73 results about "Pulse characteristics" patented technology

The invention discloses a partial discharge online monitoring method based on an ultrahigh frequency original signal, and relates to the field of partial discharge monitoring, and the method comprises the steps: autonomously switching an ultrahigh frequency signal capturing frequency band according to the real-time electromagnetic spectrum distribution of a monitoring environment, and obtaining an original signal sequence covering a potential partial discharge feature; the method comprises the following steps: preprocessing an original signal sequence through multi-scale pulse contour extraction, and suppressing environmental noise interference to retain pulse characteristics of partial discharge signals; through real-time electromagnetic spectrum analysis, ultrahigh frequency capture frequency bands are automatically switched, environmental noise changes are dynamically adapted, original signal capture accuracy is improved, through multi-scale pulse contour extraction preprocessing, noise interference is effectively suppressed, key pulse features are reserved, and the accuracy of signal capture is improved. A multi-dimensional feature data set constructed based on the pulse peak gradient, the duration spectrum and the waveform similarity provides a comprehensive basis for partial discharge recognition.

The invention relates to the technical field of signal processing and state monitoring, in particular to an intelligent lubricating state monitoring method based on oil-impact pulse, which comprises the following steps of: constructing a'fluid-solid-heat-electricity 'multi-field coupled particle double-electric-layer electrostatic model, and forming a particle electrostatic characteristic analysis and monitoring method; oil physical and chemical property parameters and mechanical equipment transmission system gear impact pulse characteristics in different lubrication states are collected in real time, an oil-impact pulse-lubrication state correlation model is constructed, and a gear transmission system lubrication state monitoring method is formed in combination with a signal processing method and a deep learning algorithm. According to the invention, the real-time performance and comprehensiveness of lubrication state evaluation are enhanced, and the work and maintenance efficiency of equipment is also improved.

The invention discloses a bionic joint humanoid robot navigation positioning method, device and equipment, and the method comprises the steps: monitoring a landing shock wave signal and reflection echo data of a robot, carrying out the time domain expansion of the shock wave signal to form a pulse characteristic spectrum, extracting multipath propagation components, and carrying out the time difference correlation to generate an environment response value. Screening a direct wave component by using an environmental response value, positioning a sound wave propagation trajectory, and detecting impedance mutation to construct an acoustic matrix. A strong reflection zone is identified through node analysis, a sound pressure gradient field is extracted to determine an obstacle boundary, and a landmark feature bit is determined in combination with an environmental response value. And adjusting a dynamic stride frequency interval based on a landmark feature bit, forming an adaptive detection sequence, and performing sparse processing to construct path configuration. The mechanical vibration component of the shock wave signal is used for piezoelectric conversion to generate recycled electric quantity, the recycled electric quantity is matched with path configuration power to determine a sensing period, intermittent sensing is executed to obtain detection data to construct a detection convergence field, and finally an accurate positioning window is determined and converted into position coordinates.

The invention is suitable for the technical field of distribution network detection, and provides a distribution network overhead line partial discharge integrated live detection system based on a ubiquitous Internet of Things architecture, which comprises a pulse signal detection end, an edge gateway and a cloud monitoring platform, the pulse signal detection end is used for detecting a partial discharge pulse signal of the overhead line and transmitting the partial discharge pulse signal to the edge gateway; the edge gateway comprises a synchronization pulse trigger module, a time calibration module and an edge analysis module. The generation module is used for generating a positioning distribution diagram of partial discharge points; the cloud monitoring platform comprises a multi-terminal data fusion module, a risk assessment module and a result feedback module; the server is used for fusing the positioning distribution map and the pulse characteristics, outputting a global discharge point correction distribution map, identifying a discharge type based on a discharge characteristic spectrogram, generating an insulation state evaluation result and feeding back the evaluation result to a terminal user; and a high-reliability, high-efficiency and low-cost solution is provided for the management and control of the insulation state of the distribution network.

The invention relates to the technical field of optical systems, and provides a full-automatic collimation method and system for an optical system, a medium and equipment, and the method comprises the steps: extracting pulse features of a pulse sequence, calculating pulse interval jitter, an energy fluctuation rate and a deviation between a pulse repetition rate and a theoretical value, and judging a mode locking state; for the output laser, reconstructing a spectral curve of the output laser, and extracting spectral features; obtaining a mode locking stability score through a deep learning model; the basic parameters of the laser cavity are optimized by maximizing the power and the collimation degree of the output laser; basic parameters of the laser cavity are used as priori, and parameters of all optical elements of the resonant cavity are adjusted by maximizing a mode locking stability score and a target pulse width score; and on the premise of the adjusted parameters of the optical elements of the resonant cavity, the pose of the lens is adjusted by maximizing the power and the collimation of the output laser. High-efficiency and progressive optimization of optical system parameters is realized, and the collimation degree is improved.

An optical metrology device includes a selectable pump arm that is used to produce a transient response in the target sample and a probe arm and may be configured to perform static Magneto-Optic Kerr Effect (MOKE) measurements or ellipsometry measurements when the pump arm is not selected, and time resolved (TR) MOKE measurements or ellipsometry measurements when the pump arm is selected. The optical metrology device may be further configured to perform opto-acoustic measurements when the pump arm is selected. A pulse shaper in the pump arm may be used to pulse duration, phase, or both, of the incident pump beam to enable measurement of the dependence of the magnetic dynamics on the pump pulse characteristics. The optical metrology device may be configured to operate at a single selectable wavelength or multiple wavelengths in a continuous or discontinuous spectrum.

The invention provides a radar working mode recognition method, device and equipment, a medium and a program product, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring a pulse description word (PDW) sequence of a radar radiation source; based on the PDW sequence, determining intra-pulse features of the radar radiation source and inter-pulse features of the radar radiation source; inputting the intra-pulse feature and the inter-pulse feature into a working mode feature extraction model to obtain an intra-pulse feature vector and an inter-pulse feature vector output by the working mode feature extraction model; and inputting a feature fusion vector of the intra-pulse feature vector and the inter-pulse feature vector into a radar working mode recognition model to obtain a radar working mode recognition result output by the radar working mode recognition model. According to the method, the intra-pulse features and the inter-pulse features are obtained firstly, then the intra-pulse features and the inter-pulse features are subjected to feature extraction, the intra-pulse feature vectors and the inter-pulse feature vectors with context information are obtained, and therefore high-precision recognition of the radar working mode can be achieved for all scenes.

The invention discloses a fan coil abnormal state diagnosis method and system, and the method comprises the steps: obtaining a fan current waveform and a coil vibration signal, and building a heat exchange efficiency reference based on a motor load change characteristic and a heat exchange tube vibration mode; identifying an efficiency sudden drop area, positioning a heat exchange dead zone expansion range, generating heat transfer coefficient jump data, and determining a heat exchange abnormal position table; obtaining temperature difference of supply water and return water and opening change of a water valve, and forming waterway unbalance characteristics through coupling analysis of water flow pulse characteristics and flow state conversion points; identifying an airflow field offset based on the heat exchange abnormal position table and the waterway imbalance feature, determining a diagnosis focusing area and generating a layered detection sequence; carrying out active and passive damage classification, extracting a damage development rate, and generating a damage superposition coefficient; and a fault coupling amplification area is identified, damage synergistic effect data is generated through chain propagation analysis, comprehensive diagnosis of the abnormal state of the fan coil is completed, and accurate identification of multiple abnormalities and accurate positioning of fault root causes are realized.

Methods and systems for identifying or classifying charge states of detected ions. An example method for classifying a charge state of detected ions may include generating a pulse for each ion in a plurality of ions detected by a detector, wherein each pulse has a pulse characteristic; generating a pulse-characteristic distribution of the generated pulses; and based on the pulse-characteristic distribution, generating an identification of the charge state of one or more ions in the plurality of ions.

The invention relates to the technical field of centrifugal machines, in particular to an automatic discharging control method and system for a top-suspended scraper discharging centrifugal machine, and the method comprises the steps that vibration data and rotating speed data of a rotary drum of the centrifugal machine are obtained; dividing the whole data acquisition time into a plurality of time windows; according to the peak pulse characteristic, the periodic characteristic, the frequency change characteristic and the high-frequency characteristic of the vibration data of each time window, acquiring a comprehensive abnormal coefficient of each time window, and acquiring an abnormal influence coefficient of each time window in combination with the dispersion degree of the rotating speed data of each time window; and according to the change characteristics of the abnormal influence coefficient of the adjacent window of each time window, the unloading abnormal coefficient of each time window is obtained, and then the optimized prediction range parameter of each time window is obtained, so that the scraper movement control of the next time window is optimized. And the unloading efficiency of the centrifugal machine is improved by adaptively adjusting the prediction range parameter of each time window.

The invention discloses a diabetes detection system based on multi-sensor pulse characteristics, and relates to the field of medical signal processing, and the diabetes detection system comprises a pulse wave data acquisition module which uses a pressure sensor and a photoelectric sensor to collect wrist pressure pulse waves and fingertip photoelectric volume pulse waves of a user; the preprocessing module is used for obtaining usable single-cycle pulse wave data; the signal fusion module is used for converting the pressure pulse wave signal and the photoelectric volume pulse wave signal into two-dimensional image signals through continuous wavelet transformation so as to reserve time-frequency domain information, and processing two two-dimensional images by using non-subsampled shear wave transformation and a pulse coupling neural network; the feature extraction and calculation module is used for extracting pulse wave image features through a ResNet network and finally performing classification through a random forest; according to the detection system, the fusion effect is improved under the condition that the calculated amount is not increased, the effect of noninvasive detection of diabetes by using multiple sensors is achieved, and the detection accuracy is improved.

The invention relates to the technical field of bionic control, and discloses a robot control method based on a pulse neural network, and the method comprises the steps: carrying out the spatial-temporal feature coding of the environment perception information and body state information of a robot, and obtaining a pulse feature sequence; performing pulse dependence fusion on the pulse characteristic sequence and the feedback pulse signal to obtain a bionic information processing process, and performing membrane potential integration on the bionic information processing process to obtain an integrated output quantity; performing pulse distribution processing on the integrated output quantity to obtain a driving pulse signal; performing path fitting on the driving pulse signal to obtain a continuous motion track; based on the continuous motion track and the bionic information processing process, performing joint space mapping on the continuous motion track to obtain an execution control instruction; the application executes the control instruction, synchronously collects a pulse response signal and feeds back the pulse response signal in real time so as to obtain an optimization control instruction of the robot; the pulse neural network-based robot control efficiency can be improved.

The invention relates to the field of discharge part detection, and particularly discloses a high-voltage transmission line discharge part intelligent detection method, which comprises the following steps of: arranging UHF (Ultra High Frequency) sensors on a plurality of side-by-side high-voltage transmission lines at equal intervals in a staggered manner, and monitoring signals in real time; screening effective signals based on the signal amplitude, and judging whether the signals are partial discharge signals or not through pulse feature matching; after a discharge range is preliminarily determined based on partial discharge signal intensity, a plurality of triangular sensor arrays are constructed, a time difference positioning method is adopted to calculate and determine suspected discharge points corresponding to the sensor arrays, and discharge parts and intensity thereof are determined through spatial clustering analysis; and finally, collecting a visible light image of a discharge part, extracting physical defect information, analyzing the defect degree, evaluating the discharge severity by combining the discharge intensity and the defect degree, and generating a detection report. The method can effectively eliminate interference, improves the positioning precision and reliability, and achieves the efficient detection and evaluation of the discharge part of the high-voltage transmission line.

The utility model belongs to the field of distributed optical fiber sensing, and particularly relates to a compact high-signal-to-noise-ratio dark pulse BOTDR (Brillouin Optical Time Domain Reflectometer) system for engineering, which comprises a laser, and the output end of the laser is connected with a first photoelectric coupler which is used for dividing light emitted by the laser into upper light and lower light. The dark pulse modulation system module is arranged, descending pump light is equivalent to a dark pulse through modulation, a computer controls dark pulse time slots so as to change pump pulse characteristics, a corresponding demodulation method is adopted in rear-end data processing, Brillouin frequency shift along a sensing optical fiber is obtained, and the Brillouin frequency shift is converted into Brillouin frequency shift along the sensing optical fiber. The lower limit of the spatial resolution of the method is equal to the fall time of the modulation system, and the spatial resolution can be greatly improved under the condition of improving the signal-to-noise ratio. In addition, due to narrow pulse width and low energy of the traditional bright pulse, an erbium-doped fiber amplifier (EDFA) is generally needed for signal amplification. Dark pulses are close to continuous light in most cases, and EDFA is not needed, so that the system design is simplified, and the cost is reduced.

The invention discloses an ultra-low background measurement device and method for discriminating beta and gamma, and relates to the field of pulse discrimination and ultra-low background measurement. The liquid scintillator outputs fluorescence through radiation of the radioactive source; the high-speed photomultiplier performs photoelectric conversion on the fluorescence and outputs a high-speed current pulse signal; the data processing unit converts the high-speed current pulse signal into a high-speed voltage pulse signal, carries out direct bias and gain adjustment, and carries out analog-to-digital conversion to obtain a digital signal; the main control unit preprocesses the digital signal, and performs feature processing and pulse position adjustment on the preprocessed digital signal to obtain pulse feature data; a built-in convolutional neural network in the neural network processing unit determines a discrimination result about beta and gamma according to the pulse characteristic data and the digital signal; and the main control unit carries out gamma background elimination according to a discrimination result so as to realize ultra-low background measurement. According to the invention, pulse discrimination is carried out rapidly and accurately, so that the background is eliminated, and ultra-low background measurement is realized.

This application discloses a diabetes detection system based on multi-sensor pulse characteristics, relating to the field of medical signal processing. The system includes: a pulse wave data acquisition module that uses a pressure sensor and a photoelectric sensor to acquire the user's wrist pressure pulse wave and fingertip photoplethysmography (PPG) pulse wave; a preprocessing module to obtain usable single-cycle pulse wave data; a signal fusion module that converts the pressure pulse wave signal and PPG pulse wave signal into two-dimensional image signals through continuous wavelet transform to retain time-frequency domain information, and processes the two two-dimensional images using non-subsampled shear wave transform and a pulse-coupled neural network; and a feature extraction and calculation module that extracts pulse wave image features through a ResNet network and finally classifies them using a random forest. This detection system improves the fusion effect without increasing computational load, achieving non-invasive diabetes detection using multiple sensors and improving detection accuracy.

A system and method for operating an implanted medical device (IMD) based on a waveform player. In one arrangement, the IMD may comprise a first module operative to effectuate a communication interface with an external device for receiving a plurality of program records for storage in a persistent memory, the program records each comprising a plurality of pulse definitions and a plurality of time interval definitions, wherein a pulse definition comprises a set of pulse characteristics to be applied in a particular time interval. A second module may be communicatively coupled to the first module, the second module including a buffer for containing a runtime image of a selected program record loaded from the persistent memory. A waveform player provided as part of the second module is operative to interpret the runtime image to generate control signals to drive an output driver circuit for applying pulse characteristics to a select set of electrodes according to the pulse definitions of the selected program record.

The invention relates to the technical field of signal processing, in particular to a time-frequency analysis method for a non-stationary signal under coexistence of harmonic waves and pulses, which comprises the following steps: acquiring a linear frequency modulation signal in which a harmonic component and a pulse component exist at the same time, and solving a threshold value compared with a chirp rate; calculating the chirp rate of each time frequency point of the linear frequency modulation signal based on a chirp rate estimator; when the chirp rate of a certain time-frequency point is less than or equal to a threshold value, considering that the time-frequency point tends to the harmonic characteristic, and redistributing the time-frequency point in the frequency direction; when the chirp rate of a certain time-frequency point is greater than a threshold value, considering that the time-frequency point tends to pulse characteristics, and redistributing the time-frequency point in the time direction; and adding the two redistribution results to obtain the time-frequency representation of the linear frequency modulation signal. According to the method, the time-frequency representation with concentrated energy can be generated for the harmonic and pulse components in the signal at the same time.

The application relates to the technical field of insulation performance detection, in particular to a method and system for detecting the insulation performance of an insulation layer of a power device, which comprises the following steps: acquiring a partial discharge signal, current data and temperature data of the insulation layer of the power device; acquiring a significant value of pulse characteristics of the partial discharge signal and a significant value of periodic similar characteristics of the partial discharge signal, then obtaining abnormal values of the window discharge signals affected by insulation deterioration, and acquiring abnormal values of the current data affected by insulation deterioration through the change randomness of harmonic components of the current data of the power device and the difference degree between the fundamental wave component of the current and the current data; calculating a consistency coefficient of the abnormal characteristic change of insulation deterioration, then obtaining an insulation deterioration degree coefficient to detect the insulation performance of the insulation layer of the power device. The application can improve the insulation performance detection precision of the insulation layer of the power device.

Apparatuses and methods are disclosed for applying laser energy having desired pulse characteristics, including a sufficiently short duration and / or a sufficiently high energy for the photomechanical treatment of skin pigmentations and pigmented lesions, both naturally-occurring (e.g., birthmarks), as well as artificial (e.g., tattoos). The laser energy may be generated with an apparatus having a resonator with the capability of switching between a modelocked pulse operating mode and an amplification operating mode. The operating modes are carried out through the application of a time-dependent bias voltage, having waveforms as described herein, to an electro-optical device positioned along the optical axis of the resonator.

The present invention relates to a small high-power microwave pulse alarm processing circuit. When a high-power microwave breaks down, a neon lamp in a signal acquisition circuit triggers a signal amplification circuit to sense and amplify a detection pulse signal, which is then sent to a controller for detection and judgment. The controller determines, based on the pulse characteristics of the detection pulse signal, that the microwave intensity reaches the intensity of a high-power microwave pulse and generates an alarm data frame. The data is modulated by a radio frequency signal sending circuit and then transmitted down through an antenna, thereby realizing the alarm and downlink of the high-power microwave pulse. The designed circuit has the characteristics of a compact, small, and simple circuit structure, greatly reducing the system volume of the high-power microwave alarm and maintaining low power consumption, thereby effectively improving system performance.

The application relates to the technical field of centrifuges, in particular to an automatic unloading control method and system of an upper-suspension type scraper unloading centrifuge. The method comprises the following steps: acquiring vibration data and rotating speed data of a centrifuge drum; dividing the whole data acquisition time into multiple time windows; acquiring a comprehensive abnormality coefficient of each time window according to the spike pulse characteristics, periodic characteristics, frequency change characteristics and high frequency characteristics of the vibration data of each time window, and acquiring an abnormality influence coefficient of each time window in combination with the discrete degree of the rotating speed data of each time window; acquiring an unloading abnormality coefficient of each time window according to the change characteristics of the abnormality influence coefficients of the adjacent windows of each time window, and then acquiring the optimized prediction range parameters of each time window, so as to optimize the scraper movement control of the next time window. The prediction range parameters of each time window are adaptively adjusted, and the unloading efficiency of the centrifuge is improved.

The application provides a high-purity germanium detector and a detection method thereof. The method comprises the following steps: collecting an electric pulse signal; extracting electric pulse characteristic data from the electric pulse signal based on a preset carrier signal sample; determining a plurality of detection intervals of the high-purity germanium detector by the energy resolution of the high-purity germanium detector and the electric pulse characteristic data; determining a main pulse peak in a first energy gathering area and a secondary pulse peak in a second energy gathering area of the electric pulse signal according to all the detection intervals and the pulse count rate of the electric pulse signal in each detection interval; determining a response bias of the high-purity germanium detector by the main pulse peak and the secondary pulse peak; performing energy spectrum reading on a confidence electric pulse signal determined by the response bias to obtain nuclear radiation energy measured by the high-purity germanium detector. The application obtains the nuclear radiation energy based on the confidence electric pulse signal, can extract the signal characteristics of the electric pulse signal under multiple noise interferences, and thus improves the detection authenticity of the high-purity germanium detector.

The invention discloses a liquid rocket turbine pump all-condition multi-fault simulation method and a related device. Comprising the following steps: constructing a turbine pump full-working-condition multi-fault simulation signal s (t) based on a periodic pulse characteristic signal, a rotor variable-working-condition rotating speed signal containing fundamental frequency and blade frequency doubling characteristics, a fault mode characteristic component signal and a random noise signal in a strong interference state; and the turbine pump full-working-condition multi-fault simulation signal s (t) is utilized to perform turbine pump full-working-condition multi-fault simulation, and the method and the related device can simulate turbine pump full-working-condition multi-fault so as to improve the fault diagnosis capability and reliability of the turbine pump.

The invention discloses a door lock control method and device and an intelligent door lock, the method is applied to the intelligent door lock, and the method comprises the following steps: obtaining a to-be-recognized image, and analyzing the to-be-recognized image to obtain facial feature data of a global face; and processing the facial feature data to obtain a blood perfusion index. Acquiring to-be-processed pulse data of the target user, and processing the to-be-processed pulse data to obtain pulse characteristic parameters. And inputting the blood perfusion index and the pulse characteristic parameter into a preset characteristic fusion model to obtain a control instruction of the intelligent door lock. According to the control instruction, the intelligent door lock is controlled to give out an early warning prompt. According to the door lock control method, signal acquisition and analysis from multiple dimensions are achieved, so that the intelligent door lock is controlled more comprehensively and accurately, finally, early warning prompt is triggered based on the control instruction, accurate and normalized monitoring of the home state of a user is achieved, and effective guarantee is provided for home safety of the user.

The invention discloses a blade breakage detection method and device, related equipment and a computer program product, and relates to the technical field of semiconductor manufacturing processes. The method comprises the following steps: acquiring a target detection signal of a to-be-detected sawtooth blade, the target detection signal comprising an electric signal acquired by converting luminous flux shielded by the to-be-detected sawtooth blade in the process of rotating the to-be-detected sawtooth blade for at least one circle; determining a pulse characteristic parameter of a detection signal segment corresponding to each circle of rotation of the to-be-detected sawtooth blade in the target detection signal; in the target detection signal, under the condition that the pulse characteristic parameters of the detection signal segments not smaller than the preset number meet the first preset damage condition, it is determined that the to-be-detected sawtooth blade is in a damaged state, and the preset number is in direct proportion to the number of rotation cycles of the to-be-detected sawtooth blade. The sawtooth blade damage detection method and device are suitable for sawtooth blade damage detection, and the sawtooth blade damage condition can be accurately recognized.

The invention provides an ambulatory blood pressure measuring device air pump control method and system based on pulse waveform dynamic adjustment, and relates to the technical field of ambulatory blood pressure measuring device air pump control. An optimal detection point is determined through pulse collection information of multiple detection points of an ambulatory blood pressure measuring device, and air pump and physiological data collection, adjustment and analysis are conducted; adjusting and analyzing the body position change according to the compensation fitting adjusting data, obtaining the dynamic control strategy information of the air pump to carry out control constraint analysis and adjustment, and obtaining the optimal control strategy information of the air pump; according to the air pump optimization strategy information, pulse feature anomaly analysis and adjustment are carried out, and air pump physiological adjustment data are obtained; performing pressure calibration adjustment according to the body position air pump adjustment data in combination with the pulse characteristic abnormal data so as to obtain inflation physiological fitting data; according to the method, multi-dimensional data are fused, and the physiological change and the accurate blood pressure measurement capability are dynamically adapted.

The invention discloses a DAS-based building attachment state monitoring and falling object early warning method and system, and relates to the technical field of building safety monitoring, and the method comprises the steps: collecting a global acoustic vibration signal of a building attachment through employing a laid distributed acoustic sensing optical fiber; the global acoustic vibration signals are separated into different modal components, after the modal components corresponding to the environmental noise are removed, the modal components corresponding to the traffic noise and the personnel activity noise are superposed to serve as reference signals, and after the reference signals are filtered through adaptive weights, defect characteristic signals of the building attachments are determined; and judging whether the defect characteristic signal is a suspected defect event based on a modal frequency change rate and a short-time pulse characteristic, if so, extracting a multi-dimensional characteristic parameter, determining a defect level according to the trained evaluation model, and positioning a defect position according to an optical fiber laying scheme. Therefore, attachment global non-blind area monitoring is realized, and the comprehensiveness, accuracy and timeliness of building safety monitoring are improved.

The application discloses a kind of wind turbine speed signal analysis method and system based on flange bolt, the method includes the following steps: based on flange bolt collection pulse basic information;Judge whether the first pulse pulse width exists abnormal condition;Accumulate original pulse information, identify abnormal condition in original pulse information, and correct pulse width variation anomaly;The proportion of abnormal pulse is counted and calculated;Identify the pulse characteristics in accumulated original pulse information, obtain N normal pulse signals according to pulse characteristics, use the N normal pulse signals instead of single pulse period to calculate initial impeller speed;Extract the pulse that meets preset period, add in impeller speed calculation;The probability of occurrence of various abnormal characteristics in speed is counted;The application corrects various abnormal signals, filters out normal bolt pulse signal, to improve the accuracy of speed calculation, reduce the abnormal fluctuation of speed caused by interference factor, provide more accurate data analysis for wind turbine.

The invention particularly relates to a high-power microwave short pulse in-pulse phase change measuring method and device, and the method comprises the steps: carrying out the coupling processing of a microwave excitation pulse signal, and obtaining a first branch signal and a second branch signal; performing delay processing on the first branch signal to obtain a reference signal for intra-pulse phase comparison; performing phase drift on the second branch signal to obtain a measurement signal for intra-pulse phase comparison; and comparing the phase of the reference signal with the phase of the measurement signal, and visualizing the comparison result. Through distribution, amplification, delay and comparison processing of excitation signals, real-time phase measurement of high-power short-pulse signals can be realized under the condition that the structure of a signal main path is not changed. The device has the advantages of being simple in structure and high in measurement precision, can effectively avoid errors caused by instrument bandwidth limitation and insufficient sampling rate in a traditional method, and is suitable for phase stability evaluation and intra-pulse characteristic analysis in a high-power microwave system.