72 results about "Peak detection algorithm" patented technology

The invention relates to the technical field of spectral signal processing, in particular to a high-precision spectral signal peak detection method and system. According to the technical scheme, the method comprises the following steps: denoising and smoothing spectral data; dividing a spectrum curve by adopting a dynamic partitioning strategy; generating candidate partitioning points based on various local features, and generating feature blocks through optimization processing; according to the method, the influence of feature differences of different regions on a detection algorithm is reduced through a dynamic partitioning strategy, a multi-scale morphological feature system is constructed to comprehensively describe spectral curve morphological characteristics, potential peaks are gradually mined by applying a layered iteration peak detection algorithm, and local characteristics of spectral signals are adapted by means of an adaptive parameter adjustment mechanism, so that the spectral curve morphological characteristics are accurately detected. The peak value correction and fusion mechanism is utilized to improve the quality and reliability of a detection result, spectral data with a complex structure and a high noise level can be effectively dealt with, an efficient and reliable solution is provided for the field of spectral analysis, and the accuracy and robustness of peak value detection and the conciseness and reliability of the result are improved.

The invention relates to the technical field of image recognition, in particular to a plate defect recognition and detection method based on an image recognition model. The method comprises the following steps: aiming at the technical problems of insufficient texture direction identification precision and difficulty in defect type distinguishing in a traditional plate surface detection method, the invention provides an intelligent detection solution based on polarization imaging. A multi-angle polarization measurement system is constructed through a fixed polarizer and a rotatable analyzer, and omnibearing polarization characteristic collection of the surface of a steel plate is achieved. A polarization intensity change model is established based on the Malus law, the polarization degree and polarization angle parameters of each pixel point are extracted, and a polarization parameter spatial distribution diagram reflecting the surface microstructure is constructed; through statistical analysis of polarization angle distribution characteristics, a rolling direction dominant angle is identified by adopting a peak detection algorithm, and accurate classification and severity evaluation of defects are realized in combination with anisotropy characteristic analysis.

The invention discloses a non-contact respiration detection method. First, an access point (AP), a base station (STA) and a capture device are arranged in a target area, and channel state information is obtained by capturing a beamforming feedback frame (BFI) periodically sent from the STA to the AP. And then, decompressing and extracting amplitude signals and time information in the BFI frame, performing phase-space reconstruction on the extracted representative subcarrier signals of the antenna combination, and extracting and evaluating the optimal antenna combination through dynamic characteristics. Thirdly, performing Hampel filtering on the subcarrier amplitude signals of the optimal antenna combination to remove abnormal values, performing phase-space reconstruction and dynamic feature extraction on all subcarriers, and selecting the optimal subcarrier by using a K-means clustering algorithm; and then, Savitzky-Golay filtering and band-pass filtering are carried out on the selected optimal subcarrier signal, and linear interpolation is carried out through principal component analysis (PCA) dimension reduction to obtain an equally-spaced sampling signal. And finally, performing fast Fourier transform (FFT) on the processed signal, and estimating the respiratory rate through an innovative peak detection algorithm. The invention provides a non-contact respiration detection method by utilizing a phase space reconstruction and dynamic feature extraction technology, and compared with a traditional method, the method can better capture nonlinear dynamic features of signals and more comprehensively evaluate dynamic features of each subcarrier, so that the subcarrier most suitable for respiration detection is selected, and the detection accuracy is improved. The breathing signal and the interference signal are effectively distinguished, and the accuracy and robustness of non-contact breathing detection based on the Wi-Fi signal are remarkably improved.

The invention discloses a wave crest detection-based train carriage automatic segmentation and weight and speed calculation method. The method comprises the following steps of: acquiring left and right wheel pressure signals and a train overall weight signal when a train passes through a wagon balance in real time; performing multi-scale extremum analysis on the left wheel pressure signal and the right wheel pressure signal by using an RA-AMPD wave crest detection algorithm, screening wheel wave crests meeting height, width and significance conditions, and filtering noise interference; according to a section close to zero value in the weight signal, identifying a compartment gap, combining a wave crest detection result, and through weight auxiliary verification and correction of the wave crest detection result; based on the corrected wave crest data and the weight zero value section information, the starting and stopping positions of each carriage are automatically recognized, and redundant wave crests are removed through a vehicle head and vehicle tail removing mechanism; and after the wave crest time of the wheel set of each carriage is obtained, the passing time of the train or the carriage is calculated, the carriage speed is calculated, and the weight of each carriage is estimated in the stable interval. Through the method, automatic segmentation and weight and speed calculation of the carriage are realized.

The present invention proposes a breathing frequency detection method based on time-frequency analysis of Hilbert-Huang transform. First, a WiFi link with strong environmental perception sensitivity is selected to construct a channel state information (CSI) ratio model. Secondly, the filtered CSI ratio time series is projected, and candidate sets of different breathing mode signals are generated by combining amplitude and phase information. Thirdly, the selected breathing modes are subjected to signal variational mode decomposition (VMD) and time-frequency analysis of Hilbert-Huang transform, so as to remove non-human breathing frequency components. On this basis, reconstruction is carried out, and the reconstructed signals are fused using principal component analysis (PCA). Finally, the breathing frequency is calculated through a false peak detection algorithm. The breathing frequency detection algorithm designed by the present invention not only expands the sensing range and eliminates "blind spots" while removing time-varying phase offsets and high-frequency noise and selecting the optimal subcarriers, but also improves the breathing frequency detection accuracy, providing a more accurate detection method for non-contact breathing frequency detection.

The invention discloses an intelligent image enhancement system and method for a multispectral night vision device, and relates to the technical field of image enhancement, and the method comprises the steps: firstly decomposing an input generative adversarial network fusion image containing artifacts into sub-band images with different frequencies through the multi-stage discrete wavelet transform; a two-dimensional fast Fourier transform and peak detection algorithm is applied to generate an artifact signature graph capable of accurately representing the spatial position of the artifact; then, the artifact signature graph is refined into a smooth space attention graph by a lightweight guide sub-network; the space attention map guides a lightweight correction network based on a U-Net architecture, so that the correction capability is focused on an artifact area; the correction network follows a residual learning normal form, only one artifact residual image is output, finally, the artifact residual image and the generative adversarial network fusion image containing the artifacts are added pixel by pixel, and a final correction image is obtained. And the fidelity and the credibility of the output result of the existing complex GAN model are obviously improved.

The invention provides an unmanned aerial vehicle nest site selection planning method and system and a related device, and relates to the technical field of unmanned aerial vehicles, and the method comprises the steps: converting the latitude and longitude of a tower and the altitude into a 3D voxel grid; extracting spatial features of a tower point group from the 3D voxel grid by using a 3D convolutional neural network; a density peak detection algorithm is applied to the spatial features to determine a clustering center, a decision value is calculated according to the local density and the minimum distance between the local density and the high-density point, and the optimal clustering point is dynamically identified based on the decision value to serve as initial site selection point distribution; and on the basis of the initial site selection point distribution, optimizing the site selection point distribution by using a reinforcement learning model. According to the technical scheme of the invention, the problems of time and labor consumption and difficulty in processing large-scale tower point group data in manual site selection in the prior art can be effectively solved; a traditional method is difficult to comprehensively consider multi-dimensional factors such as signal coverage, economic cost and geographical constraints; and a site selection strategy cannot be dynamically adjusted, and complex geographical environments and task requirements are difficult to deal with.

The invention discloses a stacked steel plate counting method and system based on machine vision. The stacked steel plates can be accurately counted. The counting method comprises the following steps: firstly, scanning the side surfaces of stacked steel plates by using a linear array camera to obtain a steel plate end surface complete image, and intercepting a steel plate area according to gray and texture differences between the steel plates and a background image; denoising and enhancing the steel plate end face image, and detecting upper and lower edge lines of the steel plate to obtain a steel plate edge graph; and finally, projecting the edge image along the edge direction, obtaining the thickness of the steel plate by using Fourier analysis, fusing thickness information into a peak detection algorithm, eliminating missing detection or excessive detection caused by imaging interference, and realizing accurate counting of the steel plate. The movable steel plate counting system is composed of the high-resolution camera, the light source, the polaroid, the industrial personal computer, the displayer, the power source and the motor lead screw transmission mechanism and has the advantages of being convenient to operate and efficient in counting, and the accuracy and reliability of the steel plate counting system are improved.

The invention provides a self-decoupling method for multi-characteristic spectrum inversion and a ribbon-parallel fiber grating sensing optical cable, and relates to the technical field of optical fiber sensing, and it needs to be explained that a grating reflection spectrum of the ribbon-parallel fiber grating sensing optical cable is obtained by determining a reference quantity according to the embodiment of each method; the method comprises the following steps: placing a ribbon-parallel fiber bragg grating sensing optical cable in a preset characteristic calibration state, collecting a reflection spectrum of the ribbon-parallel fiber bragg grating sensing optical cable, and traversing the reflection spectrum through a main peak global extremum search algorithm to obtain a central wavelength of the ribbon-parallel fiber bragg grating sensing optical cable; extracting a first-order left side lobe intensity and a first-order right side lobe intensity in symmetrical wavelength windows at two sides of the central wavelength by adopting a peak detection algorithm, and respectively calculating a wavelength reference variation, a left side lobe reference variation and a right side lobe reference variation according to the calibration reference quantity; and constructing a response characteristic calibration model so as to respectively solve the temperature variation and the strain variation of the ribbon fiber grating sensing optical cable. According to the invention, the decoupling precision and stability can be improved.

The invention provides a water quality suspended matter detection system and method based on sensing data, and belongs to the technical field of water quality suspended matter detection. Gain compensation is performed on a mixed optical signal based on backward spontaneous radiation noise power, so that the influence of self gain drift of a sensor and environmental interference on a signal baseline is effectively eliminated; the adaptive variational mode decomposition algorithm decomposes the standardized signal into a plurality of intrinsic mode functions, accurately extracts signals strongly related to the physical characteristics of the target suspended particulate matter, generates a particulate matter characteristic waveform signal with an optimized signal-to-noise ratio through the intrinsic mode functions screened out through weighted stacking, further highlights the time-frequency characteristics of the target signal, and improves the accuracy of the particulate matter characteristic waveform signal. Each independent particulate matter signal event is accurately separated by adopting a dynamic threshold peak value detection algorithm, and the detection accuracy is improved by extracting a multi-dimensional feature vector from each signal event and distinguishing target particulate matters from interferents by utilizing a pre-trained event classifier.

The invention provides an adaptive filtering method and system for a GPS circuit and a storage medium, and the method comprises the steps: scanning a radio frequency signal of a GPS in real time through broadband radio frequency sampling, and carrying out the analysis based on an energy peak detection algorithm to obtain an interference energy distribution spectrum; determining interference energy based on a preset frequency point to match a preset interference library; if a known interference frequency point is matched, a preset fixed band elimination unit is activated, and if an unknown interference frequency point is matched, LC parameters are dynamically calculated, a programmable resonance unit is configured, and interference signals are dynamically suppressed; verifying the signal-to-noise ratio based on a preset period, calculating to obtain an optimization index, and if the optimization index is insufficient, triggering LC parameter iterative updating until positioning is stable; and a core technical guarantee is provided for a high-precision positioning scene.

The invention relates to a vibration suppression method of a high-precision hardware part continuous punching machine in the field of intelligent manufacturing equipment industry in high-end equipment manufacturing industry, which comprises the following steps of: identifying a main vibration frequency and a secondary vibration frequency by adopting a peak detection algorithm according to vibration spectrum distribution, and determining a key frequency point set of multi-band vibration; through a pre-established vibration mode and damping parameter mapping model, for the dominant frequency vibration feature vector, a table look-up method is adopted to obtain a corresponding initial damping parameter combination, and a preliminary damping adjustment scheme is determined; the optimized damping parameter combination is adopted, the rigidity and the damping coefficient of a damping device are dynamically adjusted through a servo control system, multi-frequency-band vibration is restrained, and stable stamping process data are obtained; geometric dimension data of the suspender and the check claw are collected in real time through a laser measurement system, the dimensional deviation trend is analyzed through a statistical process control algorithm, and the stability of the stamping process is judged.

The invention provides a power distribution network project progress regulation and control method, system and device and a storage medium, and relates to the technical field of power distribution network management, and the method comprises the steps: obtaining project progress related data, including measure input data and progress change data; performing similarity matching on the progress change data and a theoretical progress mode cluster in a preset theoretical cluster library to obtain a matched progress cluster, and calculating progress reference features; performing feature extraction on the input data of the single measure to obtain a project basic feature, an environment interference feature and a measure core feature; calculating an instantaneous gain value sequence of the single measure in the observation time window by utilizing a time sequence gradient lifting tree model and combining the characteristics; determining the cumulative progress contribution degree of the single measure through a peak detection algorithm; generating an adjustable vector of the single measure in the to-be-executed process; and in combination with the confidence interval parameter of the matched progress cluster and the project target progress, generating a regulation and control scheme and issuing the regulation and control scheme to an execution end. According to the invention, the precision of progress regulation and control is improved.

The invention relates to a resonance peak ratio-based single-phase earth fault line selection method for a resonance grounding system, and belongs to the technical field of power system relay protection. The method comprises the following steps of: calculating zero-sequence current data within one tenth of a fundamental frequency period after a fault starting moment of each line; performing Fourier transform on the zero-sequence current of each line to obtain a spectrogram; for each line, determining a resonance peak and a resonance peak in the spectrogram by using a local peak detection algorithm; calculating the harmonic peak coefficient of each line and the average slope of two maximum harmonic peaks before and after the boundary; the resonance peak value ratio of each line is obtained; and finally, determining a fault line by judging whether the resonance peak value ratio meets the criterion or not. The key point is to detect the significant difference between the harmonic peak coefficient after the fault and the average slope of the two maximum harmonic peaks, and the method has the advantages of strong adaptability, high line selection accuracy, easy engineering realization and the like.

Factors such as activity level and sensor positioning can lead to varying results for peak detection algorithms, causing false peaks or causing true peaks to be missed, affecting calculation of Inter-

The invention relates to an underwater seismic source monitoring device with silicon microphones arranged in seven directions inside. The underwater seismic source monitoring device comprises an internal seven-direction silicon microphone device, a multi-channel acquisition system, an underwater seismic source simulation system and a data processing system, the internal seven-direction silicon microphone device comprises an internal seven-direction bracket and seven silicon microphones fixed on the internal seven-direction bracket; a groove is formed in the inner side wall of the internal seven-direction support, the groove comprises a hexagonal bottom surface and six inclined surfaces arranged around the hexagonal bottom surface, a silicon microphone is fixed on the hexagonal bottom surface, and a silicon microphone is fixed on each of the six inclined surfaces. According to the invention, the seven silicon microphones are arranged in a specific arrangement mode, so that the silicon microphones can be conveniently installed on underwater equipment to collect sound signals, the specific coordinate points of the simulated seismic source can be accurately calculated by using the collected sound signals, and the dominant wave transmission direction of the simulated seismic source can be calculated. A peak detection algorithm is adopted to analyze the distribution and evolution rules of different events in the underwater seismic source simulation experiment process.

The invention provides a multi-target angle tracking method suitable for a broadband phased array system, and belongs to the technical field of wireless communication. The method comprises the following steps: acquiring an initial angle of each target through initial angle search; generating a digital baseband signal; calculating the time delay compensation amount of each target in each array element receiving signal in a data field; performing time delay-phase joint weighting on the digital baseband signal to generate a weighted signal; synthesizing a multi-target beam directional diagram through a beam former; and through a peak detection algorithm, an angle maximum value is extracted from the multi-target beam directional diagram, the angle value of each target is updated, and loop iteration is continued. According to the method, the problems of channel inconsistency, low angle resolution and high calculation complexity of a traditional phased array system during multi-target tracking are solved. The method can track a plurality of targets at the same time, and is suitable for unmanned aerial vehicle communication and high-speed motion platforms.

The invention relates to a method and device for detecting the liquid level position in an oil casing annulus. The method comprises the following steps: acquiring a temperature distribution data sequence of an oil well acquired by a distributed optical fiber sensing system; feature points caused by the tubing coupling are recognized through a peak detection algorithm, and the number and positions of the feature points are recorded; obtaining a first liquid level according to the number of the feature points; determining a second liquid level based on local temperature characteristics of the temperature distribution data sequence; and calculating the liquid level depth according to the feature point position, the first liquid level and the second liquid level. By the adoption of the method, continuous and real-time working fluid level detection can be achieved through existing distributed optical fiber sensing data, dependence on special hardware is avoided, and system complexity and cost are remarkably reduced on the basis that measurement robustness and accuracy are improved.

The invention discloses a power transmission line steel pipe pole bearing platform assessment method based on a prefabricated pipe pile, and relates to the technical field of power transmission line steel pipe pole bearing platform assessment, and the method comprises the following steps: exciting an elastic wave of a bearing platform-pipe pile interface through a sound wave emission device, and receiving a sound wave signal through an ultrasonic array sensor at the top of the pipe pile; carrying out feature separation on the sound wave signal, and evaluating the sound velocity relative deviation according to a separation result; on the basis of an evaluation result, constructing an optimal sound velocity gradient interval by adopting a dynamic optimization algorithm comprising a Gaussian mixture model, a density peak detection algorithm and NSGA-II (Non-dominated Sorting Genetic Algorithm-II) optimization; identifying a gradient mutation region in the interval, and expanding a defect detection range by taking the region as a reference; and constructing a defect characteristic spectrum based on the expanded defect detection range, carrying out void defect positioning according to the defect characteristic spectrum, and outputting a grading evaluation result, thereby solving the problems that the defect detection range is limited and the accuracy of bearing platform evaluation is reduced due to the fact that a sound velocity gradient interval is not dynamically optimized when sound velocity is used for evaluation in a traditional method.

The present application relates to a kind of lightning intensity prediction method and system based on deep time sequence learning, belong to lightning early warning technical field, solve the problem of insufficient fine prediction ability of lightning intensity in prior art, peak time positioning is not accurate.The method comprises: obtaining current atmospheric electric field intensity data, using peak detection algorithm, determine the current peak position;According to the current atmospheric electric field intensity data, determine the current intensity time sequence data;Current peak position, and current intensity time sequence data, input lightning intensity prediction model, determine the first predicted peak position and the first predicted intensity peak;Wherein, lightning intensity prediction model includes time feature fusion layer, encoder layer and double-head output layer.The accurate timing and quantitative prediction of sparse, sudden lightning intensity peak are realized.

This invention provides a method, system, equipment, and storage medium for project schedule control in power distribution networks, relating to the field of power distribution network management technology. The method includes: acquiring project schedule-related data, including data on measure inputs and schedule changes; performing similarity matching between the schedule change data and theoretical schedule pattern clusters in a pre-set theoretical cluster library to obtain matched schedule clusters, and calculating schedule baseline features; extracting features from the input data of individual measures to obtain basic project features, environmental interference features, and core feature features of the measures; using a time-series gradient boosting tree model, combined with these features, calculating the instantaneous gain value sequence of individual measures within the observation time window; determining the cumulative schedule contribution of individual measures through a peak detection algorithm; and generating an adjustable vector for the individual measures in the pending execution process based on this; and generating a control plan and distributing it to the execution end by combining the confidence interval parameters of the matched schedule clusters and the project target schedule. This invention improves the accuracy of schedule control.

The invention discloses a guided wave radar level meter and a method for determining the position of an interface by using the guided wave radar level meter. A guided wave radar level gauge (100) for determining a filling level of a product (102) contained in a tank (104), comprising: a measurement unit (106), a probe (110), and control circuitry configured to: transmit a signal along the probe; receiving a reflected signal; detecting a first peak in the received signal, the first peak having a first amplitude exceeding a first threshold amplitude; determining that a second peak having an amplitude exceeding a second threshold amplitude is not detected; applying a sliding window peak detection algorithm to the signal to detect a second peak in the signal; and determining the position of the material interface based on the position of the second peak.

The invention relates to the technical field of frequency measurement, and discloses a short-wave carrier center frequency measurement method, which comprises the following steps: collecting short-wave signal data, and carrying out parallel processing to generate multi-domain feature representation; constructing a multi-scale time-frequency analysis framework by applying fractional order wavelet transform; fractal dimensions and self-similarity indexes of the signals are calculated, and geometric features are extracted; combining fractal features and noise type recognition results to optimize fractional order parameters; feature fusion is executed in a multi-domain multi-scale space, and enhanced frequency representation is constructed; accurately positioning the carrier center frequency by applying a hierarchical peak detection algorithm; according to the method, the accuracy and the reliability in the short-wave carrier are remarkably improved while the complexity of the algorithm is kept controllable, and particularly, the method has obvious advantages in a low signal-to-noise ratio and complex interference environment, so that powerful support is provided for improving the performance of a short-wave communication system.

The invention relates to a resonance protection method and device, equipment, a storage medium and a program product. The method comprises the following steps: acquiring voltage and current data of a target node of a to-be-tested power system; determining an inter-harmonic set and an inter-harmonic subset from the voltage and current data; if the root-mean-square values of the inter-harmonic set and the inter-harmonic subset meet the triggering condition of resonance protection, extracting peak and valley moments of voltage from the voltage and current data by using an adaptive multi-scale peak detection algorithm; determining resonance protection parameters of the target node according to the peak and valley moments; and performing resonance protection on the target node according to the resonance protection parameter. By adopting the method, the anti-interference capability can be improved.

The present application relates to the field of virtual reality, in particular to a lung rehabilitation nursing psychological counseling method and system based on virtual reality. The method collects the chest and abdominal girth change signal of the patient, calculates the respiratory flow rate curve through band pass filtering and peak detection algorithm, identifies the inspiration starting point, expiration starting point and breath holding starting point on the respiratory flow rate curve as the respiratory phase turning point. The digital phase-locked loop mechanism is introduced, the respiratory phase turning point is taken as the reference clock signal, the actual presentation time of the virtual visual and auditory stimulation is taken as the output signal of the voltage-controlled oscillator, the rendering pipeline delay of the visual and auditory generation component is adjusted through closed-loop feedback, so that the phase delay of the virtual visual and auditory stimulation is constantly within the preset neuromuscular response threshold. The present application eliminates the phase lag and drift phenomenon between the audio-visual guide signal and the real breathing action of the patient, and enhances the neural drive synchronization of the diaphragm and auxiliary respiratory muscles under the rhythmic audio-visual stimulation.

The invention relates to the technical field of electronic reconnaissance signal processing, in particular to a signal processing architecture of an electro-optical front-end reconnaissance system, which comprises a digital-to-analog conversion module for sampling an input radio-frequency signal and converting the input radio-frequency signal into a digital signal; the time-frequency conversion module is used for converting time domain information of the digital signal into frequency domain information; the frequency domain analysis module obtains frequency domain parameters by using a peak detection algorithm, a bandwidth detection algorithm and a defuzzification algorithm based on phase remainder matching; the frequency conversion filtering module is used for moving the digital signal to zero frequency and filtering the digital signal according to a frequency domain parameter to obtain mixed and filtered IQ data; and the time domain analysis module is responsible for analyzing the IQ data through the GO-CAFAR threshold detection circuit to obtain a time domain parameter of the signal, and outputting the IQ data, the frequency domain parameter and the time domain parameter to terminal equipment. According to the architecture, the transmission pressure of frequency domain reconnaissance signals can be reduced, and the frequency resolution and sensitivity of a system are improved.

The present application belongs to the field of frequency hopping network station parameter estimation, and particularly relates to a frequency hopping period estimation method based on FFT transformation. In this scenario, mainly containing frequency hopping network station signals and other interference signals, the characteristics of conventional ultrashort wave frequency hopping network station fixed frequency hopping period can be utilized, the time-frequency peak value of frequency hopping pulse is obtained through a time-frequency peak value detection algorithm, the time center sequence of frequency hopping pulse is created according to the time-frequency peak value, and the FFT transformation processing is performed on the time center sequence, so that the peak value coordinate information related to the frequency hopping period can be read in the transformed FFT spectrum, and the value of the frequency hopping period can be calculated according to the information coordinate. The present application can obtain accurate estimation value under the condition of poor detection (more missed detection frequency hopping numbers) and low time resolution of time-frequency diagram, and the method is simple and has good effect.

The invention relates to the technical field of laser-induced breakdown spectroscopy, in particular to an LIBS (laser-induced breakdown spectroscopy) technology-based real-time processing and quantitative analysis system for spectral data, which comprises a laser emission module, a spectrum acquisition module, a real-time processing unit, a quantitative analysis module, an environmental parameter monitoring module and a remote communication module, the sub-second response speed is realized through an FPGA parallel processing architecture, the signal processing precision is improved by adopting an improved wavelet threshold algorithm and a self-adaptive peak position detection algorithm, and a multi-element analysis error is controlled to be lt through a matrix effect correction algorithm; 5%. The system is provided with an environmental parameter monitoring module and a remote communication module, achieves environmental interference suppression and remote data transmission, is suitable for rapid element quantitative analysis scenes in industries such as metallurgy, environmental protection and geological exploration, and has the remarkable advantages of being high in real-time processing capacity, high in quantitative precision, high in anti-interference capacity and the like.

The present invention provides a method and system for receiving echoes with a large dynamic range by a lidar, and a lidar. The method includes the following steps: S1. The radar receiver receives the laser echo signal reflected by the object to be measured, amplifies the laser echo signal with three different gain levels to obtain three amplified echo signals, and splices the three amplified echo signals by using an accumulative splicing algorithm to obtain a laser echo input signal; S2. Smoothly filter the laser echo input signal to obtain a smooth laser echo signal; S3. Extract the optimal laser echo from the smooth laser echo signal; wherein, the peak detection algorithm is used to confirm the first laser echo and the last laser echo; obtain the fitting function, perform fitting processing on the fitting function by using the least squares method, and then decompose the optimal laser echo signal by using the least squares fitting algorithm. The present invention can achieve detection within a large dynamic range at a long distance and has high detection accuracy.

Systems / techniques are provided for facilitating post-detection chromatographic peak validation. In various embodiments, a system can cause a chromatography device coupled to a mass spectrometer to scan a specimen, thereby yielding a chromatogram and mass spectra. In various aspects, the system can identify, via a peak detection algorithm, a plurality of purported peaks in the chromatogram. In various instances, the system can separate, via execution of a machine learning classifier on respective ones of the plurality of purported peaks, the plurality of purported peaks into a set of valid peaks and a set of invalid peaks. In various cases, the system can perform a mass spectrometry analysis on first portions of the mass spectra that correspond to the set of valid peaks but not on second portions of the mass spectra that correspond to the set of invalid peaks.