the operation state of the hydroelectric generating set rotor. According to the method, the detection precision and the response speed are improved in actual hydroelectric generating set rotor acoustic anomaly detection, and automatic identification and dynamic threshold adaptive adjustment of irregular perturbation are realized.

The invention discloses a numerical control machine tool dynamic error cooperative compensation control method adaptive to high-speed machining, and relates to the technical field of numerical control machine tool error control. According to the method, a multi-source dynamic error sensing system comprising a grating displacement sensor, a six-dimensional force sensor and the like is constructed to acquire data; after wavelet threshold denoising and Kalman filtering preprocessing, inputting a three-layer LSTM error coupling prediction model combined with an attention mechanism, embedding a servo motor load characteristic curve in the model, and outputting three types of error compensation amounts; through servo-level compensation and machining-level compensation, the position of a feed shaft, the rotating speed of a main shaft, the cutting feed rate and the behavior of a micro-displacement actuator are corrected, and machining errors caused by deflection and vibration conduction of the main shaft are counteracted. And iteratively updating model parameters by using a gradient descent algorithm. According to the method, through multi-source error synchronous sensing, error coupling modeling and hierarchical cooperative compensation, dynamic error cooperative control more adaptive to a high-speed processing scene is realized, and the method has a wide application value.

The invention belongs to the technical field of deep learning, particularly relates to a two-stage blind image defogging method based on prior guide diffusion, and aims to solve the problem of distortion of a traditional decontamination method in a complex scene. Comprising the steps that a double-stage blind image defogging model is constructed, and the double-stage blind image defogging model comprises a first stage and a second stage; wherein in the first stage, physical modeling is carried out based on an improved atmospheric scattering model, the improved atmospheric scattering model is an enhanced atmospheric scattering model in which a light absorption coefficient is introduced, and a transmission image, a fogless reference image and atmospheric light parameters are output through the first stage; in the second stage, generation optimization is carried out based on a diffusion model, the transmission image, the fog-free reference image and the atmospheric light parameters output in the first stage are used as physical priori to be fused into the generation process of the diffusion model, and image defogging is carried out. In the second stage, self-adaptive difference fusion convolution is set, a fog domain multi-source fusion attention mechanism is set, and a pixel level and wavelet domain double-effect color correction strategy is adopted.

The invention discloses a fracturing equipment state monitoring and fault diagnosis system and method, and belongs to the technical field of oil and gas field fracturing equipment intelligence. The invention aims to solve the problems that in the prior art, monitoring depends on a single signal, fault early warning lags behind, and the misjudgment rate is high. The method comprises the following steps: collecting multi-source operation data of the fracturing pump in real time; establishing a theoretical pressure indicator diagram, and comparing the theoretical pressure indicator diagram with an actual indicator diagram generated by real-time data to realize first-stage fault judgment; an improved wavelet threshold noise reduction method is adopted to process the signals, and time domain and frequency domain features are extracted; and inputting the processed feature data into the combined diagnosis model for second-stage fault judgment. The joint diagnosis model combines a principal component analysis (PCA) model used for uncalibrated data anomaly detection and a BP neural network model used for calibrated data fault classification. Through deep fusion of the mechanism model and the data driving model, environmental interference is effectively resisted, and the diagnosis accuracy and the operation and maintenance efficiency are remarkably improved.

The invention belongs to the field of atmospheric boundary layer height prediction, and discloses a sea-land atmospheric boundary layer height prediction method and system under a sea fog condition. The method comprises the steps of sample screening and key meteorological element selection, multi-source data preprocessing, boundary layer height calculation, a Richardson number method after critical value optimization, a gas block method, a specific wet method, a potential temperature gradient method and laser radar wavelet covariance transformation calculation. And verifying the calculation method, screening and optimizing the optimal method, and evaluating the performance of the method. According to the method, multi-source observation data and a numerical simulation result are fused. Through a mode of combining physical process analysis and statistical verification, errors of different calculation methods can be quantified, and an algorithm combination most suitable for sea fog conditions can be identified. Calculation deviation caused by special conditions such as weak turbulence and strong temperature inversion in the sea fog environment can be improved.

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 discloses a ship key equipment impact type fault interpretable detection method based on wavelet scattering and time-frequency feature enhancement, and belongs to the field of ship equipment fault detection. According to the method, a vibration signal of ship key equipment is acquired through a sensor, and the vibration signal is converted into a time-frequency image with cross-domain consistency by adopting improved wavelet scattering transform; adaptive enhancement processing based on statistical features is carried out on the time-frequency image to realize noise suppression and fault feature enhancement; and inputting the processed time-frequency image into a pre-training model embedded with class activation mapping to complete fault identification and decision process visualization. Aiming at the impact type fault detection requirement of the ship key equipment, the method solves the problems that a traditional method is weak in generalization ability and cannot explain the diagnosis result, has the advantages of being high in detection precision, high in adaptability and traceable in diagnosis result, and can be widely applied to bearing fault detection of key equipment such as a ship main lubricating oil pump, an oil separator and a turbine.

The invention provides a crustal stress prediction method and device, electronic equipment and a storage medium, and relates to the technical field of seismic survey. The method comprises the following steps: obtaining observation seismic data of seismic wavelets in a strong VTI medium, and constructing a to-be-inverted parameter matrix based on a PP wave reflection coefficient corresponding to the strong VTI medium; constructing a posterior probability function obeyed by an inversion parameter matrix corresponding to the to-be-inverted parameter matrix based on a Bayesian inversion theory and observation seismic data, and determining a target functional based on a prior probability function and a likelihood function corresponding to the posterior probability function; determining medium density and each stiffness matrix coefficient based on an inversion parameter matrix solving result of the target functional, and determining a flexibility matrix of the strong VTI medium based on each stiffness matrix coefficient; and predicting the ground stress distribution of the target profile in the strong VTI medium based on the medium density and the positive strain matrix and the flexibility matrix corresponding to the strong VTI. Therefore, the crustal stress prediction accuracy under the strong VTI medium is improved.