369 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 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 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 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 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 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 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 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 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 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 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.

The invention discloses an intelligent civil engineering construction management method and system, and relates to the technical field of civil engineering construction management, and the method comprises the following steps: obtaining the vibration data of N construction devices and the mixed vibration data of M monitoring points; and judging whether the mixed vibration data is greater than a preset threshold, if so, performing multi-feature matching with a preset vibration fingerprint database, determining responsible equipment according to a matching result, and outputting contribution degrees and vibration measured values in a descending order. According to the method, environmental interference is eliminated through preprocessing, multi-dimensional decomposition of the mixed vibration data is realized through independent component analysis, and double criteria of time domain waveform difference and frequency domain energy matching degree are combined, so that the recognition accuracy of responsible equipment is remarkably improved, and the recognition efficiency is improved. A three-dimensional geologic model is further constructed through ground penetrating radar scanning and soil layer test data, spatial distribution and attenuation characteristics of different soil layers are accurately reflected, and misjudgment of responsible equipment caused by soil layer difference is avoided.

The invention relates to a fault detection method and device of a converter transformer, computer equipment and a medium. The method comprises the steps of obtaining a vibration time-domain waveform signal and a current time-domain waveform signal of a winding of the converter transformer in a preset time period, determining vibration frequency response data of the winding according to the vibration time-domain waveform signal and the current time-domain waveform signal, and determining the current time-domain waveform signal of the winding according to the vibration frequency response data and preset reference vibration frequency response data. And determining a fault detection result of the winding. By adopting the method, the timeliness of fault detection of the converter transformer can be improved.

The invention provides an ocean reverberation time domain waveform realization method and system based on convolution operation, and the method comprises the steps: generating a one-dimensional random number sequence meeting a random process, and the data size of the random sequence is the same as the data size of a reverberation time domain waveform to be realized; generating a sound source signal time domain waveform; performing convolution operation on the random number sequence and the sound source signal time domain waveform to obtain a stabilized time domain waveform signal subjected to reverberation intensity stabilization processing; generating an ocean reverberation intensity attenuation curve according to the reverberation intensity theoretical model; and performing intensity weighting on the stabilized time-domain waveform signal subjected to reverberation intensity stabilization processing by using the attenuation curve to obtain a reverberation time-domain waveform. The method has the advantages that cross superposition of scattering echoes of different discrete points does not need to be considered; the calculation speed is high, the requirement for the data cache capacity is small, and the amplitude and the phase of each discrete point echo and the broadening of a transmitted signal do not need to be calculated; the method is suitable for realizing reverberation time domain waveforms in various waveguide environments.

The invention relates to a positioning method and device based on an array information source, computer equipment and a storage medium, and the method comprises the steps: obtaining a time domain oscillogram of the information source based on a pre-installed array; the array comprises a plurality of array elements distributed according to a preset form; carrying out peak point search on the time domain oscillogram to obtain position coordinates of each time delay peak point; based on the position coordinate of each time delay peak point, extracting a time delay curve corresponding to the information source; and according to a pre-constructed time delay curve model, solving the time delay curve to obtain target position information corresponding to each information source. According to the method and the device, the problems that environment data needs to be actually measured in advance, the calculation amount is large and the requirement of real-time calculation is difficult to meet in the prior art are solved, and the time delay curves of a plurality of information sources can be simultaneously detected through peak point searching and extracting; and the time delay curve is solved according to the pre-constructed time delay curve model, so that the accuracy and the stability of calculating the target position information of each information source are improved, and the requirement of real-time calculation is met.

The invention relates to the technical field of safety monitoring, and particularly discloses a storage tank safety monitoring method based on the Internet of Things, and the method comprises the steps: synchronously collecting a high-frequency leakage acoustic emission signal and a reference environment noise signal through a piezoelectric acoustic emission sensor disposed on the outer wall of a storage tank and an industrial microphone disposed in a storage tank region; after time delay alignment is carried out on a reference environment noise signal and a high-frequency leakage acoustic emission signal, self-adaptive filtering processing is carried out, so that environment noise interference in the high-frequency leakage acoustic emission signal is filtered out. And then, further introducing a deep learning algorithm to carry out time domain waveform analysis and adaptive content perception enhancement processing on the denoised high-frequency leakage acoustic emission signal so as to enhance key acoustic emission characteristics in the signal, thereby realizing intelligent identification of the storage tank leakage on the basis. According to the method, the accuracy and sensitivity of storage tank leakage monitoring can be effectively improved, the influence of noise interference on an industrial site on storage tank leakage identification is overcome, and early warning of tiny leakage of the storage tank is achieved.

The invention discloses an intelligent sound signal sensing method and system based on time-frequency feature fusion, and relates to the technical field of intelligent sound signal processing, and the method comprises the steps: obtaining an original audio signal; performing short-time Fourier transform on the original audio signal to obtain a plurality of frequency spectrum features; inputting the plurality of frequency spectrum features into a plurality of neural networks for processing, and extracting frequency domain depth features; processing the original audio signal by using a one-dimensional convolutional neural network, and extracting time domain depth features; splicing and fusing the frequency domain depth feature and the time domain depth feature in a channel dimension to obtain a time-frequency fusion feature; and inputting the time-frequency fusion feature into a full connection layer classifier to obtain a final audio scene recognition result. According to the invention, by designing a double-branch network architecture of time domain and frequency domain parallel processing, time domain waveform information and frequency domain amplitude and phase information are effectively fused, and in an end-to-end training scene, the accuracy of audio recognition is remarkably improved.

This invention discloses a signal post-processing method and apparatus for improving the accuracy of train passing identification, belonging to the field of high-speed railway condition monitoring technology. Based on candidate train passing events initially identified by existing methods, the method first performs time-domain waveform screening to eliminate events whose time-domain waveforms do not conform to a preset impact pattern. Then, for the screened events, the frequency domain features of their vibration signals are extracted. Finally, a second screening is performed based on the comparison results of the frequency domain features and a preset threshold to distinguish real train passing events. This invention, through a two-stage screening architecture of first the time domain and then the frequency domain, can effectively capture and utilize the essential differences in time-domain waveforms and frequency-domain energy distribution between transient train passing impacts and persistent interference, thereby significantly improving the accuracy and reliability of train passing identification and producing a high-purity train passing event dataset.

The invention discloses an LSTM-based oil and gas pipeline transient electromagnetic detection signal analysis method and system and a storage medium, and relates to the field of pipeline defect nondestructive detection. Acquiring electromagnetic signals at a plurality of different preset frequency points and different directions corresponding to the different preset frequency points according to a preset time interval to form electromagnetic signal array data; according to the time domain waveform, the frequency spectrum curve and the signal amplitude in the electromagnetic signal array data, abnormal electromagnetic signals are screened; the resistivity of the position, corresponding to the oil and gas pipeline, of the abnormal electromagnetic signal is collected and compared with the standard resistivity, and if the comparison result exceeds a preset range, multi-dimensional features of the abnormal electromagnetic signal are extracted to construct a feature vector; and inputting the feature vector into an LSTM signal detection network model, outputting a mapping relation between the feature vector and the oil and gas pipeline defect, and obtaining an oil and gas pipeline defect condition corresponding to the current electromagnetic signal. The pipeline fault risk can be reduced, and safe and stable operation of the oil and gas pipeline is guaranteed.

The invention discloses a time-frequency fusion power line noise classification modeling method, system and device based on deep learning, and a medium, and belongs to the technical field of power line noise classification modeling, and the method comprises the steps: constructing a data set containing multiple types of typical power line noise signals; performing short-time Fourier transform on the noise signal to obtain a spectrogram, and extracting local texture features in the spectrogram; extracting global time sequence characteristics of the original time domain waveform of the noise signal; splicing the local texture features and the global time sequence features to form a fusion feature vector, inputting the fusion feature vector into a full-connection neural network, and outputting a multi-label noise prediction result; and training the full-connection neural network, and updating network parameters by optimizing a binary cross entropy loss function. According to the method, different characteristics and generation mechanisms of each type of noise are considered, through fusion of time domain information and frequency domain information, the characteristics can be extracted more comprehensively, the space relation and mode of the time domain and the frequency domain are effectively captured, and the adaptability and accuracy of the model to a noise classification task are improved.

The invention discloses a traction motor bearing fault online detection method and system, a medium and equipment. The method comprises the steps that S1, a working condition signal and a leakage current signal of a traction motor under a stable working condition are acquired; s2, performing adaptive decomposition on the leakage current signal, and capturing a sensitive mode corresponding to the state change of the bearing, namely a corresponding high-frequency mode component of the traction motor bearing in the leakage current signal; s3, extracting a peak-to-peak value Apeak of a main wave crest in the high-frequency modal component time domain waveform, a fluctuation period Ta, a resonant frequency fc of a high-frequency modal component frequency spectrum and energy Ef corresponding to the resonant frequency fc; and S4, inputting the Apeak, Ta, fc, Ef and working condition signals into a pre-constructed and trained bearing fault multi-feature fusion diagnosis model, and outputting a motor bearing diagnosis result. The method has the advantages that the fault features of the motor bearing can be effectively and sensitively obtained, and accurate traction motor bearing fault early warning is achieved.

The invention provides an AI-based loudspeaker nonlinear compensator parameter determination method, system and device, and the method comprises the steps: carrying out the real-time processing of an input audio sequence corresponding to a calibration audio based on a nonlinear compensator, and carrying out the sound amplification output; performing compensator parameter updating according to a preset updating beat until a set convergence condition is met, and determining a target compensator parameter; wherein the compensator parameter updating comprises the following steps: acquiring a reference digital sequence corresponding to the same time window, a current compensator parameter and an acoustic feedback sequence corresponding to the output of a loudspeaker to be calibrated acquired by a microphone, and processing the acoustic feedback sequence, the reference digital sequence and the current compensator parameter corresponding to the same time window through a preset AI updating network to obtain an iterative compensator parameter. The method inputs the original time domain waveform data, does not need to carry out explicit time delay alignment, synchronous compensation and other feature extraction steps, and has the characteristics of flow automation, individual difference adaptation, fast convergence, strong engineering landing and the like.

The invention discloses a time domain signal reproduction conversion method and device of an impact response spectrum and a medium. Belongs to the field of impact mechanical environment related design and test, and particularly belongs to the technical field of time domain signal reproduction of an impact response spectrum. The technical problem that the real time domain energy distribution condition of the time domain waveform corresponding to the impact response spectrum cannot be fully reflected in the prior art is solved. In the fundamental wave amplitude modulation, a Weibull sine window is adopted to process sine wavelets, and the time domain peak position and the decay time can be adjusted by utilizing the characteristic that a Weibull distribution function curve can change according to parameter adjustment. And waveform adjustment is realized by using a sine function, and the low-error convergence capability of the time domain waveform impact response spectrum is improved. Error control of the impact response spectrum of the reproduced time domain signal and the target impact response spectrum is superior to + / -2dB, parameterization control is carried out on the time domain waveform peak position and the decay time, and the distribution condition of impact energy under the time domain and frequency domain conditions is effectively reproduced.

The invention discloses a power system forced oscillation source positioning method based on ICEEMDAN-ITEO, and belongs to the technical field of power system stability analysis and control. In a signal adaptive decomposition and modal extraction part, an ICEEMDAN algorithm is utilized to decompose a wide-area measurement signal, suppress modal aliasing and noise interference and obtain a modal component with physical significance; the energy weight of each modal component is calculated by adopting ITEO, the identification capability of the dominant forced oscillation component is enhanced through an adjustable resolution parameter, and the time domain waveform of the dominant forced oscillation component is reconstructed; in the oscillation source judgment part based on the dissipation mode energy flow, the dissipation mode energy flow of each generator port is calculated according to the dominant oscillation mode component, and accurate judgment of a disturbance source is realized through linear fitting of slope characteristics of the dissipation mode energy flow; in the verification and application part of the oscillation source positioning result, the accuracy and robustness of the method in different noise and disturbance scenes are verified through a test system comprising a network-forming fan.