136 results about "Adaptive wavelet" patented technology

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 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 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 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 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 the technical field of automobile electronic intelligent fault diagnosis, and discloses a multi-modal data fusion automobile air conditioner controller abnormal state monitoring method, which comprises the following steps: deploying five types of intelligent sensor nodes at key positions of an automobile air conditioner system to realize hardware-level time synchronization and adaptive sampling; a fast ICA algorithm is combined with sliding window processing to realize signal separation, a self-adaptive wavelet basis selection mechanism is designed to carry out time-frequency analysis, and principal component analysis is used to carry out feature dimension reduction; constructing a lightweight attention fusion network, dynamically adjusting different modal feature weights, and mining feature relevance through a cross-modal interaction module; a hierarchical progressive anomaly detection architecture is designed, a statistical method and a neural network detection result are fused, a time consistency constraint mechanism is established, and multi-time scale anomaly prediction is realized; and fault type identification and severity evaluation are carried out in combination with an expert knowledge base, and a multi-priority protection strategy and an emergency plan are formulated.

The invention discloses a phase modifier bearing fault diagnosis method, which belongs to the technical field of power equipment fault diagnosis, and comprises the following steps of: analyzing independent components, performing blind source separation on a received phase modifier bearing vibration signal, and extracting three types of independent source signals of impact, abrasion and noise; the gradient driving window length is self-adaptive, and the window length of short-time Fourier transform is dynamically adjusted based on the instantaneous frequency gradient of the independent source signal; wavelet packet frequency band energy screening: performing wavelet packet decomposition on the signal after window length adaptive processing, screening a fault characteristic frequency band based on an energy contribution rate, and reconstructing the signal; and enhancing stochastic resonance, and inputting the reconstructed signal into a stochastic resonance system. According to the method, independent component analysis, gradient driving window length self-adaption, wavelet packet frequency band energy screening and stochastic resonance enhanced fourth-order diagnosis chain are constructed, so that multi-stage cooperative processing of phase modifier bearing faults is realized, and the technical problems of time-frequency resolution contradiction, insufficient feature decoupling and weak generalization ability are effectively solved.

The invention relates to the technical field of printing machine faults, and discloses a printing machine fault diagnosis method based on acoustic emission detection, which comprises the following steps of: constructing a printing machine acoustic emission signal acquisition system which comprises a plurality of acoustic emission sensors, a signal preprocessing module and a data transmission unit, the plurality of acoustic emission sensors are distributed at a key transmission part, a printing execution part and a supporting structure of the printing machine so as to synchronously collect acoustic emission original signals at different positions; the method comprises the following steps: performing multi-dimensional preprocessing on an acquired acoustic emission original signal, wherein the preprocessing comprises noise reduction processing based on a self-adaptive wavelet threshold value; acoustic emission signals of key parts of the printing machine are synchronously collected through distributed acoustic emission sensors, time domain, frequency domain and time-frequency domain features are extracted in combination with multi-dimensional preprocessing, features and position weights are dynamically given by using a fault diagnosis model fused with an attention mechanism, and accurate recognition of the fault type and position of the printing machine is achieved.

The invention discloses a self-adaptive hybrid expert method for photovoltaic power prediction, which comprises the following steps of: acquiring an input sequence Xenc of historical photovoltaic power data and a corresponding timestamp sequence, and performing first-stage decomposition on the input sequence Xenc to obtain a long-term trend component xtrend and a fluctuation component; an input sequence is decomposed into long-term trend and fluctuation components through moving average. Secondly, for the fluctuation component, a hybrid expert module is used to predict. Inputting the long-term trend component xtend into a trend prediction model ftend to obtain a first prediction result, and inputting the fluctuation component xsearch into a hybrid expert prediction module to obtain a second prediction result; and performing weighted summation on the expert prediction output according to the routing weight Wgating, combining the first prediction result and the second prediction result, and generating a final photovoltaic power prediction sequence yfinal according to yfinal = ytrend + ysearch.

The invention discloses a YOLOv11 road disease detection method and system based on multi-scale feature enhancement. The method comprises the steps of road image acquisition and marking, adaptive learning rate adjustment and data enhancement, construction of a YOLOv11 model embedded with a sub-pixel level edge enhancement module and a wavelet transform feature extraction module, staged training, model evaluation, disease detection and the like. Wherein the sub-pixel-level edge enhancement module is used for enhancing fine crack features through multi-operator fusion and a sub-pixel convolution technology; the wavelet transformation module improves the multi-scale feature perception capability through adaptive wavelet basis selection and a multi-stage decomposition and reconstruction mechanism. The system correspondingly comprises a data preprocessing module, a model building module, a training optimization module and an evaluation deployment module. On the basis of keeping the real-time performance of the YOLOv11, the detection precision and robustness of multi-scale diseases in low-contrast, sub-pixel-level cracks and complex environments are remarkably improved, and the method is suitable for road maintenance and safety monitoring scenes.

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

The invention relates to the technical field of biotechnology detection data processing, discloses a PCR detection fluorescence intensity data processing system and method, and aims to solve the problems of low signal-to-noise ratio of fluorescence signals, inaccurate baseline deduction and poor Ct value judgment consistency in the prior art. The method comprises the following steps: processing an original fluorescence signal through adaptive wavelet threshold noise reduction, dynamically determining and correcting a baseline interval, extracting amplification curve characteristic parameters, dynamically adjusting a fluorescence threshold line according to amplification efficiency, identifying an abnormal amplification mode by using a pre-trained deep learning model, and finally integrating results to generate a comprehensive detection report. By adopting the technical scheme, the signal-to-noise ratio of the fluorescence signal and the baseline deduction precision can be remarkably improved, the consistency of the Ct value is improved, multiple abnormal amplification modes are automatically detected, and a full-automatic processing flow is realized.

The invention discloses an image small target detection method based on a fusion feature visual model, and relates to the technical field of computer visual detection. And the fusion feature visual detection model performs the following processing on an input image and outputs a detection result: adopting adaptive Daubechies wavelet transform to enhance high-frequency details, combining multi-scale feature fusion with a double attention mechanism to enhance small target feature expression, utilizing regression calculation to predict bounding box coordinates, and realizing category probability distribution prediction based on a Softmax function. A multi-task loss function and an Adam optimizer are adopted for end-to-end training, and the model performance is improved by balancing classification and regression loss. The method effectively solves the problems that small target features are deficient and are susceptible to background interference, significantly improves the detection precision while maintaining the calculation efficiency, and is especially suitable for unmanned aerial vehicle patrol and other practical application scenes.

The invention discloses a GIS fault intelligent identification and visual positioning method. According to the technical scheme, the method comprises the steps that S1, a multi-source data collection platform is built, S2, an adaptive wavelet packet decomposition algorithm is adopted, and an initial point cloud is obtained; s3, constructing a GIS equipment three-dimensional point cloud topology model based on the initial point cloud; s4, setting a correlation degree threshold, screening effective candidate areas, and preliminarily determining a defect position range; and S5, identifying a fault type by adopting an adaptive quantum particle swarm optimization-graph attention network model, and outputting a fault identification result and a three-dimensional visual positioning report. The method is mainly used for fault feature extraction, defect type identification, accurate positioning and visual operation and maintenance analysis of gas insulated switchgear of various voltage classes.

The invention provides a hydroelectric generating set bolt looseness monitoring method and device, and relates to the technical field of bolt looseness monitoring. The method comprises the following steps: arranging a vibration sensor and an ultrasonic sensor in a key area of a hydroelectric generating set, synchronously collecting a vibration signal and an ultrasonic signal, and recording the vibration signal and the ultrasonic signal as original signals; carrying out adaptive wavelet threshold noise reduction processing on the original signal to obtain a sound vibration signal to be diagnosed; building a diagnosis model based on the conditional generative adversarial network and a support vector machine; extracting a time domain feature and a frequency domain feature from the acoustic vibration signal to be diagnosed, obtaining a working condition parameter vector of the hydroelectric generating set, and splicing the time domain feature, the frequency domain feature and the working condition parameter vector to obtain a fusion feature vector; and inputting the fusion feature vector into a diagnosis model, and monitoring the loosening state of the bolt through the diagnosis model. According to the invention, effective, real-time and comprehensive bolt loosening state monitoring can be realized.

The invention relates to the technical field of mechanical fault diagnosis, and discloses a deep learning-based tapered roller bearing fault diagnosis method and system, and the method comprises the steps: collecting original vibration signals of a tapered roller bearing in different operation states through an acceleration sensor, carrying out the segmentation of the collected original vibration signals according to a preset time interval, and carrying out the segmentation of the collected original vibration signals; obtaining a plurality of vibration signal samples; carrying out noise reduction processing on the vibration signal sample by combining a self-adaptive wavelet noise reduction algorithm and a soft and hard mixed threshold value, and carrying out normalization processing on the vibration signal sample after noise reduction to obtain a preprocessed vibration signal sample; inputting the preprocessed vibration signal sample into an MSCNN-BiGRU-Attention model, using a Softmax activation function to output a fault probability, and using a fault type with the maximum probability as a diagnosis result; an early warning threshold value is dynamically adjusted based on the historical operation data and the working condition, and corresponding early warning is triggered for the tapered roller bearing according to the diagnosis result and the early warning threshold value; according to the invention, the fault diagnosis efficiency is improved.

The invention discloses a DSP (Digital Signal Processor) online self-adaptive wavelet denoising and concentration inversion system and method for TDLAS (Tunable Diode Laser Absorption Spectroscopy) measurement, and relates to the technical field of gas spectrum detection. Comprises: a laser module for emitting semiconductor laser light; the absorption cell module is used for absorbing specific components of the laser through a gas absorption cell formed by the to-be-detected gas; the detection and amplification module is used for converting the transmission light signal into an electric signal and amplifying the electric signal through a photoelectric amplifier; the phase-locked amplification module is used for filtering and extracting noise; the digital signal processing module is used for performing digital sampling, performing wavelet denoising and signal reconstruction, performing wavelet inverse transformation reconstruction on a denoised waveform, and extracting a second harmonic characteristic parameter; and the upper computer module is used for obtaining the concentration of the gas to be detected based on a pre-established gas concentration inversion model according to the second harmonic characteristic parameters. The method is suitable for high-precision detection of trace gas, and has the obvious advantages of strong real-time performance, high precision, good system stability, good portability and the like.

The invention discloses an asphalt concrete mixing quality evaluation method based on digital twinning. A power sensor, a three-axis vibration acceleration sensor and an infrared temperature sensor array are arranged at key nodes of a stirring cylinder, and multi-physical field data in the stirring process are collected in real time and mapped to a three-dimensional digital twinborn model. A self-adaptive wavelet threshold algorithm is adopted to carry out noise reduction on power and vibration signals, and a dynamic time warping algorithm is utilized to realize time alignment of multi-source signals. High-dimensional features such as fluctuation variance, power spectral density peak ratio, vibration entropy, temperature variation coefficient and the like are extracted, and significant features are screened through an XGBoost model; the method comprises the following steps: constructing a time sequence evolution rule of a long-short-term memory network capture feature with a time attention mechanism, automatically weighting influence weights of different stirring stages, and finally outputting a comprehensive uniformity index to quantitatively evaluate the stirring quality of asphalt concrete, thereby providing technical support for intelligent production and quality online management and control.

The invention provides a multi-resonance enhanced photoacoustic spectrometry multi-component waste gas detection method, and relates to the technical field of photoacoustic spectrometry gas detection, a plurality of laser beams with different frequencies are divided into a reference light path and a detection light path, photoacoustic signals generated by the two light paths are respectively collected, adaptive wavelet decomposition is carried out on the photoacoustic signals, and a multi-resonance enhanced photoacoustic spectrometry multi-component waste gas detection result is obtained. Performing real-time calibration compensation on the detection light path signal based on the reference light path signal to obtain a calibration resonance characteristic value of the target component; the method comprises the following steps: arranging a multistage acoustic resonant cavity array in a waste gas sample cell, adjusting the phase difference between each stage of resonant cavity, forming a directional acoustic wave enhancement field, obtaining an enhanced calibration resonance characteristic value, and carrying out combinatorial optimization on the characteristic values of a target component under excitation of different frequencies by adopting a self-adaptive weighted fusion algorithm to obtain a concentration value of the target component. According to the invention, the energy and the signal-to-noise ratio of the photoacoustic signal are obviously improved, and high-sensitivity and high-precision detection of multi-component gas is realized.

The application relates to the technical field of analytical chemistry detection, and specifically discloses a rare earth ion chromatographic online analysis and detection method and system, which adopts a chromatography-mass spectrometry combined system, realizes multi-scale time-frequency decomposition of signals by constructing an adaptive wavelet base function library, accurately identifies and classifies interference types in combination with a convolutional neural network and vacuum degree coupling analysis; a U-Net generator with an attention mechanism and a multi-scale discriminator are designed for signal repair aiming at repairable interference; a state transition model containing a mass-to-charge ratio database is established, empirical mode decomposition and adaptive filtering technology are used to realize dynamic calibration of a mass axis; quantitative accuracy is ensured through double internal standard correction and a triple verification mechanism; when unrepairable interference is detected, a hierarchical self-checking program is started, and fault diagnosis is carried out in combination with spectrum fingerprint analysis.

The invention discloses a method and a device for positioning a low-frequency oscillation source of a wind-thermal bundling system. The method comprises the following steps: acquiring multi-source signals of the wind-thermal bundling system; carrying out adaptive wavelet threshold anti-noise preprocessing; carrying out adaptive noise intensity set empirical mode decomposition; screening a wind-fire coupling key mode; calculating a wind-fire adaptive dissipated energy flow; and oscillation source determination based on phase coupling: constructing a multi-condition fusion determination logic in combination with a phase coupling relationship between a dissipated energy flow trend and a wind-fire key mode and a phase coupling relationship between the dissipated energy flow trend and an electric energy transmission signal so as to realize oscillation source type identification. According to the invention, accurate identification of oscillation sources (a wind power side, a thermal power side and a tie line) in a frequency band of 0.1-2Hz is realized, and both positioning accuracy and engineering real-time performance are considered.

The application discloses an infrared image enhancement method, system, device and storage medium based on a wavelet threshold, which comprises the following steps: performing multi-scale discrete wavelet transform on an input original infrared image to separate low-frequency components and high-frequency components; the low-frequency components bear overall outlines and illumination distribution information of the image, and the high-frequency components contain edge, texture details and noise information of the image; performing improved adaptive wavelet threshold denoising processing on the high-frequency components to obtain denoised high-frequency components; performing layered enhancement processing based on weighted guided filtering and multi-scale Retinex on the low-frequency components to obtain enhanced low-frequency components; performing wavelet inverse transform reconstruction on the enhanced low-frequency components and the denoised high-frequency components to obtain a preliminary enhanced image; performing size upsampling on the preliminary enhanced image, and applying adaptive contrast restriction histogram equalization processing to output a final enhanced infrared image. The method can effectively suppress noise, retain details, adaptively enhance low-resolution infrared images, and is computationally efficient.