132 results about "Cross correlation coefficient" patented technology

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 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 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 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.

The application discloses a Parkinson gait evaluation method and system based on a millimeter wave radar signal. The method comprises the following steps: acquiring a millimeter wave radio frequency signal in an environment, performing data processing on the signal to obtain a distance spectrum of position change of a Parkinson patient's body and a Doppler spectrum reflecting the degree of change of the Parkinson patient's body; extracting a peak value at each moment from the distance spectrum through a filtering method, calculating a cross-correlation coefficient of the Doppler spectrum as an index of gait pattern matching, and dividing the Doppler spectrum according to each step; calculating gait features according to the filtered distance spectrum peak value and the divided Doppler spectrum; inputting the normalized gait features into a trained machine learning model for feature evaluation to obtain a UPDRS-III score of the patient. The application realizes non-invasive measurement and evaluation of Parkinson gait features in a low-cost manner, is convenient to operate, and is accurate in evaluation effect, thereby providing quantitative data and diagnosis reference for doctors.

The application provides a millimeter wave radar clutter point removal method and device based on spectrum matching, a medium and a program product. The method comprises the following steps: acquiring a multi-channel echo signal, constructing a measured angle spectrum reflecting the energy distribution of each direction through beam forming and angle traversal processing; determining each effective target angle of the measured angle spectrum; based on the geometric structure parameters of the millimeter wave radar array and the effective target angle, weighting and superimposing 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 measured angle spectrum, performing point-by-point translation processing on the theoretical angle spectrum along the angle axis, and calculating the cross-correlation coefficient distribution of the two at different angle offsets; when the cross-correlation reaches a maximum value at zero angle offset and is greater than a threshold value, it is determined that the corresponding measured angle spectrum is effective, otherwise it is regarded as a clutter point and removed. The application realizes accurate identification of real targets by using the change characteristics of the cross-correlation coefficient at different angle offsets.

The application provides a land interlayer multiple wave suppression method and device, and relates to the technical field of seismic data processing. The technical scheme is as follows: a land interlayer multiple wave suppression method comprises the following steps: acquiring seismic data which needs to be subjected to land interlayer multiple wave suppression; performing far-offset noise cutting and random noise suppression on the seismic data to obtain denoised data; based on the denoised data, fitting an amplitude curve by using middle and far offset distance data in multiple wave removal, and approximately calculating theoretical seismic data which does not contain multiple waves according to the amplitude curve; calculating a normalized cross-correlation coefficient of the theoretical seismic data and actual data; and performing data reconstruction according to the normalized cross-correlation coefficient to obtain seismic data after land interlayer multiple wave suppression. The application has the beneficial effect of suppressing land seismic near-trace interlayer multiple waves by using AVA fitting and data reconstruction based on a normalized cross-correlation coefficient.

The invention relates to the technical field of radar detection and application thereof, and provides a data processing method for ground penetrating radar data, which comprises the following steps: firstly, carrying out preprocessing and gain recovery on original radar data, then carrying out mixed denoising by adopting empirical mode decomposition and Wiener filtering, screening and reconstructing effective signals by utilizing cross correlation coefficients, and finally, carrying out data processing on the effective signals. The method comprises the following steps: firstly, constructing a filter in a frequency wave number domain to remove random noise and low-frequency interference, then constructing a fan-shaped filter in the frequency wave number domain, introducing transition band processing to suppress linear coherent interference, then carrying out wave velocity estimation based on an image entropy minimization criterion to obtain an optimal imaging wave velocity, and finally executing Stolt migration imaging by using the optimal wave velocity and outputting a result. According to the method, edge details of effective signals are effectively reserved through multi-scale decomposition, the problem of imaging defocusing is solved through self-adaptive wave velocity search, and the signal-to-noise ratio, the resolution ratio and the focusing precision of ground penetrating radar data in a complex and strong-interference environment are remarkably improved.

This improves the degradation of the cross-correlation coefficient caused by the error between the set frequency and the actual frequency of the light source, and makes it possible to improve the degradation of the measurement accuracy of strain and temperature. [Solution] The light source unit has a frequency-stabilized wavelength-tunable laser that generates continuous light, and a first optical coupler splits the continuous light generated by the light source unit into two, sending one to a second optical coupler and the other to an optical frequency measurement unit. The optical frequency measurement unit measures the frequency of the continuous light generated by the light source unit, and the second optical coupler further splits the continuous light split by the first optical coupler into two, sending one as probe light to the optical pulse generation unit and the other as local light to a detection unit. The detection unit measures the frequency change Δf by means of coherent time domain reflectometry and a means for heterodyne detection of the Rayleigh scattered light and the local light resulting from Rayleigh scattering of pulsed light in the fiber under test. max and means for acquiring the

This invention belongs to the field of data processing technology, specifically relating to an IoT data edge computing processing method for ship-shore collaborative management. The method includes: acquiring real-time operating data of the ship's engine room equipment; loading a physical association weight matrix, calculating the local spatiotemporal coupling strength index of each sensor by calculating the cross-correlation coefficient and time lag between each sensor within the current window; extracting the transient change characteristics of the data at the current moment, calculating the prediction residual of the current observation using a prediction model, and calculating the adaptive transmission priority weight of each sensor's data based on the local spatiotemporal coupling strength index; and dividing the data stream into different levels according to the adaptive transmission priority weight, and executing a dynamic queue scheduling strategy for each level. This invention can improve the accuracy of fault diagnosis while reducing redundant data uploaded by the system.

The application discloses a kind of star-based ADS-B overlap signal time of arrival estimation method and system, signal is received by uniform array antenna, based on FastICA algorithm decomposition obtains two ADS-B signals;Since there is uncertainty in signal decomposition by FastICA algorithm, therefore, two kinds of mixed signals are designed for each time delay, that is, any signal may be ahead of another signal;By cross-correlation coefficient signal processing method, the designed mixed signal is calculated with the original mixed signal;Compare two cross-correlation coefficients, find the larger value, which corresponds to the correct order of each signal in the mixed signal, the maximum cross-correlation coefficient corresponds to the relative time delay of two signals;Based on the relative time delay and the order of the mixed signal before separation obtained, the signal is effectively separated, the signal error rate is low, and the arrival time of ADS-B signal is accurately estimated.

The invention relates to the technical field of fire detection, and discloses a multi-sensor pyrolysis particle fire detection control system and device, and the system comprises a power supply and signal coupling module, a dielectrophoresis modulation detection cavity module, a multi-dimensional environment sensing module, a central processing unit and an execution module. The power supply and signal coupling module extracts a high-frequency conducted interference signal of a power supply circuit and generates a high-voltage driving signal and a reference signal respectively; the dielectrophoresis modulation detection cavity module is used for establishing a non-uniform electric field by utilizing a high-voltage driving signal and carrying out dielectrophoresis modulation on aerosol particles passing through a detection area; the central processing unit synchronously collects the photoelectric detection signal and the reference signal, and determines the fire state by calculating a cross correlation coefficient. The device integrates the modules in a shell. According to the invention, physical and signal correlation between power supply noise and particle concentration change is established, electrical fire pyrolysis particles and environmental dust are effectively distinguished, and high-sensitivity early detection of electrical fire is realized.

This invention provides a method for optimizing energy storage configuration in distribution networks based on renewable energy consumption, belonging to the field of energy storage optimization configuration. The method involves: a synchronization feature module for time-domain synchronization processing to generate feature vectors; a frequency mapping modeling module to construct an equivalent feature matrix including inter-axis cross terms; an admittance reconstruction module to inject this into the static topology to generate a real-state dynamic admittance model and solve for the impedance matrix; a sensitivity evaluation module to map and generate real-state voltage sensitivity and determine the impedance offset index; a configuration module to perform nonlinear hedging to generate configuration data; and a calibration module to perform recursive calibration. The system also generates source-grid attribution weights based on normalized cross-correlation coefficients, enabling accurate determination and differentiated correction of causes on the equipment side and grid side, and introduces a self-evolving evolutionary flow for closed-loop optimization. This invention effectively reduces asynchronous interference from heterogeneous data, improves the accuracy of risk attribution and control, and ensures the stability of the distribution network.

The application relates to a paint mist stable state change judgment method based on image sequence cross correlation, and relates to the technical field of image data processing. In order to solve the problems that the prior art can only calculate a fog cone angle through a single frame static image, cannot reflect dynamic changes of paint mist, is easily interfered by accidental factors and lacks a stable reference, the method comprises the following steps: step one, paint mist image acquisition; step two, establishment of a reference image; step three, image preprocessing; step four, calculation of a fog cone angle; step five, calculation of a fog cone angle relative error; step six, calculation of a cross correlation coefficient; and step seven, comprehensive judgment of paint mist stable state changes. The application replaces the traditional single frame image detection mode by collecting a paint mist image sequence and generating a plurality of average reference images, effectively avoids accidental interference of single frame data, establishes a stable and reliable paint mist standard reference state, and greatly improves the anti-interference ability and result reliability of detection.

The invention discloses a land subsidence monitoring system based on an INSAR technology, relates to the technical field of land subsidence monitoring, and solves the technical problems that the analysis of collected data is inaccurate and the overall analysis result is affected due to the fact that massive data are not discriminated. For precise settlement monitoring compaction data foundation, a difference screening system based on vegetation coverage indexes is constructed, EVI values of a current regional image and a historical image are compared, and the image with the minimum difference value is selected as a matching image, so that the defect of traditional rough matching is overcome, a reliable time sequence reference is embedded for settlement analysis, the long-term rule of settlement is favorably informed, and the accuracy of settlement analysis is improved. Based on cross correlation coefficient fine calculation and threshold judgment, an analysis window and a search area are flexibly selected according to area ground object resolution and features, multi-pixel sequences are accurately compared, homonymy points are locked, the interference pattern generation quality is improved, and settlement monitoring precision and source reliability are guaranteed.

The invention belongs to the technical field of lunar and deep space exploration, and relates to a simulation method, device, equipment, medium and program for a lunar seismic exploration task, which are used for realizing seismic source-propagation-observation full-link closed-loop simulation, and the method comprises the following steps: establishing a two-dimensional non-uniform lunar soil wave field propagation numerical model based on a finite difference time domain method; the whole process that lunar seismic waves are generated from a seismic source and spread in different circle layer media in the moon is simulated, and a theoretical acceleration signal at a preset station position is output; establishing an MEMS lunar seismograph system model for simulating the working characteristics of the MEMS lunar seismograph in the extreme environment of the lunar surface; the theoretical acceleration signal is input into an MEMS lunar seismometer system model for simulation calculation, and a simulation output voltage signal containing noise and instrument response characteristics is obtained; and calculating the cross correlation coefficient of the signals under different lunar earthquake station layout schemes based on the simulation output voltage signal, and further quantitatively evaluating the detection efficiency of the different station layout schemes.

The application provides an image registration method for ground-based SAR discontinuous measurement, a first image is acquired by using a ground-based SAR system, a reference coordinate system O-xyz is constructed, then the height of the ground-based SAR is increased along the z axis to acquire a second image; based on interferometric measurement, three-dimensional coordinates of reference points in the first image are acquired, and the first image is taken as a main image of discontinuous measurement; an image acquired at other time is taken as an auxiliary image of discontinuous measurement, then based on a cross-correlation coefficient method, homonymic points of the reference points in the main image in the auxiliary image are acquired, and the distance and azimuth offset between each reference point and the homonymic point are calculated; the distance and azimuth offset are respectively subjected to parameter modeling to construct a linear equation group, then a model coefficient is estimated by using a least square method to calculate accurate distance and azimuth offset; complex information of the homonymic points in the auxiliary image is acquired to realize accurate registration of the discontinuous measurement images.

The present application relates to the technical field of marine ecological environment analysis, and discloses a marine ecological environment analysis method and system based on big data, which comprises the following steps: obtaining environment monitoring and management object working condition time series data, determining a transmission lag time window based on flow field transmission logic; calculating cross-correlation coefficients in the window, and if they fall into a gray verification interval, generating a pseudo-random working condition control sequence orthogonal to the background main frequency; driving the management object to perform irregular actions through an administrative interface, calculating the local waveform matching degree of the environment data and the sequence; and determining responsibility and generating a punishment instruction when the threshold is exceeded. The system comprises a data acquisition and space-time mapping module, a cross-correlation calculation module, an active attribution module, and a fingerprint verification and decision module. Through the active pseudo-random disturbance and frequency domain orthogonal verification mechanism, the present application realizes exclusive locking of the responsible subject under complex hydrological environment, and solves the attribution problem that passive monitoring is difficult to distinguish between natural background fluctuations and human emissions.

The present application relates to the technical field of plant salt tolerance screening, and discloses a multi-parameter screening analysis method for salt-tolerant aquatic plants, which comprises the following steps: obtaining initial baseline data; injecting sodium chloride solution and calculating root system maintenance index and its first-order time derivative, locking the environmental salinity when the derivative reaches a negative threshold value and recording the root system response anchor point; calculating canopy transpiration index and its first-order time derivative under the condition of salinity locking, recording the canopy response anchor point when the derivative reaches a positive threshold value; calculating the dynamic time delay between the two anchor points and the cross-correlation coefficient of the two indexes; extracting the root system steady-state characteristic value, constructing a joint feature vector with the dynamic time delay and the cross-correlation coefficient, and outputting the salt-tolerance mechanism classification. The present application dynamically anchors the cross-organ response time sequence through the derivative of physiological parameters, converts local physiological data into global space-time coordination features, and realizes the fine and objective classification of different plant salt-tolerance mechanisms.

The embodiment of the application discloses a discharge detection method for surface defects of an insulator and related equipment, through obtaining a plurality of ultrahigh frequency electromagnetic wave signals and low-frequency electric field time-domain waveform signals of the surface defects of the insulator within a preset detection period, and performing eigenvalue extraction to obtain an amplitude mean value, an amplitude variance and a phase variance corresponding to the ultrahigh frequency electromagnetic wave signals, and a voltage zero-crossing moment electric field amplitude, a voltage and electric field waveform cross-correlation coefficient and an electric field waveform kurtosis factor corresponding to the low-frequency electric field time-domain waveform signals, multi-dimensional information reflecting the discharge state of the surface defects of the insulator is comprehensively obtained; a discharge degree evaluation factor is calculated in combination with a preset weight, the influence of various eigenvalues on the discharge degree can be comprehensively considered, and occasional and weak discharge signals can be effectively captured; whether the insulator discharges or not is judged according to the discharge degree evaluation factor and a preset threshold value, and the accuracy and reliability of the discharge detection result are improved.

The invention relates to a children rehabilitation outdoor scene interaction system based on adaptive SLAM and multi-modal time sequence fusion, and belongs to the technical field of children training, and the system comprises an adaptive projection interaction subsystem which is used for realizing + / -2mm precision three-dimensional motion capture through a distributed sensor, adaptively adjusting the projection brightness in combination with ambient light, and outputting the projection brightness; simulating tactile feedback according to the light and shadow intensity change; the multi-source data acquisition and time sequence processing subsystem is used for acquiring physiological signals, expression features and clinical scale data of children, calculating optimal lag time tau of physiological and expression data through cross correlation coefficients to dynamically align the data, adjusting data window duration T based on emotional feature variance, and constructing an emotional specificity lag correlation model; the dynamic adjustment subsystem is used for calculating a scene adjustment coefficient K by taking the normalized value S'of the rehabilitation stage index S as input, and dynamically adjusting the complexity, excitation intensity and interaction speed of a training scene; and a family-institution cooperation subsystem. According to the invention, behaviors and actions of children can be captured through the infrared sensor.

The invention relates to the technical field of image data processing, in particular to a PCB bare board blind hole defect detection method and system based on AOI, and the method comprises the steps: obtaining a to-be-detected PCB bare board blind hole image and a standard PCB bare board blind hole image; matching the blind hole image of the bare PCB to be detected with a blind hole image of a standard bare PCB by using an improved normalized cross-correlation matching algorithm to obtain a maximum normalized cross-correlation coefficient of the position of each pixel point in the blind hole image of the bare PCB to be detected, and if the maximum normalized cross-correlation coefficient is smaller than a set threshold value, determining that the blind hole image of the bare PCB to be detected is a blind hole image of the bare PCB to be detected. And if so, determining that the blind hole image of the bare PCB to be detected has defects. The method solves the problem that the existing algorithm is not high in detection accuracy when facing complex noise.

The application relates to the field of atmospheric science and discloses an El Nino event identification and La Nina transition early warning method, equipment and medium. The method uses the sea surface temperature and the near-surface 2-meter air temperature data in the fifth-generation atmospheric reanalysis product ERA5 of the European Centre for Medium-Range Weather Forecasts, calculates the lag cross-correlation coefficient between the sea temperature grid points and the air temperature grid points, and constructs a directed network between the global air temperature and the equatorial mid-east Pacific and equatorial mid-Pacific sea temperature. The average link strength is used to quantify the importance of the air temperature node, the spatial and temporal changes of the link number and the link strength are combined, the key area with a leading response to the El Nino is identified, and the transition of the El Nino event to the La Nina event is early warned. The application helps us to deeply understand the influence of the El Nino event on the global climate system, improves the prediction accuracy of the La Nina event, and thus provides a scientific basis for the formulation of disaster prevention and response strategies.