47 results about "Correlation spectrum" patented technology

The invention discloses a method and a device for determining water blocking damage degrees of different pore sizes of a rock core. Comprising the following steps: acquiring physical property data of a target rock core and simulated formation water; vacuumizing, pressurizing and saturating the target rock core to obtain a saturated water state rock core, and obtaining a first transverse relaxation spectrum and a first longitudinal and transverse relaxation correlation spectrum through nuclear magnetic resonance; performing displacement treatment on the rock core to obtain a water-bound state rock core, and obtaining a second transverse relaxation spectrum and a second longitudinal and transverse relaxation correlation spectrum; determining a pore size range, and establishing a water blocking damage identification chart to define a characteristic interval between bound water and saturated water; carrying out difference operation to obtain a difference spectrum matrix, extracting pore space areas of bound water and saturated water in the matrix, and calculating an area ratio; and determining the water blocking damage degrees of different pore sizes by combining the porosity, the permeability and the ratio. According to the method, the pore scale can be accurately divided, the damage of each scale can be quantified, the development efficiency can be optimized, and a reliable basis is provided for formulating a reservoir unblocking scheme.

The invention discloses a multi-physical-field joint target detection method based on underwater low-frequency seismic wave field and sound field signals. The method comprises the following steps: acquiring underwater low-frequency seismic signals x1 (t) and sound field signals x2 (t) through a sensor; performing cross-correlation spectrum calculation on the seismic wave signal and the sound field signal in the fixed time window t0 to obtain a frequency spectrum Xm; removing a direct current component in the frequency spectrum Xm to obtain a direct current component-removed FFT sequence XFFT; the XFFT data of the direct-current component FFT sequence in the third step are preprocessed; the detection threshold value Xt of # imgabs0 # is calculated; comparing # imgabs1 # with Xt, and judging a suspected target line spectrum frequency point according to a difference value T of # imgabs1 # and Xt: performing time integration on the difference value T, and judging that a suspected target appears if the maximum frequency Q of the suspected line spectrum frequency point in a time window is greater than a set value R; if the suspected target appears within a plurality of continuous seconds, judging that a target signal appears; suspected targets can be eliminated and the targets can be quickly locked under complex environment interference, and a basis is provided for underwater weapon development and underwater multi-physics field target recognition.

The invention discloses a method and a device for quickly realizing long-time coherent accumulation of a GPS (Global Positioning System) external radiation source signal. The method comprises the following steps: acquiring a cross-correlation spectrum, and acquiring distance and Doppler frequency migration; performing parabola sampling on the cross-correlation spectrum to obtain a sampling sequence; constructing an objective function, and performing parabola sampling, frequency compensation and Fourier transform on the cross-correlation spectrum to obtain a peak value and a corresponding speed; using an improved particle swarm optimization algorithm to maximize an objective function value to obtain an estimated equivalent velocity and an estimated equivalent acceleration, and correcting the distance and Doppler frequency migration to obtain a corrected cross-correlation spectrum; fourier transform is carried out on the corrected cross-correlation spectrum along slow time, the corrected cross-correlation spectrum is converted into a distance and Doppler frequency spectrum, peak detection is carried out, and the peak signal-to-noise ratio, the distance difference and the speed are obtained. According to the method, the calculation amount and the operation time can be greatly reduced, the accuracy of the improved particle swarm optimization algorithm for estimating the migration correction parameters is improved, and multiple targets can be detected.

The invention provides an optical frequency domain reflectometer grating array multiplexing demodulation method based on peak position estimation, and the method comprises the steps: in quasi-distributed sensing based on optical frequency domain reflectometer grating array multiplexing, in order to obtain an acceptable grating spectral measurement resolution ratio, carrying out peak position estimation; in the prior art, dense interpolation needs to be carried out on a spectrum of which the spectral resolution is lower than a threshold value required by demodulation, but the data volume is greatly increased, so that the calculation rate is slowed down, and the demodulation efficiency is low. According to the method, peak position estimation is carried out on the cross-correlation spectrum of the grating spectrum of which the spectral resolution is lower than the threshold required by demodulation, so that dense interpolation is avoided, and the problem of low demodulation efficiency caused by dense interpolation is improved; meanwhile, in order to overcome the problems of spectrum distortion caused by grating inscribing defects and spectrum splitting caused by gaps between gratings in the traditional demodulation process, the robustness of system demodulation is ensured to the maximum extent on the basis of improving the system demodulation efficiency.

The invention discloses a rapid marinating soup quality parameter acquisition method and a marinating time optimization method in a sauced product cooling process, and belongs to the technical field of sauced product production processes, and the method comprises the following steps: S1, acquiring near infrared spectrum data of a meat sample by adopting a near infrared spectrum technology; performing two-dimensional correlation analysis on the near infrared spectrum data, converting the one-dimensional spectrum data into two-dimensional correlation spectrum data, and improving the resolution of the one-dimensional spectrum data and the separation degree of overlapping peaks to obtain a two-dimensional correlation analysis result; s2, processing the two-dimensional correlation spectrum data by using a moving window technology, observing a time period in which a functional group changes along with external interference, and converting static detection of a substance into dynamic analysis to obtain a moving window technology processing result; and S3, acquiring nuclear magnetic resonance data of the meat sample by adopting a nuclear magnetic resonance technology, and reflecting the motion state of hydrogen protons. According to the invention, the meat moisture migration and fat oxidation degree can be accurately identified.

The invention relates to the technical field of epoxy floor quality detection, in particular to an epoxy floor coating thickness laser scanning and big data analysis device which comprises the steps that laser resonance spectrum scanning data and stress wave scanning data of the surface of a to-be-detected epoxy floor are acquired; based on the laser resonance spectrum scanning data, calculating a first vibration mode frequency set of the coating surface, and based on the stress wave scanning data, calculating a second vibration mode frequency set of the coating and the matrix interface; based on the first vibration mode frequency set and the second vibration mode frequency set, current thickness distribution of the coating is determined by inquiring a preset thickness and frequency correlation spectrum database; and based on the current thickness distribution and historical construction data in the thickness and frequency correlation spectrum database, performing matching degree analysis, and determining the uniformity grade and the potential defect area of the coating. And matching is carried out through data stored in the thickness and frequency correlation spectrum database, and potential defects are accurately identified.

This invention relates to a method and system for locating coaxial counter-rotating sound sources in rotorcraft, belonging to the technical field of sound source identification methods. It includes: processing the sound pressure signal of the moving sound source using continuous wavelet transform to obtain time-frequency domain variation results; performing Doppler effect correction based on experimental conditions; estimating the spatial location and relative intensity of the noise source using a cross-spectral matrix and weight vector; and performing phase-locked analysis of the rotor noise source intensity using a phase-locked averaging method. This invention differs from traditional time-domain delay superposition algorithms and frequency-domain cross-correlation spectrum methods. It can image unsteady sound sources using time-frequency domain characteristics, solving the acoustic imaging problem of counter-rotating propeller noise and possessing potential applications in the rotorcraft industry.

This invention relates to the field of signal processing technology and provides a ground clutter frequency domain suppression method based on correlation spectrum. Its key feature is the use of phase information from the autocorrelation spectral density of weather radar echo signals to identify and suppress ground clutter signals. The method includes: adaptively selecting an optimal data window function based on the clutter-to-noise ratio of the weather radar echo signal and performing power-preserving processing; calculating the autocorrelation spectral density and power spectral density, and effectively distinguishing clutter from broadband weather signals using the local deviation characteristics of the autocorrelation spectral density relative to the ideal linear phase, thereby performing clutter identification and filtering. Based on the autocorrelation spectral density after filtering out ground clutter, an inverse transform is performed to calculate the autocorrelation function, completing the time-domain estimation of the weather radar detection variables. This invention effectively solves the problem that traditional ground clutter suppression methods cannot accurately identify and eliminate ground clutter mixed in with weather signals.

This application relates to the field of data processing technology and discloses an integrated sky-ground geological environment monitoring and assessment method and system. The method includes: integrating sky remote sensing, surface monitoring, and underground exploration data into a monitoring matrix, processing the data to generate a raw database; constructing a spatiotemporal standard field based on the database and fusing it to generate a correlation spectrum; decomposing the correlation spectrum to obtain element feature chains; constructing a coupled dynamic field based on the feature chains and analytically obtaining an evolutionary parameter set; establishing a risk assessment matrix based on the parameter set and analyzing to generate an early warning indicator system; and designing a governance plan by combining the early warning system and historical cases. This application achieves deep integration of space-based remote sensing data, surface monitoring data, and underground exploration data by constructing a unified spatiotemporal standard field, a multi-level feature transfer network, and a dynamic coupling model, thereby improving the accuracy and reliability of geological environment monitoring and assessment.

The present application relates to the technical field of grain detection, and particularly relates to a grain detection device and method, which comprises obtaining original detection data and performing correlation processing to obtain correlation image data and correlation spectrum data; performing particle segmentation and invalid particle screening on the correlation image data to determine an effective particle set; extracting image features and spectrum features of each effective particle based on the correlation image data and the correlation spectrum data to form initial features; performing initial risk candidate screening to obtain a candidate risk particle set and initial risk information, and constructing a risk distribution result and a mold detection result. The scheme refines the whole batch average signal to the particle level risk identification, retains the local abnormal information without relying on large-scale crushing and mixing, is beneficial to improving the whole batch screening accuracy, reducing the risk of abnormality being covered under the condition of local aggregation and uneven batch distribution, and taking into account the detection efficiency and result stability.

The invention relates to the field of spectrum confocal thickness measurement, and discloses a coaxiality error compensation method for a bidirectional correlation spectrum confocal thickness measurement system, which comprises the following steps of: establishing a unified measurement reference coordinate system, and calibrating a reference surface of a standard gauge block to be aligned with a reference axis of the measurement reference coordinate system; obtaining optical axis inclination angles of the first spectrum confocal sensor and the second spectrum confocal sensor and a transverse offset between the two optical axes; measuring the placement gradient of the measured workpiece relative to the reference axis; and calculating through a preset geometric compensation model to obtain the real thickness of the measured workpiece. The technical effect of comprehensively compensating various error sources is achieved by systematically calibrating inherent errors such as the optical axis inclination angle and the transverse offset and combining real-time measurement of the placement inclination of the measured workpiece, and the problem that in the prior art, due to the fact that an error model is incomplete, the error is not accurate is solved. And the measurement precision is limited because the inherent error and the dynamic placement error of the system are not considered at the same time.

This invention discloses a single-frame coded phase retrieval system and parameter calibration method based on iterative filtering inversion, belonging to the field of lensless computational microscopy imaging technology. It solves the problems of mismatch between single-frame coded phase retrieval algorithms and complex coding scenarios, difficulty in balancing noise suppression and resolution, large system diffraction distance estimation errors, and inaccurate wavefront lateral offset calibration, leading to low imaging accuracy and poor stability. This invention constructs a system including a light source, replaceable amplitude / phase coding, CMOS imaging, one-dimensional electric displacement drive, and a built-in IrCPR algorithm control module, along with a process of "multi-distance calibration of coding functions → Z1 / Z2 calibration using ToG / SG according to coding board type → cross-correlation spectrum registration." This invention improves the accuracy of coding function calibration and diffraction distance estimation, achieves pixel-level alignment of the coded wavefront, and completes complex amplitude reconstruction with a single frame acquisition. The system has strong adaptability and stability, making it suitable for high-precision microscopy imaging scenarios.

The invention belongs to the technical field of mine fire early warning, and discloses a fully-distributed optical fiber CO2 sensor for fire early warning and a control method thereof.According to the sensor, sensing optical fibers are arranged at an underground fire early warning monitoring site; acquiring a signal by using an optical frequency domain reflectometer system and carrying out fast Fourier transform to obtain distance domain information; intercepting effective sensing data in a distance domain to enter loop operation; performing fast Fourier inversion on the data in the cyclic window and performing cross-correlation operation to obtain a cross-correlation spectrum offset result; a CO2 cross-correlation spectrum offset coefficient alpha co2 is obtained through a correction experiment, and distributed measurement of CO2 is achieved. According to the system, the underground large-range fully-distributed CO2 concentration is monitored on line at any time, accurate early warning of underground fire accidents can be achieved, and the problems that a current fire early warning system cannot achieve distributed measurement and is high in networking cost can be solved.

The invention discloses a micro-motion exploration frequency dispersion curve extraction method, device and medium, and relates to the technical field of micro-motion exploration, and the method comprises the following steps: S1, calculating the average cross-correlation spectrum of each station pair according to the recorded data of a plurality of stations in an observation array; s2, aiming at the average cross-correlation spectrum of each station pair, identifying a first intersection point frequency of the average cross-correlation spectrum and a frequency axis; s3, based on the first intersection point frequency, performing piecewise fitting on the average cross-correlation spectrum to extract a phase velocity: for a frequency band not greater than the first intersection point frequency, performing fitting by adopting a zero-order Bessel function J0; and for a frequency band of which the frequency is greater than the frequency of the first intersection point, performing fitting by adopting a first-class zero-order Hankel function H0 (1). In a single method, the contradiction of artifact interference in a high frequency band and insufficient detection depth in a low frequency band in the prior art is overcome at the same time, the accuracy and reliability of frequency dispersion curve extraction under the sparse array condition are improved, and the detection depth of micro-motion exploration is effectively expanded.

The application provides a continuous beam construction crack monitoring and early warning method and system, distributed sensing optical fibers are arranged at key stress sections in the continuous beam body construction stage to form a sensing network covering the beam body, and then the optical frequency domain reflection signals of the sensing network are collected; the wavelength domain difference characteristics between the beam body no-load reference signal and the optical frequency domain reflection signals are fitted to obtain the strain distribution profile along the optical fiber path in the beam body, and the suspicious sections with strain mutation of the beam body are identified according to the gradient change of the strain distribution profile; the cross-correlation spectrum of the sub-signal segment of the optical frequency domain reflection signal in each suspicious section is extracted, and then the width quantitative value of the crack in each suspicious section is inversely calculated; and the width quantitative value is converted into the early warning level of the beam body. By adopting the scheme of the application, the local crack defect can be quickly responded in the long-distance continuous beam construction scene.

The invention relates to the technical field of grain detection, in particular to a grain detection device and method, and the method comprises the steps: obtaining original detection data, and carrying out correlation processing to obtain correlation image data and correlation spectrum data; performing particle segmentation and invalid particle screening on the associated image data, and determining an effective particle set; extracting image features and spectral features of each effective particle based on the associated image data and the associated spectral data to form initial features; and initial risk candidate screening is carried out to obtain a candidate risk particle set and initial risk information, and a risk distribution result and a mildew detection result are constructed. According to the scheme, the whole batch of average signals are refined to grain level risk identification, local abnormal information is reserved under the condition of not depending on large-scale broken mixed samples, the screening accuracy of the whole batch is improved, the risk that the abnormity is covered under the conditions of local aggregation and uneven distribution in the batch is reduced, and the detection efficiency and the result stability are both considered.

The invention relates to the technical field of electrochemical in-situ spectrum monitoring, in particular to a method for monitoring dynamic change of an electrocatalytic reaction intermediate product in real time by using SERS (Surface Enhanced Raman Scattering) spectrum.According to the method, an ultrathin catalytic layer is modified on a high-SERS activity nano substrate in situ, an integrated working electrode is constructed, and a monitoring interface, namely a real reaction interface, is ensured. Through a hardware synchronization system, when dynamic electrochemical excitation is applied, synchronous acquisition of SERS spectrums and electric signals of multiple time scales is implemented. The method comprises the following steps: analyzing fingerprint information, concentration dynamics and evolution sequence of different intermediate products from a spectrum sequence by using algorithms such as dynamic differential spectrum, multivariate curve resolution and two-dimensional correlation spectrum, and finally accurately associating with electrochemical response to construct a dynamic reaction mechanism diagram. According to the invention, real-time and high-sensitivity dynamic analysis on generation, consumption and path evolution of transient intermediate products at a real catalytic interface is realized, and a powerful tool at a molecular level is provided for research on an electro-catalysis mechanism.

The invention discloses a one-dimensional nuclear magnetic resonance total correlation spectrum generation method based on a singlet filtering technology, and relates to the field of nuclear magnetic resonance spectroscopy detection, and longitudinal magnetization of a target coupling atom pair is converted into a nuclear spin singlet signal by adopting adiabatic pulses designed according to specific atomic nucleus coupling relation information; after tau waiting time, the singlet signal is converted into a traditional zero quantum signal, and the target singlet signal is converted into an observable signal by adopting a hard pulse zero quantum filtering module. Obtaining complete coupling network information of the target signal through J coupling evolution by adopting a total correlation spectrum module of zero quantum filtering, and generating a one-dimensional nuclear magnetic resonance total correlation spectrum; two acting frequency points of adiabatic pulses are arranged on the left side and the right side of the middle frequency point of the target coupling atom pair, two obtained spectrograms are subtracted, and the sensitivity and the selectivity are further improved. The method is suitable for various different sample systems, and has strong robustness and high sensitivity.

The application discloses a phase difference change rate positioning method based on a uniform circular array, first, data collection of a radiation source signal is completed by using the uniform circular array, Fourier transform is performed on two array element received signals by a frequency domain phase discrimination method, a spectrum peak position of a correlation spectrum Y(omega) is calculated, and a phase value at the position is obtained, which is a fuzzy baseline phase difference; then, all fuzzy numbers are traversed, a similarity calculation method is used, circular array defuzzification is realized, a whole week fuzzy influence generated in the phase discrimination process is removed, and a non-fuzzy phase difference of each baseline is obtained, finally, a baseline group with a 0 array element as a center array element is set as a basic group, each array element of the circular array is traversed, positioning results of different baseline groups are obtained respectively, each group of results is weighted based on a phase difference change rate, and finally, a spatial position of a radiation source is obtained; the algorithm is improved for a detection array, the whole detection is completed by using the uniform circular array, and the omnidirectional detection capability of the algorithm is improved.

The invention discloses a part machining quality monitoring method based on multi-sensor fusion, which comprises the following steps: collecting vibration, acoustic emission, cutting force and spindle current signals, and forming a standardized sequence through alignment and normalization; performing time-frequency transformation on the sequence to obtain a time-frequency spectrum; applying a combined Teager-Kaiser energy operator on the time-frequency spectrum, and extracting a first type of features; calculating an energy operator response on the sparse dictionary coefficient, and extracting a second type of features; calculating an energy cross-correlation spectrum for the cross-channel signal, and extracting a third type of features; extremum detection is carried out on the three types of features, an energy peak column is generated, statistics are calculated, and graph embedding features are obtained in combination with collaborative energy indexes and graph Laplacian constraints; and inputting the statistical characteristics, the collaborative energy index and the graph embedding characteristics into a quality evaluation model, and outputting roughness estimation, an abnormal position and a quality label. According to the invention, multi-channel fusion monitoring is realized, and the anomaly detection and quality evaluation precision is improved.

The invention relates to the technical field of drug detection and chemical analysis, in particular to a qualitative and quantitative detection method for alkyl nitrite substances by an NMR (nuclear magnetic resonance) method, and aims to solve the problems that when the alkyl nitrite substances are detected in the prior art, the alkyl nitrite substances are complex in matrix and cannot be accurately qualitatively detected, and the detection accuracy is poor. In order to solve the problems that in the prior art, a sample to be detected is directly detected after being dissolved in a deuterated reagent at room temperature by utilizing the low-temperature detection characteristic of NMR (nuclear magnetic resonance), and by combining a hydrogen nuclear magnetic resonance spectrum, a one-dimensional selective excitation total correlation spectrum and a diffusion sequencing spectrum, the content of the alkyl nitrite substances is determined. Reliable qualification in a complex matrix is realized; meanwhile, absolute quantification is realized by adopting a stable and easily available internal standard substance 1, 4-dimethoxybenzene, and a deconvolution fitting algorithm is introduced to process signal overlapping in combination with a quantitative nuclear magnetic resonance hydrogen spectrum and a quantitative nuclear magnetic resonance carbon spectrum, so that the anti-interference capability and the result credibility of quantitative detection are improved.

The application provides a milk fat adulteration detection method and an electronic device, and relates to the technical field of food detection. The method comprises the following steps: multiple to-be-detected samples are taken from a to-be-detected milk fat product; a three-dimensional data cube of the to-be-detected milk fat product is constructed according to Raman spectrum data of each to-be-detected sample under multiple preset temperature conditions; a three-dimensional synchronous correlation spectrum of the to-be-detected milk fat product is determined according to the three-dimensional data cube; and an adulteration type and an adulteration concentration of the to-be-detected milk fat product are obtained according to the three-dimensional synchronous correlation spectrum of the to-be-detected milk fat product and a pre-trained three-dimensional milk fat adulteration detection network. The three-dimensional milk fat adulteration detection network is pre-trained based on multiple milk fat samples with known adulteration types and known adulteration concentrations. The pre-trained three-dimensional milk fat adulteration detection network can effectively extract deep features related to the adulteration type and the adulteration concentration, thereby improving the adulteration detection accuracy.

The embodiment of the present application provides an OFDR measurement method and system based on correlation spectrum self-compensation, which relates to the field of optical fiber sensing technology, and includes the following steps: collecting a reference signal and a measurement signal; using a fast Fourier transform to convert both the frequency domain data of the reference signal and the frequency domain data of the measurement signal into distance domain data, and respectively calculating the spatial resolution length of the reference signal and the spatial resolution length of the measurement signal; adjusting the spectrum information of the reference signal so that the spectrum of the reference signal and the spectrum of the measurement signal match within a wide strain range; dividing the distance domain data of the reference signal and the distance domain data of the measurement signal into sliding windows based on a preset spatial resolution, and performing an inverse fast Fourier transform on the data in each window to convert it into frequency domain data; calculating the spectrum offset between the reference signal and the measurement signal by a cross-correlation algorithm, and demodulating to obtain the strain distribution along the optical fiber.

The present application belongs to the field of signal processing technology, and specifically discloses a method and device for determining acoustic timing of guided waves based on multi-resolution singular value decomposition, wherein the method comprises: preprocessing a guided wave signal S, extracting a direct wave signal SD from the preprocessed guided wave signal SP, and intercepting a reference signal Y from the direct wave signal SD; performing sliding window processing on the preprocessed guided wave signal SP, and based on the signal X in the i-th window, performing sliding window processing on the preprocessed guided wave signal SP, and ... i and Y, construct the bipartite recursive matrix H 1,i , based on H 1,i To X i Perform j-times multi-resolution singular value decomposition to obtain j-times singular correlation values, and then construct j-times singular correlation value sequences. In the waveguide singular correlation value spectrum corresponding to the j-times singular correlation value sequence, the time difference between the singular correlation spectrum peaks at the direct wave and the end echo is determined as the waveguide acoustic timing, which can realize waveguide acoustic timing detection of complex structures.

The invention relates to a novel positron annihilation lifetime-momentum correlation spectrometer and a measurement method thereof.The novel positron annihilation lifetime-momentum correlation spectrometer comprises a detector module, a data acquisition module and a data processing module, the detector module comprises two scintillator detectors and two high-purity germanium detectors, in the two scintillator detectors, one scintillator detector serves as an initial scintillator detector for detection, and the other scintillator detector serves as a second high-purity germanium detector for detection; the other scintillator detector is used as a termination scintillator detector to measure a positron annihilation lifetime spectrum; the high-purity germanium detector is used for measuring Doppler broadening during positron annihilation; the data acquisition module comprises acquisition equipment with four analog input channels, and is used for acquiring pulse signals output by the detector module; the data processing module processes the collected data and draws a positron annihilation life-momentum correlation spectrum. According to the invention, the counting rate of the positron annihilation lifetime-momentum correlation spectrometer is improved, rapid acquisition and rapid processing of pulse signals are realized, the resolution of time measurement and the precision of a time spectrum are improved, and the application of the technology in the field of material science is expanded.

The invention discloses a bearing fault monitoring method based on demodulation bispectrum, and belongs to the field of bearing fault monitoring, and the method comprises the steps: firstly, carrying out the time-frequency analysis of a collected vibration signal, and calculating the demodulation bispectrum, so as to highlight a fault modulation feature; then, bispectrum slices are extracted in the modulation frequency direction, and the correlation spectrum negentropy of each slice is calculated, so that the periodic fault information amount contained in each frequency component is quantified; and finally, identifying a fault characteristic frequency and a harmonic wave thereof according to a peak value in the correlation spectrum negentropy sequence. According to the method, fault features can be clearly and completely extracted under high-noise interference by demodulating the depiction capability of bispectrum to a modulation relation and the sensitivity of correlation spectrum negentropy to periodic pulses. Simulation and experiments show that compared with a traditional method, the rolling bearing fault diagnosis method has higher diagnosis accuracy and robustness and is suitable for early fault and weak fault diagnosis of the rolling bearing.

The present invention provides an optical frequency domain reflectometer image signal denoising method and measurement method based on deconvolution. The denoising method includes: using FFT to convert reference and measurement signals of OFDR into distance domain reference and measurement signals; dividing the measurement scattering medium into multiple segments according to the spatial resolution of OFDR, and using STFT to convert the distance domain reference and measurement signals of each segment into time domain reference and measurement signals; calculating the cross-correlation spectrum curve of the time domain reference and measurement signals corresponding to each segment to obtain a cross-correlation two-dimensional image; performing autocorrelation on the time domain reference signals of each segment to obtain an autocorrelation two-dimensional image; using the main lobe of the cross-correlation peak corresponding to each segment in the cross-correlation two-dimensional image as the wavelength cut-off range of the segment, and cutting out an image within the wavelength cut-off range of each segment of the autocorrelation two-dimensional image as the point spread function (PSF); and using deconvolution to denoise the cross-correlation two-dimensional image based on the PSF. The present invention solves the problem that OFDR cannot perform image signal denoising based on deconvolution.

The invention relates to the technical field of vibration monitoring and diagnosis, and discloses a micro-vibration automatic evaluation and alarm system based on a built-in VC criterion, which comprises a data acquisition module, a storage module, a processing module and an alarm module. The system synchronously obtains a micro-vibration signal and a real-time working condition of a key vibration source through a data acquisition module; the processing module is internally provided with a vibration source characteristic spectrum decoupling module and an environment adaptive baseline evaluation module, and the vibration source characteristic spectrum decoupling module calls characteristic spectrum fingerprints from a preset working condition-vibration source correlation spectrum model library according to real-time working conditions and decouples real-time spectrums so as to realize attribution diagnosis of vibration sources. According to the method, the problems that in the prior art, only conformity judgment can be carried out, and diagnosis and prediction capabilities are lacked are solved, accurate source attribution can be carried out on complex mixed vibration, early warning of early abnormality and environmental deterioration trend is realized, and a set of integrated evaluation scheme from trend early warning to causal diagnosis is provided.

The invention discloses a time-frequency domain cross-correlation reconstruction method and system for a rock breaking source of a drill jumbo, and relates to the technical field of seismic signal processing and intelligent detection, and the method comprises the steps: collecting the seismic signals of the rock breaking source in the drilling process of the drill jumbo, including a pilot signal and a side wall signal; respectively carrying out generalized S transformation on the pilot signal and the side wall signal to obtain respective time-frequency domain representation; performing sliding cross-correlation on the two time-frequency domain representations under set time delay to obtain a cross-correlation spectrum; and the cross-correlation spectrum is recovered to a time domain through inverse transformation of generalized S transformation, and an enhanced seismic signal is obtained. Based on generalized S transformation and time-frequency cross-correlation, underground reflection information is effectively extracted, and reconstruction of seismic source wave field features while drilling is realized.