296 results about "Window function" patented technology

The invention discloses a wellhead data acquisition modeling system. The wellhead data acquisition modeling system comprises a data acquisition module, a hydraulic characteristic processing module, a well layer coupling modeling module and an interaction control module, the invention belongs to the technical field of well water monitoring management, based on an adaptive time-frequency analysis method, a dynamic window function enhanced by local frequency spectrum entropy is introduced, feature extraction and abnormal early warning are performed on transient hydraulic fluctuation signals, and the time-frequency resolution and change recognition capability of the hydraulic signals are remarkably improved; a dynamic coupling model between a water pumping system and an aquifer is constructed by combining a multi-scale parameter coordination mechanism, and accuracy and dynamic adaptability of well layer response modeling are realized by introducing a gap compression lag function and a joint parameter calibration method; and the interaction control module implements model prediction control according to the hydraulic feature vector, the hydraulic anomaly early warning signal and the well layer coupling prediction parameter combination, and outputs wellhead data acquisition decision support data.

The invention discloses a time-frequency analysis method for impact signal positioning based on transient scale extraction transformation, and belongs to the technical field of mechanical vibration signal processing. The method comprises the following steps: collecting a rotating machine fault vibration signal, and reconstructing a signal model through Hilbert transform and a Dirac function; a transition matrix is generated by using a Gaussian window function traversal model, and matching and Fourier transform are carried out in combination with a discretized scale basis function; solving a frequency partial derivative of a transformation result to generate a time redistribution operator, and redefining by a Dirac function to obtain a transient scale extraction operator; based on the sub-time-frequency representation of scale-based rotation discretization, screening optimal matching results of each time center through a maximum kurtosis value, and integrating the optimal matching results into a complete time-frequency representation; and finally, redistributing a time-frequency coefficient by using a transient extraction operator to realize accurate positioning of the impact component. According to the method, the problems of serious impact energy diffusion and insufficient positioning precision in existing time-frequency analysis are solved, and the time-frequency representation readability and the impact positioning reliability are remarkably improved.

The invention discloses a window-based decoupling short-time fractional Fourier transform time-frequency analysis method. The method comprises the following steps: S1, acquiring non-stationary signal data generated under power quality disturbance in a power grid; s2, segmenting the non-stationary signal into a plurality of parts based on signal waveform characteristics, and setting an initialized virtual window function for discrete short-time fractional Fourier transform, wherein the window function only provides an interface for calling of a specific window function; s3, calling different window functions for each segmented sub-segment signal to obtain windowed sub-segment signals under a specific window function; and S4, discrete fractional Fourier transform is applied to the sub-segment signals obtained in the step S3 to obtain time-frequency results of the sub-segment signals under different windows, and finally, frequency spectrums of the sub-segment signals are spliced according to a time sequence and output by an oscilloscope. Based on the characteristics of non-stationary signals generated under power quality disturbance in a power grid, different window function analysis signals can be called in one task by setting a virtual window function interface, and the window-based decoupling short-time fractional order Fourier transform time-frequency analysis method is formed. According to the method, the coupling between fractional Fourier transform and a specific window is reduced, the expandability of a program is improved, and more accurate signal local spectrum features can be obtained.

The invention relates to the technical field of data monitoring, and discloses an intelligent power distribution monitoring system and method, and the method comprises the steps: collecting current and voltage signals of a preset point location, and building a signal data link; preprocessing the signal data chain based on a windowing function to obtain a windowed data chain, and determining an overlapping proportion according to the length of the windowed data chain to perform window division to obtain a plurality of frequency spectrums; the spectrums are compared in pairs, and whether abnormal early warning is carried out or not is judged according to the comparison result; when early warning is judged, processing each frequency spectrum based on Hilbert transform to obtain an instantaneous envelope and an instantaneous phase, and obtaining a phase difference value of two harmonics with the same frequency in all the frequency spectrums; and comparing the phase difference value and the spectrum similarity as feature data with a historical early warning data set for early warning. According to the method, the problems that a traditional method depends on a fixed threshold value and is slow in response and high in false alarm rate are solved, and the identification accuracy and response timeliness of dynamic harmonic abnormity are improved.

The invention discloses a calculation method and system for multi-point detection of a gas nonmetal pipeline based on an active sound source, and relates to the technical field of pipeline monitoring and management, and the method comprises the following steps: 1, monitoring the change of a sound signal in the gas nonmetal pipeline according to the change of a transmitted laser signal through an optical fiber of a distributed acoustic assembly as a sensing medium, according to the invention, the optical fiber of the distributed acoustic assembly is used for monitoring the change of the acoustic signal in the gas non-metal pipeline, the frequency offset, intensity and delay change of the laser signal are converted into the frequency, amplitude intensity and sound wave source of the acoustic signal in a correlation manner, and the adaptive filter is used for dynamically updating the filter coefficient. A window function is adopted to carry out framing processing on sound signals, short-time Fourier transform is applied, real classification labels are distributed to the sound signals through a random forest algorithm, a triangulation positioning method is applied to determine space coordinates of the sound signals, and accurate monitoring and classification of the pipeline state are achieved.

The invention discloses a high-precision spectral interference signal demodulation method for micro distance measurement. The method comprises the steps of S1, interference spectrum shaping; measuring an interference spectrum and a reference spectrum; selecting a six-item combined cosine window as a primary function of an interference signal to obtain a shaped interference spectrum; s2, performing non-uniform Fourier transform to obtain a frequency spectrum of the target signal; s3, on the basis of a multi-spectral-line interpolation algorithm, interpolation is carried out on the window function to solve a fitting function, and a center frequency k is obtained; and S4, outputting a result. And solving and outputting an optical path difference d. According to the method, a six-item cosine combination window is selected as a primary function of an interference signal, and the problem that the accuracy of signal extraction is influenced by mutual superposition of side lobes and even main lobes of a direct current item and a solved frequency item is solved; an interference spectrum is processed by using a fast non-uniform Fourier transform algorithm, accuracy can still be kept under the conditions that the distance to be measured is increased and sampling is gradually sparse, and the problem of resampling deviation caused by a traditional interpolation method can be solved.

The invention belongs to the field of structural modal identification, and particularly relates to an automatic modal parameter identification method based on a multi-scale space, which comprises the following steps: collecting a vibration data sample from a target structure, the vibration data sample comprising an acceleration signal of the structure; selecting power spectrum calculation parameters including a window function and a fast Fourier transform (FFT) length; performing frequency spectrum estimation on the acceleration signal by using interpolation power spectrum estimation to obtain frequency spectrum data; analyzing the spectrum data by using a multi-scale space algorithm, and detecting a peak value in the spectrum data to obtain a peak value frequency; through a frequency domain decomposition method, according to the peak frequency, modal parameters of the structure are calculated, the modal parameters comprise the modal frequency and the damping ratio, interpolation power spectrum estimation and a multi-scale space algorithm are innovatively combined, automatic and accurate identification of the modal parameters is achieved, and the identification precision and efficiency are remarkably improved.

The invention relates to a multi-parameter estimation algorithm combining a disturbance quantification model and a rooting method in interference measurement. In order to solve the problems that in the prior art, the disturbance degree of a sequence is difficult to quantify, and harmonic key parameters are influenced by spectrum leakage, the invention provides the following innovative scheme: firstly, a sequence multi-classification measurement entropy quantification model is constructed, and quantitative evaluation of the signal noise degree is realized; secondly, through a Hankel matrix construction and rooting parameter estimation method, realizing accurate estimation of the harmonic frequency of the real-value signal; meanwhile, an amplitude correction algorithm based on window function characteristics is designed, and the signal sampling condition is effectively judged; and finally, through matrix weighting operation of a phase demodulation function, efficient reconstruction of an optical surface shape is realized, the problems of spectrum leakage and disturbance quantification in a traditional method are solved, and the method has relatively high measurement precision.

The invention relates to a database technology, in particular to a query optimization method, a storage medium and equipment. The query optimization method comprises the following steps: obtaining a to-be-processed query statement; judging whether the to-be-processed query statement contains a window function or not; and if yes, converting the window function into transverse connection, and generating an optimized query statement based on the transverse connection. According to the query optimization method, the window function in the query statement is converted into the transverse connection, so that the window function in the query statement is eliminated, the corresponding grouping and sorting operation is removed, the problem of high performance loss caused by excessive unnecessary grouping and sorting operation due to execution of the window function is avoided, and the query efficiency is improved. And the execution efficiency of the query statement is improved, so that the performance of the database is improved.

The invention belongs to the technical field of power quality disturbance detection, and particularly relates to a power quality disturbance detection method based on improved adaptive S transformation, which comprises the following steps of: designing window width control factors of two Gaussian windows, flexibly adjusting time domain window widths and frequency spectrum characteristics of the Gaussian windows, and forming a global adaptive Gaussian window; taking upper and lower limits of time-frequency two-domain resolution as constraint conditions, taking enhanced signal energy concentration ratio as a target function of an optimization scheme, and adopting an interior point method to carry out parameter solving; performing fast Fourier transform on the signal and setting a threshold value, and defining a part higher than a threshold value point as a characteristic frequency point ki; screening calculation at a non-characteristic frequency point, only performing time-frequency transformation at a characteristic frequency point ki, and constructing a fast algorithm for improving adaptive S transformation; and obtaining a final disturbance characteristic information value. According to the method, the global adaptive Gaussian window is constructed as a kernel function for improving adaptive S transformation, the effective window length and the frequency spectrum of the window function can be automatically adjusted along with the change of the detection frequency, window parameters are prevented from being frequently switched for improving the time-frequency resolution, and various power quality disturbances are efficiently and accurately detected.

The invention discloses a power broadband signal harmonic / inter-harmonic high-precision measurement method and device. The method comprises the following steps: constructing a constrained N-element linear equation set for solution according to a windowed signal spectrum asymptotic expression, designing a normalized N-order W-window function with the performance of analyzing a digital frequency interpolation coefficient and a minimum sidelobe, performing double weighting on a broadband signal by using different-order W-windows, performing spectrum density detection based on an amplitude spectrum, and determining the spectrum density of the broadband signal according to the detected spectrum density. And a W window frequency interpolation factor and a correction formula are adaptively switched according to a detection spectral line difference value to measure a broadband signal. The method does not need to fit a frequency interpolation coefficient, is small in calculation amount, is high in robustness, is easy to achieve, and effectively achieves the real-time high-precision measurement of harmonic / inter-harmonic parameters in a broadband range.

A broadband signal spectrum analysis method based on spectrum leakage suppression and an improved FFT algorithm comprises the following steps: setting a broadband signal model comprising a fundamental wave frequency offset signal and a plurality of broadband oscillation signals according to broadband signal characteristics; based on a Fourier transform theory, analyzing a spectrum leakage generation mechanism of the broadband signal; a Nuttall-Kaiser combination window function is designed, and is used for suppressing the spectrum leakage of the broadband signal; the FFT algorithm is improved, and side lobe attenuation is suppressed by using the convolution window property so as to reduce the influence of spectrum leakage. According to the broadband signal spectrum analysis method based on spectrum leakage suppression and the improved FFT algorithm, spectrum leakage is effectively reduced while high frequency resolution of broadband signals is guaranteed, and meanwhile the method has the advantage of being high in measurement accuracy.

The invention relates to a filter coefficient determination method and device for amplitude bandwidth compensation, a compensation method and device and computer equipment. The method comprises the following steps: acquiring a first amplitude-frequency response of a to-be-compensated bandwidth of a channel in a frequency sweeping mode; determining an ideal filter coefficient of a filter for compensation based on the first amplitude-frequency response; obtaining each window function, cutting off the ideal filter coefficient through each window function, and determining a target window function based on a first error between the amplitude-frequency response corresponding to the ideal filter coefficient after cutting off and the amplitude-frequency response corresponding to the ideal filter coefficient before cutting off; and determining a target filter coefficient based on the target window function and the ideal filter coefficient. By adopting the method, FPGA (Field Programmable Gate Array) resources can be saved, and a better compensation effect can be obtained.

The invention discloses a wide-spectrum interference weak signal enhancement method for high aspect ratio micro-nano structure measurement, and relates to the technical field of semiconductor measurement. The method comprises the following steps: acquiring a wide-spectrum interferogram, and performing two-layer two-dimensional discrete wavelet transform to obtain a first-stage wavelet coefficient group; performing nonlinear enhancement on the wavelet coefficient, and outputting a nonlinear enhanced wide-spectrum interferogram; performing three-layer two-dimensional discrete wavelet transform on the wide-spectrum interferogram after nonlinear enhancement to obtain a second-stage wavelet coefficient group, and calculating a Bayesian threshold value; a neighborhood sliding window function is set, and a Bayesian threshold is combined to carry out local denoising enhancement on the second-stage wavelet coefficient; reconstructing the second-stage wavelet coefficient after local de-noising enhancement to obtain a wide-spectrum interference pattern after local de-noising enhancement; and determining an optical path position by adopting an improved gravity center method according to the wide-spectrum interferogram after local denoising enhancement, and calculating a depth value of the to-be-measured sample. According to the method, the stripe contrast is enhanced, the noise is suppressed, and the depth measurement capability is improved at the same time.

The invention provides an ultrasonic water meter flow measurement data processing method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining ultrasonic signals in a downstream direction and a countercurrent direction, and respectively forming a first time domain signal and a second time domain signal; performing window function windowing and framing processing on the two time domain signals to obtain a corresponding signal frame sequence; respectively executing Fourier transform and frequency domain filtering, and recovering the first time domain signal frame sequence and the second time domain signal frame sequence through inverse transformation; splicing the recovered frame sequence, and analyzing and extracting a propagation time difference through a cross-correlation function; calculating the flow velocity according to the propagation time difference, and obtaining the water flow in combination with the pipeline cross-sectional area. Thus, the most probable propagation time difference is extracted through the cross-correlation peak value, the signal quality is enhanced in combination with frequency domain filtering, and the anti-interference performance and the overall precision of flow velocity measurement can be effectively improved.

The invention belongs to but is not limited to the technical field of power systems, particularly relates to a harmonic and inter-harmonic parameter estimation method and system based on an improved Gaussian convolution window, and provides a new harmonic and inter-harmonic parameter estimation method based on windowing FFT (Fast Fourier Transform) on a sampling signal by utilizing an improved Gaussian window function. Harmonic and inter-harmonic parameters can be detected with high precision in a short time. The method comprises the following steps: for a signal containing harmonic waves, representing the signal as the sum of a signal component with the maximum amplitude and other signal components, estimating a parameter of the signal component with the maximum amplitude by using a bispectral interpolation algorithm based on a least square method, and reconstructing the signal component with the maximum amplitude by using the estimated parameter; and subtracting the reconstructed maximum amplitude signal component from the original signal, carrying out the process to extract a second amplitude signal component parameter, and repeating the steps until all harmonic signal parameters are extracted.

The invention relates to the technical field of pipe network acoustic detection, in particular to a water supply pipe network leakage noise detection method and system and a medium. The method comprises the following steps: constructing a physical model of leakage noise based on fluid mechanics and a sound propagation rule; generating simulated leakage noise data based on physical model parameterization so as to simulate leakage noise data under extreme physical conditions; a set of the actually measured leakage noise data and the simulated leakage noise data is used as leakage noise sample data, and time domain statistical characteristics and frequency domain statistical characteristics of the signals are obtained through quantification according to the leakage noise sample data; based on the statistical characteristics, dynamically adjusting window function parameters and performing short-time Fourier transform processing to obtain a resolution-optimized time-frequency image; and constructing a CNN classification model, training the model based on the resolution-optimized time-frequency image, and outputting prediction probabilities of leakage noise and environmental noise. While the water supply pipe network leakage noise detection precision is improved, hardware dependence is eliminated, and efficient pure acoustic end-to-end pipe network leakage detection is realized.

The invention discloses a harmonic analysis method based on double buffer window function optimization, and relates to the technical field of harmonic analysis and signal processing of a power system, and the method comprises the steps of hardware initialization, double buffer initialization, FFT module parameter configuration, data acquisition, buffer switching, data extraction and windowing and FFT processing. Parallel data acquisition and processing are realized through a double-buffer ping-pong mechanism, spectrum leakage is suppressed by applying a Hanning window function, and FFT calculation is performed after signal amplitude is recovered through a compensation factor; according to the method, the data acquisition continuity and real-time performance are improved, the harmonic amplitude error is controlled within + / -0.5%, the hardware resource utilization rate is optimized, and the method is suitable for power grid electric energy quality monitoring.

The invention provides a Paillier homomorphic encryption hardware acceleration method and a Paillier homomorphic encryption hardware acceleration device based on a residual number system (RNS) and number theory transformation (NTT), which are suitable for high throughput parallel computing of large integer modular operation. The system adopts a modular architecture, comprises an RNS conversion cluster, an NTT core array, a modular domain multiply-accumulate unit and a CRT reconstruction module, and supports homomorphic addition, multiplication and modular exponent operation on a ciphertext. Low latency and high energy efficiency of encryption operation are realized by mapping a large integer to a plurality of small module domains for parallel processing and combining NTT fast convolution and a window function table look-up strategy. The method can be widely applied to privacy computing, cloud security and edge encryption scenes.

The invention relates to the field of GNSS-R SAR image cross-correlation registration, in particular to a GNSS-R SAR image cross-correlation registration method based on an improved window function. The scheme comprises the following steps: extracting a matching region from a main image, and extracting a reference region from a slave image; performing sliding traversal in the matching region and the reference region by using an improved elliptical weighted window function, and calculating coherence coefficients under translation positions one by one to obtain a coarse registration offset; performing multiple cubic interpolation on the time sequence image to obtain a primary image and secondary image interpolation result with sub-pixel precision, re-selecting a matching area and a reference area on the interpolated image, and repeating sliding traversal and coherence coefficient calculation to obtain a fine registration offset; and performing interpolation resampling on all the slave images based on the final registration offset to obtain a time sequence image set of which the registration precision is a sub-pixel level. The GNSS-R SAR image cross-correlation registration method is suitable for GNSS-R SAR image cross-correlation registration.

The invention relates to the technical field of audio sound calibration, and discloses a room sound calibration method based on intermediate frequency reverberation time and related equipment, which are used for improving the accuracy of a sound calibration result. The room sound calibration method based on the intermediate frequency reverberation time comprises the following steps: acquiring a room impulse response of a target room and calculating the intermediate frequency reverberation time; according to the intermediate frequency reverberation time, the recommended window length of the frequency correlation window is determined by querying a predefined mapping relation, and the mapping relation is established based on acoustic association between the room reverberation time and the analysis window length; based on the recommended window length, determining the actual window length of the actual application in combination with the user setting mode; performing time domain windowing and frequency-related transfer function estimation processing on the room impulse response according to the actual window length to obtain window function weighted transfer function estimation; filter calibration coefficients are generated based on the transfer function estimates and applied for audio calibration.

The invention discloses a gearbox fault diagnosis method based on multi-channel feature fusion. The gearbox fault diagnosis method comprises the steps that vibration signals of a gearbox to be diagnosed are collected through sensors installed at different positions in a multi-channel mode; for each vibration signal, obtaining a two-dimensional time-frequency image of the vibration signal by using a sliding window function, and respectively extracting features of the vibration signal and the two-dimensional time-frequency image by using a multi-layer gated convolutional network to obtain a one-dimensional feature and a two-dimensional feature of the vibration signal; fusing the one-dimensional features and the two-dimensional features of the vibration signals collected by the channels by using a convolution attention module to obtain fusion features of the gearbox to be diagnosed; and based on the fusion features of the to-be-diagnosed gearbox, performing fault diagnosis on the to-be-diagnosed gearbox by using a gearbox fault diagnosis model. According to the method, the comprehensiveness of feature expression is improved, and the channel correlation and the spatial correlation of the features are better captured, so that the modeling capability of the model for complex features is improved.

The invention relates to the technical field of wireless communication, and particularly discloses a frequency hopping communication method fusing a self-attention mechanism and lightweight convolution, which comprises the following steps: S1, a frequency hopping signal detection and parameter estimation step: S1-1, a data preprocessing step: performing short-time Fourier transform (STFT) on a received communication signal, and generating a time-frequency graph; for a given observation signal x (t), the short-time Fourier transform is # imgabs0 #, w (t) is a window function, and e-j2piftau is in a complex conjugate form; and after sampling at equal intervals, the discrete form is # imgabs1 # imgabs2 # frequency dimension k = 1, 2,..., K, and time dimension n = 1, 2,..., N. And S1-2, a feature extraction step: inputting the generated time-frequency graph into a lightweight convolutional layer, and extracting the time-frequency feature of the frequency hopping signal. And S1-3, a multi-task processing step: sharing the features extracted by the convolutional network to a frequency hopping signal detection branch and a parameter estimation branch. By adopting the technical scheme of the invention, the requirements of efficient and accurate processing of frequency hopping signals in a complex electromagnetic environment can be met.

The invention discloses a windowing compensation method and device for frequency domain signal processing, electronic equipment and a storage medium. The windowing compensation method comprises the following steps: A, generating a window function sequence; b, generating an overlapped and added window function sequence; c, obtaining a difference value sequence; d, performing amplitude scaling and repeated expansion on the difference value sequence to generate a compensation sequence; e, obtaining a window function sequence for compensation and correction; f, obtaining a square root sequence of the compensation correction window function; and G, performing windowing processing before the FFT and after the IFFT by using the square root sequence of the compensation correction window function. According to the windowing compensation method provided by the invention, the complete distortion-free reduction of the input signal can be ensured, the problem of spectrum leakage in the FFT and IFFT conversion processes is eliminated, the overhead of computing resources is saved, and the windowing compensation method is very suitable for engineering application in a real-time scene.

The invention provides a control panel circuit module fault detection method, which relates to the technical field of electronic fault detection, and comprises the following steps: acquiring key electrical parameters of each functional unit of a control panel circuit module, establishing a dependency graph of the key electrical parameters, and constructing a multi-level parallel detection sequence; a variable-length window function is adopted to carry out synchronous sampling on detection items of the same level, a level feature matrix is generated, improved short-time Fourier transform is executed, a frequency spectrum deviation curve of each level is generated through sliding cross-correlation operation, a fault transmission chain is established, and a specific fault type is judged through a topological structure of the fault transmission chain; according to the method, the problems of low fault positioning efficiency and poor accuracy caused by difficulty in capturing fault transfer characteristics and insufficient capability of a traditional detection method in multi-level feature extraction and analysis can be solved.

The invention discloses an improved Doppler signal processing method and system, and belongs to the technical field of Doppler signal processing, and the method comprises the following steps: S1, signal collection and preprocessing: collecting an original Doppler signal, and carrying out the moving average filtering; s2, time-frequency analysis: performing time-domain analysis on the filtered Doppler signal by adopting a Hamming window function and improved wavelet threshold de-noising and combining short-time Fourier transform to obtain a motion spectrogram; s3, feature extraction: calculating a real envelope by adopting Hilbert transform, and traversing data in the motion spectrogram in combination with a peak searching function based on envelope analysis so as to extract a peak point; and S4, judging and outputting: screening peak points meeting peak height threshold conditions, and outputting the peak points and position coordinates thereof. According to the invention, a better signal extraction effect and visual speed measurement are realized, signal separation and extraction are effectively realized, and the precision and reliability of signal processing are remarkably improved.

A deviated well anisotropic formation three-dimensional induction logging data inversion method, comprising: carrying out azimuth angle conversion on pre-acquired three-dimensional induction logging data to generate a three-dimensional induction logging curve under a wellbore coordinate system (100); constructing a window function on the basis of morphological characteristics of the three-dimensional induction logging curve (200); segmenting the three-dimensional induction logging curve by means of the window function (300); and performing full-parameter inversion on each section of the three-dimensional induction logging curve to obtain a horizontal resistivity and vertical resistivity of each formation layer, formation boundaries and a well deviation angle, wherein an objective function of the full-parameter inversion is to minimize the ZZ component fitting error of the three-dimensional induction logging curve (400). The present application further relates to a deviated well anisotropic formation three-dimensional induction logging data inversion apparatus.

The invention relates to the technical field of power grid automation, and provides a fault data fingerprint construction method and system based on energy difference, and the method comprises the steps: collecting the electrical quantity data waveforms of a bus and a line side, selecting fault waveform data from the electrical quantity data waveforms, and generating a first waveform data matrix; performing time domain framing processing on the first waveform matrix, dividing the first waveform matrix into time domain frames with overlapped parts through a window function, performing Fourier transform on each time domain subframe, calculating the amplitude and frequency of data, and realizing conversion from a time domain to a frequency domain of the signal; frequency domain sub-band division is carried out according to the length of each time domain sub-frame signal, frequency domain signals are divided into non-overlapping frequency domain sub-bands, an energy matrix of each frequency domain sub-band is calculated, differential operation is carried out according to the energy matrix to construct a binary feature matrix, coding of fault features is achieved, and fault data fingerprints are formed. According to the invention, the accuracy of identifying the fault type by the device is improved, and the protection device is prevented from refusing to operate or malfunctioning.

The invention belongs to the technical field of extrusion process monitoring, and particularly relates to a cable extrusion process state online monitoring method and system based on acoustic sensing, so as to solve the technical problem of inaccurate cable extrusion process state monitoring in the prior art. The monitoring method comprises the following steps: S1, when a transient impact factor is greater than a preset threshold value, windowing a signal frame by adopting an asymmetric window function with a main lobe in front and a side lobe in back; s2, performing fast Fourier transform on each windowed signal frame to obtain a frequency spectrum of each windowed signal frame; filtering the frequency spectrum of each signal frame by using a filter bank and calculating logarithmic energy of each filtering channel; s3, selecting cepstrum coefficients of which the ratio is greater than a selection threshold to form a process state feature vector; and inputting the process state feature vector into a state classifier to obtain a current cable extrusion process state. According to the method, redundancy and noise interference are eliminated, so that the accuracy of process state monitoring is improved.

The invention provides a vibration signal detection method and apparatus, a computer device and a storage medium. The method comprises the steps of obtaining an original acceleration signal generated by vibration of a to-be-detected target; preprocessing the original acceleration signal based on a window function to obtain an optimized signal; performing feature extraction on the optimized signal to obtain a to-be-detected signal; and comparing the to-be-detected signal with a reference signal to obtain an abnormality judgment result. According to the design, before the to-be-detected signal is compared with the reference signal, the to-be-detected signal is preprocessed through the window function, filtering and noise reduction of the to-be-detected signal are achieved, the real situation of the to-be-detected signal is restored more accurately, and the accuracy of a comparison result is improved in the comparison process of the to-be-detected signal and the reference signal.