208 results about "Adaptive wavelet" patented technology

The invention discloses an acoustic emission intelligent detection method and system for hydrogen-induced damage of a high-pressure hydrogen system, and the method comprises the steps: collecting a system operation signal through an acoustic emission sensor, carrying out the combined preprocessing of variational mode decomposition and adaptive wavelet threshold noise reduction, restraining noise, constructing a lightweight MobileNet-TCN network, carrying out the deep feature extraction, and carrying out the detection of the hydrogen-induced damage of the high-pressure hydrogen system. GRU and a three-dimensional point cloud technology are fused to realize submillimeter-level positioning of an acoustic emission source, a big data damage case library is associated based on an acoustic emission parameter accumulation model, dynamic assessment and early warning of damage risks are realized, multi-physics field monitoring data are combined, damage classification is optimized through a GCNs (Graph Convolutional Networks), and the above processes are systematically integrated. And full-chain intelligent processing from signal acquisition to risk early warning is completed. The scheme has the advantages of strong anti-interference capability, submillimeter positioning precision, high edge end reasoning efficiency, high damage classification accuracy and dynamic early warning capability, and is suitable for safety monitoring of hydrogen energy storage and transportation equipment.

The invention relates to the field of equipment diagnosis, and particularly discloses an equipment predictive maintenance method based on deep learning, and the method comprises the steps: carrying out the time domain amplitude normalization and frequency domain weighted normalization of training data, and carrying out the dual-channel feature fusion, so as to obtain a fusion feature vector; performing feature extraction on the fused feature vector by using multiple groups of self-adaptive wavelet kernels to obtain a self-adaptive time-frequency feature vector; constructing a weight population through statistical characteristics, and screening the optimal initial weight of the initial diagnosis model; carrying out multi-scale depth feature calculation, time-frequency domain attention feature fusion, fault prototype comparative learning and a pre-constructed total loss function on the adaptive time-frequency feature vector, and carrying out iterative updating on the initial diagnosis model to obtain a diagnosis model; and the equipment is diagnosed through the diagnosis model. Multi-scale feature fusion and a double-path attention mechanism can cooperatively capture short-time impact and a long-period mode, and the limitation of a traditional method in diversified fault scenes is overcome.

The invention belongs to the technical field of artificial intelligence, and relates to an artificial intelligence-based electric energy quality disturbance identification and positioning method, which comprises the steps of constructing an electric energy quality disturbance signal data set, performing segmented preprocessing on electric energy quality disturbance voltage data, enhancing time-frequency joint features and encoding disturbance sensitive areas. And constructing a deep learning model for power quality disturbance identification and positioning, and identifying and positioning the power quality disturbance. According to the invention, through adaptive denoising processing, boundary detection and multi-resolution time-frequency feature extraction, the identification precision and positioning precision of power quality disturbance are significantly improved; self-adaptive wavelet denoising and dynamic segmentation are combined, noise interference is effectively suppressed, and the edge characteristics of voltage sudden change points are kept; according to the dual-task sharing network, disturbance identification and positioning tasks are cooperatively optimized, so that the network can consider disturbance classification and time positioning at the same time; and through Bayesian reasoning, the system can output confidence estimation, provides credibility quantification of identification and positioning results, and effectively improves the reliability of the system.

The invention discloses a digital twinning system and a construction method thereof, and relates to the technical field of digital twinning, and the construction method comprises the steps: carrying out the classified collection of multi-source heterogeneous data of a digital twinning entity, carrying out the filtering and denoising through a self-adaptive wavelet threshold, mapping the data into a multi-dimensional feature vector, screening key features, and outputting the key feature data; the method comprises the following steps: establishing a physical mechanism layer based on a general physical rule, defining core physical parameters and a constraint equation, initializing a particle swarm to establish a data driving layer, constructing an LSTM time sequence prediction module, executing particle filter state calibration, and selecting a model to configure a communication protocol to establish a virtual-real interaction layer, so as to realize state mapping and instruction feedback of a digital twin entity and a virtual model; and calculating virtual and real state deviation in real time and performing attribution diagnosis, adjusting model parameters or key features according to a deviation source, generating an optimization parameter combination based on a long time sequence prediction result and performing simulation verification in a virtual environment, and controlling entity parameter adjustment through an instruction feedback channel so as to realize predictive optimization.

A system and method are disclosed for low-light image enhancement using hierarchical adaptive wavelet decomposition with cross-scale feature fusion. The system analyzes a raw input image to determine image characteristics and preprocessing parameters. A hierarchical adaptive wavelet decomposition process creates a variable-depth decomposition tree comprising frequency domain nodes, with decomposition depth determined by local image complexity. Cross-scale feature fusion implements attention mechanisms between nodes at different decomposition levels, enabling bidirectional information flow across scales. A dynamic network pool allocates specialized neural networks to process nodes based on their frequency characteristics, with weight sharing between similar nodes for efficiency. An adaptive reconstruction engine traverses the decomposition tree using learned filters and multi-scale residual learning to produce an enhanced image. The hierarchical approach enables superior low-light image enhancement by allocating computational resources based on content complexity, achieving better quality than fixed decomposition methods while maintaining compatibility with existing image signal processing pipelines.

The invention provides an under-mine video stream dynamic denoising method based on multi-modal fusion, which comprises the following steps: constructing a time sequence synchronous fusion mechanism of visible light, infrared and laser radar data, and realizing time-space alignment of multi-source heterogeneous data; a dynamic noise model is established by introducing a fractional calculus optical flow field concept and combining a Gaussian mixture model, so that a dynamic noise region is accurately identified; an improved self-adaptive wavelet threshold function is constructed, a function threshold parameter can be linked with a dust concentration sensor in real time, and the de-noising intensity is dynamically adjusted according to the actual dust concentration; designing a dual-path feature enhancement neural network to effectively separate and enhance structural features and texture features in the video image; a cascaded detection decision system is created, a lightweight network is used as a primary detector, a high-confidence detection result is directly output, and a low-confidence detection result is input into a Transform correction module for secondary reasoning. According to the invention, dynamic denoising, feature enhancement and target intelligent monitoring of the video stream under the mine can be realized.

The invention provides a partial discharge on-line monitoring method and system based on multi-modal fusion and adaptive noise reduction, and the method comprises the steps: employing an ultrahigh frequency UHF sensor, a miniature ultrasonic sensor, and a miniature detector for gas dissolved in oil, which are disposed on a transformer; synchronously acquiring electric signals, sound signals and characteristic gas concentration data in oil generated in the partial discharge process; performing format unification, abnormal value elimination and time alignment processing on the electric signal, the sound signal and the gas concentration data in an edge calculation unit to obtain aligned multi-source original data; performing adaptive wavelet noise reduction processing on the electric signal to obtain a de-noised UHF signal; respectively extracting time domain, frequency domain and chemical features from the de-noised UHF signal, the aligned sound signal and the gas concentration data to form a multi-dimensional feature vector; and inputting the multi-dimensional feature vector into a pre-trained defect traceability model, and outputting a partial discharge defect type, severity level and development trend prediction result.

The invention discloses a phase modifier unit state monitoring and load control method and system for reactive power compensation of a power grid, belongs to the technical field of automatic control of power systems, and mainly solves the problem that service life loss is aggravated or a control target is single due to the fact that the health state of equipment is not fully considered when an existing phase modifier unit responds to reactive power requirements of the power grid. The method comprises the following steps of: calculating reactive vacancy and extracting vibration frequency band energy characteristics by acquiring voltage and frequency of a power grid as well as vibration, temperature and exciting current signals of a unit in real time, and constructing a health state index HSI in combination with self-adaptive wavelet basis selection; taking minimum equipment life loss and optimal power grid voltage stability as multiple objectives, dynamically generating a load control instruction by adopting a particle swarm algorithm, realizing weight adaptive adjustment based on HSI, and introducing an online life loss model as a constraint condition; finally, reactive power output is adjusted through an excitation system, and cooperative control of power grid stability and equipment life extension is achieved.

The invention discloses a distribution transformer state evaluation method, system and device and a storage medium, and the method comprises the steps: obtaining current and voltage long sequence data through high-frequency sampling, and forming a high-quality data set through expert labeling; signal preprocessing is realized by adopting dual-channel adaptive wavelet packet denoising and cross-correlation sinchinger interpolation phase correction; constructing an asymmetric convolution pyramid to extract current high-frequency and voltage low-frequency multi-scale features, and performing cross-modal fusion by using compressed multi-head attention and a channel-space double-gating mechanism; dynamic random depth regularization is introduced, and a time domain loss function in a fault sensitive period is focused, so that high-accuracy and low-delay real-time state evaluation is realized; the capacity of capturing and recognizing early weak fault features of the distribution transformer in a complex operation environment is remarkably enhanced, and therefore more sensitive and more accurate state early warning is achieved.

The invention discloses an airborne radar identification method based on multi-scale signal decomposition and depth feature fusion, belongs to the technical field of radar target identification, is used for unmanned aerial vehicle radar target identification, and comprises the following steps: decomposing an original dynamic radar cross section signal into a plurality of intrinsic mode components by adopting an adaptive noise complete set empirical mode decomposition method; self-adaptive wavelet soft threshold denoising is carried out on all the components in a unified mode so as to improve the signal-to-noise ratio and suppress background interference; effective components are screened out by calculating correlation coefficients of all intrinsic mode components and original signals, a radar cross section signal sequence after noise reduction is reconstructed, and multilevel feature fusion is carried out; and inputting the feature vector into a stacked auto-encoder deep neural network classification model, and introducing a particle swarm optimization algorithm to optimize the classification model. According to the method, the signal-to-noise ratio of the signal and the recognition accuracy of the classification model in a low signal-to-noise ratio environment are remarkably improved, and high-precision recognition and classification of different types of radar cross section targets are realized.

The invention relates to the technical field of fault diagnosis, and discloses a cable fault diagnosis method, system and terminal, and the method comprises the steps: applying a broadband excitation signal to a target cable, so as to excite the target cable to generate a first response signal; extracting multi-dimensional time-frequency features of the first response signal after adaptive wavelet threshold denoising, and matching a fault template of a preset cable reference model according to energy distribution features in the multi-dimensional time-frequency features; determining a fault area in a fault template corresponding to the target cable according to the propagation time and the propagation speed of the first response signal; applying the narrowband excitation signal to a fault area of the target cable to obtain an impedance spectrum; the resonant frequency point based on the impedance frequency spectrum and the corresponding impedance amplitude value are compared with a typical fault mode in a fault feature database, and the fault type and the fault position of the target cable are obtained; generating a diagnosis report of the target cable according to the fault type and the fault position; the cable fault diagnosis accuracy can be improved.

The invention relates to a GIS partial discharge optical detection method and device, and a medium. The method comprises the following steps: collecting a GIS partial discharge optical signal through a light guide rod, and carrying out the preprocessing of the GIS partial discharge optical signal; self-adaptive wavelet packet decomposition is carried out on the preprocessed signals, noise signals are separated through a self-adaptive threshold value adjustment algorithm, multi-scale time-frequency characteristics are extracted from the signals after noise separation, and according to the self-adaptive wavelet packet decomposition, a wavelet basis function is selected in a self-adaptive mode according to statistical indexes of the extracted signals so as to carry out wavelet packet decomposition; and constructing a lightweight convolutional neural network model, classifying the extracted multi-scale time-frequency features, and identifying a partial discharge signal. Compared with the prior art, the method has the advantages that the anti-interference capability is high, weak partial discharge signals can be effectively extracted, and the real-time requirement is met.

The invention relates to the cross technical field of geological disaster monitoring and signal processing, and provides a GNSS tunnel portal slope deformation time sequence noise reduction method, system and medium combining CEEMD and an adaptive wavelet packet threshold function, the method integrates the advantages of CEEMD and wavelet packet adaptation, processing of mixed entropy demarcation, adaptive threshold, dynamic smoothing and multi-path compensation is used, and the noise reduction efficiency is improved. Useful deformation features can be reserved while more comprehensive and finer noise reduction is performed, smooth compression can be performed in a large signal part through improved threshold function processing, excessive suppression can be avoided, smooth transition is performed in a small signal part through exponential attenuation, features of the large signal part are reserved, noise suppression is realized, signal details are reserved to the greatest extent, and the noise suppression effect is improved. The noise reduction requirement of tunnel portal side slope monitoring data is met, and particularly in side slope monitoring of a tunnel portal in a complex environment, the precision and reliability of GNSS deformation monitoring are improved.

The invention relates to a building structure health monitoring method and system based on machine learning, and the method specifically comprises the following steps: firstly, building a target building three-dimensional numerical model through finite element simulation, generating a simulation signal, injecting Gaussian white noise, and adjusting model parameters to form a data set with health category labels; performing data enhancement by combining adaptive wavelet denoising with dynamic normalization, and extracting and enhancing high-resolution time-frequency features through adaptive window short-time Fourier transform and adaptive frequency band enhancement; then, a neural network model fusing structure physical prior guidance and multi-scale space-time interaction is constructed, a feature matrix is modulated, fused and coded to obtain a refined feature vector, and damage state probability distribution output is achieved; and training is carried out by using a feature consistency and prediction smoothness regularization term constraint model, and finally, the trained model is deployed, so that building structure health state evaluation and safety early warning are realized, the monitoring accuracy and reliability are improved, and effective technical support is provided for building safety guarantee.

The invention discloses a vegetable disease incubation period detection method and system based on bimodal time sequence collaborative fusion, and the method comprises the steps: obtaining a leaf image through building an acquisition environment, constructing a training sample data set, and constructing a downy mildew incubation period spectral feature adaptive enhancement (AW-FPF) module; the method comprises the following steps: decomposing a hyperspectral signal into low-frequency and high-frequency components through spectrum time sequence adaptive wavelet decoupling, obtaining an enhanced feature tensor through spectrum multi-scale pathological feature frequency-time dual-path aggregation fusion frequency domain and time domain paths, and obtaining a first feature sequence through weighted screening by using a multi-head attention mechanism; a downy mildew incubation period prediction (HyChl-TFNet) model containing a hyperspectral branch, a chlorophyll fluorescence parameter branch, a feature fusion branch and a classifier is constructed, bimodal features are processed and fused to output a day number prediction result, accurate recognition of the downy mildew incubation period is achieved, the detection precision can be controlled to the day number level, and the detection accuracy is improved. And an accurate time basis is provided for early prevention and control of diseases.

The invention belongs to the technical field of wireless sensing and artificial intelligence, and discloses a high-robustness non-contact accurate electrocardiogram monitoring method based on a millimeter wave radar. Firstly, the distance and angle of a potential target are obtained through distance fast Fourier transform and digital beam forming technologies, and static background removal and thoracic cavity position detection are achieved in combination with mean filtering and a two-dimensional constant false alarm rate algorithm. And then a continuous phase is extracted by using a differential cross multiplication method, a two-step heartbeat-related phase extraction scheme is designed, body micro-motion and breathing interference are removed by adopting B-spline fitting and differential operation respectively, and a stable heartbeat-related phase signal is obtained. A heart rate-guided adaptive wavelet decomposition method is designed to obtain multiband features, and time-frequency joint features are extracted through a double-branch attention mechanism and a gating fusion part. Finally, the time-frequency joint features are input into an electrocardiosignal time domain reconstruction module based on a TransUNet architecture, high-quality reconstruction of electrocardiosignals is achieved, and the method has the advantages of being non-contact, continuous and convenient.

The invention discloses a vibration signal processing method based on self-adaptive wavelet packet and deep learning fusion, and belongs to the field of sewage plant equipment fault diagnosis. The method aims at solving the problems that traditional signal processing is poor in flexibility, the non-stationary signal processing capacity is weak, the deep learning data requirement is large, and the high-frequency weak feature capturing capacity is limited. According to the method, the high-frequency acceleration sensor is adopted, the vibration signals of the sewage plant equipment are accurately collected, the self-adaptive wavelet packet decomposition technology is applied, the primary function is dynamically selected, the number of decomposition layers is optimized, self-adaptive threshold noise reduction is achieved, and the signal processing quality is improved. Meanwhile, in combination with a one-dimensional convolutional neural network and a bidirectional LSTM model, local and global features of the signal are extracted respectively, and pre-processed data are formed through gating weighted fusion. According to the method, the signal-to-noise ratio and the weak fault detection rate are remarkably improved, feature redundancy and data requirements are reduced, the calculation efficiency and diagnosis accuracy are improved, the method is suitable for sewage plant equipment fault diagnosis, and the industrial applicability is enhanced.

The invention relates to the technical field of water conservancy projects, in particular to a dam construction period displacement monitoring and safety early warning system and method based on physical information deep learning, and the method comprises the following steps: S1, data preprocessing: carrying out the self-adaptive wavelet denoising and auto-encoder reconstruction of original displacement data, and completing the standardization processing; s2, hybrid model displacement prediction: after training a PINN-LSTM / GRU model through historical monitoring data, predicting future displacement, and using NeuralProphet to correct a prediction residual error; and S3, multi-factor dynamic safety evaluation and early warning: adjusting threshold parameters and weight coefficients in combination with a construction stage, dynamically evaluating a safety risk level, and issuing the safety risk level through a graded early warning signal. According to the invention, high-precision displacement prediction and multi-factor dynamic safety early warning are realized through fusion of physical information and deep learning, and the monitoring reliability and early warning response capability of the dam construction period are effectively improved.

The invention relates to the technical field of geological survey, in particular to an unmanned aerial vehicle multispectral geological survey method and system, and the method comprises the steps: fusing a multispectral image, a digital elevation model, geophysics and historical geological data, systematically constructing a geological feature priori knowledge model, including lithology, construction and alteration feature libraries, and mapping with multispectral data; multi-scale geologic features are extracted through adaptive wavelet transform and morphological analysis, and feature weight adaptive adjustment is achieved; geological units are accurately divided by adopting geological scene perception superpixel segmentation and combining geological boundary constraint and similarity recursion combination; identifying an interference mode, generating an adaptive filtering matrix, and enhancing image quality; cooperatively interpreting multi-source information by using a deep auto-encoder network to generate a high-precision geological interpretation map and a confidence map; geological professional knowledge is introduced, so that the geologic body recognition accuracy is remarkably improved; the adaptive flight control strategy ensures the consistency of complex terrain data, and improves the precision and efficiency of geological survey.

The invention discloses a marine geological profile rapid imaging and stratigraphic structure inversion system and a marine geological profile rapid imaging and stratigraphic structure inversion method. Comprising a multi-source marine geological data synchronous acquisition module, an acquisition signal noise reduction and space-time registration module, a stratum interface initial positioning and feature enhancement module, a multi-scale geological section rapid imaging module, a stratum physical parameter inversion initialization module, a constraint stratum structure inversion optimization module and an inversion result verification and visual output module. According to the invention, through GPS and Beidou synchronization and Kalman registration, the problem of spatial-temporal dislocation of multi-source data is solved, and deep fusion of sound wave, gravity and magnetic survey data is realized; based on self-adaptive wavelet threshold noise reduction, effective signals are reserved, and meanwhile, marine environment noise interference is greatly reduced; the multi-scale fusion algorithm gives consideration to shallow details and deep structures, and meets the requirements of different exploration targets.

The invention discloses an adaptive signal decomposition and denoising method, device and system, and a storage medium. The method comprises the following steps: acquiring an original signal; carrying out global optimization on key parameters of continuous variational mode decomposition (SVMD) by adopting an improved snake optimization algorithm ISO which introduces a dynamic bidirectional population evolution dynamics (DBPED) strategy, and carrying out accurate self-adaptive decomposition on an original signal; and for all the decomposed modal components IMFs, calculating correlation coefficients of all the decomposed modal components IMFs and an original signal, matching different adaptive wavelet denoising strategies for each component according to the magnitude of the correlation coefficients to carry out differencing and refining processing, and finally reconstructing all the processed components to realize deep denoising of the signal. By adopting the technical scheme of the invention, aiming at a time sequence signal under a non-stable and strong noise background, the signal and the noise are accurately separated through a self-adaptive decomposition means, and a high-quality signal with high fidelity and high signal-to-noise ratio is finally reconstructed.

The invention relates to the technical field of motor detection, in particular to a generator set fault diagnosis and detection method based on deep learning, and the method comprises the steps: firstly, synchronously collecting three-phase voltage, current and rotating speed signals, and constructing a multi-dimensional time sequence matrix through data cleaning and sliding window segmentation; then, carrying out multi-scale decomposition on the matrix by adopting adaptive wavelet packet transformation, combining each frequency band reconstruction coefficient with an original signal channel, and constructing an enhanced feature tensor; then, a CNN-BiLSTM parallel network is constructed; and finally, dynamically fusing the features of the two branches through a self-adaptive weighted fusion strategy, and inputting a multi-layer full-connection classifier to output a fault type. According to the method, early weak fault features are effectively enhanced, bearing faults, rotor eccentricity, electrical imbalance and composite faults thereof can be accurately recognized, the intelligent level and accuracy of fault diagnosis of the generator set are remarkably improved, and the method can be widely applied to online monitoring and health management of power generation equipment of a power system.

The invention provides a remote sensing image rotating target detection method based on dual-path feature enhancement, and relates to the technical field of computer vision and remote sensing image processing. The method comprises the following steps: constructing a dual-path feature enhanced remote sensing image rotating target detection network comprising a texture enhancement path and a direction modeling path; wherein in the texture enhancement path, a self-adaptive wavelet reconstruction module is adopted to enhance texture details and edge features in the input feature map; in the direction modeling path, performing spatial alignment and direction consistency modeling on the input feature map by adopting a multi-scale angle guide deformable encoder, and extracting features containing structure and direction information; fusing the features output by the two paths; constructing a joint loss function, and performing end-to-end training on the network; and detecting and positioning a rotating target in the remote sensing image by using the trained network. By adopting the method, the detection precision and robustness of multi-direction, multi-scale, densely distributed and small-size targets in the remote sensing image can be effectively improved.

The invention relates to a SiC MOSFET (Metal-Oxide-Semiconductor Field Effect Transistor) surge failure real-time discrimination method and system, and the method comprises the steps: 1, collecting SiC MOSFET surge waveform data, and carrying out the multi-scale physical feature extraction, and obtaining a physical frequency band energy feature; 2, performing feature alignment on the physical frequency band energy features to form a standardized feature vector; and 3, constructing a surge failure prediction model, performing temperature compensation on an output result of the surge failure prediction model, and judging a failure state. Current and voltage waveforms in a surge event are captured through a high-speed data acquisition system, key physical frequency band energy features are extracted by adopting an adaptive wavelet packet decomposition technology, a pulse width normalization coding model is constructed to realize feature alignment, and a physical guide degradation path auto-encoder model is designed to perform failure prediction. The method innovatively solves the problem of failure discrimination of surges with different pulse widths, and has the characteristics of high precision and strong real-time performance.

The invention relates to an intelligent dam safety state prediction method and system based on a time sequence large model, and the method comprises the steps: constructing a space-time embedding mechanism comprising dynamic graph position coding and adaptive wavelet position coding through fusing sensor time sequence data, environment factor data and unstructured text data; and cross-time and cross-dam knowledge migration is realized by adopting a multi-scale memory enhancement encoder, and potential causal factors are identified through a causal decoupling decoder, so that the model interpretability is improved. And the prediction result is processed by the physical constraint output layer to ensure that the engineering mechanics law is met. The system supports collaborative deployment of an edge end and a cloud end, the edge end processes data in real time and generates preliminary prediction, and the cloud end operates a complete model and periodically updates edge model parameters. The method is suitable for real-time prediction and grading early warning of the health state of the dam structure, and is widely applied to intelligent water and electricity, infrastructure monitoring and AI-driven predictive maintenance systems.

The invention provides a multi-band radio signal abnormal interference detection method, and relates to the technical field of radio signal interference detection. The method comprises the following steps: acquiring a to-be-detected multi-band radio signal to obtain signal data; de-noising the signal data by using an adaptive wavelet threshold de-noising algorithm in combination with a time-frequency analysis method to obtain pre-processed data; performing feature extraction on the preprocessed signal data by applying a feature extraction network based on deep learning to obtain a multi-dimensional feature vector; performing time sequence analysis on the multi-dimensional feature vector by using a long short-term memory network so as to identify a normal state and an abnormal state of the signal data; performing deep feature extraction on the signal data of the abnormal state by using a convolutional neural network, and determining a suspicious signal and a first abnormal signal according to a threshold value; and determining a normal signal and a second abnormal signal according to the suspicious signal. According to the invention, the problem of low radio signal interference detection accuracy and efficiency in the prior art is solved.

The invention relates to the technical field of power grid detection, and discloses a power grid fault identification method, device and equipment based on deep learning, and a medium, and the method comprises the steps: collecting the multi-modal data in the operation process of a power grid in real time through a sensor and monitoring equipment disposed at each key node of a main network of the power grid; denoising and time-frequency feature extraction are carried out on the multi-modal data through adaptive wavelet transform and Hilbert-Huang transform, and multi-modal features are obtained; carrying out dynamic weighted fusion on the multi-modal features by adopting a self-adaptive multi-head attention mechanism to obtain fused features; inputting the fused features into a fault recognition model for fault recognition to obtain a corresponding fault recognition result; and performing fusion decision based on the fault identification result, generating a fault processing suggestion, and feeding back the suggestion to a dispatching center and operation and maintenance personnel of the power grid main network in real time. The fault type, position and severity of the power grid can be accurately identified in real time, and a powerful guarantee is provided for safe and stable operation of the power grid.

The invention relates to the field of intelligent manufacturing and printing processes, and discloses a digital printing equipment image optimization system based on dynamic texture mapping, which comprises the following steps: constructing a dynamic manifold representation space of printing parameters, and realizing global optimization through a curvature-driven quantum annealing algorithm; designing a contact coefficient online correction mechanism and a multistage fault-tolerant protocol, and establishing differential homeomorphic mapping from a quantum optimization result to an execution instruction; manifold adaptive wavelet transform is adopted to extract optimization features, and a pulse shaping control quantity is generated in combination with deep reinforcement learning. According to the method, traditional optimization local extreme value limitation is broken through, self-adaptive control under material characteristic drift is achieved, the problem of control mismatch caused by multi-physics field coupling is solved, and the precision stability and working condition adaptability of complex texture printing are improved.

The invention relates to the technical field of biological radar signal processing, in particular to an FMCW radar-based heart beat interval estimation method, which comprises the following steps of: S1, converting a chest vibration echo phase time sequence obtained by irradiating a chest area of a monitored object by an FMCW radar into an acceleration time sequence by using a second-order time derivative; s2, dynamically mapping the center frequency and the wavelet order in a preset frequency analysis interval, constructing a self-adaptive wavelet dictionary, then executing multi-order wavelet time domain convolution operation on the acceleration time sequence by using the dictionary, and aggregating time-frequency energy distribution to generate a time-frequency energy diagram; and S3, performing a deconvolution operation reconstruction strategy based on an energy-guided multi-stage time-frequency feature screening technology and wavelet function conjugation, generating an approximate time domain signal of heart beat vibration, and completing heart beat information inversion. According to the method, the accuracy and robustness of IBI extraction can be effectively improved under the condition of low signal-to-noise ratio, so that stable monitoring of light and moderate HRV (heart rate variability) is supported.

The invention relates to an electroencephalogram signal multi-source physiological artifact real-time removal method and system based on a self-adaptive hybrid model. The types of artifacts such as electro-oculogram, myoelectricity and the like are dynamically identified through fusion preprocessing, time-frequency domain feature rapid extraction and a lightweight online classifier; an intelligent hybrid processing engine is innovatively constructed, and an optimal denoising strategy is adaptively selected according to an artifact type: an improved regression model is adopted for ocular artifacts, adaptive wavelet threshold or ICA component elimination is adopted for myoelectric artifacts, and component separation is performed on hybrid artifacts in combination with sparse representation and variational mode decomposition; and parameters are optimized online through a real-time feedback mechanism. According to the method, additional hardware reference electrodes are not needed, high-precision artifact removal is achieved under millisecond-level delay, the electroencephalogram signal quality is remarkably improved, and the method is suitable for scenes with strict requirements for real-time performance and robustness such as brain-computer interfaces and nerve disease auxiliary diagnosis.