541 results about "Time domain waveforms" patented technology

The invention discloses an electrical equipment operation state monitoring method, system and terminal, full life cycle health management of equipment is realized through multi-dimensional perception and intelligent analysis, a composite sensor network can be constructed from the level of the method, and high-frequency current, ultrahigh frequency, fiber grating temperature vibration, multi-parameter gas and acoustic sensors are integrated. Electromagnetic characteristics, mechanical states, environmental parameters and voiceprint characteristics are covered; the adaptive signal processing technology performs classification and noise reduction on multi-source data, and the three-dimensional digital twin model realizes time-space fusion of a temperature field, a vibration field, an electric field and a sound field; a lightweight space-time convolutional network is deployed to fuse a time domain waveform, a spectrogram and spatial distribution characteristics for diagnosis, and a hidden semi-Markov model and a particle filter algorithm are combined to dynamically predict the residual service life of equipment. The system architecture comprises a self-organizing sensor network with edge computing capability, a time-sensitive industrial communication network and a containerized analysis engine, and supports mixed reality visual interaction.

The invention provides a manifold constraint-based sonar imaging reverberation elimination method and system, and is applied to the field of imaging data processing. According to the method, the time domain waveform of the echo signal is collected in real time, whether the irregular reflection peak value is detected or not is judged, potential interference and reflection in the underwater environment can be rapidly recognized, and particularly, in the complex underwater environment, non-target objects (such as seabed obstacles or sundries) possibly cause irregular reflection signals, so that the interference and reflection in the underwater environment can be rapidly recognized. Meanwhile, the underwater propagation characteristics of sound waves can be accurately modeled by collecting the reflection path of the echo signal and predicting the propagation parameters of the reverberation signal. And accurate background data can be provided for the propagation modeling of the reverberation signal through the pre-recorded information of submarine topography, geological characteristics, sound wave propagation characteristics and the like.

The invention provides a weak signal multi-module networking self-adaptive monitoring system for power transmission and transformation construction, and relates to the technical field of multi-module networking, and the system comprises a multi-mode module which is used for deploying a multi-mode sensing unit at a key monitoring point of power transmission and transformation equipment, collecting vibration, partial discharge, leakage current and environmental parameter signals, and transmitting the signals to a monitoring center; the signals are processed through packaging of an electromagnetic shielding shell and a built-in harmonic filter circuit so as to obtain original monitoring signal data; and the detection module is used for inputting the original monitoring signal data into the chaotic enhanced weak signal detection device, and extracting a fault feature vector by capturing an attractor phase diagram abrupt change and a time domain waveform threshold. Through the multi-mode sensing and self-adaptive networking technology, monitoring of power transmission and transformation weak signals and timely extraction of fault features are achieved, and the fault early warning accuracy and the self-adaptive capacity are improved.

The invention relates to the technical field of industrial equipment diagnosis, in particular to an AI-driven industrial equipment fault analysis method and system, and solves the problems of insufficient multi-source data fusion, weak working condition adaptability and inaccurate fault positioning in the prior art. According to the method, high-frequency time sequence signals are synchronously collected through a vibration sensor, a temperature sensor and the like, working condition parameters are marked, a dynamic confidence coefficient weight is generated based on a working condition matching historical fault library, and cross-modal features are extracted through a space-time convolutional network after multi-source data are fused. A fault mode separation module is used for decoupling the fault contribution degree of mechanical transmission, electric power and heat dissipation systems, precise diagnosis is achieved in combination with frequency domain fingerprints, time domain waveform analysis and cross decision verification, finally, the remaining life is predicted through a long-short-term memory network and a Weibull model, and the system integrates multi-source sensing, dynamic fusion and intelligent diagnosis units. The fault early warning accuracy and the equipment maintenance scientificity under complex working conditions are improved, and a solution is provided for intelligent diagnosis of industrial equipment.

The invention discloses a radio frequency signal interference intelligent suppression management system and method based on image recognition. The system comprises a multi-source sensing module, a fusion analysis module, a dynamic suppression module and an execution feedback module. The multi-source sensing module forms a radio frequency original signal set containing frequency spectrum features and time domain waveforms; the fusion analysis module receives the radio frequency original signal set and extracts an interference source feature vector in the radio frequency original signal set through a pre-trained convolutional neural network to generate a fusion decision signal; the dynamic suppression module receives the fusion decision signal and activates a corresponding algorithm in a preset suppression strategy library according to the interference type identifier; and the execution feedback module receives the real-time control signal and drives the tunable filter, the phased-array antenna and the power amplifier to execute interference suppression operation. According to the intelligent suppression management system and method for the radio-frequency signal interference based on image recognition, the problems of fuzzy positioning of the radio-frequency interference source, single suppression strategy and lack of environmental perception capability can be solved.

The invention discloses a GIS field-electricity fusion real-time state sensing and early warning system and method. The system comprises a signal synchronous acquisition and measurement embedded device and an industrial personal computer, and a simulator, a position searcher and an early warning device are deployed in the industrial personal computer; when the embedded device monitors that the electromagnetic field signal in the GIS contains the partial discharge signal according to the strong correlation characteristics, obtaining a full time domain oscillogram containing a time domain oscillogram and a frequency domain oscillogram of the partial discharge signal; the simulator performs coarse positioning on the partial discharge source based on the full time domain oscillogram of GIS partial discharge, and performs simulation enhancement calculation by taking a coarse positioning result as an initial injection point to obtain simulation enhancement data of the partial discharge source; the position searcher carries out iterative search on the actually measured discharge intensity obtained through calculation according to the full-time-domain oscillogram and the simulation enhancement result, and the actual discharge position of the partial discharge source is obtained; the early warning device carries out early warning based on the time domain full waveform and early warning information including the actual discharge position of the partial discharge source.

The invention discloses a resting electroencephalogram quality evaluation method and system based on double-branch comparative learning, and the method comprises the steps: collecting an original EEG signal X, carrying out the data preprocessing and data enhancement, generating two different enhanced views, transmitting the two different enhanced views to a double-branch encoder in parallel, respectively extracting a time domain waveform and a time-frequency domain rhythm feature, and carrying out the deep fusion, the fused features X1 and X2 are sent to a projection head, and through a self-supervised contrast learning mechanism, the network weight is optimized by using contrast loss; a small amount of labeled fine-tuning data sets including X and corresponding labels y are adopted, after flowing through a pre-trained double-branch encoder, the fine-tuning data sets are directly sent to a classification head connected with the back of the double-branch encoder so as to output a prediction result of an input data segment, and a mixed loss function including classification loss and comparison loss is adopted for training in the optimization process. According to the invention, a complete online real-time quality control system is established, and end-to-end real-time closed loop from data acquisition to quality evaluation is realized.

The invention discloses a target voice extraction method based on multi-reference clue fusion, and the method comprises the steps: enabling a to-be-extracted multi-channel mixed voice to pass through a feature extraction module, calculating the complex spectrum and spatial features of the mixed voice, and obtaining a mixed voice feature map; enabling the azimuth information and the reference voice of the target speaker to pass through an embedded feature extraction module, and combining the direction features and the voiceprint features to obtain embedded features of the target speaker; inputting the mixed speech feature map and the embedded features of the target speaker into a main extraction network based on a dual-path Fastform neural network to obtain high-dimensional feature representation of the speech to be extracted; the high-dimensional feature representation is converted into a plurality of masks for estimating the separated voice through a mask estimation module, and the masks act on a plurality of spectrums of the mixed voice signals; estimating and extracting a time domain waveform of the speech through inverse short-time Fourier transform reconstruction; and the extracted voice of the target speaker is input into the comparison learning module, so that the correct extracted voice can be output when the mixed voice is absent by the target speaker.

The invention discloses a multi-source partial discharge diagnosis system and method for power equipment. The system comprises a signal acquisition unit and a signal processing unit, and the signal processing unit comprises a signal preprocessing module, an atlas generation module and an image generation module; in the method, a signal acquisition unit acquires an optical signal generated by partial discharge of multiple discharge sources of power equipment, converts the optical signal into a current signal and sends the current signal to a signal processing unit, and a signal preprocessing module in the signal processing unit obtains a time domain waveform signal and a PRPD statistical sequence based on the optical signal; the map generation module obtains a two-dimensional time-frequency map based on the time-frequency waveform signals and the PRPD statistical sequence, the image generation module generates an IFCNN network, and fusion feature maps of the optical signals generated by the discharge sources are output in a classified mode based on the time-frequency waveform signals and the two-dimensional time-frequency map through the IFCNN network. The partial discharge of different discharge sources of the power equipment can be effectively distinguished.

The invention relates to the technical field of tunnel fire detection, and provides a rapid tunnel moving fire detection method, which comprises the following steps of: calculating signals received by sensors in parallel by an intelligent algorithm, extracting undistorted characteristic signals in time, and combining data from a plurality of sensors through a data fusion technology; false alarms possibly generated by a single sensor are reduced, so that the fire detection accuracy is improved, and the time from fire occurrence to alarm giving is shortened; by fusing data of a plurality of sensors, the system is more robust to faults or abnormities of a single sensor, and the stability and reliability of the system are improved; the intelligent algorithm can be combined with the time domain oscillogram of a plurality of detectors, space and time information can be extracted in time, the specific position of the fire can be positioned more accurately, and fire extinguishing measures can be taken quickly.

The invention discloses a steam turbine safe operation health assessment method and system, and belongs to the technical field of steam turbine monitoring. The method comprises the following steps: acquiring running data of a steam turbine in real time through a multi-source sensor network deployed on key components of the steam turbine; the method comprises the following steps: preprocessing original operation data of a steam turbine by adopting edge computing equipment to generate a time domain waveform analysis diagram or a time sequence analysis diagram; transmitting the preprocessed structured feature data to an operation and maintenance management platform, inputting a fault diagnosis model for analysis, pre-training through a historical fault case library, and aligning feature distribution of the historical case library and current equipment data through a domain adaptive algorithm in transfer learning; and inputting a diagnosis result into a self-adaptive dynamic evaluation model based on a reinforcement learning engine, dynamically adjusting a model weight and an alarm threshold according to real-time working condition parameters, outputting a health score and a maintenance decision, and forming a closed-loop process of perception-edge processing-cloud diagnosis-dynamic optimization-decision output.

The invention, which belongs to the technical field of electromagnetic signal monitoring, relates to a rapid judgment method for a spatial spill electromagnetic signal, and the method specifically comprises the following steps: S1, receiving a spill electromagnetic signal in a space; s2, dividing the monitoring total frequency band into a plurality of sub-frequency bands with the same width, rapidly analyzing the obtained electromagnetic signal frequency spectrum in real time based on multi-scale spectrum entropy analysis, and positioning the frequency band where the characteristic signal is located; and S3, converting the frequency spectrum of the frequency band where the characteristic signal is located into a time domain signal, and simultaneously displaying a spectrogram and a time domain waveform. Through spectrum segmentation, multi-scale spectrum entropy analysis, adaptive threshold judgment and frequency domain-time domain combined display, the problems of poor real-time performance, weak anti-noise capability and single dimension in the prior art are solved.

The invention provides a space-based radar time domain waveform adaptive method based on reinforcement learning, and the method comprises the following steps: firstly, constructing a radar signal echo model and a radar clutter echo model according to an inter-pulse frequency modulation pulse Doppler radar; calculating a signal clutter echo distance Doppler matrix according to the radar signal echo model and the radar clutter echo model; establishing a reinforcement learning environment and introducing a reward function to obtain a large amount of reinforcement learning data pairs; taking a large amount of reinforcement learning data as input, and combining with a reinforcement learning algorithm to train the intelligent agent; and finally, outputting an optimized inter-pulse frequency modulation pulse Doppler waveform by applying the trained intelligent agent, and realizing clutter position regulation and control. According to the method, the pulse Doppler waveform of inter-pulse frequency modulation is provided, so that the radar has the capability of actively regulating and controlling the clutter position.

The invention discloses a 2 [mu] m nanosecond pulse fiber laser and a time domain waveform adjustment method thereof. The laser comprises a seed source module and an amplification module connected with the seed source module. The seed source module comprises a first high-reflectivity fiber bragg grating, an acoustic optical modulator, a first beam combiner, a first thulium-doped fiber, a 2 * 2 coupler and a second high-reflectivity fiber bragg grating which are connected in sequence, as well as a driving source connected with the acoustic optical modulator and a first pumping source connected with the first beam combiner; the amplification module comprises an online isolator, a second beam combiner, a second thulium-doped optical fiber and an output jumper which are connected in sequence, and a second pumping source connected with the second beam combiner; the rising edge time and the starting time of the acousto-optic modulator are subjected to parameter setting through a human-computer interface of the driving source; the wavelengths of lasers output by the first pumping source and the second pumping source are both 793 nanometers. According to the invention, the multi-peak phenomenon of the Q-switched pulse laser of the 2um waveband can be conveniently and effectively eliminated, the performance of the laser is improved, and the application requirements of low cost and mass production are met.

The invention discloses a radar interference identification method based on multi-representation domain feature interactive fusion and a graph structure semantic classifier, and the method comprises the steps: constructing a multi-representation domain feature interactive fusion module, taking a time domain waveform, a spectrogram and a time-frequency spectrogram as input data, and outputting fusion features; constructing a graph structure semantic classifier, inputting the fused features into the graph structure semantic classifier, analyzing semantic association of different types of signals by the graph structure semantic classifier, and outputting a classification result; dividing the data set into a training set and a test set, and storing network parameters with the best classification effect in the training set according to a classification result; and loading network parameters, and inputting the test set into the radar interference identification network for processing to obtain an identification result. According to the method, the interference signals can be fully represented, the identification and processing capability of the interference signals is improved, robustness representation is carried out on the interference signals, and the accuracy of radar interference identification is improved.

The invention relates to the technical field of interdisciplinary research of biomedicine and computer science, and relates to an arrhythmia classification method and device based on a multi-modal diffusion model, and a medium. The method comprises the steps that input original electrocardiosignals are preprocessed, and three kinds of modal data are generated to serve as training set data; a personalized arrhythmia classification model is constructed; freezing diffusion driving feature extractor parameters, performing personalized optimization on training set data, inputting the data into a personalized arrhythmia classification model, and training the module; the method comprises the following steps: collecting an electrocardiosignal to be detected, preprocessing the electrocardiosignal into three modals of a time domain waveform, a short-time Fourier transform spectrogram and a Lorentz scatter diagram, and carrying out personalized optimization; and inputting the optimized data into the trained personalized arrhythmia classification model to obtain a classification result. According to the invention, high-precision, low-label-dependence and individual-adaptive arrhythmia judgment is realized.

The invention provides a bearing health state evaluation method and system based on high-pass filtering. The method comprises the following steps: acquiring a real-time vibration acceleration signal of a rolling bearing by using data acquisition equipment; the method comprises the following steps: performing fast Fourier transform to obtain a complex sequence containing time amplitude spectrum and phase spectrum information, performing high-frequency band-pass filtering processing in different frequency intervals by using high-frequency band-pass filters of different grades, and further performing inverse Fourier transform on multiple filtered complex sequences to obtain time domain waveform data; and respectively calculating envelope spectrums, calculating peak factors based on a set observation frequency range, selecting a maximum value from the peak factors, and determining the health state of the rolling bearing based on the maximum value in combination with a preset evaluation analysis rule. By adopting the scheme, the problems that the prior art depends on a full-cycle data model and hyper-parameter optimization and is complicated in evaluation operation are solved, the fault frequency characteristics of the envelope spectrum after high-frequency band-pass filtering are quantitatively described through the dimensionless indexes, the calculation complexity is low, and the health state evaluation timeliness is good.

The invention discloses an electric arc detection method based on differential increase and structured attention, and the method comprises the steps: firstly carrying out the collection and preprocessing of a current signal, carrying out the differential enhancement according to a sample type, carrying out the strong enhancement of an electric arc sample, improving the generalization capability, and carrying out the weak enhancement of a normal sample, thereby avoiding the overfitting; the problem of class imbalance is relieved, and the model generalization ability is improved; secondly, obtaining six complementary feature representations of time domain waveform, frequency domain frequency spectrum, time frequency analysis, envelope features, statistical distribution and related features from the differentially enhanced current signal through a multi-modal feature extraction method, and fusing to generate a multi-modal image; then, designing a deep learning model integrated with structured attention, carrying out distinguished attention on different feature analysis areas of the multi-modal image, and directionally enhancing arc features; and finally, dynamically quantifying the trained model, reducing the size of the model and reasoning delay, and supporting efficient deployment of various edge computing devices.

The invention discloses a cutter state real-time monitoring and compensating method, system and equipment and a medium, and relates to numerical control precision machining. In the method, a cutting edge optical image, an acoustic emission signal time domain waveform, a vibration acceleration parameter and real-time temperature data of a cutting area of a cutter in the machining process are collected; measuring the maximum width and depth of a wear belt through an image to form a wear characteristic quantized value, and judging a wear over-limit state; judging the damage state based on the continuous ultralimit time of the instantaneous mutation rate of the acoustic emission signal; judging the abnormal vibration grade and state through vibration spectrum analysis; and inputting the vibration spectrum and the temperature data into a prediction model to obtain a position offset prediction quantity. And calculating a size deviation value according to a wear / damage state, calculating a coordinate offset according to vibration abnormity, generating a cutter geometric compensation instruction or a motion trail compensation instruction, and completing real-time compensation operation. According to the scheme, the fine abnormal change of the cutter in the machining process can be captured in time, and compensation is conducted according to the fine abnormal change.

The invention relates to the technical field of physical measurement and data processing, in particular to an intelligent detection and screening system for combined harvesting quality of forage grass and grass seeds. An excitation and acquisition module; the signal processing module is used for separating single seed data from the mixed signal, extracting high-discrimination features and outputting an acoustic feature vector of each seed; a classification and sorting module; wherein the preprocessing module is connected with the excitation and acquisition module through seed matrix coding information, the excitation and acquisition module is connected with the signal processing module through multichannel time domain waveform data, and the signal processing module is connected with the classification and sorting module through acoustic feature vectors. By means of the combined harvesting quality intelligent detection and screening system, the efficiency and quality of forage grass seed harvesting are remarkably improved, the loss rate is reduced, various technical problems faced by the current industry are effectively solved, and therefore a solid foundation is laid for sustainable development of the forage grass industry.

The invention relates to the technical field of pulse electric field measurement, and discloses a time drift compensation method of a pulse electric field measurement system based on model parameterization. According to the method, a parameterized model is constructed in simulation software, and the parameterized model is additionally provided with two servo zeroing compensation circuits on the basis of an original measurement system, and the two servo zeroing compensation circuits are connected to the output ends of two in-phase operational amplification circuits respectively to form a negative feedback loop used for dynamically suppressing low-frequency baseline drift; acquiring electric field time domain waveform data of the output end of the third in-phase operational amplifier circuit; calculating a transfer function of a pulsed electric field measurement system in the parameterized model, converting the transfer function from a frequency domain state to a time domain state through inverse Fourier transform, and then performing deconvolution on the transfer function of the time domain state and electric field time domain waveform data to obtain real electric field intensity; according to the invention, time drift caused by operational amplifier input imbalance, power supply fluctuation and temperature change is automatically compensated in real time, and the stability and precision of system measurement are improved.

The invention relates to the technical field of control and adjustment systems, in particular to an LED display matrix dynamic control system, which comprises a time sequence feature extraction module, a self-adaptive threshold calibration module, a self-adaptive threshold calibration module, a self-adaptive threshold calibration module, a self-adaptive threshold calibration module, a self-adaptive threshold calibration module and a self-adaptive threshold calibration module, wherein the time sequence feature extraction module is used for collecting time domain waveforms of scanning signals and data signals; a display scene is recognized based on a k-nearest neighbor classifier, a composite reference value is generated in combination with historical optimization parameters, a hierarchical compensation execution module is used for executing a three-level compensation strategy according to the composite reference value to correct time sequence deviation, a dynamic model updating module is used for optimizing scene recognition and threshold generation parameters through a genetic algorithm, and a scene library is periodically updated. All the modules cooperate to form a closed-loop link, static, dynamic, high-speed motion and temperature change scenes are dynamically adapted, and the time sequence synchronous control precision and the system robustness of the row driving circuit and the column driving circuit are improved.

The invention provides a voice reconstruction method and system based on entropy coding residual quantization and frequency spectrum restoration, and relates to the technical field of artificial intelligence voice signal processing, and the method comprises the steps: obtaining an original voice waveform, inputting the voice waveform into a neural voice coding and decoding model, firstly entering a coder to map the input voice waveform into acoustic potential representation, and then entering a frequency spectrum restoration model; performing residual quantization on the acoustic potential characterization layer by layer through a residual vector quantization module, introducing a gating-based dynamic layer number selection mechanism and entropy regularization constraint, enabling bits to be adaptively distributed among different voice segments, reconstructing reconstructed acoustic features of the potential characterization, inputting the reconstructed acoustic features into a decoder, restoring the reconstructed acoustic features into a time domain waveform, and outputting the time domain waveform. And mapping to a logarithmic magnitude spectrum domain through a spectrum repairing module, predicting a residual error in the logarithmic magnitude spectrum domain and performing confidence gating fusion to obtain a complex spectrum, and outputting after time domain synthesis to obtain reconstructed speech. According to the invention, high fidelity, intelligibility and transmission reliability of the voice can be considered at an extremely low bit rate.

The invention provides a signal processing method for a partial discharge signal and a related device, and the method comprises the steps: carrying out the frequency band extraction of a first sound pulse signal, and obtaining a second sound pulse signal corresponding to an audible sound frequency band and a third sound pulse signal corresponding to an ultrasonic frequency band; determining a first time domain waveform of the second sound pulse signal, and determining a second time domain waveform of the third sound pulse signal; determining a first frequency domain waveform of the second sound pulse signal, and determining a second frequency domain waveform of the third sound pulse signal; inputting the first time domain waveform and the second time domain waveform into a first convolutional neural network model for feature extraction to obtain an image feature vector; inputting the first frequency domain waveform and the second frequency domain waveform into a second neural network model for feature extraction to obtain a voiceprint feature vector; splicing the image feature vector and the voiceprint feature vector to obtain a target feature vector; and inputting the target feature vector into a classification network to obtain a target classification result.

The invention discloses an underwater acoustic target classification and identification method based on multi-domain feature deep fusion and multi-platform sonar data learning. The method comprises the following steps: firstly, preprocessing target tracking beam data collected by a multi-platform sonar, and constructing a time domain waveform sample set and a time-frequency spectrogram sample set; secondly, constructing an underwater acoustic target classification and recognition network model which comprises a primary feature extraction parallel double-branch network module and a feature deep fusion and classification recognition module, the primary feature extraction parallel double-branch network module extracts time domain and time frequency features respectively, and the feature deep fusion and classification recognition module realizes feature deep fusion and classification through a bidirectional cross attention mechanism; training the model by adopting a multi-stage training strategy, wherein the training comprises pre-training, special training, end-to-end training and fine tuning and incremental training; finally, unknown target data is preprocessed and then input into the trained model for forward reasoning, and a classification recognition result is output according to the majority voting principle. According to the method, target multi-domain information can be fully mined, multi-source data are effectively utilized, and the model generalization ability and the recognition precision are improved.

The invention discloses a concrete crack detection method based on coupling of an infrared thermal field and a Rayleigh wave field, which comprises the following steps of: comprehensively scanning concrete in a target area by adopting an infrared thermal imaging method, and identifying the position of a concrete crack; calculating the depths of a superficial crack, a deep crack and a junction region crack; extracting Rayleigh wave amplitude attenuation characteristics to calculate crack width, and inverting a crack inclination angle based on time domain waveform head wave arrival time difference; superposing an infrared thermal imaging method detection result and a Rayleigh wave detection result, and constructing a three-dimensional visual crack model by adopting a linear interpolation algorithm; the crack position, the crack depth, the crack width, the crack inclination angle and the three-dimensional visual crack model are input, and a mass concrete crack detection report is generated. Through coupling detection of an infrared thermal field and a Rayleigh wave field, seamless connection of a shallow crack and a deep crack is achieved, a three-dimensional visual crack model is constructed, and the problems that a traditional method is low in efficiency, poor in precision and incomplete in coverage are solved.

The invention discloses an interference suppression measurement method for a grounding resistor, and relates to the technical field of interference resistance, and the method comprises the steps: injecting a detection current into a grounding electrode, and synchronously collecting an original voltage signal at two ends of the grounding electrode and a time domain waveform of the detection current; performing time domain segmentation on the original voltage signal, performing linear frequency modulation wavelet transform on the signal in each time period, generating an initial time-frequency matrix, extracting a time-frequency ridge line from the initial time-frequency matrix, identifying interference information, obtaining an interference-removed time-frequency matrix, optimizing the interference-removed time-frequency matrix through robust kernel principal component analysis, and obtaining an interference-removed time-frequency matrix; a synchronous redistribution operator is used for carrying out signal purification, a pure time-frequency matrix is obtained, linear frequency modulation Z transformation is carried out on the pure time-frequency matrix, voltage amplitude and frequency are extracted, an energy centroid method is used for correcting the voltage amplitude and frequency, interference can be effectively restrained, and the grounding resistance measurement precision is remarkably improved.

The invention relates to the technical field of signal processing, and provides a time domain waveform reproduction signal smooth processing method, which is used for detecting the fatigue durability of automobile parts in a vehicle vibration acquisition system, and comprises the following steps: S1, obtaining the initial impedance of the automobile parts and multiple frames of to-be-reproduced signals; s2, calculating a first frame first driving signal, and smoothing the first driving signal to form a target driving signal; s3, calculating and generating a first driving signal of a corresponding frame based on the to-be-reproduced signal of each subsequent frame and the updated impedance corresponding to the previous stage in sequence, and forming a target driving signal; and S4, performing stress analysis on the multi-frame response signals, quantifying the damage accumulation process of the automobile part under the simulated working condition, and evaluating the fatigue durability of the automobile part. According to the invention, the generation of response pulses can be avoided, and the signal waveform reproduction precision is improved, so that the accuracy of fatigue durability detection of automobile parts is improved.

The invention discloses a high-frequency and high-speed wire harness test system data analysis processing method, and relates to the technical field of signal processing crossing, and the method comprises the steps: inputting a three-dimensional fusion tensor into a space-time-frequency three-dimensional convolutional neural network model, capturing time sequence features through a bidirectional LSTM, and obtaining defect features through a multi-head attention mechanism; fusing the time sequence features and the defect features to generate time sequence-defect fusion features, and performing comparative analysis on the time sequence-defect fusion features and historical defect data to obtain a risk defect area; by generating a three-dimensional fusion tensor and acquiring defect features through a multi-head attention mechanism, high-dimensional coupling of multi-modal data and adaptive focusing of key defect modes are realized; based on a three-dimensional joint feature space, a quantum coding mapping fusion method is utilized to perform row mapping combination on a time domain waveform sequence, a spatial heat distribution matrix and a frequency domain S parameter spectrum, so that collaborative coding of a time process, a spatial position and frequency change information is realized, and the joint characterization capability of a multi-source signal is improved.

The invention discloses an ultrasonic orthogonal scanning measurement method for mechanical properties of a fiber reinforced composite material. The method comprises the following steps: aiming at a composite material unidirectional plate to be detected, fixing receiving (transmitting) points on the surface of one side of a material, positioning the transmitting (receiving) points on the surface of the other side of the material, performing linear scanning along any two orthogonal directions, and recording transmission quasi longitudinal wave and quasi transverse wave time domain waveform signals which are acquired by combining each pair of transmitting and receiving points and are propagated along different paths; establishing an acoustic model for quantitatively describing the quantitative relationship between the ultrasonic trend and the independent elastic constant, thickness and fiber direction angle of the to-be-tested fiber reinforced resin matrix composite so as to simulate an ultrasonic data acquisition experiment and generate a corresponding simulated ultrasonic data set; and updating the independent elastic constant, the thickness and the fiber direction angle value of the composite material by taking the similarity degree between ultrasonic data sets obtained by experiments and simulation as a target function. According to the invention, multi-parameter high-precision in-situ nondestructive measurement of mechanical properties, thickness and fiber orientation of materials can be synchronously realized.