528 results about "Peak detection" patented technology

The invention relates to the technical field of power equipment state detection, in particular to an ultrahigh-frequency partial discharge online detection system, method and device and a medium, and the system comprises the steps: collecting an initial discharge signal in real time through an ultrahigh-frequency sensor array, and carrying out the preprocessing of the initial discharge signal, so as to obtain an ultrahigh-frequency discharge signal; carrying out peak detection on the ultrahigh-frequency discharge signal, and triggering a high-speed analog-to-digital converter to collect an original waveform when the amplitude exceeds a preset threshold value; performing multi-dimensional feature extraction on the original waveform by using a digital signal processor to obtain multiple groups of dimensional features; identifying and classifying the multiple groups of dimension features based on a random forest algorithm, generating discharge type labels and confidence coefficients, and storing the discharge type labels and the confidence coefficients in a dynamic database; carrying out spatial position calculation on the ultrahigh-frequency discharge signal by adopting a time difference method, and determining a three-dimensional coordinate of a discharge source; the dynamic database and the three-dimensional coordinates of the discharge source are subjected to space-time correlation and multi-dimensional analysis, a defect analysis result is generated, and high-precision online detection of the partial discharge defect of the high-voltage equipment is achieved.

The invention relates to the technical field of valve leakage detection, and particularly discloses an ultrahigh pressure gas valve leakage detection method and system. According to the method, a unified leakage feature vector is obtained through multi-source acquisition and processing; capturing a time sequence dependency relationship, and determining a micro leakage mode; carrying out abnormal peak detection to obtain a preliminary leakage probability; combining the initial leakage probability with real-time environmental parameters, and dynamically adjusting a judgment standard; comparing the unified leakage feature vectors through a judgment standard, fusing position coordinate triangulation positioning, judging whether micro crack leakage occurs or not, and obtaining leakage position estimation; the historical data sequence is fused, the micro crack development trend is predicted, and the early warning level is determined; and generating a corresponding response signal according to the early warning level, and outputting micro crack leakage early warning. Multi-source data fusion, dynamic threshold adjustment and position prediction can be carried out, the accuracy and robustness of micro crack leakage detection are remarkably improved, and efficient guarantee is provided for safe operation of a pipeline.

The invention discloses an ion chromatography wave crest boundary extraction method based on fitting residual and tangent judgment, and belongs to the technical field of ion chromatography analysis. Comprising the following steps: S1, reading an original waveform data sequence, and executing frequency reduction sampling according to a set step length; s2, calculating the slope of the data after underclocking sampling by using a difference method, and searching positive and negative slope sections by performing first-order derivation on the waveform after underclocking sampling; s3, noise interference is eliminated, slope areas are grouped, then a chromatographic peak detection limit value is set, and an effective peak left section and an effective peak right section are screened out; s4, combining the effective wave crest left side section and the effective wave crest right side section to obtain an effective wave crest and a preliminary left boundary point and a preliminary right boundary point of the effective wave crest; s5, fitting a straight line through the preliminary left boundary point and the preliminary right boundary point to position an error integral region; and S6, updating the optimal boundary point by traversing the evaluation indexes of all points in the error integral region. The boundary search range is more accurate, and the calculation efficiency is higher.

There is described an artificial intelligence wearable ECG skin patch (400) to detect sudden cardiac arrest. The wearable ECG monitoring patch (400) with AI based predictive analytics and remote based cardiac monitoring (615) system that can detect cardiac arrhythmias automatically in real-time and make a diagnosis with AI models trained with acquired data. The wearable skin has a biocompatible polymer patch (400) which captures the electrical signal through a flexible printed electronic technology based conducting ink and a substrate. The microcontroller controls (201), store and transmit the data packets. The IoT connected signal transmission is capable of recording and transferring the data packets through wireless communication. The AI engine is capable of analysing, evaluating, testing and providing the data packets of sudden cardiac arrest through a peak detector algorithm. The ECG skin patch (400) to detect and measure the sudden cardiac arrest with the R-R interval time series to obtain heart rate variability.

The invention relates to the technical field of medical data processing, and provides a medical image data processing method based on deep learning, which comprises the following steps of: acquiring data according to an acquisition template, extracting a pulse time sequence by self-adaptive threshold peak detection, calculating an instantaneous phase according to linear interpolation, calculating a statistical magnitude, comparing a quantitative index with a preset threshold value, and calculating a pulse time sequence according to the statistical magnitude. Judging a steady state by combining a peak loss rate and an abrupt change detection rule, and calculating phase consistency between channels for verification; the method comprises the following steps of: splitting acquired data according to a concept entity to form a data relation model, implementing rapid global rigid estimation and applying affine transformation, estimating a pixel-level displacement field by adopting a pyramid dense optical flow network, applying the displacement field to an original pixel, and performing time domain fusion by taking optical flow confidence and a registration residual error as weights; and cutting the short-time image stabilization sequence after registration compensation, and outputting a pixel-level risk thermodynamic diagram, a candidate focus list and each output confidence interval by taking a hybrid network of a convolution front end and a space-time Transform backbone as a prediction model.

The invention discloses a chromatographic data optimization processing method for a liquid chromatograph, and relates to the technical field, and the method comprises the following steps: collecting an original signal flow, carrying out preliminary peak detection, and generating an original peak list; extracting modal exclusive characteristics of each chromatographic peak; converting the modal exclusive features into a unified multi-dimensional semantic feature vector; calculating a time interval weight, and calculating a semantic relevancy weight based on the multi-dimensional semantic feature vector; correcting the time interval weight, generating a network edge weight, and constructing a peak correlation topology network by taking the multi-dimensional semantic feature vector as a node; identifying node clusters connected with high edge weights, and aggregating the node clusters into candidate compound entities; tracking a node signal intensity change track, and adding a conflict mark; conflict resolution arbitration is carried out on the candidate compound entities with the added conflict marks, dominant detector evidence is output, and an analysis report is generated. According to the method, the problems of poor data collaboration of multiple detectors, inaccurate peak identification and association and isolated data processing flow are effectively solved.

The invention discloses a pulling and lifting cable frequency detection and cable force measurement method and system based on a millimeter wave radar, and belongs to the field of pulling and lifting cable force measurement. The method comprises the steps that vibration signals are collected through the millimeter wave radar to obtain complex data streams, time-frequency analysis is conducted on the complex data streams, and an interference model is established; the method comprises the following steps: reconstructing a noise-reduced vibration signal by adopting adaptive filtering, extracting candidate vibration frequencies through power spectral density estimation and spectral peak detection, calculating corresponding stability scores, and obtaining a first verification factor used for evaluating wind disturbance and a second verification factor used for evaluating vibration frequency deviation; according to the method, through multi-source sensing data fusion and self-adaptive signal processing, complex environment interference is effectively overcome, non-contact high-precision cable force measurement is achieved, and the method has the outstanding advantages of being high in anti-interference capacity, high in measurement reliability and the like.

The invention relates to a health index monitoring tooth socket and a monitoring method thereof, and the method comprises the steps: collecting an optical signal in an oral cavity in real time through a collection module, and extracting a DC component and an AC component from the optical signal; and evaluating the signal quality of the direct-current component and the alternating-current component according to the standard signal proportion, and obtaining a target direct-current component and a target alternating-current component which meet the standard signal quality. On the basis of the cross correlation between the target direct-current component and the target alternating-current component, the blood oxygen saturation health index is obtained through a piecewise function. Based on the target alternating component, the heart rate health index is obtained through peak value detection. The problem of low monitoring accuracy of a monitoring method for monitoring health indexes in related technologies is solved.

The utility model relates to the technical field of satellite navigation, in particular to a satellite navigation anti-interference signal intensifier, which comprises a receiving antenna, a prefilter, a fixed amplification module and an automatic gain module which are sequentially connected, the output end of the fixed amplification module is further connected with a control module, and the control module comprises a peak value detection unit and a switch control unit. Two output ends of the switch control unit are connected with a control end of the automatic gain module, the peak detection unit is used for detecting a signal peak value, and the switch control unit controls the amplification factor of the automatic gain module based on the signal peak value output by the peak detection unit. According to the utility model, the prefilter is used for filtering interference signals, then the peak value detection unit is used for judging the magnitude of the signals, and then the switch control unit is used for selecting different resistors, so that the amplification factor of the automatic gain module is changed, the signals are enhanced, the final signals are in a proper range, subsequent signal processing is facilitated, and the practicability is higher.

The invention discloses an underwater sound double-expansion channel parameter estimation method based on Newton orthogonal matching pursuit. The method comprises the following steps: firstly, realizing frame synchronization by using a cross-correlation peak of an LFM signal and a received signal; a coarse estimation value of a Doppler coefficient is obtained on a discrete time frequency grid through an ambiguity function method by utilizing the self-correlation characteristic of m-sequence pilot frequency, multipath time delay of a channel is detected by utilizing a matched filtering peak value of a signal after frequency offset compensation, and a coarse estimation result of the channel is obtained; executing Newton iteration optimization in a continuous parameter space by adopting a Newton orthogonal matching pursuit algorithm to realize super-resolution off-grid estimation of the Doppler coefficient of the first data block; and finally, proposing an iterative block tracking strategy, initializing the compensation of the rear block through the estimated value of the front block, and updating the time-varying Doppler coefficient by using the balanced signal feedback to realize the dynamic tracking of the time-varying Doppler coefficient. According to the invention, the Doppler estimation root-mean-square error is obviously reduced, and the calculation efficiency is high.

The invention discloses a carriage groove positioning and steel coil placement center determining method based on three-dimensional point clouds, and relates to the technical field of industrial automation, and the method comprises the steps: firstly carrying out the preprocessing of an original point cloud, and extracting a carriage main body point cloud through ground removal, outlier filtering and DBSCAN clustering; on the basis, the overall orientation of the carriage point cloud is determined through principal direction analysis, a central area point cloud is obtained through cutting in the principal direction, and after x and z coordinates of the central area point cloud are extracted, groove valley points are positioned through sliding window calculation, Gaussian smoothing and peak value detection. As the size of the compartment groove is generally larger than that of the steel coil, in order to guarantee the loading safety of the steel coil, sole timbers need to be arranged on the edge of the groove, so that the sudden drop point of the z value is detected forwards based on the valley point, and the edge position of the groove is determined. And calculating the center coordinate of the steel coil to be placed by combining the edge of the groove, the size of the sole timber and the radius of the steel coil, and finally mapping the two-dimensional detection point back to a three-dimensional space by using KD tree nearest neighbor search to obtain the three-dimensional coordinate of the steel coil placing center.

The invention discloses a cable elbow-shaped head ponding recognition method based on knocking peak alignment and wavelet scattering, and belongs to the technical field of voiceprint recognition. Comprising the following steps: receiving a cable elbow type terminal knocking sound signal, realizing a knocking signal peak alignment algorithm through knocking peak value detection, windowing processing and signal mirror image filling, and outputting an alignment sound signal; wavelet scattering coefficients are extracted from the aligned sound signals to serve as characteristic parameters, and a voiceprint characteristic parameter set under the corresponding ponding degree is obtained; inputting the voiceprint characteristic parameter set into an LSTM model for training, outputting a water level discrimination label, and obtaining a water level state recognition model through hyper-parameter optimization; and identifying the water accumulation state of the cable elbow-shaped head by using a water level state identification model. Safety risks caused by water inflow of the elbow-shaped connector can be avoided, manpower and material resource cost consumption caused by a traditional maintenance mode is remarkably reduced, and the elbow-shaped connector is of great significance in improving operation safety of an electric power system.

The invention discloses a multi-scale time sequence modeling video abstract generation method fusing semantic enhancement and boundary perception, and belongs to the technical field of computer vision. The method comprises the following steps: carrying out feature coding on an input video frame sequence, and extracting a corresponding time sequence feature representation; inputting the extracted time sequence features into a video abstract generation model, and outputting importance scores by the model; performing time sequence smoothing and peak value detection on the importance score, and determining a key frame candidate set; and generating a final video abstract fragment based on the key frame candidate set in combination with a non-maximum suppression and fragment combination strategy. Experimental results obtained on reference data sets SumMe and TVSum prove the advancement of the method. According to the video abstract generation method provided by the invention, the problems of fuzzy event boundary and insufficient multi-scale time sequence understanding in the existing video abstract generation method can be effectively solved.

The application discloses a radar plot and AIS track adaptive parallel matching method and system, and belongs to the technical field of radar and navigation information. In order to reduce the algorithm complexity and the dependence of AIS data, the application comprises the following steps: identifying the database arrival data, performing voting on the projected radar plot in a parameter space by a system error extraction module based on an error distribution function to obtain a plurality of counters and filtering the counters by a filter, finally accumulating the results of all the filters and performing peak value detection to extract the system error of the radar plot; performing speed correlation checking on the searched radar track point by a radar plot index module, and searching the radar track point with a speed parameter meeting the requirements; performing sum and optimization on an error function according to a PARZEN window based on the radar track point with the speed parameter meeting the requirements by an error function optimization module; and updating the membership ship number in the AIS table of the database by using the radar track point with the speed parameter meeting the requirements.

The invention relates to a method for realizing high-adaptability peak detection suitable for a blind signal spectrum. The method comprises the following steps: generating candidate peaks based on non-adjacent maximum search; and performing intelligent peak value screening based on clustering analysis and intensity hierarchical decision. By adopting the method and the device for realizing high-adaptability peak detection suitable for the blind signal spectrum, the processor and the computer readable storage medium, the dependence on manual setting of key parameters is thoroughly eliminated. According to a traditional method, an operator needs to repeatedly carry out trial and error type parameter adjustment based on prior knowledge, the process is tedious and time-consuming, and a result has strong subjectivity. According to the method, a parameter setting process is internalized into a self-adaptive decision driven by data characteristics through a two-stage framework of'fast non-adjacent search 'and'intelligent screening based on clustering', so that cross-device and cross-sample'zero parameter 'operation is realized, and the automation level, objectivity and result consistency of peak detection are greatly improved.

The invention belongs to the technical field of wind power generation control, and relates to a wind turbine variable pitch control method based on load prediction feedforward compensation, which comprises the following steps: firstly, analyzing the frequency spectrum of a wind speed and a load signal, and distinguishing the current interference as periodic load interference or instantaneous load interference according to discrete peak detection, a cross-power spectrum and an energy ratio; then analyzing the lateral energy ratio and multiband energy distribution of the wind wheel shaft to determine the direction of periodic load interference or the intensity change amplitude of instantaneous load interference; generating a corresponding feed-forward compensation signal; generating a synchronous reverse sine compensation signal for periodic load interference; for the instantaneous load interference, generating a filtering compensation signal in direct proportion to the intensity change amplitude of the instantaneous load interference; and finally, fusing the feedforward signal subjected to amplitude limiting and speed limiting processing with a power feedback signal, and outputting an integrated pitch angle instruction. According to the method, through distinguishing and corresponding compensation of the load types, the active inhibition capability of the variable pitch system on the complex load is improved.

The invention relates to the technical field of biomedical signal processing and intelligent detection, and particularly discloses a PPG peak detection method based on a signal quality mask and dual-stage verification, and the method comprises the steps: obtaining an original PPG signal sampling sequence and a three-axis acceleration signal, and carrying out the data preprocessing of the original PPG signal sampling sequence; calculating a signal quality index SQI per second, and calculating a body motion variable coefficient CV; carrying out linear fusion on the SQI and CV to obtain a comprehensive quality score Score model; screening local candidate peaks according to a sliding window; backward verification and dynamic correction are carried out; updating a start bit of the sliding window according to the physiological rhythm, confirming a peak value and counting the peak value into a peak value list; setting a score threshold, and rejecting abnormal peaks in the peak list according to a Score model; according to the method, the candidate peak value is searched through the signal quality mask and the sliding window, and a two-stage verification mechanism of the final candidate peak value is determined through backward verification, so that the peak value detection accuracy is high, the motion interference resistance is high, and the real-time performance is good.

The invention relates to the technical field of navigation and positioning, in particular to a pedestrian dead reckoning optimization method based on improved wave crest detection and CNN-LSTM, comprising the following steps: S1, data acquisition: acquiring pedestrian motion sensor data including acceleration, angular velocity and magnetic field intensity through an accelerometer, a gyroscope and a magnetometer; s2, on the basis of an improved wave crest detection algorithm, real-time monitoring and analysis are carried out on the acceleration signal by setting a dynamic threshold value, and an effective gait cycle is extracted; s3, extracting reference features of the step length from the pedestrian motion sensor data, training a step length prediction model of the CNN-LSTM network, and optimizing the step length prediction model; and S4, obtaining a pedestrian course angle based on quaternion attitude calculation, realizing gait cycle segmentation and step length prediction in combination with an improved wave crest detection algorithm and a CNN-LSTM model, and finally calculating the position of the pedestrian at the next moment through the course angle and the step length so as to complete continuous navigation positioning. The method can achieve the dynamic extraction and fusion of the spatial-temporal features of multiple sensors, improves the gait detection robustness and step length estimation precision in a complex scene, remarkably optimizes the overall positioning performance of a PDR, and solves a problem that the positioning precision is reduced because a signal is affected by sensor noise, behavior diversity and the like.

The invention discloses an electromagnetic interference detection method and device and a nonvolatile storage medium. The method comprises the following steps: acquiring an initial voltage signal; fourier transform is carried out on the initial voltage signal, and a target spectral line corresponding to the initial voltage signal is determined; the target spectral line is input into a preset phase-locked loop detector, a target signal component is obtained based on a transmission mode in the phase-locked loop detector, and the target signal component comprises amplitude and frequency; demodulating and filtering the target signal component in sequence to obtain a target filtering signal; processing the target filtering signal by adopting a preset quasi-peak value detector to obtain a target voltage quasi-peak value; based on the target voltage quasi-peak value, an interference detection result of the initial voltage signal is determined, and the interference detection result comprises existence of electromagnetic interference or absence of electromagnetic interference. According to the invention, the technical problems of inaccurate measurement result, complex measurement process and high cost of the existing detection technology are solved.

The invention relates to the technical field of image correction, and particularly discloses an X-ray image correction method and device based on deep learning and terminal equipment. The method comprises the following steps: acquiring a pulse full-time original image and an energy spectrum time sequence signal, and extracting to obtain a distortion core feature; a distortion transmission path is extracted based on the distortion core features, a reverse mapping model is constructed to output distortion magnification times, and a calibration coefficient matrix is established; screening a core energy interval and a corresponding core trace element peak; the calibration direction is bidirectionally constrained through a bimodal linkage calibration mechanism, and a corrected signal and image are output; the device comprises a feature extraction module, a distortion calibration module, a core extraction module and a linkage calibration module. The terminal device executes the steps of the program implementation method through the processor, and the storage medium stores the corresponding computer program. According to the invention, the trace element peak detection precision and calibration stability are improved, and the reliability of X-ray detection is enhanced.

The invention relates to the technical field of Bluetooth headset control, and discloses an AI-based Bluetooth headset voice control method, system and equipment, and the method comprises the steps: obtaining real-time audio and voice signals, and obtaining frequency distribution characteristics through Fourier transform; and obtaining a matched peak value position set through multi-layer convolution operation and peak value detection. Single sound source feature subsets are extracted and clustered, and classified sound source groups are obtained; and carrying out fusion analysis on the resonance feature vectors, generating a preliminary tuning parameter set and an alternative list, and determining an optimal set. And a dynamic tuning scheme is obtained through smooth processing and weight adjustment. And after the voice signal instruction is matched, activating the scheme, adjusting the real-time audio, and outputting an optimized signal. According to the method, intelligent real-time audio optimization and multi-band adaptive adjustment based on the voice instruction can be realized, and the tone quality performance and auditory experience of the Bluetooth earphone in different scenes are improved.

The invention discloses a smart watch arrhythmia early warning method and system based on deep learning. The method comprises the steps that firstly, PPG signals are collected through a photoelectric volume pulse wave sensor, and denoising and standardization processing are conducted through an adaptive filtering algorithm; then, the heart beat position is recognized through a wavelet transform peak value detection algorithm, and a heart rate variability feature sequence is obtained; converting the one-dimensional heart rate variability feature sequence into a two-dimensional heart rhythm image by using an improved Gramian angular field transformation algorithm; and training an arrhythmia classification model by adopting a ResNet-50 convolutional neural network to realize automatic identification of different types of arrhythmia. And finally, continuous monitoring and timely alarming are realized through a sliding window technology and an early warning mechanism. The key technical problems of low detection precision, poor real-time performance, high equipment cost, poor user experience and the like in the existing arrhythmia detection technology are effectively solved, and an innovative solution is provided for heart rhythm health monitoring of the intelligent wearable equipment.

The invention relates to the technical field of flow signal processing and leakage early warning, in particular to an oil engine intelligent monitoring system and method based on multi-source data analysis. A data acquisition unit acquires an oil return pipe flow pulse signal, an engine body vibration signal and an oil pump load rate parameter in real time; the vibration direction analysis unit distinguishes vertical and horizontal vibration dominant modes according to the load rate and outputs directional data and compensation and early warning signals, and the eddy current pseudo peak value detection unit is matched with a pulse template by means of wavelet decomposition, identifies the amplitude, frequency and dislocation characteristics of a pseudo peak value at a pipe diameter sudden change position, and outputs the directional data and the compensation and early warning signals. The dynamic baseline correction unit constructs a vibration and eddy current coupling factor model, compensates a pseudo peak lifting effect at a low load and corrects time sequence offset at a high load, the leakage judgment unit calculates corrected flow deviation degree and weighted residual energy distribution characteristics, leakage alarm is triggered when an alarm threshold value is exceeded, misjudgment caused by interference is eliminated, and the oil engine leakage monitoring accuracy is improved.

The embodiment of the invention provides a water level measuring method and system, electronic equipment and a storage medium. The method comprises the following steps: acquiring echo analog signals of a target water area detected by a millimeter wave radar; performing fast Fourier transform on the preprocessed echo analog signal to obtain a signal frequency spectrum; performing amplitude and phase extraction on the signal spectrum to obtain an amplitude spectrum and a phase spectrum; carrying out peak detection and adjacent point selection based on the amplitude spectrum, and determining a main peak position and an adjacent point position; performing phase difference calculation and fine interpolation on the main peak position and the adjacent point position based on the phase spectrum to obtain a fine estimation frequency; according to the fine estimation frequency, the analog-to-digital conversion acquisition rate of the millimeter wave radar and the linear frequency modulation bandwidth, a preliminary water level measurement value is obtained through calculation; and performing dynamic weighted average and adaptive Kalman filtering on the initial water level measurement value and the historical water level measurement value to obtain a target water level measurement value, so that the embodiment of the invention can realize high-precision water level measurement by applying the millimeter wave radar.

The invention discloses a LoRa signal demodulation and decoding method and system based on blind identification, and the method comprises the steps: carrying out the down-conversion processing of a LoRa signal, and generating a baseband signal corresponding to the LoRa signal; identifying linear sweep frequency characteristics of the baseband signal based on short-time Fourier transform, and judging whether the baseband signal is a CSS modulation signal according to the linear sweep frequency characteristics; if the signal is a CSS modulation signal, detecting the signal bandwidth based on spectrum estimation, and identifying a spreading factor by adopting a peak detection and zero-cross detection method; and configuring parameters of a preset LoRa demodulation and decoding module in real time according to the signal bandwidth and the spreading factor, and demodulating and decoding the LoRa signal according to the configured LoRa demodulation and decoding module to obtain LoRa communication signal content. Through development of a deep learning-based adaptive detection algorithm, blind identification and parameter estimation of LoRa signals in a complex electromagnetic environment are realized, and then robust demodulation and decoding are completed.

The invention provides a multi-modal fusion radar tide level intelligent adaptive filtering method, which belongs to the technical field of radar tide, and comprises the following steps: through dual-polarization radar signal acquisition, performing three-layer wavelet decomposition on vertical and horizontal polarization digital intermediate frequency signals, and marking clutter coefficients based on variance and skewness threshold values; performing adaptive soft threshold processing, evaluating residual clutter confidence coefficient by using a convolutional neural network, dynamically adjusting suppression intensity, calculating a polarization phase difference sequence, inputting the polarization phase difference sequence into a long-short-term memory network to identify a false target distance unit, and calculating multipath path parameters by combining ray tracing of a digital elevation model to perform phase compensation and amplitude correction; finally, an accurate tide level height value is obtained by detecting a real tide level distance Doppler spectrum peak value, and the technical problem that the measurement precision is reduced due to the fact that radar tide level measurement cannot effectively distinguish clutter interference and multipath false targets in a complex marine environment is solved.

The invention provides a non-contact traditional Chinese medicine exterior and interior syndrome identification method and system based on HRV data, and relates to the technical field of biological detection.The method comprises the steps that non-contact face video collection is conducted on an object to be detected through remote photoplethysmography, and an original face video is collected; preprocessing each frame of face image in the original face video to obtain an ROI pixel mean value of the face image, and forming a channel pixel change curve according to the ROI pixel mean value; performing source signal separation on the channel pixel change curve to obtain a plurality of source signals, and selecting the source signal with the highest Pearson correlation coefficient in the green channel as an rPPG signal; performing spline interpolation on the rPPG signal, performing peak detection to obtain an RR interval sequence, and calculating an HRV feature set according to the RR interval sequence; and constructing a syndrome identification model, and discriminating the HRV feature set of the to-be-detected object to obtain a traditional Chinese medicine exterior and interior syndrome identification result of the to-be-detected object. According to the invention, the perception capability of syndrome identification on the slight difference of the physiological state is improved.

The invention relates to the technical field of industrial process control and fluid dynamic measurement, and discloses a high-precision liquid flow detection method based on a sensor. The method comprises the following steps: arranging a sensor, initializing a measurement section, receiving an ultrasonic signal, extracting a phase difference updated along with time, executing two-point temperature calibration, converting liquid temperature and correcting sound velocity, and obtaining static propagation time delay and a theoretical phase; constructing a net phase difference, inverting an axial instantaneous flow velocity according to a propagation difference, and applying non-negative and upper bound constraints; collecting pressure fluctuation to estimate a turbulence equivalent speed and superposing the turbulence equivalent speed with the main speed; calculating the volume flow according to the cross section area and accumulating the volume; phase anomaly, temperature rate and pressure peak detection is carried out, and substitution, recalculation and interpolation are adopted respectively; and counting and normalizing the maximum values of the speed and the flow, and outputting the speed, the flow, the accumulated volume and a normalized curve.

The invention relates to the technical field of remote sensing image processing, in particular to a geographic information acquisition method and system based on remote sensing image.The method comprises the following steps that a narrow-band spectrum sensor obtains multiband image data, reflectivity peak value coordinates are extracted through peak value detection, and band peak value displacement data are calculated; the method comprises the steps of extracting peak displacement of wave bands, analyzing stability through Kalman filtering, screening a stable wave band set, constructing a standard spectrum feature library based on the set, calculating spectrum similarity values through difference values, outputting land utilization types through Bayesian classification, and generating a geographic information distribution diagram. The spectral expression is constructed based on stable reflection characteristics, the influence of imaging condition fluctuation on samples is reduced, the type distinction degree is enhanced through stable wave band difference value symbol matching, the category judgment accuracy based on likelihood inference is improved, classification keeps coherence in an illumination change scene, and the reliability and consistency of spatial distribution results are improved.

The utility model relates to a sine wave excitation circuit for a magnetic flux type current sensor. The device comprises a square wave generator used for generating a square wave signal; the resonant oscillator is connected with the square wave generator, at least generates sine wave signals of fundamental frequency based on the square wave signals generated by the square wave generator and loads the generated sine wave signals to the magnetic core, and the frequency of the fundamental frequency is consistent with the frequency of the square wave signals; the excitation signal sampling feedback circuit is connected with the square-wave generator and is adaptively connected with the magnetic core, so that a magnetic core excitation signal adaptively connected with the magnetic core is sampled and then transmitted to the square-wave generator, and the excitation signal sampling feedback circuit comprises a signal conditioning circuit and a peak detection circuit. According to the utility model, the magnetic core can be effectively modulated, and the noise is lower.