245 results about "Cross correlation coefficient" patented technology

The invention belongs to the technical field of production monitoring systems, and discloses a numerical control machine tool dynamic fault diagnosis method and system. The method comprises the following steps: generating a global time reference signal through a main shaft encoder and a clock synchronization protocol; the method comprises the following steps: collecting vibration data of a main shaft bearing in each unit time, current data of an electric cabinet and process parameters, and generating a preprocessed data sequence through transmission delay compensation and multi-rate frequency raising processing; inputting the vibration data and the current data into a preset mechanical-electrical transfer function model, and calculating a time delay parameter; performing phase alignment on the preprocessed data sequence based on the time delay parameter to generate an aligned data sequence; inputting the aligned data sequence into a time sequence neural network, and outputting a fusion feature vector; calculating a cross correlation coefficient of the fusion feature vector, and generating a fault diagnosis result based on a preset cross correlation threshold value; the problem of failure of fault feature extraction caused by data asynchronization in the prior art is solved.

The invention relates to the field of ventilation equipment early warning, in particular to a ventilation equipment early warning method and system for a smart laboratory, and the method comprises the steps: building a window with a target moment as a center, and obtaining a multi-dimensional state parameter sequence of ventilation equipment in the window; calculating the maximum cross correlation coefficient of the target moment, obtaining the optimal lag, weighting the optimal lag to obtain a final lag coefficient, and obtaining the cross correlation difference significance according to the maximum cross correlation coefficient of each sampling moment in the window; and aligning the state parameter sequences of different dimensions in the window based on the final lag coefficient, obtaining the correlation of the state parameter sequences of any two dimensions, calculating a risk value of the target moment, and correcting the risk value to obtain a risk correction value. According to the technical scheme, the early warning precision of the ventilation equipment can be improved, and the influence of hysteresis on early warning is reduced.

The invention provides an exoskeleton man-machine motion intention cooperative control method and device and a storage medium, and relates to the technical field of robot motion control signal processing. According to the method, signal phase differences are acquired through energy buttons at human joints, and an original phase difference sequence is formed and preprocessed; calculating a cross correlation coefficient of the target and the associated joint based on the preprocessed sequence, and constructing a motion feature set in combination with the joint speed and acceleration; constructing a prediction model by using a long-short-term memory network, and outputting motion intention and joint position prediction information through multi-feature fusion training; and analyzing the prediction information by adopting a second-order linear impedance model, generating a driving torque instruction and sending the driving torque instruction to the servo motor for execution. According to the method, signal phase difference analysis, LSTM prediction and impedance control are fused, the problems of response lag and poor adaptability of a traditional exoskeleton are solved, the control real-time performance, accuracy and cooperative fluency are improved, and the use requirements of different users in different scenes are met.

The invention relates to the technical field of safety monitoring, and provides an oil and gas pipeline leakage monitoring method and system, a medium and equipment, and the method comprises the steps: obtaining a leakage vibration signal of each measurement point of an oil and gas pipeline, and extracting a time domain feature, a frequency domain feature, a spatial-temporal feature and an initial distance from a leakage point to a starting end of an optical fiber; performing cross-correlation analysis on the leakage vibration signals of the adjacent measuring points to obtain a propagation time difference and a cross-correlation coefficient, and calculating a correction distance from the leakage point to the starting end of the optical fiber; if the leakage point is in the spiral section, calibrating the correction distance to obtain a calibration distance; calculating the difference value between the initial distance from the leakage point to the optical fiber starting end and the corrected distance to obtain a preliminary positioning error value; and based on the time domain feature, the frequency domain feature and the spatial-temporal feature, in combination with a cross correlation coefficient, a calibration distance, an amplitude enhancement coefficient and a preliminary positioning error value, obtaining a leakage medium type through a medium identification model. The method gives consideration to the positioning efficiency and precision, and greatly improves the accuracy and robustness of a leakage medium type recognition result.

The invention provides a method for testing flow-induced noise of a pipeline. The method comprises the following steps: S1, determining measurement positions at an upstream flow channel and a downstream flow channel of a measured element, carrying out normalization processing on measured signals, and calculating the pressure power spectrum density of the inner wall surface of a pipe; s2, calculating a wall surface pressure fluctuation time domain cross correlation coefficient, and analyzing correlation characteristics between wall surface pressure pulsation; s3, spectrum intrinsic orthogonal decomposition is carried out on the pressure pulsation field, and space-time decoupling is achieved through time domain-frequency domain conversion; s4, extracting a dominant flow-induced noise mode, constructing an empirical frequency-wave number spectrum, and decomposing fluctuating energy distribution into contributions of different frequency and wave number combinations; s5, analyzing the decoupled flow-induced noise characteristic result, and identifying the amplitude-frequency characteristic of the flow-induced noise; according to the invention, real-time measurement of pressure pulsation in the pipeline can be realized, decoupling analysis of flow field pulsation and sound pressure pulsation can be carried out on the measured signal, and characteristics and a generation mechanism of pipeline flow-induced noise can be identified.

The invention discloses a bearing electrostatic signal noise reduction method based on VMD-OMP. In order to solve the problem that early fault features are difficult to extract due to the fact that bearing electrostatic signals are easily affected by power frequency interference and environmental noise, a noise reduction technology based on combination of VMD (variational mode decomposition) and OMP (orthogonal matching pursuit) is provided. The method comprises the following steps of: firstly, performing automatic optimization on a modal number and a secondary penalty factor of VMD decomposition by taking an envelope entropy as a fitness function through a grey wolf optimization algorithm to realize adaptive decomposition; and then, screening effective modal components according to the cross correlation coefficient and the kurtosis, determining the optimal sparseness by adopting a saturation value method, respectively reconstructing the components through an OMP algorithm, and finally, directly adding reconstructed signals to obtain a noise reduction result. According to the method, power frequency noise and broadband interference in the bearing electrostatic signal can be effectively suppressed, and the feature definition and the fault recognition capability of the electrostatic signal are improved.

The invention discloses an integrated pediatric vital sign intelligent monitoring and early warning method and system, and relates to the technical field of biomedical signal processing. The method is used for solving the problems of large monitoring interference and early warning lag in a dynamic environment. The method comprises the following steps: firstly, synchronously collecting a multi-modal signal to generate a motion intensity envelope signal and an R wave peak value interval sequence, carrying out orthogonal projection denoising on a photoelectric volume pulse wave signal by utilizing motion envelope based on a recursive least square algorithm, and outputting a reconstructed blood oxygen waveform; then, calculating a cross correlation coefficient of a heart rate trend and a motion envelope, quantifying a physiological coupling degree to generate an independent heart rate anomaly index, and accurately identifying a non-motion related risk; finally, the real-time blood oxygen value is analyzed, the index is used for dynamically increasing the low oxygen threshold value so as to tighten the alarm boundary, physiological parameter linkage grading early warning is achieved, and the recognition sensitivity of the compensation period risk is improved while interference resistance is guaranteed.

The invention provides a water chiller predictive maintenance method and system based on digital twinning, and the method comprises the steps: obtaining the evaporation pressure, suction temperature, refrigerant flow and outlet water temperature parameters during the operation of a main module of a water chiller, and carrying out the synchronous collection through multiple sensors, thereby forming a time series data set; according to the time sequence data set, a sliding window method is adopted to extract the mean shift amount, variance volatility and cross correlation coefficient of evaporation pressure and suction temperature, and a collaborative drift mode reflecting micro leakage is obtained; according to the corrected drift rate and cross correlation coefficient, generating a feature vector of a collaborative change direction among the evaporation pressure, the suction temperature and the refrigerant flow; and by calculating the Euclidean distance between the feature vector of the cooperative change direction and the health baseline, whether the micro leakage reaches the early warning triggering degree is judged, and the strength of an early warning signal is obtained.

The invention provides an effective signal filtering and noise reduction method and system for a micro-seismic event of a tunnel water and mud inrush disaster, and relates to the technical field of signal processing. Comprising the following steps: acquiring original signals of water-inrush and mud-inrush micro-seismic events; performing modal decomposition on the original signal by using a variational modal decomposition method to obtain a plurality of modal components; calculating a cross correlation coefficient between the original signal and each modal component; based on the center frequency of each modal component and in combination with the cross correlation coefficient, performing primary noise reduction on the plurality of modal components; performing de-trend fluctuation analysis on the modal components after the primary noise reduction screening to obtain scale indexes of the modal components after the primary noise reduction screening, and performing secondary noise reduction based on the scale indexes; and reconstructing the modal component after the secondary noise reduction screening to obtain a micro-seismic signal after noise reduction filtering. According to the method, the problem that noise and effective signals in part of modal components are mixed is solved, and the weak signal extraction capacity is improved.

The invention discloses a wave height short-term prediction system and method based on variational mode decomposition and wind and wave time correlation, and belongs to the technical field of ocean engineering.The method comprises the steps that firstly, original storm time series data are decomposed into IMF components with different center frequencies through variational mode decomposition; secondly, performing data cleaning on the decomposed IMF component based on a Pearson's correlation coefficient; then combining two wind speed IMF components with the strongest correlation with the wave height IMF component based on correlation analysis to form a wind speed wave height component combination, and determining an input time length through an autocorrelation coefficient ACF of wave height data and a cross correlation coefficient CCF of the wind speed IMF components and the wave height IMF components; and finally, according to the formed wind speed wave height component combination and the determined input time length, carrying out short-term prediction on the wave height based on a long short-term memory neural network. According to the system and the method provided by the invention, the influence of noise on prediction is reduced, and the prediction precision is improved while the prediction efficiency is improved.

The invention provides a mine vibration safety detection method and system during mining blasting. The method comprises the following steps: acquiring three-component signals of explosion fluctuation at a plurality of sampling points in a warning range through an intelligent sensor network during mine blasting; decomposing each three-component signal into a plurality of attenuation waves, determining cross correlation coefficients of the attenuation waves in the same component direction among different sampling points, and performing reverse time back propagation on each sampling point according to all the cross correlation coefficients to obtain a reverse time wave field of blasting vibration at each sampling point; inputting all the reverse time wave fields into a pre-trained multi-seismic-source positioning model to obtain a plurality of explosion points; and determining the risk value of miss-fire of each blast hole in the detonation network according to the coordinate positions of all the explosion points, and performing explosion aftershock evaluation on the mine according to all the risk values. According to the scheme, the miss-fire risk of each blast hole can be evaluated after mine blasting.

The invention discloses a TIADC mismatch calibration method and circuit based on a frequency domain cross correlation coefficient. According to the method, after ADC output of each sub-channel is converted to a frequency domain, a cross correlation coefficient between adjacent channels is solved, so that a sampling moment mismatch parameter is estimated and obtained, then a calibration module is controlled to carry out data calibration through the sampling moment mismatch parameter, and sampling moment mismatch compensation is finally completed through repeated iteration. And RLT design is carried out on the FPGA, so that a mismatch calibration circuit is obtained. Compared with the prior art, the method is suitable for a multi-channel TIADC system, an auxiliary channel is not needed, and calibration can be completed only through fewer sampling points and first-order differential compensation. A simulation result shows that under the input of any signal, the overall performance is superior to that of an existing sampling moment mismatch calibration algorithm.

The invention relates to the technical field of bridge deformation monitoring, in particular to an online monitoring method, system and equipment for bridge structure deformation, and the method comprises the steps: analyzing the similarity degree of a spectrogram between each transverse component and a reference component thereof, and determining the transverse feature value of each transverse component in each GPS receiver, so as to obtain a reconstructed transverse displacement signal; determining a vertical characteristic value of each vertical component in each GPS receiver by analyzing a correlation time fitting result of all the vertical components and combining the cross correlation coefficient so as to obtain a reconstructed vertical displacement signal; and respectively analyzing extreme value distribution of the reconstructed transverse displacement signal, the longitudinal displacement signal and the reconstructed vertical displacement signal, and determining a deformation monitoring result of the bridge floor of the to-be-detected bridge. The interference of external wind power and traffic load on bridge structure deformation monitoring is solved, and the accuracy of bridge structure deformation monitoring is improved.

The invention discloses a rock mass fracture early warning method for a hydraulic tunnel, belongs to the technical field of tunnel safety monitoring, and can solve the problem that an existing method cannot specifically recognize isolated impact noise and rock mass real fracture signals in the construction period of the hydraulic tunnel under the background of strong noise. The method comprises the following steps: S1, acquiring an original micro-seismic signal synchronously acquired by a sensor array in a hydraulic tunnel; s2, detecting a micro-seismic event from the original micro-seismic signal to obtain an event signal set, and determining a reference channel signal according to the event signal set; s3, extracting cross correlation coefficient peak values of event signals of each channel in the event signal set and reference channel signals, and counting the number of channels of which the cross correlation coefficient peak values exceed a cross correlation coefficient threshold value as the number of coherent channels; and S4, calculating a frequency band energy ratio of the reference channel signal, and generating a rock mass fracture early warning signal when the number of coherent channels and the frequency band energy ratio meet preset conditions. The method is used for rock mass fracture early warning of the hydraulic tunnel.

The invention relates to the technical field of mechanical equipment fault diagnosis, and discloses a fault diagnosis method and system for an automatic tensioning control valve of a scraper conveyor. The method comprises the following steps: acquiring pressure and flow data of a main flow channel and an auxiliary flow channel through a sensor array, and extracting frequency domain characteristics to obtain a standardized frequency spectrum; detecting parameter coupling abnormity based on the spectrum cross correlation coefficient and generating an abnormal index; environment interference and medium viscosity data are fused, and non-linear feature distribution and correction deviation representing the mechanical state are obtained through classification and filtering analysis; matching the correction deviation with a preset fault mode, determining a fault type, and generating a fault positioning coordinate in a three-dimensional space by using an optimization algorithm; and finally, performing fluid dynamic simulation through the high-fidelity virtual model to verify the coordinate stability, and outputting accurate early warning when conditions are met. Through cooperation of multi-source information fusion and an intelligent algorithm, the accuracy of early-stage internal fault diagnosis of the complex double-flow-channel valve is improved.

The invention discloses a high arch dam operation modal parameter automatic identification method and system based on discharge excitation. The method comprises the following steps: 1) obtaining a vibration displacement response signal: determining a penalty factor and an optimal decomposition layer number based on an adaptive multivariate variational mode decomposition algorithm to obtain an optimal IMF component of each sensor channel signal, and performing IMF component screening and signal reconstruction through a frequency domain cross correlation coefficient to realize adaptive noise reduction of the signal; 2) establishing a Monte Carlo three-dimensional stability diagram based on a random subspace recognition algorithm driven by a covariance matrix; and 3) modal parameter automatic identification based on an intelligent clustering algorithm. According to the method, the noise is suppressed by automatically optimizing the modal component reconstruction signal of the multi-sensor vibration signal; a Monde-Carlo three-dimensional stability diagram is established in combination with a Monde-Carlo theory and a covariance driven random subspace method to determine a model order, and automatic interpretation of the stability diagram is realized by applying improved fuzzy clustering, so that operation modal parameters of the high arch dam are accurately identified.

The invention provides a radar signal preprocessing method for adaptive filtering and noise reduction. The method comprises the following steps: decomposing a radar signal into a multi-order IMF by performing CEEMDAN on the radar signal, then judging the selection of the IMF through a fuzzy entropy and a cross correlation coefficient, then performing noise positioning and noise reduction processing on the selected IMF through a normalized correlation coefficient, and finally obtaining a noise-reduced signal through signal reconstruction. According to the method, the background noise can be effectively suppressed to the maximum extent while effective signal energy is reserved, and the precision of radar signal characteristic analysis is improved.

The invention discloses an offshore wind power early-stage fault feature extraction method. The method comprises the specific steps that vibration signals in an offshore wind power unit are collected through a motion sensor; performing wavelet decomposition on the vibration signal, and decomposing the vibration signal into four sub-signal components according to the frequency from high to low; processing the vibration signal based on the continuous kurtosis to obtain a continuous kurtosis value of the signal; the wavelet instantaneous energy and the continuous kurtosis are fused, a fault feature signal is reconstructed, a local cross correlation coefficient of the wavelet instantaneous energy and the continuous kurtosis is calculated, and fused feature instantaneous energy is obtained; and calculating an envelope spectrum, and extracting a fault feature frequency. According to the method, the kurtosis index and the wavelet transform are combined, the kurtosis index is extremely sensitive to impact components in the signals, the wavelet transform has good time-frequency localization characteristics, the time-varying characteristics of the signals can be depicted in detail, the defect of a single method is effectively overcome through combination of the kurtosis index and the wavelet transform, and the extraction precision of weak fault characteristics is improved.

The invention discloses a parameter selection method for adaptive characteristic mode decomposition based on three criteria, and belongs to the technical field of mechanical fault diagnosis and signal processing analysis. Firstly, a mechanical vibration signal is preprocessed and reconstructed by adopting an iterative envelope spectrum method, and the periodicity of the signal is enhanced; and then the reconstructed signal is input into adaptive characteristic mode decomposition for processing. Similarity modals and modals containing high noise are removed by setting triple criteria of correlation kurtosis, modal cross correlation coefficient and envelope harmonic signal-to-noise ratio. Information corresponding to the maximum value of the WEHNR is used as parameter output of adaptive characteristic mode decomposition, envelope spectrum analysis is carried out on an optimal decomposition mode, fault characteristic frequency is extracted, and finally faults are detected and fault positions are identified. According to the method, adaptive decomposition of the fault signal can be realized, over-decomposition and under-decomposition can be avoided, impact and periodicity of mechanical fault response can be considered, and the method has relatively good robustness and practicability.

The invention provides an improved RPSEMD-based millimeter wave radar vital sign detection method, which comprises the following steps of: capturing an echo signal of a target by using a frequency modulation continuous wave radar module to carry out a signal preprocessing step, carrying out distance dimension fast Fourier transform on the captured signal, and positioning a distance unit where a target object is located by using a distance gate locking technology; phase extraction is carried out on the signal, and phase unwrapping processing is carried out; signal stability is enhanced through differential operation, and signal quality is improved through a median filtering technology; vital sign signals are separated and extracted by improving an RPSEMD algorithm model, and millimeter wave radar vital sign detection is achieved. The quadratic sum of the cross correlation coefficients is used as a target function, Bayesian optimization is utilized to optimize parameters of a traditional RPSEMD algorithm, and a group of optimal parameters are found out in a given parameter range. The algorithm verification result of the actually measured signal shows that the modal aliasing problem of the traditional algorithm in the signal decomposition process is reduced.

The invention discloses a hyperspectral image noise detection and evaluation method and system, and relates to the technical field of image processing, and the method comprises the steps: firstly, carrying out the superpixel segmentation of an original hyperspectral image through employing an improved linear iteration clustering method, and obtaining a plurality of superpixel sub-blocks; then, calculating a de-trending cross correlation coefficient and a de-trending cross correlation coefficient for the spectral reflectivity sequences of every two superpixel sub-blocks by adopting a de-trending cross correlation method; meanwhile, obtaining a noise sequence characteristic value range according to a randomly generated Gaussian white noise sequence, and taking the noise sequence characteristic value range as a reference of a hyperspectral signal noise range; and finally, performing spectral decomposition on a correlation matrix constructed by the de-trending partial cross correlation coefficients of every two superpixel sub-blocks to obtain a partial correlation matrix characteristic value under each preset scale, and performing calculation according to a noise sequence characteristic value range to obtain a noise proportion value and a de-trending partial cross correlation coefficient matrix after de-noising. According to the invention, the accuracy of noise detection evaluation of the hyperspectral image can be optimized.

The invention relates to the technical field of medical treatment, in particular to a multi-channel surface electromyogram signal noise reduction and de-aliasing method which comprises the steps that S1, a wireless terminal collector is used for collecting multi-channel surface electromyogram signals (sEMG) at the same time; s2, performing noise reduction on the sEMG by using second-generation wavelet transform; s3, carrying out independent component analysis on the collected data; and S4, verifying a de-aliasing effect through a cross correlation coefficient, the signal subjected to noise reduction by adopting the improved threshold function has the advantages of good approximation degree with an original signal, smooth signal and high signal-to-noise ratio, and the noise reduction effect of the method applied to the electromyographic signal is obviously superior to that of a traditional soft and hard threshold function method.

The invention relates to a cable partial discharge detection and identification method and system based on a pulse polar coordinate vector. The method comprises the following steps: installing an Internet of Things intelligent sensor for sensing a current pulse signal of a cable grounding lower lead; background noise in the current pulse signal is removed based on wavelet multi-resolution decomposition and an improved wavelet threshold; calculating a polar coordinate vector of the current pulse signal according to the denoised current pulse signal so as to draw a polar coordinate vector domain distribution diagram; and calculating a cross correlation coefficient between the polar coordinate vector domain distribution diagram and polar coordinate vector diagrams of various signal types in a preset sample library so as to determine the signal type of the current current pulse signal. Compared with the prior art, the signal type can be judged without obtaining the power frequency synchronizing signal, hardware investment is saved, and cable insulation state evaluation can be rapidly and effectively achieved.

The invention provides an intelligent monitoring system and method for liquefied petroleum gas supply, and the method comprises the steps: determining a cross correlation coefficient of pressure attenuation between different sensor nodes through the dynamic pressure characteristics of liquefied petroleum gas at each sensor node; determining a thermodynamic distribution diagram of liquefied petroleum gas pressure attenuation according to all cross correlation coefficients and a topological structure of the sensor nodes; evaluating the pressure leakage state of the transportation section between the adjacent sensor nodes according to the mutual support degree of the pressure fluctuation amplitude between all the adjacent sensor nodes to obtain the confidence probability of the pressure leakage of each transportation section, and further determining an association detection tree of the leakage of the liquefied petroleum gas supply network; and performing fusion leakage detection on the liquefied petroleum gas supply network through the thermal distribution diagram of the liquefied petroleum gas pressure attenuation and the correlation detection tree to obtain a leakage occurrence area of the liquefied petroleum gas supply network. By adopting the scheme of the invention, the confidence leakage detection of the liquefied petroleum gas supply network can be realized based on the multi-source monitoring point information.

The embodiment of the invention discloses an efficient response spectrum method for soil-structure interaction anti-seismic analysis. The efficient response spectrum method comprises the steps that a finite element model of a to-be-analyzed soil-structure system is obtained; performing modal analysis on the structure model under the rigid foundation, and calculating the effective inherent frequency and modal vector of each order of the soil-structure system; according to the effective inherent frequency and the modal vector of each order, calculating a modal participation coefficient of each effective modal and a cross correlation coefficient between the effective modals; a model bottom response spectrum is obtained according to earthquake risk analysis of a specific site, and for an engineering site without earthquake risk analysis of the specific site, the model bottom response spectrum is obtained through inversion of a ground surface design spectrum through an inversion method; and determining the peak response of the structure according to the bottom response spectrum of the model. According to the embodiment of the invention, soil-structure interaction is considered in the structural seismic analysis response spectrum method, and the execution efficiency is relatively high.

The invention relates to the technical field of vision measurement, and discloses a three-dimensional vision measurement system based on multi-view structured light, and the system comprises a data collection module which obtains image data, system data and point cloud data through a sensor and a system; the synchronous link module is used for generating a synchronous pulse interval based on the system data; calculating a cross correlation coefficient to judge interference and finish construction of a synchronous link; the three-dimensional measurement module is used for adjusting and calibrating by utilizing a composite structure, adjusting brightness through double modulation, constructing a target section data representation model for iterative optimization, analyzing to obtain an absolute phase value, constructing an energy function, obtaining a matching point pair based on image data and calculating a depth error; the data architecture module is used for dividing point cloud data, carrying out gridding discretization on an integral region, carrying out parallel calculation on energy values, summarizing the energy values, and dynamically adjusting the order of multi-pole expansion; and the enhanced anti-interference module is used for carrying out anti-interference processing on the image data, adjusting measurement parameters and adjusting the projection light intensity based on the predicted light intensity.

The invention provides a millimeter wave radar miscellaneous point removing method and device based on frequency spectrum matching, a medium and a program product, and the method comprises the steps: obtaining a multi-channel echo signal of a millimeter wave radar array, and constructing an actual measurement angle spectrum reflecting the energy distribution in each direction through beam forming and angle traversal processing; carrying out peak value search on the actually measured angle spectrum, and determining an effective target angle corresponding to each power peak; according to geometric structure parameters and effective target angles of the millimeter-wave radar array, performing weighted stacking on each target response based on a theoretical echo signal model to obtain a theoretical angle spectrum; performing spectrum matching analysis on the theoretical angle spectrum and the actually measured angle spectrum, and calculating cross correlation coefficient distribution of the theoretical angle spectrum and the actually measured angle spectrum under different angle deviations; and when the cross-correlation obtains a maximum value at the zero offset and is greater than a threshold value, judging that the corresponding target is an effective target, otherwise, regarding the target as a miscellaneous point and removing the miscellaneous point. According to the method and the device, accurate identification of a real target is realized by utilizing the change characteristics of the cross correlation coefficient under different angle deviations.

The invention relates to a time-lapse seismic data time difference correction method and device, electronic equipment and a medium, and relates to the field of seismic data processing, and the method comprises the steps: carrying out the preprocessing of two groups of time-lapse seismic data collected at a preset time interval; based on a first constraint of a preset time window length and a second constraint of a preset time-shifting maximum value, performing cross-correlation matching calculation on each sampling point in the two groups of time-shifting seismic data after preprocessing to obtain a cross-correlation coefficient; according to a preset time window length, the distance between the sampling points in the two groups of time-lapse seismic data and the cross correlation coefficient, generating a matching error function between the sampling points in the two groups of time-lapse seismic data; performing dynamic planning according to the matching error function, and determining an optimal matching path for connecting corresponding matching sampling points in the two groups of time-lapse seismic data; and carrying out time difference correction on the corresponding matched sampling points according to the optimal matching path. The time difference correction of the time-lapse seismic data can be realized more accurately, and the time consistency of the time-lapse seismic data is ensured.

The invention discloses a multi-temporal optical remote sensing image robust registration method based on multi-scale joint similarity measurement, and the method comprises the steps: 1, inputting a dual-temporal image, and obtaining uniformly distributed control point pairs through SIFT extraction and interactive manual auditing; 2, constructing a global dense displacement field by using a thin-plate spline, completing coarse registration and partitioning according to an overlapping strategy; 3, performing three-stage coarse-to-fine template matching on each image block, inheriting an initial displacement value step by step, and realizing radiation difference and geometric deformation synchronous self-adaption by taking a product of normalized mutual information and a normalized cross correlation coefficient as joint similarity measurement; and 4, performing sub-pixel-level deformation correction on the reliable displacement field by using a local thin plate spline, and outputting a registration image and metadata. According to the method, a global sparse-local dense framework and a multi-scale progressive + joint measurement + quality constraint mechanism are coupled, and a high-robustness and high-precision registration solution is provided for quantitative remote sensing application such as change detection and disaster assessment.

The application discloses a double-lead finger clip electrode biological wave detection method and device, and belongs to the technical field of biomedical engineering, and comprises a mechanical contact terminal, an intelligent adaptive module and a display terminal; the mechanical contact terminal comprises left and right two finger clip electrodes and is used for receiving biological wave signals in real time; the intelligent adaptive module is used for pre-processing the received biological wave signals, dividing the biological wave signals into low, medium and high frequency bands to extract key features, calculating cross-correlation coefficients and baseline drift amplitudes, constructing a classification model, identifying a user age range and automatically adjusting mode parameters, checking contact stability through a fast resampling mechanism and a dynamic threshold, and performing impedance compensation; and the display terminal is used for decomposing biological wave signals, extracting multi-domain features, modeling through a space-time attention LSTM network, calculating a physiological age and a health risk index, and generating an intervention measure recommendation list.