2047 results about "Fast Fourier transform" patented technology

The invention discloses a virtual synchronous generator grid-connected current harmonic suppression method and system, and relates to the technical field of power systems, and the method comprises the steps: S1, carrying out the digital sampling of intermittent high-frequency disturbance through a wind power plant grid-connected current signal in a power grid through an analog-to-digital converter, and obtaining an original digital current sequence; s2, according to the original digital current sequence, analyzing a wind load frequency component by adopting a fast Fourier transform algorithm, and determining amplitude and phase characteristics of a high-frequency harmonic component; s3, if the amplitude of the high-frequency harmonic component exceeds a preset threshold value, extracting an intermittent disturbance harmonic sequence from the frequency component to obtain a to-be-suppressed harmonic subset; according to the virtual synchronous generator grid-connected current harmonic suppression method and system, the stability of a remote wind power plant power grid is remarkably improved, continuous monitoring and real-time suppression of intermittent disturbance are achieved, and high efficiency and reliability of power grid operation are guaranteed.

The invention discloses a glass ceramic thickness detection method and a related device, and the method comprises the steps: obtaining a vibration signal generated in a glass ceramic thinning and grinding process, and extracting a vibration dominant frequency, a frequency spectrum centroid frequency and a frequency band energy distribution ratio as thickness characteristic parameters through fast Fourier transform processing; three-dimensional position coordinates of the grinding head are obtained, space mapping and weighted average processing are conducted on the thickness characteristic parameters, and space distribution of the thickness characteristic parameters is obtained; performing process interference compensation and sliding window time sequence smoothing processing according to the grinding process parameters to obtain stable thickness characteristic distribution; based on the stable thickness feature distribution and the current grinding process, final thickness distribution is predicted, and a thickness qualification pre-judgment result is obtained; and extracting a time sequence change characteristic of the thickness characteristic parameter as a vibration evolution index, and performing comprehensive quality evaluation in combination with a thickness eligibility pre-judgment result. According to the scheme, the thickness can be detected in real time in the thinning and grinding process of the microcrystalline glass, and the production efficiency is improved.

The invention relates to the technical field of power supply detection, and discloses a fire-fighting equipment power supply detection method and system.The detection method comprises the following steps that voltage fluctuation, current phase and ripple coefficients are collected through a multi-channel sensor group, a feature sequence is generated through sliding window noise reduction, the Euclidean distance is calculated through Z-score standardization, and a calibration instruction is generated when a threshold value is exceeded; kalman filtering adjusts frequency output detection parameters, fast Fourier transform is executed to extract harmonic amplitude to generate a suppression vector, and a least square reconstruction model outputs a state identifier; according to the method, voltage, current and ripples are collected through multiple channels, anti-interference performance and data integrity are enhanced through sliding window noise reduction, a threshold value is adjusted through Z-score and Euclidean distance self-matching, sampling frequency and detection parameters are optimized through Kalman filtering, harmonic characteristics are extracted through FFT, and the state is evaluated through a least square reconstruction model. And a multi-dimensional classification system is constructed by fusing the distortion rate and the phase deviation, so that the detection precision, the sensitivity and the response real-time performance are improved.

The invention discloses a DC voltage divider parameter anomaly detection method based on harmonic characteristics, and the method comprises the steps: deploying an isolation type voltage sensor at a bus of a DC power transmission system, carrying out the voltage sampling through combining with an ADC, carrying out the data buffering and preprocessing, extracting a fundamental wave component and a target harmonic component through the fast Fourier transform, and calculating the harmonic distortion rate. And constructing a low-voltage arm capacitance and harmonic distortion rate model based on a voltage divider secondary circuit equivalent circuit and an energy distribution principle, and calibrating the low-voltage arm capacitance and harmonic distortion rate model. And designing a multi-objective optimization scheduling algorithm based on energy storage capacity and capacitance characteristics, and realizing dynamic power distribution and harmonic suppression of the system. And after abnormal triggering, the system automatically loads a capacitance compensation coefficient, adaptively adjusts algorithm parameters in combination with a measurement loop PI control link, reduces a harmonic amplification effect, and ensures that a measurement error is within an acceptable range. Harmonic monitoring precision and capacitance compensation optimization capability of the direct-current power transmission system are effectively improved, and stable operation of the system is guaranteed.

The invention discloses an adaptive modulation anti-multipath unmanned aerial vehicle swarm communication method and system based on MIMO-OFDM, and relates to the technical field of wireless communication, and the method comprises the steps: receiving a down-conversion baseband; synchronization and compensation are carried out; removing a cyclic prefix and carrying out fast Fourier transform; estimating a channel and noise on the training and pilot frequency, and correcting a common phase; performing frequency domain equalization and soft decision, forward error correction and cyclic redundancy check according to the estimation to obtain a control parameter, decoding a payload according to the control parameter, and performing statistics to form a link quality indication; modulating coding, bandwidth and spatial mode, configuring mapping, power and weight, and packaging physical layer protocol data; carrier sensing conflict avoiding sending, confirmation and retry are adopted to generate media access statistics; and the routing selects the next-hop multi-hop forwarding according to the transmission quality, and performs end-to-end statistics and recharge. Through cooperation of hierarchical synchronization and phase correction with cyclic prefix and FFT, crosstalk is suppressed, channel noise scale optimization soft decision is robust, data decoupling is controlled, and end-to-end availability is improved through multi-hop self-healing recharge.

The invention relates to the technical field of road surface detection, in particular to a road surface skid resistance detection system based on multi-feature fusion, which comprises the following steps: applying broadband sweep frequency excitation by using a frequency modulation vibration sensor, matching with the inherent frequency of road surface texture to generate local resonance, and collecting the temperature, humidity and rainfall of a road surface in real time; performing fast Fourier transform on the collected vibration signals to obtain a resonance response spectrum, extracting key parameters through Gaussian fitting, and fusing frequency domain, material and environment data to form a comprehensive feature set; the frequency domain features are converted into three-dimensional energy distribution of pavement microtextures, the actual contact area ratio is calculated according to the three-dimensional energy distribution, an environment temperature and humidity compensation factor is introduced, and a dynamic friction attenuation coefficient is calculated; and comparing the calculated dynamic friction attenuation coefficient with a third-level safety threshold, and outputting a corresponding anti-skid performance level. Multi-source data fusion enables a detection result to be more fit with an actual driving scene, and misjudgment caused by single data is avoided.

Systems, methods, and apparatus for automatic signal detection in a radio-frequency (RF) environment are disclosed. At least one node device is in a fixed nodal network. The at least one node device is operable to measure and learn the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data. The at least one node device is operable to create a spectrum map based on the learning data. The at least one node device is operable to calculate a power distribution by frequency of the RF environment in real time or near real time, including a first derivative and a second derivative of fast Fourier transform (FFT) data of the RF environment. The at least one node device is operable to identify at least one signal based on the first derivative and the second derivative of FFT data.

The invention relates to a method and system for measuring the three-dimensional morphology of a metal hydrophobic surface based on white light microscopic interference, and belongs to the field of precision measurement, and the method comprises the steps: firstly, measuring a sample, scanning the surface of the sample, and synchronously collecting a white light interference image sequence containing surface contour information; adopting a bicubic interpolation algorithm to carry out interpolation processing on the acquired white light interferogram group, and constructing a high-resolution interference image sequence; analyzing the time-frequency domain interference signal through fast Fourier transform, effectively inhibiting noise interference in combination with a filtering technology, and accurately extracting an envelope curve of the interference signal; and carrying out Gaussian fitting on the envelope peak by adopting a least square method and positioning a peak value coordinate, and finally reconstructing a high-precision three-dimensional surface topography. According to the method, through advantage complementation of the bicubic interpolation algorithm and the fast Fourier transform method, the transverse resolution is remarkably improved while the longitudinal resolution is kept, and an effective technical means is provided for accurate characterization of the three-dimensional morphology of the metal hydrophobic surface structure.

The invention discloses a fault diagnosis method and device, computer equipment and a computer readable storage medium, relates to the technical field of power systems and automation thereof, and solves the problem of fault misjudgment caused by important fault characterization of a method which is easy to lose when single modal information is used for fault diagnosis at present. The method comprises the following steps: converting a bus voltage signal into a two-dimensional time-frequency diagram based on a continuous wavelet transform model, and converting the bus voltage signal into a one-dimensional frequency spectrum sequence based on a fast Fourier transform model; performing feature extraction on the two-dimensional time-frequency graph based on a time-frequency image feature extraction model to obtain a first feature vector, and performing feature extraction on the one-dimensional frequency spectrum sequence based on a frequency spectrum sequence feature extraction model to obtain a second feature vector; performing feature fusion on the first feature vector and the second feature vector based on a feature fusion model, and obtaining a category probability vector by using a fault diagnosis model based on the fused feature vector; and determining the highest probability value in the category probability vector, and taking the corresponding fault type as a target fault type.

The invention discloses an industrial image change anomaly detection method and system based on artificial intelligence, and relates to the technical field of image recognition, and the method comprises the steps: collecting a dual-light-source industrial image, employing frequency domain saliency to guide fusion, and carrying out visual enhancement processing through color mapping and edge enhancement; inputting the enhanced image into a CNN convolutional network to generate a multi-scale feature map, extracting a multi-scale high-pass residual image through two-dimensional fast Fourier transform and a high-pass filtering template, and splicing all scales and coding to generate a token sequence through local attention guide fusion; constructing a self-induction visual model, and performing feature reconstruction on the token sequence to generate a reconstructed feature map; and calculating and reconstructing an error scoring graph by adopting double error indexes, sampling to obtain an abnormal smooth graph, and segmenting an abnormal region based on the abnormal smooth graph. And finally, a dual anomaly detection system of image-level judgment and region-level identification is constructed.

The invention relates to the technical field of wireless communication, and provides a wireless interference source automatic identification and classification system and method based on deep learning, and the method comprises the steps: 1, receiving an original wireless interference signal, and carrying out the denoising and filtering processing; signal energy is converted into a power spectrum and a time-frequency diagram through fast Fourier transform (FFT) and continuous wavelet transform (CWT); step 2, constructing a time-domain graph branch and a frequency-domain graph branch through a convolutional neural network CNN and a self-attention mechanism Transform architecture, and capturing frequency-domain features and time-frequency features of the interference signal at the same time; 3, fusing the frequency domain and time-frequency domain features through a multi-scale feature fusion network MT in combination with a convolutional neural network and a self-attention mechanism; and step 4, adopting a Softmax activation function to carry out classification identification on the fused features, and outputting the category of the wireless interference source. According to the invention, the wireless interference source can be efficiently, accurately and automatically identified and classified.

The invention discloses an egg quality detection system and method based on multi-sensor fusion, and relates to the technical field of multi-dimensional data process.The method comprises the steps that texture features are extracted from visible light images, and feature wavelengths are extracted from spectrums; pressure distribution and shell deformation in the pressing process are synchronously collected; recording a pressure-time curve and a deformation-time curve, extracting characteristic parameters, and constructing a three-dimensional dynamic model; performing wavelet transformation on the response curve of each sensor, extracting time-frequency domain characteristics, and generating a smell fingerprint spectrum; scanning the surface of the egg to excite resonance response, recording a sound pressure signal, and converting the sound pressure signal into frequency domain data through fast Fourier transform; and presetting a business rule, and generating a final decision by combining the model output with a rule matching result. The pressure feature extraction module, the smell feature extraction module and the crack degree quantification module are arranged to realize data depth association, and the quality inspection credibility is improved through an interpretable decision model.

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

The invention relates to a lithium ion battery thermal runaway early warning method and device and a medium, and the method comprises the steps: setting an AC excitation power supply parameter, and outputting a sine excitation current with a preset frequency and amplitude; injecting the polymer into a single battery through a four-probe method; synchronously acquiring an injection current signal and a response voltage signal; through fast Fourier transform analysis, the amplitude and the phase of a signal fundamental frequency component are extracted; and calculating the electrochemical impedance under the frequency. And in combination with an electrochemical impedance-thermal runaway precursor mapping database established by a pre-experiment, converting the impedance value into a corresponding thermal runaway risk level, and outputting corresponding early warning information. According to the method, lithium dendrite growth and internal micro short circuit signs can be captured in advance, and the early warning time efficiency is remarkably improved compared with traditional voltage / temperature monitoring.

The invention belongs to the field of edge computing acceleration, and relates to a fast Fourier transform and neural network reasoning collaborative computing acceleration method, which comprises the following steps of: deploying a computing method which is based on butterfly computing merging and a tensor mapping strategy and is mixed with DFT (Discrete Fourier Transform) and FFT (Fast Fourier Transform) in an operator deployment level; on the interface integration level, a bus interface of a register access path in a tensor accelerator control path is subjected to lightweight reconstruction, so that the bus interface is adaptive to an edge computing platform; in an operation level, a user-defined instruction is introduced into a tensor calculation unit, and an accelerator is enabled to independently complete a whole-process task of FFT signal processing and NN intelligent identification. According to the method, an operation instruction set is expanded on hardware, and delay overhead caused by returning a large amount of intermediate data to a processor is avoided, so that efficient execution of FFT and neural network tasks on unified hardware is guaranteed, and multiple requirements of high real-time performance, high precision and low power consumption are met.

The invention discloses a grid-connected inverter control method based on second-order linear active disturbance rejection, and particularly relates to the field of grid-connected inverter control. Voltage and frequency error signals between the output side of the inverter and the power grid are obtained, a pre-synchronization control model is constructed, and voltage amplitude and frequency synchronization is achieved; a second-order linear active-disturbance-rejection controller is introduced in the frequency synchronization control process, an extended state observer is adopted to estimate the output frequency, the frequency change rate and a disturbance term, and a state error feedback control law is combined to generate a frequency regulation control quantity, so that accurate frequency control is realized; after grid connection, a dual-channel LADRC power regulation controller is constructed, and decoupling tracking control is carried out on active power and reactive power; meanwhile, on the basis of fast Fourier transform processing as a stable grid-connected state criterion, the method has the advantages of being high in control precision, high in anti-interference capability, good in current waveform quality, high in adaptability and the like, and is suitable for various grid-connected application scenes.

The invention discloses a power equipment defect intelligent identification method, system and device and a medium, and the method comprises the steps: collecting an original image of power equipment, and screening the original image of the power equipment to obtain a channel image; carrying out enhancement processing on the channel image and then calculating a gray scale difference value to obtain a gray scale image; converting the gray level image into a frequency spectrum image by adopting fast Fourier transform, and constructing a Gaussian filtering function to carry out convolution and inverse transformation on the frequency spectrum image to obtain a spatial domain image; and performing adaptive threshold segmentation on the spatial domain image, dividing the image into a defect area and a non-defect area, obtaining a segmented image, and performing morphological processing on the segmented image to obtain an electrical equipment defect identification result. According to the method, the problem of aliasing in traditional spatial domain processing is solved, the defect area is accurately extracted, short-time interference and real defects can be effectively distinguished, and the segmentation accuracy is improved.

Disclosed in the present invention is a deep learning-based power distribution network load prediction method, comprising: acquiring regional load data and renewable energy power generation data to form a data set, and preprocessing the data set to obtain a first data set; using a convolutional neural network to extract a time series feature in the first data set, and converting the time series feature into a data form of a deep learning model by means of an embedding layer to obtain one-dimensional time series data; performing fast Fourier transform on the one-dimensional time series data to obtain a frequency curve, extracting amplitude values on the frequency curve to calculate corresponding periods, and selecting the corresponding periods to slice the one-dimensional time series data and form same into two-dimensional matrixes; using a two-dimensional convolutional network to perform feature extraction, reshaping the two-dimensional matrixes that have undergone feature extraction into one-dimensional arrays, and performing adaptive fusion on the one-dimensional arrays to obtain a first time series feature; and inputting the first time series feature into a fully connected layer for weight calculation and linear transformation to obtain a power distribution network load prediction result, thereby improving the stability and reliability of electric power supply.

The invention relates to the technical field of automatic control, and discloses a control method and system for an injection molding process of automotive trim. The method comprises the following steps: synchronously acquiring a pressure pulsation signal, flow velocity distribution data and flow front position information of a plastic melt through a high-frequency pressure pulsation sensor array and a melt flow velocity sensor, and performing fast Fourier transform processing on the pressure pulsation signal to obtain a characteristic frequency component; a flow impedance coefficient and a shear stress distribution parameter are calculated in combination with flow velocity data, the impedance coefficient is compared with a reference value to identify an abnormal type, a pressure compensation amount is calculated through a classification compensation algorithm, and finally a servo hydraulic execution system carries out rapid pressure adjustment. The technical problem that the flow impedance characteristic change of the plastic melt cannot be recognized in real time and the injection pressure cannot be dynamically compensated in the existing automotive trim injection molding process is solved.

The invention discloses a steam turbine regulating valve state evaluation method, and belongs to the technical field of thermal power plant steam turbine valve state monitoring, and the method comprises the steps: collecting the video stream data and operation data of a to-be-evaluated steam turbine regulating valve rod, recognizing the contour of the valve rod based on a YOLOv5 model according to the video stream data, and outputting the feature points of the valve rod; tracking the two-dimensional coordinates of the feature points of the valve rod to calculate the vibration parameters of the valve rod; the method comprises the following steps: calculating a Pearson correlation coefficient of operation data, screening vibration parameters related to a jam fault to generate a time domain displacement sequence, performing time alignment with the operation data, performing fast Fourier transform to obtain a frequency spectrum, and identifying a dominant frequency component, a frequency multiplication component and a sideband frequency; calculating a frequency deviation and an energy distribution difference based on a preset reference spectrum library; and judging the jam grade according to the frequency deviation and the energy distribution difference. The technical problem that the jamming fault level cannot be accurately quantified by an existing turbine regulating valve state monitoring method is solved.

The invention discloses a locomotive wheel polygon damage detection method based on a lightweight neural network, and the method comprises the steps: obtaining axle box vertical vibration acceleration response signals of a heavy-load locomotive under the combination of different operation speeds and different wheel polygon abrasion degrees, and carrying out the preprocessing of the signals, the method comprises the following steps: acquiring frequency domain characteristics of a signal through fast Fourier transform, constructing a sample data set based on the frequency domain characteristics, constructing a wheel polygon damage detection network model, training the model by using the sample data set, and identifying a wheel polygon abrasion amplitude by using the trained polygon damage detection network model. And completing damage detection of the polygon of the wheel. The method can realize accurate and quantitative detection of the polygon abrasion degree of the heavy-load locomotive wheel, has the characteristics of accuracy, high efficiency and strong robustness, and also has relatively good interpretability.

The invention discloses a wide-frequency-domain weak signal data acquisition method, system, equipment and medium, and relates to the technical field of power system monitoring and fault diagnosis, and the method comprises the steps: collecting an input signal, analyzing the signal characteristics in real time, obtaining the frequency composition and amplitude change information of the signal, and dynamically adjusting the sampling rate according to the signal characteristics. Performing noise reduction processing on the collected signals, eliminating noise interference and retaining effective signal components, performing time alignment processing on the data to form a data set with a unified time reference, extracting multi-category features based on the data set, performing fusion judgment, identifying whether the system is in a fault state, and when it is judged that a fault occurs, judging whether the system is in a fault state or not; if yes, fault analysis and positioning are executed, and an analysis result is generated and output. According to the method, fast Fourier transform analysis is carried out on the signals, the dominant frequency and harmonic components can be accurately recognized, the energy ratio can be calculated, and a reliable data basis is provided for signal feature extraction and fault diagnosis.

The invention discloses a mixed Mamb-Attention air quality prediction model based on multi-scale decomposition and a construction method of the mixed Mamb-Attention air quality prediction model. The method comprises the following steps: firstly, constructing a multi-scale decomposition module (MSD), decomposing an input time sequence into a trend term, a season term and a residual term through a parallel sliding window group, and realizing cross-scale feature fusion by utilizing group normalization and convolution; then designing a periodic pyramid module, extracting multi-level periodic features based on fast Fourier transform (FFT), and enhancing the perception ability of the model to different time scale periodic laws; the Mama branch is used for capturing long-range dependence, the self-attention branch is used for extracting a local dynamic mode, and the output of the Mama branch and the output of the self-attention branch are fused through residual connection and layer normalization; and finally, the prediction head module completes feature aggregation and result output. The model gives consideration to long sequence modeling capability and calculation efficiency, can accurately capture multi-scale dynamic change and non-stationary features in air quality data, improves the precision and stability of air quality prediction, and has good practical value and popularization prospect.

The invention provides a power distribution network single-high-frequency resonance rapid tracking and adaptive suppression method, a multi-mode dynamic cooperative adaptive suppression system is established based on a PCCVF adaptive method, and the damping characteristics of a power distribution network are remodeled by injecting compensation current into a power distribution network system so as to realize broadband resonance suppression of the power distribution network. The compensation current injection method comprises the following steps: step 1, detecting resonant frequency deviation of a power distribution network through real-time FFT (Fast Fourier Transform); 2, performing dynamic phase angle correction based on a phase prediction residual error of real-time frequency deviation; step 3, constructing a layered impedance remodeling module, and ensuring stable power transmission; 4, establishing a frequency-variable impedance model of the power distribution network based on resonance energy spectral density analysis, dynamically optimizing parameters through fuzzy logic of a bell-shaped membership function, and feeding harmonic compensation current into the power distribution network by using a space vector pulse width modulation technology; according to the invention, single high-frequency resonance can be effectively suppressed, the response and tracking performance of the system is improved, and the spectrum analysis efficiency and bandwidth occupation are optimized.

The invention belongs to the technical field of deep learning and time sequence analysis, and particularly relates to a multivariable time sequence prediction method and device based on spatial-temporal feature fusion. The method comprises the following steps: acquiring multivariable traffic time series data, and processing the traffic time series data; dividing the processed traffic time sequence data into overlappable patches, and generating a patch embedding sequence through linear mapping; applying bimodal time attention to the patch embedding sequence to obtain fused attention features; based on the learnable node embedding matrix, generating a time-varying adjacency matrix through dynamic graph construction, executing graph convolution to obtain a time domain graph propagation result, executing fast Fourier transform, multiplying by a learnable scaling factor and then performing inverse transformation to obtain an inverse transformation time frequency result; adding the time domain graph propagation result and the inverse transformation time frequency result to obtain a final spatio-temporal characteristic; and flattening the final spatio-temporal characteristics, and generating predicted values of the variables in a future prediction window through a linear prediction head.

The invention provides a multivariable time series prediction method and device, electronic equipment and a storage medium, and relates to the technical field of computers, and the method comprises the steps: carrying out the processing of time series data through a period coding function and a seasonal factor coding function, and obtaining preprocessing feature data; performing periodic analysis on the preprocessed feature data through fast Fourier transform to obtain two-dimensional time sequence data; based on a convolutional neural network, obtaining local dependence features according to the two-dimensional time sequence data; on the basis of a self-attention mechanism, global dependency features are obtained according to the preprocessed feature data; obtaining a fused multi-view feature through the local dependency feature and the global dependency feature; and obtaining predicted time series data according to the fused multi-view features. According to the invention, the precision of time sequence prediction is improved.

The invention discloses a construction method of a cardiac ultrasound basic model. Firstly, a cardiac ultrasound image big database is constructed; secondly, preprocessing the image, including data enhancement, fast Fourier transform, frequency domain enhancement, frequency domain random covering and inverse Fourier transform; then, an encoder, a space decoder and a frequency domain decoder are constructed, and the space decoder and the frequency domain decoder are respectively used for recovering space domain information and frequency domain information of the randomly covered image; finally, pre-training the encoder to obtain a cardiac ultrasound basic model; in the pre-training stage, convolution blocks in the first stage and the second stage of the encoder multiply the embedded vectors and the covering masks to obtain randomly-covered embedded vectors, and the randomly-covered embedded vectors serve as input of diffusion convolution operation; the shallow layer features output by the encoder in the first stage and the second stage are fused with the deep layer features output by the encoder in the third stage after being subjected to convolution and flattening operation, and the fused features are used as input of a space decoder and a frequency domain decoder after being subjected to linear projection. By repairing the randomly covered image from the spatial domain and the frequency domain, the feature extraction of the encoder is realized, and the accuracy is improved.

The invention discloses an instantaneous torque measuring system and method based on the front end of an engine, and relates to the technical field of engine testing and signal processing, and the system comprises a signal acquisition module, a noise analysis module, a signal processing module and a signal processing module, the noise frequency distribution and noise amplitude range extraction module is used for extracting noise frequency distribution and a noise amplitude range from the smoothed torque data and determining filtered torque data containing low-frequency drift noise and periodic interference signals, and the parameter extraction module is used for analyzing the noise frequency distribution and the periodic interference signals by adopting fast Fourier transform according to the filtered torque data so as to obtain the low-frequency drift noise and the periodic interference signals. Acquiring noise duration time and a noise source identifier, and determining an amplitude compensation coefficient and a frequency suppression parameter; according to the instantaneous torque measuring system and method based on the engine front end, accurate acquisition and noise suppression of engine torque signals are achieved, the accuracy and reliability of torque data are improved, and effective support is provided for engine performance monitoring and fault diagnosis.

The invention provides an ultrasonic nonlinear static parameter monitoring method for creep damage of a high-temperature alloy component, and particularly relates to the technical field of material monitoring. The method comprises the following steps: S1, arranging an ultrasonic probe; s2, determining the emission frequency of the excitation end; s3, respectively calculating an incident angle and an emergent angle of the signal; s4, exciting fundamental frequency ultrasonic waves; s5, receiving the signal and uploading the collected signal to a cloud processing system; s6, carrying out fast Fourier transform and calculating a nonlinear parameter of a static component; s7, establishing a linear relation with creep time, and constructing a creep damage linear quantitative model; s8, calculating a static component nonlinear parameter of the healthy sample; and S9, completing quantitative analysis of the damage by using nonlinear parameters. The method solves the problems that a traditional nonlinear ultrasonic method depends on second harmonic nonlinear parameters, phase velocity matching conditions in the middle and later periods are seriously damaged, response shows a nonlinear trend, unique creep time cannot be determined according to the parameters, and then the unique damage state cannot be determined.

The invention relates to a switch cabinet operation reliability evaluation method, system and device and a medium, and belongs to the technical field of power distribution equipment monitoring, and the evaluation method comprises the steps: receiving real-time monitoring data of a to-be-detected switch cabinet; performing fast Fourier transform on the current waveform data, and calculating to obtain a harmonic distortion rate sequence; performing feature extraction on the vibration spectrum data to obtain a vibration dominant frequency amplitude sequence; correlation analysis is carried out on the harmonic distortion rate sequence and the vibration dominant frequency amplitude sequence, a sliding covariance matrix is constructed, and a dynamic coupling coefficient is calculated; generating a three-dimensional feature space distribution diagram according to the harmonic distortion rate sequence, the vibration dominant frequency amplitude sequence and the dynamic coupling coefficient sequence; and dividing the real-time monitoring data into a plurality of feature regions based on density clustering, determining a current fault state corresponding to the to-be-detected switch cabinet according to the feature regions mapped by the real-time monitoring data, determining a corresponding maintenance strategy, generating a reliability evaluation report, and sending the reliability evaluation report to the management terminal. The operation stability of the switch cabinet equipment can be improved.