1019 results about "Frequency characteristic" patented technology

The invention discloses a data line surface defect rapid nondestructive detection method based on intelligent image recognition, relates to the technical field of image data processing, and aims to solve the technical problems of difficult defect feature separation and low detection accuracy under complex weaving texture noise interference, and the method comprises the following steps: S1, collecting a data line image and carrying out graying processing; s2, constructing a multi-scale image pyramid, accurately segmenting the image by adopting a self-adaptive threshold algorithm, and realizing rapid detection of surface defects of the data line in combination with Hough transform; s3, frequency domain separation of weaving texture and background noise is realized through Fourier transform, interference is filtered out in combination with a band-pass filtering technology, and then the defect contrast is enhanced through histogram equalization; according to the method, the three-layer Gaussian pyramid is constructed for multi-scale decomposition, and the band-pass filtering technology is combined, so that the weaving texture and the defect signal are effectively separated, the detection accuracy is greatly improved, and the problems of missing detection and misjudgment caused by frequency characteristic confusion are thoroughly solved.

The invention relates to the field of frequency converter fault detection, and discloses a frequency converter fault prediction method and system based on machine learning, and the method comprises the steps: obtaining multi-dimensional real-time data in the operation process of a frequency converter; constructing a dynamic mapping relation to obtain a basic feature set; generating a time sequence feature vector capable of reflecting the state change of the equipment based on the basic feature set; comparing, analyzing and judging whether the equipment state deviates from a normal operation interval or not based on the historical operation data and the time sequence feature vector, and outputting a state deviation index; performing abnormal fluctuation judgment on the time sequence feature vector; extracting fluctuation amplitude and frequency characteristics of the key indexes to obtain quantitative description data of abnormal fluctuation; inputting the quantitative description data of the abnormal fluctuation into an abnormal prediction model; and generating a coping strategy and a triggering condition of the coping strategy based on the risk prediction result. The method has the advantages that the abnormal state of the frequency converter is recognized in time, and potential risks are predicted.

The invention relates to an audio noise reduction method, device and system based on deep learning, and the method comprises the steps: obtaining an input audio signal with noise, and carrying out the multi-scale time-frequency decomposition, and obtaining a mixed time-frequency feature and a noise fingerprint spectrum; performing parameter parallel processing on the noise fingerprint spectrum through a preset dynamic kernel generation network, and performing preliminary noise reduction processing on the mixed time-frequency characteristics to obtain noise-reduced mixed data; performing dual-path processing structure construction on the noise reduction mixed data to obtain amplitude optimization data and phase optimization data; performing dynamic time-frequency domain cross fusion on the amplitude optimization data and the phase optimization data to obtain fused audio data; and carrying out differentiable acoustic equation constraint adversarial training on the fused audio data, and carrying out inverse time-frequency transformation processing to obtain a target noise-reduced audio signal. According to the invention, the overall efficiency and effect of audio signal processing can be effectively improved.

The invention relates to an intelligent microphone pickup and speech enhancement method, system and device, and the method comprises the steps: obtaining an audio signal collected by a dual-silicon microphone array, and carrying out the cross-correlation analysis of the audio signal, and obtaining a corresponding human voice signal correlation feature; performing sound source positioning analysis on the audio signal based on the human voice signal correlation feature to obtain a corresponding target human voice signal; performing frequency characteristic analysis on the target human voice signal, and performing segmented dynamic gain processing on the signal according to a preset frequency band range to obtain a corresponding voice enhancement signal; performing noise component adaptive filtering processing on the target human voice signal to obtain a corresponding noise suppression parameter; and inputting the speech enhancement signal and the noise suppression parameter into a preset echo cancellation model for joint optimization to obtain a corresponding output human voice signal. According to the invention, the coupling problem of multiple acoustic interferences can be effectively solved.

The invention discloses a coal and rock mass fracture instability multivariate signal fusion analysis method, and belongs to coal and rock mass analysis. The method comprises the following steps: carrying out time-frequency domain processing on an acoustic emission signal to obtain an accumulated ringing characteristic value # imgabs0 # and an accumulated b characteristic value # imgabs1 #; a main control frequency characteristic value # imgabs2 # and an amplitude characteristic value # imgabs3 # are accumulated; a spatial characteristic value # imgabs4 # of acoustic emission; fitting a fitting function # imgabs7 # from the characteristic value # imgabs5 # to the characteristic value # imgabs6 # respectively; according to the fitting function # imgabs8 #, calculating a correlation coefficient matrix R between the characteristic values # imgabs9 # to # imgabs10 #; according to the matrix R, determining a weight coefficient # imgabs11 # of each characteristic value; a first-order derivative function # imgabs13 # and a second-order derivative function # imgabs14 # of the fitting function # imgabs12 # are obtained, and according to the first-order derivative function # imgabs15 # and the second-order derivative function # imgabs16 #, a demarcation time point # imgabs17 # in the coal and rock mass damage process is determined; and the boundary time point # imgabs19 # is corrected by using the weight coefficient # imgabs18 #, and a corrected boundary time point # imgabs20 # is obtained. In order to solve the problems that an existing coal and rock mass fracture instability judgment index is single, and coal and rock mass fracture instability early warning is low in single index precision, the early warning precision is improved through multi-element signal fusion.

The invention provides an intelligent grabbing control method and system based on tactile perception, and the method comprises the steps: obtaining a tactile feedback signal through a sensor array of a humanoid robot, carrying out the frequency domain decomposition of the signal through a Fourier transform method, and carrying out the analysis of the pressure and deformation data of each contact point on the surface of a to-be-grabbed object, calculating to obtain frequency characteristic distribution of the tactile feedback signal; if the initial estimation value of the object rigidity gradient exceeds a threshold value, performing finite element analysis on the object rigidity gradient, and combining a time domain processing result of the tactile feedback signal to obtain object rigidity gradient distribution; carrying out probability modeling on the deformation field by adopting a Bayesian inference method according to the gradient distribution of the rigidity of the object, and carrying out iterative optimization on a modeling result to obtain dynamic update parameters mapped by the deformation field; and regenerating an action sequence of the end effector of the humanoid robot based on the optimized strategy according to a trigger condition of grabbing failure detection, and determining a final grabbing control parameter.

The invention relates to the technical field of converters, in particular to a multi-module converter resonance suppression method and system for a mobile energy storage system. The dynamic response of the system is optimized through the controller, it can be ensured that the system can keep good stability and performance under different operation conditions, the robustness of the multi-module converter system under different working conditions can be improved through mathematical modeling and accurate design of a control algorithm, and the real-time monitoring and frequency recognition mechanism is adopted, so that the reliability of the multi-module converter system is improved. A control strategy can be adjusted according to frequency characteristics of resonance, active intervention can be performed on the resonance phenomenon of the system by adjusting the on-off state of the converter in real time and a control algorithm, the system can automatically adapt according to different loads, environment conditions and equipment states by adjusting control parameters in real time, long-term stable operation is ensured, and the system is safe and reliable. By actively suppressing resonance and reducing excessive oscillation of the system, energy waste and equipment loss caused by resonance can be significantly reduced.

The invention relates to the technical field of vibration monitoring, in particular to a slurry pump motion monitoring management system based on data analysis, which comprises a multi-source acquisition module, a period judgment module, a trend focusing module and a dynamic management and control module. According to the method, vibration data are collected through a three-axis acceleration sensor, a fundamental frequency amplitude is extracted through FFT, time-frequency characteristics and periodic distribution are fused in combination with an extreme value interval sequence, the data representation dimension is enhanced, the rising slope is processed through moving average, the periodic variation is calculated, random noise is restrained, and trend continuity is enhanced. Monotonicity test is combined with dynamic threshold judgment to improve anomaly recognition sensitivity; a sliding window is used for segmenting multi-cycle data; a sudden change interval is positioned based on a pressure amplification rate second derivative; the limitation of a frequency domain on transient feature recognition is broken through, a pressure amplification rate three-dimensional vector is constructed, cosine similarity is calculated, and a working condition difference degree is quantified to dynamically bind a maintenance instruction; and closed-loop feedback is formed to improve the early warning and decision matching degree.

The invention provides a bearing fault diagnosis method and system, a storage medium and a computer, and the method comprises the steps: obtaining a bearing vibration signal, and extracting time-frequency features in the bearing vibration signal according to variational mode decomposition and fast Fourier transform; performing convolution operation on the time-frequency features, and processing a convolution output result by adopting an ECA attention mechanism to extract frequency domain features in the time-frequency features; capturing a global time sequence dependency relationship in the time-frequency features based on a Transform model to extract long-distance dependent time domain features in the time-frequency features; and fusing the extracted frequency domain features and time domain features through a bidirectional cross attention mechanism, transmitting the fused features into a full connection layer for classification, outputting fault categories of the bearing, converting a classification result into probability distribution, and selecting the category with the highest probability as a diagnosis result. The bearing fault diagnosis method provided by the invention is high in fault diagnosis accuracy.

The invention discloses a motor control method and device based on an encoder, a medium and a program product, and relates to the field of motor control, and the method comprises the steps: receiving a position signal and a speed signal outputted by the encoder, and carrying out the filtering processing to obtain an operation state parameter; collecting mechanical vibration and electromagnetic noise generated in the operation process of the motor to obtain acoustic parameters of the motor, and extracting frequency characteristic parameters representing the rotating speed and motion characteristics of the motor; performing data fusion on the operation state parameters and the frequency characteristic parameters according to a preset weight to generate an operation state model; the operation state model is compared with a standard motion trail, and the position deviation value and the speed deviation value of the actual motion trail of the motor are calculated; and calculating a compensation control quantity according to the position deviation value and the speed deviation value, converting the compensation control quantity into a driving signal and outputting the driving signal to a motor driver so as to drive a motor to perform operation adjustment. According to the invention, the control precision of the motor in actual operation can be improved.

The invention relates to a millimeter wave radar vital sign modeling method based on time-frequency characteristic decoupling. According to the method, a millimeter wave radar array is arranged, reflection echo signals are collected, and pure initial signal data are obtained; performing time-frequency transformation on the initial signal data based on a multi-scale sliding window to generate a time-frequency energy distribution map; according to the energy concentration degree and stability difference of different frequency components in the time-frequency spectrum, frequency components which are high in energy concentration degree and have continuous, stable and periodic changes on a time axis are extracted and serve as effective signal components corresponding to human respiration and heartbeat characteristics; extracting a corresponding frequency change trajectory according to the effective signal components, establishing a vital sign signal trajectory model based on a time-frequency characteristic change trend, and determining an optimal vital sign trajectory path according to the stability and continuity of the trajectory on a time axis; and establishing a vital sign monitoring model by using the optimal vital sign track path to realize high-precision monitoring of human respiration and heart rate.

The invention discloses a running state monitoring method of an electric energy metering box, a storage medium and equipment, and relates to the technical field of intelligent power grid monitoring. The method comprises the following steps: performing multi-scale frequency domain decomposition on a multi-dimensional electrical signal to generate subsequences with different frequency characteristics; a dynamic window mechanism is adopted to adaptively segment signals and optimize a filtering strategy; extracting spatio-temporal joint features through multi-scale time sequence convolution and a physical constraint graph network; inputting the features into an attention prediction module integrated with a historical knowledge base for knowledge enhancement reasoning; and generating prediction uncertainty estimation based on Monte Carlo Dropout, calculating a composite abnormal index in combination with the distribution deviation degree, and triggering graded early warning. According to the method, accurate detection of transient abnormality and long-term trend is realized, the prediction accuracy is improved through spatio-temporal feature fusion and dynamic knowledge retrieval, the false alarm rate is reduced by using an adaptive threshold strategy, and a multi-dimensional and high-reliability state monitoring solution is provided for the electric energy metering box.

The invention provides a noise reduction processing method and system for Bluetooth audio equipment, and the method comprises the steps: obtaining an audio signal, and generating spectrum distribution data based on the audio signal; according to the intensity value of each frequency band in the spectrum distribution data, extracting a frequency characteristic set of a multi-source interference component; for the frequency characteristic set, determining a dynamic fluctuation parameter corresponding to the frequency characteristic; adjusting the configuration of the filter according to the corresponding relation between the dynamic fluctuation parameter and the preset noise width; synchronously updating the time domain response parameter of the filter based on the amplitude change rate of the dynamic fluctuation parameter; calculating dynamic threshold upper and lower limits according to the time domain response parameters, and generating a dynamic threshold sequence; and performing gain compensation processing on the environment audio signal based on the dynamic threshold sequence to generate a noise reduction audio signal. Adaptive time domain response parameters and a dynamic threshold sequence are adopted, so that filter configuration can be adjusted in real time according to noise characteristics.

Disclosed in the present invention are a dual-end monitoring-based partial discharge source localization method and system for high-frequency partial discharge of a high-voltage cable. The method comprises: acquiring operation data and phase velocity test data of a high-voltage cable; when a partial discharge fault occurs in the high-voltage cable, collecting partial discharge signals on two sides of the high-voltage cable by means of sensors on the two sides of the high-voltage cable; performing Fourier transform respectively on the basis of the partial discharge signals on the two sides to obtain amplitude-frequency characteristics of the partial discharge signals, and preprocessing the collected partial discharge signals on the basis of the amplitude-frequency characteristics to optimize and improve a phase difference algorithm; and by incorporating the phase velocity test data of the high-voltage cable, using the improved phase difference algorithm to perform partial discharge source localization on the faulty cable. In the present invention, dual-end monitoring is used to reduce the attenuation of signals caused by long-distance transmission, and synchronization of two sensors is performed using a PTP protocol, effectively reducing errors; and in addition, during monitoring, partial discharge source localization is performed using the improved phase difference algorithm, which is conducive to resisting external interference, and there is no need to determine the time of arrival, greatly improving the positioning accuracy.

The invention discloses a poultry health condition detection method, system and device and a storage medium, and relates to the technical field of poultry health condition detection.The poultry health condition detection method comprises the steps that poultry audio data are collected and preprocessed; based on the time-frequency characteristics of the audio data, constructing an MAE model; and performing pre-training and fine tuning on the MAE model, and evaluating the pre-trained MAE model. Based on a sound spectrum analysis technology, pre-emphasis filtering and Mel-spectrum conversion are combined, the key feature extraction capacity of poultry sound is enhanced, and the method adapts to the complex environment of a livestock farm. An MAE model is adopted, time-frequency characteristics are learned through self-supervised pre-training and a Transform structure, automatic audio acquisition and analysis and real-time health state output are realized, and the labor cost is reduced. The attention visualization technology is introduced, key frequency bands are identified in an auxiliary mode, the model interpretability is enhanced, and the whole scheme has the advantages of automation, high precision and high robustness.

The invention discloses a small sample radar target identification method based on multi-feature fusion. According to the invention, the natural resonant frequency characteristics of radar echo signals are introduced into a small sample learning framework, and the advantages of certain stability and simple and rapid extraction under the condition of attitude and observation angle change are fully utilized; attention multi-feature weighted fusion based on energy guidance is carried out on natural resonant frequency features and time frequency features, scattering features are described from different feature mechanisms, and the completeness of the features under the small sample condition is improved; in addition, a loss function based on combination of feature similarity measurement and classification loss is also designed, so that the model can learn feature representation with higher distinction degree while the feature space discrimination is optimized, and the performance is improved in a feature fusion-classification network. According to the method, on the premise that the reasoning time and the calculation complexity are not remarkably increased, the recognition accuracy under the condition that the observation angle changes is effectively improved.

The invention discloses an anti-seismic and anti-power-frequency-interference system and method for a vortex shedding flowmeter, and relates to the technical field of flow measurement, the system comprises a signal receiving module, a vibration monitoring module, a frequency identification module, a temperature and pressure measurement module and an interference signal processing module, interference signals in a flow-free state can be accurately processed, whether actual flow exists or not can be accurately judged by measuring the temperature and pressure of fluid in a pipeline and combining the temperature and pressure with vibration and frequency characteristic information, wrong measurement data generated due to the interference signals in the flow-free state are avoided, and the measurement accuracy is improved. According to the method and the system, the measurement problem caused by misjudgment can be reduced, meanwhile, information about the equipment operation state and the pipeline environment is provided for different types of interference signals through comprehensive analysis and judgment of the system, potential equipment faults and abnormal working conditions can be found in time, and a powerful data basis is provided for maintenance and management of the equipment.

The invention discloses a distributed energy storage stability adjusting method, and relates to the technical field of stability adjustment, and the method comprises the following steps: obtaining distributed energy storage multi-source disturbance data, constructing a disturbance signal time-frequency characteristic matrix, and extracting a disturbance type index; collecting first data of the energy storage unit, evaluating the physical suitability based on the disturbance type index, and generating an adaptation list containing the respondable unit identifier; calling a pre-stored response template according to the adaptive list, and generating a control instruction set; issuing a control instruction to the target energy storage unit, executing a control action and synchronously acquiring second data; and performing correlation analysis on the second data and the disturbance type index, updating the disturbance-control effect mapping matrix, and optimizing a calling priority strategy of the response template through reinforcement learning. According to the method, the dynamic adaptation list is generated, so that each energy storage unit executes the optimal adjustment strategy in the physical boundary of the energy storage unit, and the response precision to different types of disturbances and the system stability recovery speed are effectively improved.

The invention relates to the technical field of pipeline detection, and provides a non-contact pipeline fluid flow velocity measurement method based on a frequency wavenumber domain spatial spectrogram, and the method comprises the steps: collecting a turbulence signal in a pipeline through a piezoelectric film sensor; carrying out snapshot number segmentation on the turbulence signal to obtain a plurality of segmented turbulence time domain signals; fourier transform and narrowband signal processing are carried out on the segmented turbulence time domain signals, and a spatial spectrum corresponding to each narrowband signal component is obtained; constructing a three-dimensional frequency-wave number domain spectrogram according to the spatial spectrum function; and according to the three-dimensional frequency-wavenumber domain spectrogram, calculating the flow velocity of the pipeline fluid. According to the method, a three-dimensional spectrogram characteristic space is constructed through conjoint analysis of the frequency and the wave number, the angle, distance and frequency characteristics of the turbulence signals are decoupled through spatial spectrum estimation methods such as the MUSIC algorithm, multipath interference and noise are effectively restrained, the flow speed of fluid in a pipeline can be accurately estimated, and limitation in a traditional method is overcome.

The invention provides a concrete spraying rebound rate optimization decision-making method and a concrete spraying rebound rate optimization decision-making system. The method comprises the following steps: firstly, collecting and extracting time-frequency characteristics in a jet impact acoustic emission signal, collecting laser Doppler array measurement data in a concrete jet process to obtain concrete flow velocity distribution, then carrying out multi-source fusion processing on the time-frequency characteristics and the concrete flow velocity distribution, and extracting principal component characteristic vectors associated with springback; the method comprises the following steps: firstly, constructing a main component feature vector, then constructing a space velocity field model to obtain an identification result representing a concrete rebound state, then carrying out clustering analysis on the main component feature vector and the identification result, identifying a high rebound mode category and a low rebound mode category to generate a risk partition map, and finally, dynamically generating a jet parameter adjustment instruction by applying a self-adaptive control algorithm. Outputting a concrete rebound rate optimization decision scheme; according to the technical scheme provided by the invention, the problem of feature drift caused by space-time asynchronization of physical field data is solved, and the overall rebound rate prediction precision is greatly improved.

The invention relates to the technical field of hydraulically-driven industrial control, and particularly discloses a self-adaptive control method and system for a hydraulically-driven guniting manipulator, which are characterized in that a pressure fluctuation signal, a flow change signal and a mechanical arm displacement signal of a hydraulic system are collected in real time, and a frequency domain decomposition processing technology is combined to realize self-adaptive control of the hydraulically-driven guniting manipulator. Separating the composite signal into a low-frequency characteristic signal reflecting load change and a high-frequency characteristic signal reflecting pressure impact; for a low-frequency signal, a dynamic weight adjustment strategy is adopted to generate a load compensation control quantity; for high-frequency signals, pressure oscillation is suppressed through differential order adjustable phase compensation processing; and finally, inputting the two paths of control characteristic values into a driving control model of the guniting manipulator, and generating an adjusting instruction of the hydraulic actuating mechanism.

The invention provides a low-illumination perception method and system based on space-frequency fusion, and relates to the technical field of image processing, and the method comprises the steps: obtaining a normal light image and a low-illumination image corresponding to the normal light image; performing feature extraction on the normal light image and the low-illumination image through an encoder to obtain multi-scale spatial features; decomposing the multi-scale spatial features through stationary wavelet transform to generate a low-frequency feature component and a high-frequency feature component; performing optimization processing on the low-frequency characteristic component and the high-frequency characteristic component based on a parameter learnable frequency domain adaptive filtering mechanism; fusing the optimized low-frequency feature component and the optimized high-frequency feature component by introducing a cross attention learning mechanism to obtain a frequency domain feature; performing adaptive complementary fusion on the frequency domain features and the multi-scale spatial features to generate fusion features; step-by-step up-sampling is carried out on the fusion features through a decoder; and outputting a segmentation result of the low-illumination image through a sensing head according to the fusion features after up-sampling.

The invention discloses a coal rock mass main fracture precursor rock sound information analysis method. The method comprises the following steps: acquiring multiple groups of coal rock samples by adopting a core drilling mode, and processing the coal rock samples to form coal rock standard samples; placing the coal rock standard sample in a loading control system, and receiving an acoustic emission signal in the loading process of the coal rock standard sample through loading of a servo hydraulic machine; extracting a plurality of main frequency characteristic frequency bands in the received acoustic emission signal; analyzing each extracted main frequency characteristic frequency band to obtain the change rate of each main frequency characteristic frequency band; and according to the change rate of each main frequency characteristic frequency band, carrying out early warning grade division on the main fracture of the coal and rock mass. By means of the method, the problems that the sound emission impact number and energy are always located at high values due to mixing of multiple main frequencies, and the report rate is reduced are solved, meanwhile, early warning is conducted according to the main frequency characteristic frequency bands, and the sound emission early warning effect is further improved.

The invention provides a capacitor health state assessment method and device, a storage medium and computer equipment. The method comprises the steps of collecting and preprocessing a voltage signal and a current signal of a capacitor; extracting real-time multi-frequency characteristics of the capacitor from the voltage signal and the current signal, and generating a real-time multi-frequency characteristic curve according to the real-time multi-frequency characteristics; the multi-frequency characteristic curve offset between a reference multi-frequency characteristic curve corresponding to the real-time multi-frequency characteristics and the real-time multi-frequency characteristic curve is calculated, the offset amplitude is determined according to the multi-frequency characteristic curve offset, and the reference multi-frequency characteristic curve is used for representing that the capacitor is in a healthy state; and calculating a health state evaluation index of the capacitor according to the offset amplitude, and predicting the residual life of the capacitor according to the health state evaluation index of the capacitor. Therefore, online real-time monitoring is realized, and different degradation states of the capacitor are comprehensively reflected.

The invention provides a multiple longitudinal and transverse wave joint detection method based on a seabed crawler, which belongs to the technical field of geophysical exploration, and constructs an original longitudinal and transverse wave signal matrix by transmitting multi-frequency longitudinal and transverse wave signals and receiving stratum reflection signals. Fourier transform is utilized to extract frequency characteristics, physical analysis is carried out in combination with a wave equation, and a longitudinal and transverse wave interference matrix is constructed and weighted fusion is carried out. And constructing an error compensation matrix according to the crawl device attitude data, and performing interference compensation on the joint interference matrix. And performing time-frequency analysis through wavelet transform, extracting specific time-frequency features, inputting the specific time-frequency features into a pre-trained stratum classification prediction model, realizing anti-interference prediction on the type and thickness of the seabed stratum structure, finally drawing a seabed three-dimensional geological profile map according to a prediction result, and marking a key geological structure and a resource distribution region. The technical problem that accurate recognition and resource distribution positioning of the seabed stratum structure are affected due to much external interference in the seabed geological exploration process is solved.

The invention provides an underground structure boundary identification method and system based on distributed optical fiber sensing, and relates to the technical field of underground structure detection and boundary identification. The method comprises the following steps: firstly, arranging a sensing array in a to-be-detected area, establishing a channel corresponding to a space coordinate, and collecting a non-excitation base line; medium and noise are estimated through low-energy probe, excitation frequency band, energy and repetition rate are optimized, and the signals are emitted by an adaptive seismic source; reflection and transmission responses are synchronously collected under the unified time reference; performing wavelet denoising, temperature and dispersion compensation and time alignment on the data, and extracting amplitude, phase and frequency characteristics; gradient is calculated on the feature field, non-maximum suppression is carried out, and stable boundary points are obtained by adopting self-adaptive double thresholds based on local statistics and combining time continuity and space connectivity constraints; a two-dimensional section is obtained through spline fitting, a three-dimensional model is reconstructed under the constraint of multi-section consistency, a section map and the three-dimensional model are output, and high-precision, real-time and visual detection of small-size boundaries is achieved.

The invention provides a self-adaptive algorithm and system for improving frequency measurement precision and response speed of a speed regulator. The method comprises the following steps: Step 1, signal acquisition and preprocessing; step 2, dynamically adjusting filtering parameters, and suppressing power grid signal noise; 3, extracting frequency characteristics in real time, and improving the frequency measurement resolution; 4, a synchronous clock is adopted as a reference frequency source, a clock reference value is periodically and automatically corrected, and long-term drift is eliminated; 5, a nonlinear dynamic model of the hydroelectric generating set is constructed, the nonlinear relation between the valve opening degree and output of the speed regulator is compensated in real time, and the frequency modulation precision is improved; and step 6, outputting the instantaneous frequency. According to the scheme, through the self-adaptive frequency measurement algorithm, high-precision clock calibration, a hardware acceleration mechanism and a nonlinear compensation strategy, a multi-dimensional optimization system is constructed, high-precision frequency measurement is carried out, a rapid effect can be achieved, and a hydroelectric system has long-term stability and high adaptability.

The invention belongs to the technical field of hydroelectric generating set fault diagnosis, and particularly discloses a hydroelectric generating set cavitation fault diagnosis method based on multi-channel acoustic emission signal fusion. Through the technical means of acquiring the acoustic emission signals at multiple positions, a more comprehensive cavitation phenomenon data basis is provided; the representation of time-frequency characteristics is optimized through the Mel time-frequency diagram, and the problem that the dynamic characteristics of acoustic emission signals cannot be effectively captured through a traditional method is solved; multi-channel feature extraction is performed on the Mel time-frequency graph through a preset deep convolutional neural network model, so that automatic learning of deep cavitation working conditions is realized; and multi-channel fusion and classification identification are carried out on a feature extraction result through the model, so that effective integration and fault classification of features are realized. Compared with the prior art, a more accurate and comprehensive cavitation fault diagnosis effect is realized, and the diagnosis precision is improved.

The invention relates to the technical field of ground wire damage detection, and provides a ground wire damage detection method and system based on magnetostrictive guided waves, and the method comprises the following steps: generating a white noise signal through a magnetostrictive transducer to excite a ground wire, and collecting a guided wave response signal of the guided waves; identifying the guided wave response signal through a spectrum analysis model to obtain the inherent frequency characteristic of the ground wire, and obtaining the optimal excitation frequency according to the inherent frequency characteristic and the amplitude frequency mapping relation; adjusting the excitation frequency of the white noise signal according to the optimal excitation frequency to obtain an optimized guided wave signal; identifying the optimized guided wave signal through a defect detection and tension monitoring model to obtain a defect tension detection result; and based on the defect tension detection result, judging the damage level of the ground wire, and generating a detection report. According to the invention, defect detection and tension abnormity monitoring are carried out at the same time, the reliability of ground wire detection is improved, and an effective monitoring means is provided for safe operation of the ground wire.

The invention discloses a motor imagery electroencephalogram signal decoding method and system based on a Sinc filter, and belongs to the field of brain-computer interfaces, and the method comprises the following steps: carrying out convolution calculation on a motor imagery electroencephalogram signal and the Sinc filter to obtain a frequency characteristic signal of the motor imagery electroencephalogram signal; performing channel connection on the characteristic signals of the motor imagery electroencephalogram signals of different frequency bands to generate high-dimensional electroencephalogram characteristic signals; obtaining a parallel multi-branch space-time convolutional network, performing time domain feature extraction, spatial feature extraction and average pooling dimension reduction processing on the high-dimensional electroencephalogram feature signal to obtain a channel weight, and performing weighted calculation on the target feature map based on the channel weight to obtain a space-time feature map; and obtaining class labels of the motor imagery electroencephalogram signals after mapping processing and activation processing, and obtaining a decoding result. According to the method, the Sinc filter and the space-time convolutional neural network are combined, and the accuracy and robustness of motor imagery electroencephalogram signal classification are improved.