93 results about "Time frequency decomposition" patented technology

The invention provides a trajectory control video generation method and device based on depth information and time-frequency optimization, and relates to the technical field of image processing, and the method comprises the steps: optimizing a 3D trajectory through multi-entity segmentation, depth estimation and time-frequency decomposition in combination with a user instruction, and generating a control signal through a multi-scale fusion network; finally, the signals and original images are input into an improved Stable Video Diffusion model to generate a video potential representation sequence, the problems that an existing video generation method is insufficient in dynamic entity motion control precision and poor in cross-frame consistency are solved, and through 3D trajectory modeling guided by depth information and a time-frequency joint optimization mechanism, the video potential representation sequence is generated. And the motion smoothness, the space authenticity and the time-frequency stability of the generated video are obviously improved.

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 the technical field of fault detection, and discloses a diesel generating set fault detection method and system based on deep learning, and the method comprises the steps: obtaining first vibration signal data, and carrying out the time-frequency decomposition, and obtaining a dynamic change feature; de-noising processing is carried out on the dynamic change features to obtain a time-frequency feature sequence; extracting a peak energy distribution data set, and calculating each frequency band entropy value to obtain a frequency band entropy value sequence; classifying the frequency band entropy sequence, determining a random fluctuation reference mode, and separating to obtain an abnormal frequency component; calculating a spectral line spacing and amplitude ratio, obtaining a spectral line feature data set, classifying the spectral line feature data set, and determining a fault classification result; obtaining current second vibration signal data, performing similarity calculation on the current second vibration signal data and a pre-established normal mode library, and outputting a fault feature vector; and verifying the fault feature vector to obtain a final fault detection result. According to the method, closed-loop diagnosis from signal acquisition to fault classification can be realized, and the fault detection precision of the diesel generating set is improved.

The invention relates to the technical field of Ethernet power supply, and discloses a POE power supply power control method, device and equipment and a storage medium. The method comprises the steps of obtaining port electrical parameter data, device temperature data and historical power supply operation data of a target POE power supply, and performing multi-dimensional calibration compensation, multi-window time-frequency decomposition, dynamic and static feature extraction and feature hierarchical mapping on the port electrical parameter data based on the device temperature data; performing classification boundary construction and load feature mapping conversion on the mapping result to obtain a load classification mapping table, and performing progressive prediction of multi-level power supply parameters and power demand integration based on historical power supply operation data to obtain a power demand prediction result; and performing multi-constraint optimization calculation on the power demand prediction result, generating a power distribution strategy, and based on the power distribution strategy, controlling the POE power supply to perform multi-power closed-loop control, and generating a power control result. According to the invention, efficient and accurate control of the POE power supply power is realized.

The invention provides a power transmission and distribution line suspended foreign matter detection method and system based on image processing and deep learning. Wherein a historical point cloud set on the surface of the power transmission tower is acquired, a deformation gradient map is generated through dynamic registration of curvature differences of adjacent point clouds, a super-threshold region is marked as a deformation correlation region, and foreign matter contour parameters are extracted; performing time-frequency decomposition on the node strain signal, retaining a steady-state component, and mapping the steady-state component to an association region to form a strain map; calculating the displacement offset of the deformation association interval to construct a deformation propagation network, and extracting load over-threshold nodes to construct a stress diffusion network; and superposing the network, fusing the displacement and the load value in the superposition area to generate a risk index, and outputting early warning in combination with the foreign matter coordinates and the risk index. According to the technical scheme provided by the invention, the cross-modal dynamic analysis of deformation propagation and stress diffusion paths is fused, and the early warning efficiency of foreign matter attachment and structure abnormity risks of the power transmission tower is improved.

The invention provides a power transmission network equipment insulation aging state evaluation and life prediction method and system, and relates to the technical field of power equipment state monitoring, and the method comprises the steps: obtaining multi-dimensional operation monitoring data, extracting features through multi-scale time-frequency decomposition, and carrying out the orthogonal transformation to obtain a decoupling feature vector; calculating a mahalanobis distance to obtain an aging state deviation metric value; constructing a stress accumulation damage factor to perform nonlinear modulation on the degradation rate; an aging state trajectory is predicted based on the modified degradation rate and a remaining life is determined. The method can accurately evaluate the insulation aging state and predict the residual life of the equipment, and improves the evaluation precision and reliability.

The invention, which relates to the technical field of education recommendation, discloses a student portrait-driven education agent recommendation system based on a cognitive diagnosis map, comprising a time-frequency decomposition processing module, a weight rhythm matching module, a time sequence coupling damping module, a dynamic difference readjustment module and a time-frequency domain self-balancing control module. And the time-frequency decomposition processing module is used for establishing a time-frequency decomposition processing layer based on drifting characteristics of student portrait parameters in a time dimension, and performing energy distribution analysis on multi-source dynamic data streams from learning behaviors, test performance and emotion feedback according to time slices. Through time-frequency decomposition and non-resonance rhythm matching, dynamic coordination of weight adjustment and student portrait drifting is realized, characteristic fluctuation amplification is prevented, and the stability of a learning path is guaranteed; and a dynamic closed loop is formed through time sequence coupling damping and time-frequency domain self-balancing control, stable convergence of cognitive features is realized, and the accuracy and continuity of educational agent recommendation are improved.

The invention discloses a frequency modulation continuous wave radar multi-target dynamic separation and anti-interference method and system based on synchronous extraction transformation, and the method comprises the steps: S1, generating a linear frequency modulation continuous wave, and transmitting a high-frequency signal through a radio frequency front end; s2, receiving a target reflection signal, mixing the target reflection signal with the emission signal through a mixer, extracting an intermediate frequency beat signal, and performing denoising; s3, performing time-frequency decomposition on the signal by adopting synchronous extraction transformation to generate a high-resolution time-frequency distribution matrix; s4, separating overlapped targets based on the time-frequency matrix by adopting a density clustering algorithm, eliminating a distance-speed coupling error in combination with a dynamic decoupling compensation algorithm, and outputting a time-frequency track of an independent target; constructing a dynamic time-frequency domain mask filter to filter noise, and optimizing the detection sensitivity and performing false alarm control through an adaptive threshold detection mechanism; according to the method, dense targets are accurately separated through high-resolution time-frequency analysis, and the anti-interference capability and the dynamic target tracking precision are improved in combination with a dynamic Doppler decoupling algorithm.

The invention discloses a cross-domain-oriented tool wear monitoring method and a cross-domain-oriented tool wear monitoring system. The method comprises the following steps: carrying out time-frequency decomposition on vibration signals of a source domain and a target domain by utilizing physically guided wavelet kernel initialization, and extracting high-frequency characteristics with physical interpretability; a bat bionic attention mechanism is constructed, and dynamic enhancement and cross-domain feature alignment of wear features are realized in combination with energy guidance, echo alignment and a time-frequency attention module; splicing enhanced features of the source domain and the target domain, inputting the spliced enhanced features into a shared convolution and circulation network, and extracting space-time fusion features; and constructing an improved MMD loss function fused with the processing parameter similarity, and performing joint optimization by combining regression errors to realize cooperative training of feature alignment and wear prediction. By means of the method, the high-precision and high-robustness monitoring task of tool abrasion under different working conditions on an industrial site can be met.

The invention relates to the technical field of seismic signal detection, in particular to a multi-dimensional multi-level attention seismic first arrival pickup novel artificial intelligence method, which comprises three steps of model building, model training and first arrival pickup. The core components of the model building model comprise an efficient discrete wavelet device, an encoder, a feature extractor, a decoder and an inverse wavelet converter. According to the method, time-frequency decomposition is performed on the seismic waveform by using efficient discrete wavelet transform, the characteristics of the signal on different frequencies and time dimensions are extracted, and local and global correlation modeling is performed on the extracted characteristics through a hierarchical attention mechanism, so that the complex characteristics of the seismic signal are more effectively captured, and the accuracy of the seismic signal is improved. The seismic wave first arrival pickup method has the remarkable advantages of first arrival pickup precision and stability in a noise complex environment, and the accuracy of seismic wave first arrival pickup can be greatly improved.

The invention provides an intelligent switch load identification method and device, and the method comprises the steps: carrying out the multi-scale time-frequency decomposition processing of the load data of an intelligent switch, and obtaining a multi-scale feature matrix; based on the dimension of the multi-scale feature matrix, performing adaptive convolution processing on the multi-scale feature matrix to obtain an adaptive convolution output matrix; performing dynamic pooling processing on the adaptive convolution output matrix to obtain a fixed dimension feature matrix; performing multi-task adaptive meta-learning model construction on the fixed dimension feature matrix to obtain a load type identification result; according to a load type identification result, performing weighted Focal loss calculation to obtain an optimized loss function value; and according to the optimized loss function value, a multi-task adaptive meta-learning model is updated, and the multi-task adaptive meta-learning model is used for predicting the load change of the intelligent switch.

The invention provides a postoperative bleeding risk monitoring method and system for a hepatobiliary patient. The method comprises the following steps: collecting real-time multi-modal data; constructing an LSTM-CNN hybrid model by using the time-frequency decomposition features, and obtaining a local tissue hypoxia index and a vasomotor function anomaly probability; the low-frequency impedance change rate and the albumin level are fused through a random forest algorithm, and the ascites occurrence probability and the effusion amount predicted value are obtained; and generating a bleeding point positioning coordinate and a thermodynamic diagram risk grade. And calculating a comprehensive bleeding risk probability and positioning a bleeding area. And generating graded early warning signals and recommending personalized treatment schemes or nursing suggestions. According to the invention, the LSTM-CNN hybrid model, the random forest algorithm and the three-dimensional convolutional neural network are adopted to deeply extract different data features, so that the limitation of single index evaluation is avoided. A causal relationship model is established through the Bayesian network, and the comprehensive risk probability calculation preciseness is improved; the early warning threshold is dynamically adjusted by combining the individual characteristics of the patient, and the traditional problem of easy misjudgment is solved.

The invention belongs to the technical field of equipment state monitoring, and discloses a relay state monitoring and diagnosis method and system. Vibration time domain signals of all monitoring points in the working process of a relay are collected, a time delay characteristic matrix is constructed through segmented cross-correlation operation, and a near-field sound source candidate area is analyzed and recognized in combination with the spatial divergence of a time delay gradient vector field. A sound-vibration coupling propagation model is introduced, near-field sound source excitation and structure conduction vibration components are distinguished, multi-component signal decoupling is realized through spatial constraint and frequency domain sparsity constraint, and a relay body vibration signal is effectively separated. The method comprises the following steps: performing time-frequency decomposition on a body vibration signal, extracting a transient impact feature of a contact action and a steady-state vibration feature of coil excitation, constructing a multi-dimensional state feature vector, and realizing state diagnosis through similarity matching with a preset feature library. According to the invention, the early fault detection sensitivity and diagnosis accuracy of the relay are improved, and a reliable basis is provided for preventive maintenance.

The invention provides a multi-mode biological signal dynamic fusion fitness guidance method and system. The method comprises the steps that a signal acquisition layer synchronously acquires dynamic force and static pressure through a flexible voltage capacitance sensor; in the feature processing layer, frequency domain energy distribution is extracted from voltage signals through short-time Fourier transform, static pressure gradient is obtained from capacitance signals through wavelet packet decomposition, time-frequency decomposition is carried out, and feature dimensionality reduction is carried out; the fusion modeling layer is used for eliminating time migration of sensor data through dynamic time warping to carry out time alignment and carrying out feature fusion based on a Transform attention mechanism; and the decision generation layer performs incremental learning and large model driven fine tuning to generate a personalized exercise prescription. Based on the technical scheme of the invention, the muscle state analysis precision is effectively improved through the dynamic time-frequency fusion of the dynamic and static signals; real-time dynamic feedback of the system is improved; the model power consumption is reduced, and the noise suppression rate is improved; and the personalized adaptation degree is improved by a dynamic threshold value.

The invention relates to the technical field of audio signal processing, and discloses an industrial noise identification method and system, and the method comprises the steps: obtaining an audio signal, and carrying out the time-frequency decomposition, and obtaining preliminary decomposition data; classifying the coupling features of the preliminary decomposition data to obtain a coupling region boundary value; if the boundary value of the coupling region exceeds a preset boundary threshold value, performing filtering processing to obtain separated noise spectrum data; calculating an overlapping proportion of the separated noise spectrum data and a preset target sound; if the overlapping proportion causes extraction deviation, extracting dynamic change characteristics of the separated noise spectrum data and fusing the dynamic change characteristics into a spectrum extraction process to obtain an accurate noise spectrum; calculating an intensity value according to the accurate noise spectrum, and determining an intensity evaluation result; and comparing the intensity evaluation result with a preset pollution threshold, calculating a noise pollution degree and outputting a noise identification result. The method is suitable for industrial environment noise monitoring, and can accurately extract the noise spectrum.

The invention discloses a porosity real-time prediction method and system based on alkane gas and carbon isotope characteristics thereof and a storage medium, relates to the technical field of oil and gas reservoir development, and aims to solve the problem of insufficient precision in a heterogeneous reservoir due to neglect of dynamic characteristics of alkane gas carbon isotope in an existing method. The method is characterized by comprising the following steps: S100, collecting alkane gas concentration and carbon isotope data thereof in real time, and performing time-frequency decomposition on the data to obtain characteristic data on different time and frequency scales; s200, performing multi-scale feature extraction on the data to obtain multi-scale features; s300, performing feature fusion on the multi-scale features; s400, constructing a GNN network model, introducing an adaptive attention mechanism, residual connection, jump aggregation and time sequence dynamic fusion, constructing a graph structure by taking a reservoir unit as a node, and mapping a fusion feature to a graph node attribute; and S500, constructing a reservoir space structure based on the GNN network model, and combining physical constraint optimization to realize real-time prediction of the porosity.

The invention provides a fault detection and early warning method and system for an insulating layer of an oil-immersed high-voltage wiring harness, and relates to the technical field of power equipment detection, and the method comprises the steps: obtaining a multi-physical field coupling signal, carrying out the time-frequency decomposition, obtaining degradation feature distribution, extracting a feature response amplitude, calculating a gradient change rate, and recognizing an abnormal feature cluster; deducing residual life distribution of the insulating medium according to the degradation rate and generating a risk index; according to the risk index, the early warning level and the intervention time window are determined, early detection and early warning of the fault of the insulating layer of the oil-immersed high-voltage wire harness can be realized, and the operation safety of power equipment is improved.

The application relates to the technical field of analytical chemistry detection, and specifically discloses a rare earth ion chromatographic online analysis and detection method and system, which adopts a chromatography-mass spectrometry combined system, realizes multi-scale time-frequency decomposition of signals by constructing an adaptive wavelet base function library, accurately identifies and classifies interference types in combination with a convolutional neural network and vacuum degree coupling analysis; a U-Net generator with an attention mechanism and a multi-scale discriminator are designed for signal repair aiming at repairable interference; a state transition model containing a mass-to-charge ratio database is established, empirical mode decomposition and adaptive filtering technology are used to realize dynamic calibration of a mass axis; quantitative accuracy is ensured through double internal standard correction and a triple verification mechanism; when unrepairable interference is detected, a hierarchical self-checking program is started, and fault diagnosis is carried out in combination with spectrum fingerprint analysis.

The invention discloses a converter transformer fault diagnosis method and system based on voiceprints and thermal signals, and the method comprises the steps: carrying out the time-frequency decomposition of each voiceprint original signal data and temperature thermal signal data of a converter transformer, and obtaining a sound time-frequency matrix and a temperature time-frequency matrix; the kurtosis and the sound pulse duration of the time-frequency matrix of the sound are calculated to serve as basic time-frequency characteristics of the sound; calculating an energy gradient and an entropy change rate of the time-frequency matrix of the temperature as basic time-frequency characteristics; coupling characteristics of the time-frequency matrixes of the sound and the temperature in the same frequency band are extracted; performing feature screening on all basic time-frequency features and coupling features by calculating a maximum mutual information coefficient, and outputting an optimal feature subset; and constructing a converter transformer fault diagnosis-oriented identification model fusing ensemble learning and random forest, and training the identification model according to the optimal feature subsets in different operation states to perform fault diagnosis. The fault risk sensing capability of the converter transformer can be effectively improved.

This invention discloses a bearing remaining life prediction method based on frequency domain degradation perception. It constructs an integrated prediction system through a time-frequency decomposition dual-branch feature extraction module, a frequency domain degradation perception weight allocation module, and a life prediction loss function optimization module. First, the time-frequency decomposition dual-branch feature extraction module decomposes the original time series into high and low frequencies and inputs it into an sLSTM and mLSTM dual-branch structure for feature extraction. Second, the frequency domain degradation perception weight allocation module performs degradation perception processing on the fused features, generating dynamic weights and modulating the features to obtain the final fused features, which are then mapped to the prediction space. Finally, the life prediction loss function optimization module calculates the loss, optimizes the model output, and obtains the bearing remaining life prediction result. This invention comprehensively covers the full-stage features of bearings from healthy to severely degraded, improves the sensitivity of early fault detection, and still possesses excellent generalization and robustness under complex operating conditions.

The invention discloses a bridge beam end track state evaluation method, system and equipment based on time-frequency decomposition and Shannon entropy measurement, and a medium, and the method comprises the steps: S1, obtaining track irregularity historical dynamic detection data of a long-span railway bridge, carrying out the alignment preprocessing, and building a beam end track irregularity data set; s2, using an improved VMD algorithm to decompose the beam end track irregularity data set to obtain a plurality of IMF components; s3, calculating the power spectrum density of each IMF component, and determining the maximum energy concentration wavelength L1 of each IMF component; s4, measuring the information density of different track irregularity data which can be accommodated by different fixed window lengths by using Shannon entropy, and determining the maximum information window length L2; s5, calculating a relative deviation between L1 and L2, and determining a fixed calculation window length L3; and S6, calculating a beam end track quality index based on the L3, and evaluating the smoothness state of the current bridge beam end track. According to the method, the time-frequency decomposition theory and the information entropy theory are combined, and the irregularity state of the long-span railway bridge beam end track is evaluated more accurately.

The invention discloses an AI large model-based atmospheric water vapor content prediction method, system and device. The method comprises the following steps of: obtaining atmospheric water vapor content data and related meteorological variable data; performing time-frequency decomposition on the atmospheric water vapor content data and the related meteorological variable data by using wavelet transform, and extracting a multi-scale feature subsequence; constructing an AI large model based on a long short-term memory network, and inputting the extracted multi-scale feature subsequences into the large model for multi-factor nonlinear training; and performing space-time prediction on the future atmospheric water vapor content by using the trained large model to obtain a prediction result. Through the WT-LSTM mixed large model, the interannual prediction R2 is improved by 36%, the RMSE is reduced by 56.52%, the nonlinear bottleneck of a traditional model is solved, and high-precision space-time prediction is provided.

The application provides a multi-directional stress prediction method, device and equipment in a milling cutter milling process and a storage medium. It relates to the field of numerical control machine tool processing digital twin technology. The method comprises: obtaining small sample experimental data based on an orthogonal test method, time-frequency decomposition of the milling force test signal, and extraction of multi-dimensional features of the milling force dynamic characteristics; analyzing the correlation between the processing parameters and the features, screening the key features, establishing a physical mapping model of the process parameters to the key features and solving the cutting coefficients; constructing a time-varying signal prediction model based on a recurrent neural network, predicting the multi-directional dynamic milling force of the milling cutter with the key features in the small sample test data; and based on the cutting coefficient, designing an adaptive filter to post-process and optimize the predicted signal and inverse normalize it, and output the final prediction value. Based on small sample data, the application can accurately predict the multi-directional dynamic stress of the milling cutter only with the process parameters, and effectively improve the virtual-real mapping and dynamic optimization capability of the processing process.

The application discloses a CNN-LAE-LSTM hybrid network centrifugal pump cavitation diagnosis method and device, comprising: collecting different degree cavitation data, including cavitation pressure signal and cavitation visual image; performing Kalman filtering and modal time-frequency decomposition on the cavitation pressure signal; performing gray scale processing on the cavitation visual image; combining the processed cavitation data to form a training set and a test set; constructing a CNN-LAE-LSTM-based centrifugal pump cavitation diagnosis model, wherein the CNN is used for extracting local key features of the cavitation pressure signal and the cavitation visual image; the LAE is used for feature dimension reduction, redundancy elimination and noise suppression; the LSTM is used for capturing signal time sequence correlation and adapting to pump operation dynamic characteristics; the training set is used for training the centrifugal pump cavitation diagnosis model, and the test set is used for verifying the model accuracy; and the to-be-tested data is input into the trained centrifugal pump cavitation diagnosis model to obtain a fault diagnosis result.

The invention provides a method and device for predicting multidirectional stress in the milling process of a milling cutter, equipment and a storage medium. Relates to the technical field of numerical control machine tool machining digital twinning. The method comprises the following steps: acquiring small sample experimental data based on an orthogonal test method, performing time-frequency decomposition on a milling force test signal, and extracting multi-dimensional characteristics of milling force dynamic characteristics; analyzing the correlation between the processing technological parameters and the features, screening key features, establishing a physical mapping model from the technological parameters to the key features, and solving a cutting coefficient; constructing a time-varying signal prediction model based on a recurrent neural network, and predicting the multidirectional dynamic milling force of the milling cutter under small sample test data by using the key features; and designing an adaptive filter based on cutting coefficient constraint to perform post-processing optimization and inverse normalization on the predicted signal, and outputting a final predicted value. According to the method, based on the small sample data, the multi-directional dynamic stress of the milling cutter is predicted in a high-precision mode only through the technological parameters, and the virtual-real mapping and dynamic optimization capacity in the machining process is effectively improved.

Methods, apparatuses, and systems for analysing the state of power transformers are described. A method may include providing at least one sensor arranged relative to a power transformer, retrieving at least one vibroacoustic signal from said at least one sensor, performing a time-frequency decomposition of said at least one vibroacoustic signal from a time domain to a frequency domain, identifying one or more vibroacoustic harmonic frequencies provided by the fast Fourier transformation of the at least one vibroacoustic signal, calculating an amplitude value and a phase angle related to the one or more harmonic frequencies, retrieving an electromagnetic signal emitted from the power transformer, a temperature generated by the power transformer, or both, and providing at least one analysed information from the amplitude value and the phase angle related to the one or more harmonic frequencies, the electromagnetic signal, the temperature, or any combination thereof.

This invention discloses an activity pattern recognition method and system based on wavelet transform and CNN / Transformer. First, wavelet transform is used to perform multi-scale time-frequency decomposition on the original signal to enhance the perception of subtle local features and high-frequency components. Then, convolution operations are used to further extract and fuse effective features in the time-frequency domain. Finally, leveraging the self-attention mechanism of Transformer, global dependencies are captured in the enhanced feature sequence, thereby achieving efficient collaborative modeling of local information and global context. This invention overcomes the insensitivity of Transformer models to local details and high-frequency information, effectively avoiding the neglect or smoothing of crucial subtle action patterns and transiently changing high-frequency signals during recognition, significantly improving the model's recognition accuracy and enhancing the reliability and interpretability of the entire system in practical applications.

The application provides a brain-computer interface cognitive function decline monitoring system, method, device and medium, which can be applied to the field of electroencephalogram signal analysis. The brain-computer interface cognitive function decline monitoring system comprises a signal monitoring module, a signal processing module, a result processing module and a medium. The signal monitoring module is used for collecting multi-channel electroencephalogram signals in multiple time periods. The signal processing module is used for performing time-frequency decomposition and global field power determination on the multi-channel electroencephalogram signals. The probability of the multi-channel electroencephalogram signals belonging to each microstate mode is determined, and a microstate feature vector is generated. According to the phase locking value of the electroencephalogram signals between nodes and the similarity between the microstate feature vectors corresponding to the nodes, a topological heterogeneous brain network is generated. The topological heterogeneous brain network is subjected to graph feature extraction and state classification to obtain an analysis result. The result processing module is used for generating a brain load change trend according to multiple analysis results, and generating an adjustment suggestion based on the brain load change trend.

The application discloses a kind of acoustic signal feature extraction methods of gas insulated equipment, comprising: the sound signal collected is carried out noise reduction processing, and the mean and variance of signal are normalized processing;Using short-time Fourier transform STFT and wavelet transform WT, the sound signal of pre-processing is carried out time-frequency decomposition;Comprehensive MFCC, AWPC adaptive wavelet packet coefficient and instantaneous frequency feature IF are carried out comprehensive extraction to the feature of sound signal;The voiceprint feature extracted is carried out feature fusion, and MFCC, AWPC and IF feature are spliced, form a high-dimensional feature vector, then using principal component analysis PCA is carried out dimension reduction;The feature after dimension reduction is standardized, and correlation analysis method is further used to screen features.The application can extract effective voiceprint features from the sound signal generated by gas insulated equipment defects and reduce the influence of noise on detection, thereby improving the accuracy of detection.

The invention discloses a foam cleaning agent proportion control method based on online parameter regulation and control, and relates to the technical field of fine chemical process automatic control, and the method comprises the following steps: establishing a unified time base line in a feeding loop, and generating a phase reference field; low-amplitude calibration pulses are injected into all the feeding pumps, flow response is collected, inherent pulse characteristics are extracted, and a zero phase point is determined; and on the basis of the unified time base line and the zero phase point, constructing a pulsation tensor, and performing time-frequency decomposition on flow, pressure and valve position signals of each feeding pump to generate a synchronous phase track. According to the method, the time base line is constructed, the pump pulsation characteristics and the synchronous phase track are extracted, accurate recognition of the pump set interaction state is achieved, and the resonance event is judged in combination with disturbance verification; after high-risk judgment, a resonance path is cut off in a high-frequency fragmentation and time staggering mode, the matching stability is guaranteed, precise, automatic and intelligent control over the cleaning agent preparation process is achieved, and the product consistency and the system safety are improved.