1336 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 provides a bridge structure risk prediction method and system based on digital twinning, and relates to the technical field of bridge structure risk prediction, and the method comprises the steps: obtaining bridge stress node displacement data, environment humidity data, and bridge visual and acoustic monitoring data through the system; the system constructs a digital twinborn model, simulates stress and deformation of a bridge under different load conditions, and calculates a stress deformation potential risk coefficient according to a maximum stress value and an average deformation value. The monitoring risk coefficient is calculated by extracting the total crack length in the visual monitoring data and the frequency characteristics of the acoustic data. And calculating a final risk coefficient by combining the stress deformation potential risk coefficient, the monitoring risk coefficient and the humidity data so as to judge the safety condition of the bridge. According to the method, the accuracy and reliability of bridge safety assessment in a complex environment are enhanced.

According to the harmonic amplification detection method and system of the electric power system, the frequency response of the impedance of the electric power system containing inter-harmonic frequency components is analyzed by obtaining the topological structure and the load distribution condition of the electric power system, and the impedance frequency characteristic scanning is performed on the electric power system at different frequencies by adopting a harmonic injection method; obtaining an impedance frequency response curve; according to the frequency interval of harmonic amplification, the hazard coefficient of the harmonic is evaluated through the voltage distortion rate and the current distortion rate caused by the harmonic, and the type of harmonic source equipment causing harmonic amplification is determined according to the hazard coefficient. According to the scheme, the harmonic source equipment can be detected, the power system can be conveniently maintained in time, and the stability of the power system can be improved.

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 relates to the technical field of electrical fault detection, in particular to a photovoltaic inverter fault detection system based on an electric signal, and the system comprises a signal collection module which continuously collects the output electric signal of an inverter, records the amplitude, frequency and phase of the electric signal, and generates an original signal record; and based on the original signal record, screening the electric signal, eliminating noise and non-characteristic data, and generating screened signal data. According to the method, the time frequency characteristics of the signals are extracted in real time through continuous wavelet transform, and the sensitivity and accuracy of fault detection are improved. According to the detailed feature recognition method, even tiny abnormal waveforms or frequency deviations can be captured, and therefore early warning of potential faults is achieved. Meanwhile, the fault detection threshold value is dynamically adjusted through the environmental parameters, flexible response is allowed according to the actual operation environment change, the false alarm and missing alarm conditions are reduced, and the adaptability and the reliability are improved.

The invention provides a three-phase inverter direct current side electric leakage fault type discrimination method and electronic equipment, and the method comprises the steps: carrying out the frequency spectrum time sequence characteristic analysis of the residual current of the alternating current side of a three-phase inverter, and analyzing the change of the amplitude of the residual current along with the time under different frequencies; the fault type of the direct current side electric leakage of the inverter can be known; by performing continuous energy monitoring on the residual current signal of the inverter, real-time current monitoring on the inverter is realized, and sudden change of the fault type of electric leakage or instantaneous electric leakage can be well monitored and distinguished. Aiming at the specific characteristics of the harmonic component of the leakage current of the inverter, different frequency spectrum characteristics of the leakage current under different fault types are determined, the electric leakage fault type discrimination method based on harmonic time-frequency characteristic extraction is provided on the basis, the specific fault type of the inverter can be discriminated under various working conditions, the position of the electric leakage fault is further positioned, and the reliability of the inverter is improved. The problem of inaccurate electric leakage fault type judgment and positioning in the current engineering application is solved.

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 a voltage quality composite disturbance identification method considering complex signal time-frequency feature fusion. The method comprises the following steps: S1, obtaining time-frequency domain distribution information of a voltage quality disturbance signal; s2, dividing disturbance identification input characteristic indexes into time domain statistical characteristics, frequency domain statistical characteristics and energy characteristics; s3, extracting a depth feature from the voltage quality disturbance information, and fusing the depth feature with a disturbance identification input feature index; s4, weighting the fusion feature set; and step S5, adopting an integrated learning device XGBoost based on a Boosting strategy as a disturbance classifier, and inputting the fusion feature set into the XGBoost after empowerment so as to realize identification of the composite disturbance signal. The technical problems of power quality disturbance traceability and power system stability can be solved.

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 relates to the field of degradation trend evaluation of rotary machinery, in particular to a motor abnormity identification method fusing low-frequency characteristics and an auto-encoder, and the method comprises the following steps: S1, collecting a vibration signal of target motor equipment; s2, data preprocessing; s3, building an auto-encoder network model; s4, training an auto-encoder network model; s5, calculating an equipment anomaly detection threshold value index; and S6, deploying applications. According to the method, vibration signal low-frequency feature mapping of the healthy operation state of the motor is self-learned through the self-encoder, the mean square error of the reconstructed feature and the original low-frequency feature is tracked, and the statistical abnormality trigger threshold value is calculated, so that feature information loss caused by direct extraction of a vibration signal monitoring index is avoided; extraction of original vibration low-frequency signal features is enhanced, the abnormal state of the motor is estimated in advance, and early warning and operation and maintenance of early degradation of the motor are achieved.

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 discloses an on-load tap-changer fault diagnosis method based on lightweight YOLO11, and is used for solving the problem of low mechanical fault diagnosis efficiency of a traditional on-load tap-changer. Comprising the following steps of: converting vibration signals in a motor starting stage and a change-over switch action stage into high-resolution time-frequency images through continuous wavelet transform (CWT) so as to comprehensively reflect time and frequency characteristics of the signals; starNet is adopted to replace a YOLO model backbone network, and the feature extraction capability is enhanced while the network compactness is kept through star operation; and designing an efficient joint convolution detection head EJCD, and realizing model lightweight and multi-scale target detection through grouping normalization and convolution parameter sharing. According to the invention, through combination of the time-frequency image representation capability of continuous wavelet transform and an efficient target detection framework of deep learning, the limitation of a traditional method in non-stationary signal processing is overcome, and the diagnosis efficiency and the model performance are significantly improved.

The invention relates to the technical field of wireless communication testing, and particularly discloses a Bluetooth headset anti-interference performance testing system which comprises an interference signal generation module, an interference signal dynamic simulation module, a signal stability monitoring and evaluation module and an anti-interference capability identification module. An interference signal comprising a modulation signal, a cross-band noise signal and a dynamic radio frequency signal is generated and injected into a test environment, and a real interference scene is simulated by using dynamic conversion of signal strength and frequency; the monitoring module obtains the intensity and frequency characteristics of the interference signal in real time, and comprehensively evaluates the signal stability based on an intensity abnormal coefficient and a frequency abnormal coefficient; and the anti-interference capability identification module calculates an anti-interference capability coefficient by using a support vector machine in combination with performance data of the Bluetooth earphone under different interference levels, and realizes graded evaluation of earphone performance.

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 provides an open earphone audio optimization method based on a self-adaptive compensation algorithm, and aims to improve the audio auditory experience of a user. The method comprises the following steps: firstly, acquiring an environmental noise signal through an earphone microphone, receiving a target audio signal, analyzing auditory perception of a user in a current environment by combining an ISO226 loudness curve and the like, and determining auditory sensitivity of the user to different frequency bands (including high frequency, intermediate frequency and low frequency); and then, enhancing the high-frequency part, deepening the low-frequency part and performing smooth transition compensation on the intermediate-frequency part by using a self-adaptive compensation algorithm. And then, dynamically adjusting the low-frequency part by using a hyperbolic filter, generating an adjusted audio signal, and generating a harmonic signal based on the amplitude and frequency characteristics to further optimize the tone quality. Finally, the optimized audio signal and the generated harmonic signal are provided for a user together, and the effects of automatically optimizing audio output in different environments and enhancing tone quality performance are achieved.

The invention relates to the technical field of valve internal leakage identification, aims to solve the problem of low leakage rate prediction precision in valve internal leakage acoustic emission detection, and discloses a nuclear power valve internal leakage detection method based on acoustic emission signal time-frequency characteristics. Extracting the characteristics of the time domain, the frequency domain and the time-frequency domain of the leakage signal, analyzing the linear relation between the characteristics and the leakage rate of the valve, and predicting the leakage rate and the leakage grade. According to the invention, remote online detection can be carried out on the internal leakage state of the valve, meanwhile, the internal leakage rate of the valve is predicted, the safety of maintenance personnel is guaranteed, the scheme feasibility is high, the measurement is accurate, the applicability is high, the improvement of automation and intelligence levels is facilitated, and good practicability and safety benefits are achieved.

The invention discloses a voiceprint-based power transformation equipment anomaly detection method and device, relates to the field of power transformation equipment monitoring, and can solve the technical problem of low accuracy of existing voiceprint-based power transformation equipment anomaly detection. The voiceprint-based power transformation equipment anomaly detection method comprises the steps of obtaining historical voiceprint feature data of a plurality of power transformation equipment, and based on a two-dimensional convolutional network, obtaining a plurality of historical voiceprint feature data of the plurality of power transformation equipment; extracting a local time-frequency feature of the historical voiceprint feature data to obtain a first voiceprint feature; performing feature extraction on the first voiceprint feature based on a bidirectional recurrent neural network to obtain a second voiceprint feature; according to the second voiceprint feature, multiple times of iterative training is carried out on a preset initial voiceprint detection model, joint optimization is carried out on model parameters based on comparative learning and perceptron classification during iteration, training is completed until the model parameters meet preset values, and a voiceprint detection model is obtained; and obtaining real-time voiceprint features of the to-be-detected power transformation equipment, and inputting the real-time voiceprint features into the voiceprint detection model to obtain an anomaly detection result of the to-be-detected power transformation equipment.

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 terahertz horn antenna and a preparation method thereof, and relates to the technical field of mobile communication systems. The antenna layer is arranged to be the antenna layer with the horn antenna groove structure, and due to the advantages of the horn antenna, the terahertz horn antenna has the advantages of being simple in structure, convenient to control gain, excellent in frequency characteristic, small in loss and the like. Moreover, the antenna layer and the radio frequency layer provided by the invention are fixed through bonding of the radiation electrode and the metal film, so that a low-reflection interconnection channel is realized, THz waves can be limited in a bonding area, the problem of electromagnetic leakage caused by stacking of the antenna layer and the radio frequency layer is avoided, and the performance of the terahertz horn antenna is improved. According to the terahertz horn antenna, the antenna layer and the radio frequency layer both adopt the glass substrates, the glass substrates have the advantages of high mechanical strength, small dielectric loss and the like, and the glass substrates can bear higher temperature, so that the problems of warping and deformation of the substrates are reduced, and the preparation quality of the terahertz horn antenna can be improved.

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.