141 results about "Instantaneous amplitude" patented technology

The invention provides a freezing circle watershed hydrological prediction method and system based on multi-source data fusion, and relates to the technical field of hydrological monitoring and sediment monitoring, and the method comprises the steps: obtaining a vibration signal of the interaction of a water flow and a riverbed and meteorological data in a region, and carrying out the preprocessing; the preprocessed meteorological data and vibration signals are input into a multi-source data fusion model, sequential feature extraction is conducted on the meteorological data and the vibration signals through a multi-layer perceptron in the multi-source data fusion model, the extracted sequential features are fused through a gating circulation unit, and multi-source data fusion features are obtained; and carrying out empirical mode decomposition and Hilbert spectrum analysis on the preprocessed vibration signal to obtain a plurality of signal characteristics such as instantaneous frequency and instantaneous amplitude of the vibration signal, inputting the signal characteristics and the multi-source data fusion characteristics into a hydrological parameter prediction model, and outputting corresponding predicted values of the water flow velocity, the flow and the sediment transport capacity.

The invention discloses an HPLCamp (High Performance Liquid Chromatography) based on deep learning. The invention discloses an HRF dual-mode communication adaptive coding modulation and anti-noise method. The method comprises the following steps: acquiring an optical radio frequency signal amplitude-phase change rate and synchronously sampling and normalizing; calculating a node amplitude-phase residual error to generate a nonlinear mapping coefficient; monitoring coherent change to solve a drift trend, adjusting a modulation coding optimization scheme, compensating distortion and outputting an anti-noise result. According to the method, the instantaneous amplitude and phase of the optical radio frequency dual-mode signal are extracted, a multi-dimensional amplitude-phase characteristic matrix is formed in combination with time domain synchronization and a normalization template, differential residual modeling and nonlinear mapping coefficient calculation are carried out between impedance nodes, and dynamic compensation of amplitude-phase mismatch and envelope offset is achieved. A drift trend quantity is generated based on coherent offset parameter differentiation, feedback is provided for modulation format and coding strategy optimization, amplitude equalization and phase correction are completed, the signal synchronization degree and amplitude-phase consistency are improved, and the steady-state response and anti-disturbance performance of a transmission link are enhanced.

The invention provides a forging and pressing equipment detection method and system based on multi-source data fusion, specifically, each time sequence signal is decomposed into a group of intrinsic mode functions, and the instantaneous amplitude sequence and the instantaneous frequency sequence of each intrinsic mode function are calculated through Hilbert transform; combining the instantaneous amplitude sequence and the instantaneous frequency sequence of all the intrinsic mode functions obtained by decomposing each sensor source signal to obtain a time-frequency spectrum characteristic pattern corresponding to the sensor; extracting features of different time steps from each time-frequency spectrum feature map by using a plurality of parallel circulation unit branches comprising phase gates, connecting a time step attention layer behind the circulation unit of each parallel branch, and constructing a sensor association map based on the time sequence features; using the features of the adjacent nodes in the graph convolution network aggregation graph with residual connection to update the nodes to obtain fusion features; and obtaining a detection result of the forging and pressing equipment based on the fusion features.

The invention discloses an underground physical characteristic prediction method and system combining seismic waves and drilling data, and relates to the technical field of geophysical exploration, and the method comprises the steps: collecting original seismic records, carrying out the preprocessing, obtaining a three-dimensional seismic data body, carrying out the complex channel analysis of the three-dimensional seismic data body, extracting the instantaneous amplitude, instantaneous phase and instantaneous frequency attributes, and carrying out the calculation of the instantaneous amplitude, instantaneous phase and instantaneous frequency attributes. Fault features are identified through a coherent body algorithm, an earthquake feature matrix is obtained, depth alignment and correction are carried out on density logging, interval transit time and neutron porosity curves of drill holes, and a standardized drill hole data table is generated; and based on the seismic feature matrix and the standardized drilling data table. According to the method, deep complementation of the earthquake macroscopic information and the borehole local truth value is realized through the geometric perception multi-modal graph neural network and the dynamic gating fusion mechanism, the problems of insufficient cross-modal feature fusion and misalignment of physical rules in the prior art are solved, and the prediction precision and reliability under complex geological conditions are remarkably improved.

The present invention relates to the technical field of fault diagnosis, and provides a fault diagnosis method for a wind-turbine drive train based on time-frequency plane expectation maximization. The method comprises the following steps: configuring a cylindrical MEMS acceleration sensor on a drive chain of a wind turbine, and acquiring a vibration signal of the drive chain of the wind turbine via the cylindrical MEMS acceleration sensor; adopting a spectrogram-zero-based unsupervised classification method, acquiring a vibration signal and a rotational speed signal of the wind turbine during operation, generating a time-frequency representation of the vibration signal by using a short-time Fourier transform, and extracting spectrogram zeros and performing unsupervised classification to implement denoising processing of the vibration signal; on the basis of a multi-component signal estimation method under time-frequency plane expectation maximization, accurately estimating an instantaneous frequency and an instantaneous amplitude of a multi-component signal on a time-domain plane of the denoised signal; and performing order spectrum analysis to identify vibration signal characteristics of the drive chain of the wind turbine, so as to achieve fault diagnosis of the drive chain of the wind turbine under variable rotational speeds.

The invention provides a vibration nonlinear signal energy analysis method and system based on variational mode decomposition, and relates to the technical field of signal processing, and the method comprises the following steps: obtaining a monitoring signal of a vibratory roller, determining a corresponding state based on root-mean-square data of the monitoring signal, and determining the state of the monitoring signal; performing segmentation processing on the monitoring signal based on the state corresponding to the monitoring signal to obtain a signal segmentation result; performing variational mode decomposition processing on the signal segmentation result to obtain at least two second mode components; performing Hilbert transformation processing on the second modal component, and combining the instantaneous frequency and the instantaneous amplitude of each modal component obtained through transformation to obtain frequency spectrum information of the monitoring signal; and performing marginal spectrum calculation and integration on the frequency spectrum information to obtain the total energy of the monitoring signal. According to the method, interference signals can be efficiently identified and eliminated in the vibration signals, so that the precision and robustness of compaction quality evaluation are improved.

The invention discloses a flow field data modal decomposition method, device and equipment suitable for a non-stationary flow field of an aircraft, and a storage medium, and relates to the technical field of hydrodynamics, and the method comprises the steps: obtaining the spatial-temporal data of the non-stationary flow field of the aircraft, determining a physical representation form, and obtaining the spatial-temporal data of the non-stationary flow field of the aircraft; the method comprises the following steps of: decomposing a plurality of low-order dynamic process components consisting of spatial modals and time evolution coefficients by utilizing a frequency modulation technology, constructing a frequency modulation operator, converting the time evolution coefficient of each component into a narrowband frequency modulation component time sequence signal, and optimizing the narrowband signal bandwidth by taking minimization of the narrowband signal bandwidth as an optimization target; and constructing a to-be-solved variational optimization problem by combining the flow field reconstruction constraint and the frequency modulation structure constraint. According to the method, a time evolution coefficient, an instantaneous frequency, an instantaneous amplitude and a spatial mode are obtained through iterative solution by adopting an alternating direction multiplier method, and a modal decomposition result is determined based on a solution result, so that adaptive extraction and dynamic characteristic description of the non-stationary flow field are realized, and the modal separation precision and the extraction efficiency of nonlinear and transient characteristics of the non-stationary flow field are improved.

The invention relates to an equipment fault diagnosis method and system. The method comprises the following steps: performing signal decomposition on an original vibration signal to obtain a group of intrinsic mode components; for each intrinsic mode component, calculating a kurtosis value and an order cyclic spectral density amplitude; based on the kurtosis value and the order cyclic spectral density amplitude, constructing a frequency band selection index used for representing fault impact energy and periodical modulation certainty; determining one or more intrinsic mode components enabling the frequency band selection index to be optimized, and determining an optimal filtering frequency band according to the center frequency and the bandwidth of the optimized intrinsic mode components; performing Hilbert transform on the filtered signal to obtain an analytic signal; extracting the instantaneous amplitude of the analysis signal to obtain an envelope signal; performing cepstrum editing on the envelope signal to obtain an enhanced envelope signal, and performing fast Fourier transform on the enhanced envelope signal to obtain an enhanced envelope spectrum; and performing fault diagnosis based on the fault characteristic frequency in the enhanced envelope spectrum.

The invention discloses an inertial navigation system periodic oscillation suppression method based on time-frequency transformation, and relates to the technical field of underwater navigation, and the method comprises the following steps: S1, modeling an error propagation model of an INS, and defining coordinate systems required by INS navigation, including an inertial coordinate system, a navigation coordinate system and a body coordinate system; based on the INS navigation equation, obtaining an INS error model, and carrying out time domain analysis to obtain an analytical solution of time domain error divergence; s2, designing a time-frequency transformation NTFT, proposing a normal phase time-frequency transformation method, and constructing a complete observation equation; s3, substituting a null method into a calculated longitude and latitude result, and accurately extracting frequency, phase and instantaneous amplitude in inertial navigation longitude and latitude; and S4, establishing a resolving framework of the inertial navigation system periodic oscillation suppression method based on time-frequency transformation, and constructing a negative feedback loop through input gyroscope and acceleration information to suppress periodic errors.

The invention discloses a pre-distortion calibration method and system based on a single chip microcomputer, a medium and a product, and relates to the field of radio frequency front ends. The method comprises the following steps: extracting an instantaneous amplitude value of an input signal sequence to obtain a first envelope to represent instantaneous nonlinearity; the periodic average power of the input signal is calculated and filtered through timer interruption, and a second envelope representing the heat effect is obtained. According to the method, the first envelope and the second envelope serve as double indexes, the preset two-dimensional lookup table is inquired, so that the pre-distortion calibration coefficient capable of being matched with the instantaneous nonlinearity and the thermal memory effect of the power amplifier at the same time is determined, and the input signal is calibrated. In addition, a feedback signal output by the power amplifier is obtained to calculate a distortion calibration error, and the two-dimensional lookup table is adaptively updated according to the error. According to the scheme, a composite calibration model is constructed on a low-cost single-chip microcomputer platform, and the accuracy and stability of nonlinear pre-distortion calibration of the power amplifier are guaranteed.

The invention provides a gear continuous spectrum vibration signal identification method based on EMD (Empirical Mode Decomposition) and HHT (Hilbert-Huang Transform), and belongs to the field of signal processing of mechanical power. The method comprises the following steps: acquiring an original vibration signal; performing FFT (Fast Fourier Transform) on the original vibration signal to obtain an amplitude spectrum; eMD decomposition is carried out on the original vibration signal to obtain a plurality of IMF components and a residual component; performing HHT (Hilbert-Huang Transform) on each IMF component to obtain an analytic signal, extracting an instantaneous amplitude and an instantaneous phase of the analytic signal, and calculating an instantaneous frequency according to the instantaneous phase; performing FFT (Fast Fourier Transform) on the instantaneous amplitude to obtain amplitude modulation information; performing FFT (Fast Fourier Transform) on the instantaneous frequency to obtain frequency modulation information; overlapping the amplitude modulation information and the frequency modulation information of each IMF component to generate a Hilbert envelope spectrum; and carrying out peak value labeling on each Hilbert envelope spectrum, and comparing the Hilbert envelope spectrums with the amplitude spectrums to finish the characteristic frequency identification of the gear vibration signals. According to the method, the problem that a traditional fast Fourier transform (FFT) method is insufficient in complex modulation signal analysis capacity is solved.

The invention provides an electronic load grid voltage adjusting method based on an improved DQN algorithm, and the method comprises the steps: employing a DQN reinforcement learning architecture, collecting the high-frequency waveform data of the load pulling current of an electronic load, obtaining an instantaneous amplitude signal, an instantaneous phase signal and an instantaneous frequency signal through Hilbert transformation, and enabling the instantaneous amplitude signal, the instantaneous phase signal and the instantaneous frequency signal to serve as an environment state st; the environment state st is input into a multi-mode DeepSeek distillation model intelligent agent, and the intelligent agent outputs an electronic load MOS tube grid voltage regulating variable delta Pt as an action at; establishing a reward function rt with optimal control performance as a target; the intelligent agent executes the action at in the environment state st to obtain a new environment state st + 1, and (st, at, rt, st + 1) is stored in an experience playback buffer pool; and the network parameters are replayed, optimized and updated by adopting priority experience, so that the accurate control of the load pulling current of the electronic load is realized.

The invention discloses a communication signal classification and identification method based on multi-sequence feature fusion, and belongs to the technical field of communication, and the method takes an I / Q sequence, an A / P sequence and a PSD sequence of a signal as the input of a multi-channel feature fusion network model: the I / Q sequence retains the complete time domain in-phase / orthogonal component information of the signal, and provides basic waveform features; the A / P sequence highlights the instantaneous amplitude fluctuation and phase jump characteristics of the signal through polar coordinate transformation; the PSD sequence reveals the frequency domain energy distribution characteristics of the signal, and effectively captures a modulation mode with remarkable frequency domain characteristics. Three-dimensional complementation of a time domain, a transform domain and a frequency domain is formed, and the representation limitation of a single domain feature in a complex channel environment is overcome. And constructing a multi-channel feature fusion network model by using complementary gain information in different data to classify and identify a communication signal modulation mode. And deep processing is performed on different types of input data by using different network structures, so that the overall model recognition performance is improved.

The invention relates to the field of railway data analysis and processing, in particular to a railway irregularity data processing method based on cEEMDAN-Hilbert. The method comprises the following steps: acquiring and preprocessing track irregularity original signals to obtain a target track irregularity signal sequence; performing CEEMDAN (adaptive noise complete ensemble empirical mode decomposition) on the target track irregularity signal sequence, and performing iterative decomposition on the signal added with Gaussian white noise to obtain a plurality of IMF (intrinsic mode function) components and a residual component; performing Hilbert transformation on each IMF component, calculating an instantaneous frequency and an instantaneous amplitude, constructing an analysis signal in a complex form, and extracting time and frequency joint distribution information; according to the instantaneous frequency and the energy result, false wavelength components are removed, signal reconstruction is carried out, and reconstructed track irregularity signals are generated. According to the invention, the identification capability of local structure disturbance can be enhanced.

The invention discloses a W-Cu alloy laser ultrasonic detection method based on GA-EE-VMD and a longitudinal wave enhancement algorithm, and the method specifically comprises the steps: 1, collecting a W-Cu alloy at a T temperature through employing a laser ultrasonic system, and obtaining an ultrasonic signal; 2, performing decomposition, reconstruction and de-noising processing on the acquired ultrasonic signals by adopting a GA-EE-VMD method to obtain K modal functions; 3, calculating instantaneous amplitudes and frequencies of different IMFs components, and carrying out weighted enhancement processing to obtain an enhanced longitudinal wave signal; and 4, the thickness of the W-Cu alloy at the high temperature is calculated through the enhanced longitudinal wave signals. According to the invention, the identification precision of the longitudinal wave is improved, so that the thickness detection precision is improved.

The invention relates to the technical field of nuclear reactor fault detection, in particular to a method and system for extracting weak collision and abrasion noise of loose parts in a reactor based on signal processing and machine learning, and the method comprises the steps: obtaining a reactor vibration signal, and decomposing the vibration signal into a plurality of sub-signals through empirical mode decomposition; performing Hilbert transform processing on each sub-signal, extracting an instantaneous frequency and an instantaneous amplitude of each sub-signal, and constructing a time-frequency two-dimensional matrix of each sub-signal; superposing the time-frequency two-dimensional matrixes of the sub-signals along the dimensions of the sub-signals to generate a three-dimensional instantaneous time-frequency matrix; and inputting the three-dimensional instantaneous time-frequency matrix into a pre-trained convolutional neural network model, inhibiting the operation noise of the reactor, and extracting a weak collision and abrasion noise signal after noise reduction. The method aims at extracting weak collision and abrasion noise signals of the loose part in the reactor and achieving collision and abrasion fault recognition of the loose part in the reactor.

The invention relates to the technical field of feature detection, in particular to a radar micro-motion feature extraction method based on deep learning, and the method comprises the following steps: collecting the instantaneous amplitude, slope, time interval and phase change of a radar signal, normalizing the instantaneous amplitude, slope, time interval and phase change into a time sequence feature group, constructing and fusing a feature matrix, and extracting the feature matrix; and inputting a time sequence convolutional network model to carry out multi-layer extraction and modeling, implementing multi-stage judgment and dynamic screening, and finally outputting a fragment sequence through threshold judgment and multivariate joint screening based on the attribution confidence probability. According to the method, quaternary time sequence features are constructed by capturing instantaneous amplitude, slope, extreme point interval and phase change, internal association of weak dynamic of a target is revealed, spatial-temporal correlation is automatically modeled to mine nonlinear and non-stationary features, and multi-stage discrimination, dynamic screening and attribution confidence probability joint determination are performed on the features. Therefore, effective micro-motion signal segments are accurately locked under complex background interference, and the robustness and accuracy of feature extraction are remarkably improved.

The application provides an oil and gas detection method and device based on instantaneous energy frequency value, a computer readable storage medium and an electronic device. The method comprises the following steps: for each seismic signal in a three-dimensional seismic data volume of a target area, decomposing the seismic signal into a data set composed of a plurality of IMF components, extracting the instantaneous amplitude and instantaneous frequency of each IMF component, calculating the instantaneous energy frequency value of each IMF component based on the instantaneous amplitude and instantaneous frequency, and calculating the instantaneous energy frequency value of the seismic signal based on the instantaneous energy frequency value of each IMF component; analyzing the change characteristics of energy and frequency in the stratum of the target area based on the instantaneous energy frequency value of each seismic signal in the three-dimensional seismic data volume and the instantaneous energy frequency value of each IMF component of each seismic signal, and further analyzing the oil and gas bearing property of the stratum. The method provided by the application can more accurately identify the energy and frequency anomalies in the stratum and can also assist in oil and gas identification.

The invention relates to the technical field of electrical variable measurement, in particular to a detection method and a detection system for a starting power supply cell. The method comprises the steps of obtaining a local abnormal trend of a data point according to a difference between an instantaneous amplitude of the data point and an instantaneous amplitude mean value of a window of the data point, an instantaneous amplitude standard deviation and a window fitting slope mean value of the data point and adjacent data points on two sides of the data point, so as to obtain a proportion of a suspicious data point in a current time sequence, constructing a slope value sequence based on instantaneous amplitude slope values between adjacent data points to obtain a global abnormal trend of the current time sequence; calculating the box separation width of the current sequential sequence, wherein the box separation width is in negative correlation with the global abnormal trend; in the HBOS algorithm, the box separation width of the current sequential sequence is used to obtain the abnormal detection result of each data point, so that the accuracy of the detection result of the starting power supply cell is effectively improved.

The application discloses a rolling bearing fault diagnosis method based on acoustic framing. Firstly, the collected acoustic signal of the rolling bearing is subjected to Hilbert transform to construct a double-channel analytic representation containing instantaneous amplitude and instantaneous phase information. Then, the analytic signal is divided into short-time frame segments by the acoustic framing embedding module according to a preset frame length and step, and a uniform dimension frame-level embedding vector is generated by linear mapping, so as to highlight the local transient impact feature and effectively compress the sequence length. An improved lightweight Transformer encoder is adopted for timing feature extraction, the encoder combines a low-dimensional multi-head self-attention mechanism and a feedforward network based on SiLU activation and deep convolution, so as to simultaneously model the cross-frame global dependence and the short-range local structure, and finally, the intelligent identification of the multi-class faults of the rolling bearing is completed through the global feature vector of the class token.

The invention relates to the technical field of radio frequency signal processing, in particular to a broadband radio frequency signal processing method and system based on multi-stage frequency conversion, and the method comprises the following steps: S1, inputting a radio frequency signal, mixing the radio frequency signal into a first intermediate frequency signal through a first-stage mixer, and filtering the first intermediate frequency signal through a first filter and a second filter to obtain a second intermediate frequency signal; frequency mixing is performed through a second-stage frequency mixer to obtain a second intermediate frequency signal; s2, sampling the second intermediate frequency signal to obtain a modulation signal, and extracting an in-phase component and an orthogonal component of the modulation signal; s3, performing frequency mixing on the in-phase component and the orthogonal component of the modulation signal and an orthogonal signal, and performing multi-stage decimation filtering through a filter to obtain an orthogonal baseband signal; and S4, calculating an instantaneous amplitude, an instantaneous phase and an instantaneous frequency of the orthogonal baseband signal, and completing frequency modulation signal demodulation through a phase difference algorithm to obtain a modulation signal source. The method provided by the invention has the advantages of wide frequency band coverage, high signal quality and high demodulation efficiency.

The application provides a replay voice attack detection method for a voiceprint security authentication system. First, the voice signal is preprocessed, then a plurality of sub-band signals are obtained through linear equal-width Gabor filters, each sub-band signal is processed through FDEO to obtain an instantaneous amplitude and an instantaneous frequency, then the instantaneous amplitude and the instantaneous frequency are respectively taken as inputs of an SENet to obtain enhanced IACC and IFCC features, and the features are respectively processed through windowing and averaging and discrete cosine transformation to obtain respective low-dimensional feature vectors. Then, the extracted IACC and IFCC feature vectors are respectively used to train respective Gaussian mixture model classifiers to obtain respective classifier model parameters. In detection, the IACC and IFCC feature vectors of the voice to be detected are respectively input into the respective GMM classifiers and are scored for credibility, and finally score level fusion is performed to realize discrimination of true and false voices.

A method, apparatus, and processor-readable medium for detecting a physical change in an environment are provided, the method comprising: acquiring a first set of sound frames representing sound waves reflected in the environment; applying frequency filtering to extract from the first set of sound frames a corresponding first set of filtered frames limited to a predefined frequency band, the predefined frequency band corresponding to a frequency band of a predefined cyclic acoustic detection signal; calculating an instantaneous amplitude of the filtered frame in the first set of filtered frames; evaluating whether the first set of sound frames is indicative of a physical change in the environment based on the calculated instantaneous amplitude and a predefined reference parameter corresponding to a reference state of the environment; when the evaluation indicates the physical change, a predefined operation is caused to be performed.

This application relates to a method, apparatus, device, medium, and product for under-range protection of a line. The method includes: upon detecting a fault in the protected line, determining the zero-mode current component corresponding to the voltage and current data of the protected line; decomposing the zero-mode current component to obtain two preset sub-frequency band signals, and determining the instantaneous amplitude and instantaneous phase corresponding to each preset sub-frequency band signal; determining the actual time difference between the two preset sub-frequency band signals based on their respective instantaneous amplitudes; and determining the theoretical time difference between the two preset sub-frequency band signals based on their respective instantaneous phases; and performing protection processing on the under-range protection area of ​​the protected line based on the actual time difference and the theoretical time difference. This application can promptly perform under-range protection actions and improve the accuracy of under-range protection actions.

The present invention discloses a method for predicting karst caves in the karst area during shield tunneling, belonging to the technical field of karst cave prediction. In the present invention, reflection waves in the karst area are collected by geophones during the operation of the shield machine, and the instantaneous frequency, instantaneous amplitude, and instantaneous phase of P-waves and S-waves are calculated respectively, reflecting the changes in frequency, amplitude, and phase at each moment. By capturing the phase reversal coefficient, amplitude change coefficient, frequency change coefficient, and energy attenuation coefficient of P-waves, as well as the amplitude change coefficient and energy attenuation coefficient of S-waves, the response characteristics of P-waves and S-waves to karst caves can be effectively reflected. Combining these characteristic coefficients with the processing of the karst cave prediction model can further improve the accuracy of karst cave prediction and provide a strong guarantee for the safety of shield construction.

A kind of DAS signal adaptive demodulation method and system based on FPGA, wherein, the method comprises: obtaining the in-phase component signal and quadrature component signal of DAS signal;When the address of write pointer occurs incremental change, the historical in-phase component signal and historical quadrature component signal in the preset history sampling window are read;The instantaneous amplitude energy of each time stamp is calculated, and the instantaneous amplitude energy sequence is obtained;The amplitude energy mean and variance eigenvalue of DAS signal are calculated;Adaptive gain factor is generated;The in-phase component signal and quadrature component signal are compensated and iteratively demodulated by adaptive gain, and the phase output value and demodulation module length value are obtained;The module length deviation amount of demodulation module length value and preset standard module length is calculated, and when the cumulative value is greater than preset locking threshold, the correction coefficient is calculated according to the current amplitude energy mean and module length deviation amount;The storage value of index address is updated based on correction coefficient.The application can improve the reliability of DAS system.

The invention discloses a power distribution terminal automatic test method and system based on a dynamic fault record and analysis oscillograph, and the method comprises the steps: S1, obtaining a signal collected by the dynamic fault record and analysis oscillograph, carrying out the wavelet packet decomposition and noise reduction processing, carrying out the transformation, obtaining a signal feature containing an instantaneous frequency and an instantaneous amplitude, and carrying out the detection of the instantaneous frequency and the instantaneous amplitude; time domain features and frequency domain features in the signal features are extracted, and fault feature classification is carried out; s2, reconstructing the fault feature vector, carrying out time sequence feature association mapping, and predicting the action response of the power distribution terminal through a pre-trained intelligent prediction model; generating a test case according to the prediction result and the corresponding fault feature vector; and S3, reconstructing a test signal, outputting the test signal to the power distribution terminal to generate an actual response signal, evaluating the actual response signal by using a prediction result of the intelligent prediction model, and generating a test evaluation result responded by the power distribution terminal. The intelligent level of power distribution terminal testing can be improved.

The invention relates to the technical field of sound field control, in particular to a sound equipment sound field low-frequency increasing and decreasing method and system based on volume amplitude recognition, and the method comprises the following steps: collecting an original audio stream, and extracting instantaneous amplitude features; calculating a low-frequency compensation coefficient according to the nonlinear auditory model; constructing a dynamic safety threshold and correcting a coefficient in combination with the physical limit of the equipment; and adjusting the low-frequency component based on the corrected parameter, and generating an optimized sound field signal. According to the invention, by establishing a dynamic mapping and constraint mechanism between the instantaneous volume amplitude and the low-frequency gain, the technical defects that the low-frequency hearing feeling of a traditional sound system is deficient under the low volume and overload distortion is extremely easy to occur under the high volume are thoroughly solved, and under the premise of strictly guaranteeing the physical safe operation of loudspeaker hardware, the sound quality is greatly improved. And full-dynamic-range sound field adaptive equalization and restoration conforming to human ear psychological acoustic characteristics are realized.

The application provides a medium-voltage distribution network line fault positioning method and a smart switch, and belongs to the technical field of distribution networks. The method comprises the following steps: performing Hilbert-Huang transformation on a distribution network fault current signal connected with a distributed energy source to obtain the instantaneous frequency and instantaneous amplitude of a key component signal; performing multidimensional signal phase space reconstruction based on the instantaneous frequency and instantaneous amplitude of the key component signal to obtain a phase space reconstruction signal; using a convolutional neural network to extract the characteristic quantity of the time sequence of the distribution network fault current signal and the characteristic quantity of the phase space reconstruction signal, mixing them, and determining the fault occurrence position according to the mixed characteristic quantity. The application firstly uses Hilbert-Huang transformation to mine the fault current signal and its time-frequency characteristics in the medium-voltage distribution network containing distributed resources, then uses a phase space reconstruction method to capture the internal law of the fault signal, and finally realizes fault line selection and accurate positioning of the fault in the medium-voltage distribution network containing distributed resources through a convolutional neural network.

The application provides a speech separation post-filtering method and system based on sub-band envelope characteristics, which comprises the following steps: designing an m times frequency band pass filter set, performing sub-band decomposition on the separated estimated sound source signal and the original mixed signal to obtain the frequency band division representation thereof; performing Hilbert transform on each sub-band of the estimated sound source signal and the original mixed signal to construct an analytic signal, calculating the instantaneous amplitude thereof and removing the high frequency component through low pass filtering to obtain the corresponding sub-band envelope; calculating an initial masking value, limiting the initial masking value by setting a lower threshold, performing nonlinear mapping on the initial masking value through a sine function to obtain an envelope masking coefficient; applying the envelope masking coefficient to the corresponding sub-band signal of the estimated sound source; and performing full-band reconstruction on each sub-band signal subjected to the masking processing to obtain an enhanced target sound source signal. The application has the advantage that the performance of a speech separation system in a complex acoustic environment is effectively improved.