95 results about "Time frequency distribution" patented technology

The invention relates to a bridge structure vibration monitoring and analysis method based on artificial intelligence, and the method specifically comprises the following steps: setting an acceleration sensor network at a key part of a bridge, collecting and marking a vibration signal sample, and forming a data set; a high-resolution time-frequency matrix of sample adaptive optimal kernel time-frequency distribution is calculated, a damage sensitive resonance frequency band is positioned according to spectrum kurtosis, and the time-frequency matrix is enhanced by the resonance frequency band subjected to adaptive gain enhancement; dividing a matrix frequency axis multi-scale binary tree, calculating and normalizing sub-band average energy, quantifying energy distribution uniformity through information entropy, and splicing multi-scale entropy values into feature vectors; then constructing and training a bridge detection data model; and finally, preprocessing new data, inputting the preprocessed new data into the trained model, and automatically analyzing to obtain a bridge health monitoring result. Through time-frequency optimization, multi-scale entropy and deep learning technologies, the problems of large noise, difficult feature extraction and low automation and accuracy of traditional monitoring can be solved.

The invention discloses a live detection method for insulation defects of high-voltage power equipment. The method comprises the following steps: firstly, synchronously arranging ultrahigh frequency sensors and acoustic emission sensors on a plurality of monitoring points on the surface of a gas insulated switchgear shell to form an array, and synchronously acquiring signals for filtering pretreatment; then, pulse events are extracted from the two types of signals respectively, and associated pulses in a time window are matched into matched pulse pairs representing the same discharge source; then, carrying out time-frequency transformation on the ultrahigh frequency and acoustic emission pulse waveform in each matched pulse pair, carrying out joint noise reduction by calculating a coherence coefficient between time-frequency distribution matrixes, and extracting a joint feature vector containing an energy ratio, a time parameter ratio and a frequency difference from the denoised time-frequency matrix; according to the invention, multi-source signals are fused, and high-sensitivity detection, high-precision positioning and high-accuracy identification of insulation defects are realized.

The invention discloses a coal-fired power plant belt conveyor driving gear box bearing fault diagnosis method, which relates to the field of fault diagnosis and evaluation, and is used for extracting gear box bearing fault features based on an order tracking analysis method of tachometer-free instantaneous rotation frequency estimation. The method specifically comprises the following steps: S1, obtaining an instantaneous rotation frequency ridge line through the time frequency distribution of a vibration signal, carrying out the rotation frequency estimation of a rotating shaft, and improving the precision of the instantaneous rotation frequency ridge line through an improved multi-source ridge line fusion algorithm; and S2, extracting bearing fault feature order information by using a calculation order tracking COT algorithm, and adaptively processing a bearing fault diagnosis problem of the driving gearbox of the belt conveyor. According to the bearing fault diagnosis method for the rubber belt conveyor driving gearbox of the coal-fired power plant, the bearing inner ring fault is successfully diagnosed, and the feasibility of a bearing fault diagnosis strategy for the rubber belt conveyor driving gearbox is verified.

The invention belongs to the technical field of rotating machinery signal processing, and discloses a multi-shafting rotating machinery rotating speed estimation method based on a displacement vibration signal, and the method comprises the steps: collecting the displacement vibration signal, and carrying out the short-time Fourier transform to obtain a time-frequency distribution matrix; screening out a safety harmonic set of each shafting based on the estimated rotating speed range; calculating a definition evaluation value of each safety harmonic wave and selecting an optimal harmonic wave as a target tracking harmonic wave; constructing a cost function fusing amplitude and frequency abrupt change penalty, and performing ridge line tracking in the local time-frequency matrix after noise reduction to obtain a preliminary estimation time-frequency curve; and constructing a demodulation phase function based on the curve, performing generalized demodulation and narrow-band filtering on an original signal, extracting a pure harmonic component, and finally reconstructing a final rotating speed curve of a corresponding shaft system through instantaneous phase differential. According to the method, high-precision and anti-interference decoupling and estimation of the rotating speed of each shafting under a strong interference background are realized.

The invention provides a rotating speed extraction method, system and equipment based on time-frequency analysis and a medium. The method comprises the following steps: acquiring a signal x (t) to be analyzed; performing spectral analysis transformation on the to-be-analyzed signal x (t) to obtain a spectral analysis result; acquiring a three-dimensional time-frequency distribution diagram A (t) (f) of the to-be-analyzed signal x (t) according to the spectral analysis result; searching a peak frequency in the three-dimensional time-frequency distribution diagram A (t) (f) according to a preset extraction rotating speed frequency Nfn and a preset search range so as to obtain the rotating speed fn (t); according to the method, the rotating speed of the equipment can be accurately extracted according to the monitoring data under the condition of not depending on an external rotating speed sensor; the system has the same beneficial effects.

The invention discloses an ant colony optimization energy focusing cable insulation damage positioning method and device, and relates to the technical field of cable detection engineering, and the method comprises the steps: generating a Gaussian envelope linear frequency modulation chirp detection signal, injecting the detection signal into a to-be-detected cable, and collecting an incident signal and an echo signal containing a reflection component; constructing a parameterized time-frequency analysis kernel function family under a Cohen type time-frequency distribution framework, and searching and optimizing kernel parameters by using an ant colony optimization algorithm; and calculating the time frequency distribution and energy-time curve of the signal based on the energy focusing time frequency analysis kernel function obtained through optimization, extracting the arrival time corresponding to an energy peak value, and realizing cable insulation damage positioning according to the reflection time delay and the propagation wave velocity. According to the method, energy peak focusing and peak position stability are enhanced while cross terms are restrained, defect positioning precision and robustness are remarkably improved, and the method is suitable for engineering online detection and automatic implementation of cable insulation damage.

Example systems, methods, and apparatus are disclosed herein for a high-resolution time-frequency analysis for nonstationary signals. Time-frequency distributions (TFDs) are essential tools for analyzing nonstationary signals in various applications. The proposed technology, Zoom, a novel time-frequency distribution (TFD) designed to enhance the resolution and reduce cross-term interference. The performance of the proposed TFD is rigorously evaluated by studying its behavior across different smoothing values, allowing for a detailed analysis of its time-frequency localization capabilities. To quantify its effectiveness, the proposed technology computes several performance metrics, including time-frequency concentration and cross-term reduction.

The invention relates to a rotary machine composite fault detection method and system under a time-varying working condition, and belongs to the technical field of rotary machine fault diagnosis. The method comprises the steps of collecting a non-stationary vibration signal under a variable speed working condition, fusing a third-order Renyi entropy and kinematics prior to construct an adaptive time-frequency enhancement model, and outputting weighted time-frequency distribution; global optimization is carried out through a priori constraint Viterbi algorithm, and an instantaneous frequency ridge line is extracted under the condition of no rotating speed sensor; performing angular domain resampling by taking the ridge line as a phase reference, and converting a non-stationary signal into an angular domain stationary signal; constructing an extended difference mode decomposition convex optimization model, and iteratively solving an optimal difference spectrum; and determining a frequency spectrum segmentation threshold value based on self-adaptive change point analysis, performing decoupling separation and time domain reconstruction on the optimal difference spectrum, and completing composite fault diagnosis through feature comparison. The method does not need an external tachometer, can adaptively extract and separate the composite fault features under the variable speed working condition, is high in diagnosis accuracy, and is good in engineering application value.

The invention relates to the technical field of ultrasonic signal processing, in particular to an ultrasonic data processing method and system and electronic equipment, and the method comprises the steps: obtaining an original ultrasonic echo signal, and carrying out the time gain compensation, band-pass filtering and down-sampling preprocessing; extracting dynamic frequency dispersion characteristic parameters based on Coann class time-frequency distribution; constructing a depth self-adaptive phase compensation model, and correcting nonlinear phase distortion segment by segment; hilbert transform is adopted to extract an envelope, and after logarithmic compression and linear mapping are carried out, image reconstruction is completed by utilizing delay summation beam forming and an adaptive contrast enhancement algorithm. According to the method, the imaging definition is improved through cooperation of frequency dispersion modeling and phase correction, and the method has good depth adaptability and image visualization effect and is suitable for high-precision image acquisition requirements in scenes such as medical ultrasonic imaging and industrial nondestructive testing.

The application provides a method for reducing impurities in electrolyte based on a copper foil production device, comprising: identifying a pressure fluctuation abnormal area from a pressure time-frequency distribution map as an input of a pre-constructed high deposition area distribution model, outputting a position distribution of the high deposition area, and determining a potential high-risk node of impurity deposition in a pipeline; deploying a filtering device at the potential high-risk node of impurity deposition according to the determined potential high-risk node of impurity deposition in the pipeline, separating impurity particle sizes through a multi-stage filtering structure built in the filtering device, directionally collecting different particle size impurities separated, and obtaining impurity data.

The application discloses a wheeled traffic target classification and identification method, system, device and medium based on micro-motion information, and the method comprises the following steps: acquiring a micro-Doppler echo signal of a wheeled target; performing a short-time Fourier time-frequency transformation on the micro-Doppler echo signal to obtain a time-frequency distribution of the micro-Doppler echo signal; calculating an instantaneous frequency of the micro-Doppler echo signal, performing an EMD transformation on the instantaneous frequency, obtaining a plurality of modal components, and selecting a single-frequency component from the plurality of modal components to obtain a wheel rotation period; according to the time-frequency distribution of the micro-Doppler echo signal, the number of flicker points in the wheel rotation period is obtained, and the corresponding maximum speed in the time-frequency distribution is also obtained; and the data set composed of the number of flicker points in the wheel rotation period, the maximum speed and the corresponding vehicle type is input into a support vector machine to classify and identify the vehicle. The application can finely classify and identify different wheeled vehicles.

The invention discloses a target deformation detection method based on Doppler feature guidance, and the method comprises the following steps: 1, obtaining radar echo signal data, and calculating distance-slow time two-dimensional data; 2, acquiring signal data at a target position and signal data at a reference position, and calculating a phase at the target position and a phase at the reference position; 3, calculating time-frequency distribution of the signal data at the target, extracting Doppler features of the signal data, and recording an existence time set of the Doppler features; 4, compensating the phase at the target position by using the phase at the reference position to obtain target deformation; and 5, in the Doppler feature existence time set, performing deformation detection on the target deformation to obtain a target deformation displacement set and an occurrence time index set. According to the method, the radar is used for counteracting shaking of the platform perceived by the phase at the reference position, detection is guided by combining Doppler features related to target deformation, and the high deformation detection rate can be achieved with the low false alarm rate.

This application relates to a terahertz video synthetic aperture radar moving target imaging method based on time-frequency analysis. The method includes: constructing a radar echo model of the moving target based on instantaneous range and radar echo; processing the radar echo model by performing a short-time Fourier transform on the compressed echo signal; obtaining the Doppler center frequency of the scene echo from the obtained time-frequency distribution map; performing Doppler frequency zero-padding, range migration correction, and azimuth processing on the compressed echo signal according to a pre-set function; observing the range cell position of the moving target from the obtained imaging results; and extracting and performing time-frequency analysis on the moving target signal. After determining the Doppler center frequency of the moving target through time-frequency analysis and the time-frequency map, velocity estimation is performed; and the image of the moving target is refocused based on the characteristics of the time-frequency distribution line of the moving target echo. This method can quickly achieve velocity estimation and image refocusing of moving targets.

The application discloses a ship active signal time-frequency analysis method based on time-frequency parameter fine estimation, and comprises the following steps: (1) obtaining a ship active signal to be processed; (2) calculating a single-frequency signal energy fluctuation characteristic of the ship active signal to be processed; (3) calculating a received single-frequency signal time existing range based on the single-frequency signal energy fluctuation characteristic; (4) calculating a received single-frequency signal frequency based on the received single-frequency signal time existing range; and (5) calculating a received frequency modulation signal time-frequency distribution based on the received single-frequency signal frequency. The method is suitable for a low signal-to-noise ratio condition, can effectively calculate a time-frequency distribution of a frequency modulation signal in a two-component ship active signal composed of the frequency modulation signal and a single-frequency signal, and provides a basis for target feature extraction based on the ship active signal.

This application belongs to the field of rotating machinery signal processing technology, and discloses a method for estimating the rotational speed of multi-axis rotating machinery based on displacement vibration signals. The method involves acquiring displacement vibration signals and performing short-time Fourier transform to obtain a time-frequency distribution matrix; selecting a safe harmonic set for each axis system based on the estimated rotational speed range; calculating the sharpness evaluation value of each safe harmonic and selecting the optimal harmonic as the target tracking harmonic; constructing a cost function that fuses amplitude and frequency mutation penalties; performing ridge tracking in the denoised local time-frequency matrix to obtain a preliminary estimated time-frequency curve; constructing a demodulation phase function based on this curve; performing generalized demodulation and narrowband filtering on the original signal to extract clean harmonic components; and finally reconstructing the final rotational speed curve of the corresponding axis system through instantaneous phase differentiation. This method achieves high-precision, anti-interference decoupling and estimation of the rotational speed of each axis system under strong interference conditions.

The application relates to the technical field of audio processing, and discloses a voice activity detection method, a system, a terminal and a storage medium. The method comprises the following steps: continuously monitoring an input audio signal through a first detection layer, performing preliminary voice judgment on each frame of audio signal based on basic acoustic characteristics, and generating a wake-up signal to start a second detection layer when the judgment result meets a first preset trigger condition; the second detection layer extracts at least two acoustic characteristics reflecting voice harmonic structure and time-frequency distribution characteristics of the audio signal in response to the wake-up signal; all acoustic characteristics reflecting voice harmonic structure and time-frequency distribution characteristics are subjected to fusion analysis, and when the fusion analysis result meets a second preset trigger condition, it is determined that the current audio signal is a candidate audio signal, and an activation signal is generated to start a third detection layer; the third detection layer inputs the candidate audio signal into a preset deep learning model after processing the candidate audio signal in response to the activation signal, so as to output a voice activity detection result.

The invention belongs to the technical field of time-frequency spectrum decomposition, and discloses a time-frequency spectrum decomposition method and device based on empirical matching pursuit and a storage medium, the time-frequency spectrum decomposition method comprises the following steps: constructing an empirical mode decomposition (EMD) dictionary suitable for time-frequency analysis according to input seismic data; performing matching pursuit on a seismic signal to be decomposed by using the EMD dictionary to obtain a time-frequency atom combination of the seismic signal; and performing multiple synchronous compression transformation on the time-frequency atoms of the seismic signal to obtain the time-frequency distribution of the seismic signal. According to the method, a new time-frequency characterization method is deduced, a time-frequency spectrum decomposition strategy based on empirical matching pursuit is designed, and the time-frequency resolution of seismic signals is improved by introducing multiple synchronous compression transformation.

The application discloses a Doppler through-wall radar positioning method based on neural network time-frequency analysis, comprising the following steps: constructing a multi-target motion echo signal after demodulation of a Doppler through-wall radar; performing time-frequency analysis processing on the signal through Winger-Ville distribution to obtain corresponding time-frequency distribution and construct a fusion data set; constructing a multi-input neural network model, training the model by using the fusion data set, updating the model parameters, and constructing an optimal time-frequency distribution prediction model; acquiring real-time collected echo signals, performing time-frequency analysis processing through the distribution to obtain corresponding time-frequency distribution, and constructing a real-time fusion data set; obtaining a predicted time-frequency distribution; extracting the multi-target instantaneous frequency of the predicted time-frequency distribution, estimating the distance and angle information of the target through a Doppler through-wall radar positioning algorithm, and completing the positioning of the multi-target; and the method can eliminate the interference of time-frequency distribution cross terms, improve positioning accuracy, and improve time-frequency analysis effect.

The invention discloses a denoising radar intra-pulse signal modulation high-precision identification method, which comprises the following steps of: 1, acquiring a radar signal to be identified, and carrying out Choi-Williams time-frequency distribution preprocessing on the radar signal to be identified to obtain a noise-containing two-dimensional time-frequency image to be identified with corresponding Choi-Williams time-frequency distribution; 2, constructing and training a real image denoising network, and enabling the real image denoising network to convert the input noise-containing two-dimensional time-frequency image input into a corresponding noise-free two-dimensional time-frequency image; and step 3, constructing and training a Swin-Transform network, and obtaining a feature extraction intelligent identification network model. And step 4, inputting the noise-containing two-dimensional time-frequency image to be identified in the step 1 into the real image denoising network which completes training to obtain a noise-free two-dimensional time-frequency image. And step 5, inputting the noise-free two-dimensional time-frequency image in the step 4 into the Swinin-Transform network obtained in the step 3, and obtaining a corresponding recognition result of the radar signal.

The invention provides an aero-engine strong non-stationary cross frequency signal time-frequency analysis method and system, and relates to the technical field of aero-engine signal processing and fault diagnosis, and the method comprises the steps: obtaining a vibration signal, and obtaining initial time-frequency distribution; performing adaptive ridge line extraction on the initial time-frequency distribution, and determining a time-frequency block range of an intersection region; carrying out local sub-blocking on the time frequency distribution in the intersection area to obtain optimized time frequency distribution; performing component reconstruction on the vibration signal by adopting Vold-Kalman order filtering to obtain final time frequency distribution; according to the method, the ridge line fracture time-frequency block is determined through bidirectional retrieval repair, the intersection area and the hierarchical cascade reconstruction component are optimized through comprehensive evaluation indexes, the ridge line continuity and instantaneous frequency extraction precision of the intersection area are remarkably improved, and high-resolution representation and accurate separation of strong non-stationary intersection frequency signals are achieved.

The application discloses a GIS device partial discharge wide-frequency electromagnetic signal characteristic analysis method and system, and relates to the technical field of partial discharge detection. The method comprises the following steps: collecting a GIS partial discharge wide-frequency electromagnetic signal, and adopting wavelet transform processing to obtain a time-frequency distribution matrix; uniformly dividing the time-frequency distribution matrix according to a frequency axis to obtain initial sub-frequency bands; calculating the effectiveness indexes of the sub-frequency bands and adaptively aggregating the sub-frequency bands to obtain candidate characteristic intervals; and performing collaborative contribution degree analysis on the candidate characteristic intervals to obtain optimal characteristic intervals. The technical problem that, in the existing GIS partial discharge wide-frequency electromagnetic signal characteristic extraction process, frequency band division relies on experience and lacks an adaptive optimization mechanism, resulting in inaccurate characteristic interval selection and weak anti-interference capability is solved, and the technical effect that the accuracy and anti-interference capability of partial discharge characteristic extraction are improved through adaptive sub-frequency band division of the wide-frequency electromagnetic signal and collaborative analysis of the characteristic intervals is achieved.

The invention relates to the technical field of lead screw guide rail data acquisition, in particular to a lead screw guide rail data acquisition method and system, and the method comprises the following steps: obtaining a vibration signal of a lead screw guide rail; preprocessing the vibration signal to obtain a preprocessed vibration signal; self-adaptive noise suppression is carried out on the preprocessed vibration signal, and a noise-suppressed vibration signal is obtained; performing time-frequency domain conversion on the vibration signal subjected to noise suppression to obtain time-frequency distribution of the vibration signal; extracting transient high-frequency impact characteristics from the time-frequency distribution; dynamically adjusting the judgment rule of the early damage state according to the operation condition of the lead screw guide rail; according to the transient high-frequency impact characteristics and a judgment rule, the early damage state of the lead screw guide rail is judged; according to the judgment result of the early damage state, graded early warning is sent out; through obtaining the vibration signal of the lead screw guide rail and carrying out preprocessing and self-adaptive noise suppression, the purity of the signal is effectively improved.

The invention relates to the technical field of pathological characteristic sound detection, and provides a Velcro rale detection method and system based on respiratory auscultation sound, and the method comprises the steps: firstly determining the collection position of lung sound, obtaining the lung sound data of a subject, carrying out the preprocessing, carrying out the initial feature extraction of a preprocessed respiratory sound data set, and obtaining a frequency spectrum representation; then, a pre-trained double-flow multi-view Velcro rale detection model is used for processing: the Velcro rale characteristic acoustic biomarker features represented by the frequency spectrum are enhanced, feature fusion is performed on the enhanced frequency spectrum representation, and multi-view fusion advanced feature representation is obtained; time domain feature representation is extracted from the preprocessed breath sound data set; and finally, integrating the multi-view fusion advanced feature representation and the time domain feature representation to form multi-dimensional time-frequency joint representation, and outputting a detection result. According to the method, the time-frequency distribution mode of the Velcro rale is fully considered, the Velcro rale is accurately and reliably detected, and a non-invasive, economical and efficient means is provided for early screening of IPF.

The invention discloses a pressure pipeline elbow curvature measurement method and device, a curvature measurement module is mounted on the surface of a pressure pipeline elbow, and the curvature measurement module comprises an inclinometer, a strain gauge, a transducer mounting groove, a flexible connecting rod and an ultrasonic transducer; reading the angle value of the inclinometer, and adjusting the curvature measurement module according to the angle value to calibrate the ultrasonic transducer; according to the strain value measured by the strain gauge and the original scale distance of the flexible connecting rod, the actual distance between the calibrated transducers is calculated; an ultrasonic transducer is controlled to emit ultrasonic waves, and ultrasonic guided wave signals are collected; according to the ultrasonic guided wave signal and the actual distance between the transducers, characteristic parameters used for inversion calculation of the curvature are extracted, and the characteristic parameters comprise the guided wave speed, the guided wave attenuation coefficient and the time-frequency distribution characteristic; the characteristic parameters are input into a preset curvature inversion calculation model, and the curvature value of the pressure pipeline elbow is obtained through calculation. The bend curvature can be rapidly measured in a limited space, and the detection efficiency and the detection precision are improved.

This invention belongs to the field of pipeline eddy current damage localization technology. It proposes a method, system, medium, and computer equipment for pipeline eddy current damage localization. The method involves acquiring multi-channel original discrete signals from the pipeline, performing mean-reduction processing, and determining the dominant frequency. Based on the dominant frequency, an optimal bandwidth parameter is adaptively determined from a candidate bandwidth set using a kurtosis maximization criterion. A complex Moray wavelet function is constructed using the dominant frequency and the optimal bandwidth parameter, and a continuous wavelet transform is performed on the signal after removing the DC component to generate a multi-channel time-frequency distribution matrix. A frequency-weighted operator is applied to this time-frequency distribution matrix for energy enhancement, resulting in a multi-channel weighted energy map. Time-frequency coherent fusion is performed on the multi-channel weighted energy map, and a global energy peak is searched in the fused energy field. The coordinates corresponding to this peak are determined as the pipeline damage location. This invention effectively improves the accuracy and robustness of damage localization.

The application provides a kind of unmanned plane fast positioning method and system based on telemetry data, it is related to unmanned plane technical field, the application is by obtaining the wideband digital signal of bearing telemetry data and the preset feature parameter set;Time-frequency distribution data is generated based on wideband digital signal, to determine the target carrier frequency, target baud rate and target phase information of the downlink signal of target unmanned plane;According to target carrier frequency and target phase information, generate baseband symbol sequence;According to the preset constraint length and generating polynomial, the state sequence corresponding to state transition path is backtracked and mapped into telemetry data stream;Protocol frame structure analysis and unpacking are carried out on telemetry data stream, to determine the geographic coordinate position of target unmanned plane, realizes the blind demodulation of weak unmanned plane telemetry signal and the fast direct acquisition of high-precision geographic coordinate in complex electromagnetic environment.

A high-resolution spaceborne SAR imaging processing method for a ship target under complex sea conditions, which method relates to the technical field of radar imaging processing. In the method, by means of a dynamic adjustment strategy from a fine-tuning index, a cumulative energy ratio threshold value can be flexibly adjusted on the basis of real-time signal conditions; a phase compensation function is introduced as a basis function and a time window parameter is added, so as to better adapt to the instability of echoes from strong scattering points under complex sea conditions, thereby further improving the robustness of an algorithm; parameter estimation, residual updating and component extraction are performed in a frequency domain, thereby reducing search dimensions and increasing the operation speed; and a parameterized time-frequency distribution construction method is proposed, thereby effectively reducing the impact of cross terms and sidelobes. Upon verification, after processing is performed by using the method, the image quality of a ship under complex movement conditions is significantly improved.

The invention relates to an anti-interference robustness wireless channel prediction method. The method comprises the following steps: acquiring observation uplink CSI (Channel State Information) sequences of a plurality of time slots in the past; inputting the observation uplink CSI sequences of the plurality of time slots into an interference perception gating prediction model to obtain ideal downlink CSI sequences of a plurality of time slots in the future; wherein the interference sensing gating prediction model comprises an interference sensing module which is used for carrying out normalization operation on the observation uplink CSI sequence, detecting interfered time-frequency distribution of the observation uplink CSI sequence to generate an interference mask, and inhibiting an interfered area through a self-adaptive gating factor to obtain a purified CSI sequence; and the channel prediction backbone network is used for modeling long time sequence correlation for the purified CSI sequence and predicting an ideal downlink CSI sequence at a future moment. According to the invention, adaptive suppression of interference and accurate prediction of a channel can be realized.

The invention provides a frequency spectrum real-time sensing system with edge calculation enabling, which relates to the technical field of frequency spectrum sensing, and comprises a generation module used for performing primary processing and analysis on original frequency spectrum signal data, determining a basic characteristic distribution mode of a signal by extracting a main energy region of the signal in time frequency distribution, and generating a frequency spectrum signal; generating a signal analysis correction parameter; the analysis module is used for calibrating and preprocessing the original spectrum signal data by using the signal analysis correction parameters to obtain standardized to-be-analyzed signal data; and based on the standardized to-be-analyzed signal data, respectively performing power spectral density fusion analysis and eigenvalue statistical analysis of the received signal covariance matrix, and generating a first signal existence judgment result and a second signal existence judgment result. According to the invention, the data transmission demand and the back-end processing burden are reduced, and the real-time performance and the response speed of spectrum sensing are improved.