6727 results about "Echo signal" patented technology

The invention relates to the technical field of defect detection, and discloses a flange forging defect detection method and system. The method comprises the following steps: performing three-dimensional scanning and material acoustic characteristic measurement on a to-be-detected wind power flange to obtain layer partition sound isolation path data; generating an array element excitation control file of the double-array phased array ultrasonic detection system; applying the array element excitation control file to a double-array phased array ultrasonic detection system, and performing micro defect feature enhancement on a received echo signal to obtain a feature-enhanced signal data set; performing ultrasonic emission and data acquisition on the wind power flange to obtain a global detection data set; and inputting the global detection data set into the two-stage defect detection model for defect feature extraction and classification evaluation, and generating a defect detection evaluation report. According to the method, the detection rate and the classification accuracy of the micro forging defects are improved.

The invention provides an efficient flaw detection method of an ultrasonic flaw detector for automatic detection of a welding seam, and belongs to the technical field of welding seam detection.The efficient flaw detection method comprises the steps that firstly, a welding seam three-dimensional digital model is established, an optimal scanning path is determined, reflection echo signals are processed in real time through an adaptive beam forming algorithm, and defect features are extracted and subjected to quantitative analysis; and establishing a neural network model based on the defect characteristic spectrum, and performing deep learning training by using the sample library. A multi-angle scanning strategy is adopted to implement full-coverage detection, and cross scanning is applied in a key area to improve the detection probability. And calculating the accurate position of the defect by using a time-difference method, and constructing a defect three-dimensional distribution diagram by combining a multi-angle data fusion technology. The optimal filtering parameter is determined through the acoustic feature optimization function, and the defect signal-to-noise ratio is improved. And finally, a quality evaluation report is automatically generated based on a detection result, so that the problems of defect identification accuracy and detection efficiency in weld flaw detection are effectively solved.

The invention provides a manifold constraint-based sonar imaging reverberation elimination method and system, and is applied to the field of imaging data processing. According to the method, the time domain waveform of the echo signal is collected in real time, whether the irregular reflection peak value is detected or not is judged, potential interference and reflection in the underwater environment can be rapidly recognized, and particularly, in the complex underwater environment, non-target objects (such as seabed obstacles or sundries) possibly cause irregular reflection signals, so that the interference and reflection in the underwater environment can be rapidly recognized. Meanwhile, the underwater propagation characteristics of sound waves can be accurately modeled by collecting the reflection path of the echo signal and predicting the propagation parameters of the reverberation signal. And accurate background data can be provided for the propagation modeling of the reverberation signal through the pre-recorded information of submarine topography, geological characteristics, sound wave propagation characteristics and the like.

The invention relates to a calibration method of an ultrasonic flaw detector calibrating device, which comprises the following steps of: acquiring and transmitting ultrasonic waves of which the frequency is matched with the theoretical frequency of a material to be detected, acquiring and dynamically correcting the actual propagation path of the ultrasonic waves in real time, generating a calibration parameter of a frequency-delay compensation coefficient, and compensating the transmitted ultrasonic waves based on the calibration parameter. After the compensated ultrasonic waves enter a to-be-detected material, time-base line signals are collected and input into the trained deviation calculation model to calculate time axis scale deviation, time-base line linearity is adjusted based on the time axis scale deviation, stepped ultrasonic attenuation signals are generated, echo signal amplitude values of the flaw detector are collected, and the amplitude values of the echo signals of the flaw detector are calculated. And comparing the echo signal amplitude value with a preset amplitude threshold value to obtain a dynamic difference value of the two, and adjusting the attenuation amount in real time based on the dynamic difference value. The problems of insufficient calibration efficiency and consistency, poor dynamic parameter adaptability, limited sensitivity calibration precision and complex working condition simulation capability in the prior art are solved.

The present application discloses a multifunctional health monitoring method and system based on a millimeter wave radar, and a computer readable storage medium. The method comprises: receiving an echo signal in a space, and on the basis of the echo signal, calculating point cloud data of a user and phase change data caused by the respiration and heartbeat of the user; calculating behaviors of the user on the basis of the point cloud data; calculating vital signs of the user on the basis of the phase change data, wherein the vital signs comprise respiration rate and heart rate; and on the basis of the behaviors and / or the vital signs, determining that the user triggers a set alarm event, and on the basis of the type of the triggered alarm event, performing a corresponding alarm operation.

The invention discloses a composite board ultrasonic defect detection method which comprises the following steps: S1, fixing a carbon fiber circumferential winding composite pressure container on a bracket, and establishing a coordinate system with the axis of the container as a z axis; s2, a 40 MHz dry coupling phased array ultrasonic probe is attached to the scanning starting point, and the contact angle of the probe is recorded; s3, moving the probe along the spiral track of the outer surface of the container at the speed of 20mm / s, and collecting A-scanning echo signals of all array elements; s4, performing pulse compression and time domain deconvolution processing on the acquired signal to obtain a time domain echo sequence; s5, delay time is calculated according to the shell curvature, the array element signals are compensated, and focusing B-scanning data are generated; s6, splicing the B-scanning data at the step length of 0.5 mm * 0.5 mm, and reconstructing C-scanning data corresponding to the space coordinates; and S7, inputting the C-scanning data into the trained Transform network, and outputting defect types, sizes and three-dimensional coordinates. The method realizes high-frequency ultrasonic composite board defect accurate detection, remarkably improves the microcrack recognition rate, and is widely applied to safety evaluation scenes of high-pressure hydrogen storage tanks and pressure vessels.

A transmission tower deformation monitoring device based on a phased array radar technology relates to the field of irregular contour metering, and comprises a map construction module used for constructing a regional digital map; the boundary determination module is used for near-field region boundaries; the signal acquisition module is used for acquiring a near-field echo signal of a near-field region through a narrow beam and acquiring a far-field echo signal of a far-field region through a wide beam; the calibration table construction module is used for constructing a signal intensity comparison table; the data processing module is used for determining effective monitoring data; the error calculation module is used for performing error calculation on the effective monitoring data to obtain a phase error and an amplitude error; and the parameter determination module is used for determining iron tower deformation parameters. According to the invention, the accuracy of transmission tower deformation monitoring can be improved.

The invention relates to the technical field of new material detection, and particularly discloses an ultrasonic flaw detection method and system for a carbon steel bar. The method comprises the following steps: acquiring an appearance image and a section label, constructing a probe path, generating a sound field compensation factor, collecting and preprocessing an echo signal to form a multi-modal signal sequence, further extracting time sequence and spatial characteristics of the multi-modal signal sequence to generate fusion representation, and finally realizing defect classification and label generation. And dynamically updating model parameters and path configuration based on a classification result. The method can improve the detection intelligence and robustness under a complex structure, has high adaptability, high precision and high self-optimization capability, and is suitable for intelligent defect identification of industrial grade carbon steel materials.

The invention provides a double-mechanism cooperative type curved surface component water immersion ultrasonic intelligent detection method and system, and relates to the technical field of ultrasonic detection, and the method comprises the following steps: scanning a workpiece by using a double-mechanism water immersion ultrasonic detection system, collecting and processing an ultrasonic echo signal, and generating an ultrasonic C scanning image. And performing defect identification on the ultrasonic C scanning image by using the constructed multi-scale feature fused deep convolutional neural network model to obtain the type, position, size and confidence of the defect. Quantitative evaluation is carried out based on the defect recognition result, a report is generated, and meanwhile visual display and database management of the defect recognition result are achieved. Through double-mechanism cooperative scanning and deep learning intelligent identification, the precision and efficiency of curved surface component defect detection are improved, and automatic identification, quantitative evaluation and visual display of defects are realized.

The invention discloses an intelligent material conveying deviation remote monitoring system based on Internet of Things, which relates to the technical field of material conveying deviation remote monitoring and comprises a signal monitoring module, an abnormal window calibration module, a distortion evaluation module, a data correction module and a remote optimization module. The distortion evaluation module is used for acquiring ultrasonic propagation distortion characteristic information in a signal propagation abnormal window in real time, analyzing and evaluating the echo signal distortion degree under the condition of ultrasonic signal propagation path distortion after acquisition, and dividing the echo signal distortion degree into three types, namely a light distortion degree, a medium distortion degree and a heavy distortion degree; and the data correction module is used for taking corresponding correction measures on the to-be-corrected measurement data under the condition of different echo signal distortion degrees based on the evaluation result. According to the method, the problem of measurement errors caused by propagation path distortion of ultrasonic signals in conveyor belt deviation monitoring is solved, accurate distortion evaluation and adaptive data correction are realized, and the monitoring precision and stability are improved.

The invention provides a lightweight AI personnel perception method and system based on a millimeter wave radar, and relates to the technical field of personnel perception, and the method comprises the steps: carrying out the phase compensation, amplitude calibration and bidirectional combined filtering processing of a millimeter wave echo signal, and constructing three-dimensional space point cloud data. Then, constructing a spatial distribution matrix based on the point cloud data, calculating the center position of a point cloud cluster, establishing an adaptive sampling radius to extract point cloud features, and calculating a motion vector and attitude parameters; and finally, inputting the motion vector and the attitude parameter into a double threshold judgment module, judging whether a person exists in the target area, generating a person position thermodynamic diagram based on the center position of the point cloud cluster, and outputting a person perception result. According to the method, the calculation complexity is reduced, the real-time performance and accuracy of personnel perception are improved, and the method is suitable for smart home, security monitoring and other scenes.

The invention discloses an electromagnetic ultrasonic thickness detection method and system based on a miniaturized low-power-consumption internet of things, and the method comprises the steps: carrying out the parameter optimization processing through a pulse coding excitation algorithm according to the electromagnetic characteristic parameters of a target material and a preset detection precision requirement, and outputting a low-power-consumption excitation parameter set; driving an electromagnetic ultrasonic transducer to generate a detection signal based on the low-power-consumption excitation parameter set, and outputting a compressed echo signal; performing multi-scale intrinsic mode decomposition on the compressed echo signal, and outputting a thickness correlation characteristic parameter set; inputting the thickness correlation characteristic parameter set into a depth residual error-attention network model, and outputting a thickness prediction result; and performing multi-node data fusion on the thickness prediction result based on an Internet of Things cloud platform to generate a final thickness detection report. According to the embodiment of the invention, high-precision and traceable thickness detection can be realized based on the miniaturized low-power-consumption Internet of Things.

The invention relates to an emotion recognition method based on a millimeter-wave radar and a robot. The method comprises the following steps: receiving a transmitting signal of the millimeter-wave radar and a corresponding echo signal, converting the transmitting signal and the corresponding echo signal into a point cloud format, and removing static reflection to obtain dynamic point cloud data and a timestamp of a surrounding environment; grouping the dynamic point cloud data into clusters; obtaining a centroid estimation value of the target, and tracking the centroid estimation value; sending the millimeter wave signal which horizontally moves at a constant speed to a target person, and receiving echo signals which are reflected by the target person and are uniformly spaced; point spread function optimization is carried out, and SAR image reconstruction is carried out by using a fast frequency domain near-field imaging method; calculating to obtain phase change of the human body signal, and separating to obtain respiration and heartbeat signals of the target person; and extracting emotion related features to obtain the current emotion of the target person. Compared with the prior art, the specific position of the human body can be accurately recognized, and non-contact privacy protection emotion recognition can be remotely achieved.

The invention provides a ship radar identification method based on deep learning, and relates to the technical field of ship radar signal processing. The method comprises the following steps: denoising and filtering echo signals reflected by a target, and generating spatial features and filtered target feature signals based on the denoised and filtered signals; carrying out enhancement and normalization processing on the filtered target characteristic signal, and further carrying out space-time decomposition and nonlinear fusion to obtain a fused space-time signal matrix; windowing processing is carried out on the fused space-time signal matrix, and space-time feature optimization is carried out; generating data after spatio-temporal feature fusion through a spatio-temporal feature adaptive optimization algorithm; and inputting the data after spatio-temporal feature fusion into the target recognition model, outputting a prediction result and a confidence value of the target category, and optimizing the target recognition model. The problems that a traditional radar target recognition method is difficult to effectively extract accurate spatio-temporal features and lacks adaptive ability, and the generalization ability of a target recognition model is insufficient are solved.

The invention relates to a millimeter wave radar vital sign modeling method based on time-frequency characteristic decoupling. According to the method, a millimeter wave radar array is arranged, reflection echo signals are collected, and pure initial signal data are obtained; performing time-frequency transformation on the initial signal data based on a multi-scale sliding window to generate a time-frequency energy distribution map; according to the energy concentration degree and stability difference of different frequency components in the time-frequency spectrum, frequency components which are high in energy concentration degree and have continuous, stable and periodic changes on a time axis are extracted and serve as effective signal components corresponding to human respiration and heartbeat characteristics; extracting a corresponding frequency change trajectory according to the effective signal components, establishing a vital sign signal trajectory model based on a time-frequency characteristic change trend, and determining an optimal vital sign trajectory path according to the stability and continuity of the trajectory on a time axis; and establishing a vital sign monitoring model by using the optimal vital sign track path to realize high-precision monitoring of human respiration and heart rate.

The invention discloses a sonar target recognition system based on a residual network, and the system comprises a sonar preprocessing module which is used for collecting and preprocessing an original sonar echo signal; the frequency spectrum construction module is used for generating a multi-channel frequency spectrum image; the spectrum enhancement module is used for performing spectrum enhancement processing; the residual feature extraction module is used for extracting sonar target feature representation; the label screening module is used for calculating a label credibility score and constructing a label training set; the joint training module is used for combining the label training set and the real labeling samples to form a joint training set and executing iterative optimization of the SonarResBlock model; and the identification output module is used for performing reasoning identification by using the updated model and outputting a category label of the sonar target. According to the method, multi-class spectrum construction and a residual attention mechanism are fused, accurate sonar target recognition is achieved, and the method has the advantages of being comprehensive in modeling, high in robustness and efficient in training.

The invention provides a joint denoising method and system based on multi-source noise suppression and sparse representation, and belongs to the technical field of noise suppression in ultrasonic detection of power equipment, and the method comprises the steps: synchronously collecting an ultrasonic echo signal, a vibration signal and an electromagnetic signal of a composite insulator; decomposing the ultrasonic echo signal into a plurality of modal components and undecomposed signal margins; extracting a dominant frequency component in the vibration signal, and identifying a resonance noise feature overlapped with the frequency band of the ultrasonic signal; detecting a pulse interference event in the electromagnetic signal, marking occurrence time and a frequency range, and generating a pulse event feature; performing frequency band suppression and time domain threshold processing on the modal component according to the resonance noise characteristics and the pulse event characteristics; performing sparse representation coding on the processed modal components, executing multiple rounds of iterative screening and orthogonalization processing, and screening an atom set matched with defect echoes; and reconstructing the de-noised ultrasonic signal by using the screened atom set, and combining the de-noised ultrasonic signal with the undecomposed signal margin to output a final result.

The invention discloses a space-variant self-focusing imaging method for a missile-borne synthetic aperture radar sea surface moving target. The method comprises the following steps: acquiring a radar fundamental frequency echo signal; performing range pulse pressure and range migration correction on the radar fundamental frequency echo signal to obtain a correction signal, and further obtaining a coarse focusing radar image; extracting a single defocusing target image in the coarse focusing radar image, and inverting the single defocusing target image to an echo data domain to obtain data of single target energy; performing correction to obtain data after correction of the residual range walk; selecting a part of distance units from the data after the residual range walk is corrected, performing Doppler frequency modulation rate estimation on the selected part of distance units by adopting fractional Fourier transform, fitting the estimated Doppler frequency modulation rate by adopting a space-variant phase error model, and constructing a space-variant phase error compensation function of the target; and compensating the data after the correction of the residual range walk, and imaging. According to the invention, the refocusing effect of the moving ship target can be improved.

The invention relates to an ultrasonic flow metering device and a metering method thereof, and relates to the field of ultrasonic measuring devices. According to the metering device, through a cross-correlation operation circuit, a microcontroller, a double-threshold comparator, a peak detector, a gain adjustment circuit and a head wave threshold adjustment circuit, self-adaptive processing and adjustment of echo signals and head wave thresholds are achieved, and the accuracy and reliability of flow metering are improved. The metering method adopts an adaptive adjustment strategy of echo signal amplifier gain based on an output signal of an echo signal peak detection circuit, and a head wave threshold adaptive adjustment strategy based on a correlation operation value of echo signal sampling data and pre-stored reference wave data. According to the scheme, the stability of the echo signal and the rationality of the head wave threshold value are guaranteed, the method has adaptability to echo contour feature changes caused by transducer aging or contamination, and then the reliability of the flow metering process is guaranteed.

A multipath identification method and apparatus, a vehicle, and a medium. The method comprises: preprocessing an echo signal of a millimeter wave radar, so as to obtain static point cloud data and dynamic point cloud data in the echo signal; clustering the dynamic point cloud data by means of improved DBSCAN clustering algorithm, so as to obtain multi-cluster dynamic data; on the basis of an object reflection boundary parameter and cluster center information in the multi-cluster dynamic data, determining at least one matching set, the matching set containing at least two cluster centers, and the object reflection boundary parameter being determined on the basis of the static point cloud data; and for each matching set, performing identification on the cluster centers in the matching set to obtain a target cluster center, and determining the target cluster center and the point cloud data in a cluster where the target cluster center is located as a multipath object in the echo signal. The method can effectively identify a multipath object, thereby improving the accuracy and robustness of vehicle-mounted millimeter wave radar detection.

The present invention belongs to the technical field of LiDARs, and particularly relates to a system and method for synchronous detection of atmospheric data in the same path. The detection system comprises a transmitting unit, a receiving unit and a data processing unit, wherein the transmitting unit comprises a plurality of laser devices, and is used for synchronously transmitting laser having different wavelengths in a common path; the receiving unit comprises a receiving telescope, which is symmetrically provided with a plurality of sub-apertures used for receiving echo signals that are generated after the laser having different wavelengths interacts with the atmosphere; and the data processing unit is used for calculating atmospheric data parameters by means of the echo signals. By means of the present invention, synchronous detection of a plurality of atmospheric data parameters in the same path can be implemented.

The invention discloses a rubber-lined pipeline defect detection method and system, and the method comprises the steps: arranging an ultrasonic probe array, obtaining large-area scanning information of a rubber-lined pipeline, collecting an ultrasonic signal, an echo signal and a resonance signal, generating a resonance signal curve, and reflecting a mapping image of the interior of the rubber-lined pipeline. And calculating a half-power bandwidth and a damping characteristic parameter, generating a corresponding characteristic vector through the identification model, identifying and positioning defects in the rubber-lined pipeline, evaluating the severity of the defects, and outputting an identification result. Through the structural design of arranging a plurality of array elements of the array probe, the coverage range is increased, ultrasonic wave emission and receiving are carried out on the rubber-lined pipeline from different angles and positions, the probability of discovering small-area debonding is improved, in addition, the machine learning technology is combined, an intelligent recognition model is trained, rich features are provided, and the detection accuracy is improved. The capability of identifying different types of defects is enhanced, the sensitivity to different types of defects is improved, and the risk of missing detection of small-area debonding is reduced.

The invention relates to the technical field of remote medical treatment, in particular to a remote ultrasonic diagnosis support system based on cloud computing, which comprises an echo feature analysis module, a dynamic focus screening module, a case image comparison module, a focus trend classification module and a remote diagnosis support module. According to the method, by analyzing echo signals in a remote ultrasonic image data stream and calculating the echo change rate between image frames, the dynamic characteristic capturing capacity of a lesion area is improved, calculation of a gray gradient change value is combined, a suspected lesion area conforming to a change rate stable interval is screened, lesion shape edge curve contour parameters are utilized, and the dynamic characteristic capturing capacity of the lesion area is improved. The method comprises the following steps: performing matching analysis with an ultrasonic case image database, enhancing the accuracy of lesion category attribution, analyzing the morphological change trend of a lesion area, combining trend stability index calculation, realizing lesion evolution classification, ensuring the reasonability of lesion type division, and marking a progressive lesion area, so that remote diagnosis is more intuitive; and the auxiliary decision-making capability is improved.

The invention provides a sprayed concrete hardening detection method and system, and relates to the technical field of material detection.The sprayed concrete hardening detection method comprises the steps that detection requirements are determined based on physical characteristics of sprayed concrete, and a progressive multi-frequency excitation sequence is established; after each time of multi-frequency excitation is completed, echo signals in the concrete are monitored in real time, and excitation parameters are dynamically adjusted according to the quality of the echo signals; the collected echo signals are preprocessed; performing multi-dimensional characteristic parameter extraction on the preprocessed echo signals, including time domain characteristics, frequency domain characteristics and energy domain characteristics, to form representative characteristic vectors; removing redundant features according to the feature vector, and calculating a hardening score by using residual features; and outputting a detection result and early warning information according to the hardening score. According to the method, the hardening state of the sprayed concrete can be accurately evaluated, so that reliable technical support is provided for engineering construction quality control.

The invention provides a self-adaptive radar target detection method, system and device and a medium, and belongs to the technical field of self-adaptive radar target detection, and the method comprises the steps: carrying out the high-resolution decomposition and Gaussian fitting of a radar echo signal, analyzing the spectral centroid and spectral width characteristics, and determining the frequency domain interval of the radar echo signal; decomposing the radar echo signal into a plurality of intrinsic mode components, and screening out the mode components in a target frequency domain interval for reconstruction; key features in the reconstructed radar echo signals are extracted from the time domain dimension and the frequency domain dimension, and features with distinguishing force on target detection are reserved through a feature screening method; the method comprises the following steps: segmenting a long-time signal in a radar echo signal into a plurality of small windows, and respectively carrying out VMD decomposition and reconstruction; and carrying out target detection on the extracted key features by using a CA-CFAR algorithm. The method improves the precision of target detection under the background of complex sea conditions and strong sea clutters, and improves the accuracy and robustness of target detection.

The invention discloses a single-channel wallboard damage positioning method based on a Lamb wave coding metamaterial interface. The method comprises the following steps: constructing a prior database for wallboard damage detection; the prior database stores an arrangement form of a damage monitoring area on the wallboard, arrangement parameters of a coding unit, Lamb wave parameters of an excitation signal applied during damage detection, an echo signal of the wallboard in a healthy state and a neural network model used for identifying a damage direction; for the wallboard to be subjected to damage positioning, arranging a coding unit and an exciter according to the information in the prior database, applying an excitation signal, and then collecting an echo signal; a polar angle region corresponding to the damage is obtained based on a neural network model, and orientation identification of the damage is achieved; the flight time of Lamb waves is obtained based on signal processing, so that the distance from the damaged part to the center of the damage monitoring area is determined, and the damage distance is determined; and finally, wallboard damage positioning under single-channel data acquisition is realized.

The invention discloses a small sample radar target identification method based on multi-feature fusion. According to the invention, the natural resonant frequency characteristics of radar echo signals are introduced into a small sample learning framework, and the advantages of certain stability and simple and rapid extraction under the condition of attitude and observation angle change are fully utilized; attention multi-feature weighted fusion based on energy guidance is carried out on natural resonant frequency features and time frequency features, scattering features are described from different feature mechanisms, and the completeness of the features under the small sample condition is improved; in addition, a loss function based on combination of feature similarity measurement and classification loss is also designed, so that the model can learn feature representation with higher distinction degree while the feature space discrimination is optimized, and the performance is improved in a feature fusion-classification network. According to the method, on the premise that the reasoning time and the calculation complexity are not remarkably increased, the recognition accuracy under the condition that the observation angle changes is effectively improved.

The invention provides a high-imaging-resolution method for identifying a lead sealing defect part of a cable, and relates to the field of nondestructive testing. Comprising the steps that a linear array probe array is utilized, a plurality of array elements emit ultrasonic waves to a to-be-detected defect part at the same time, and a received echo signal reflects the waveform shape and the waveform phase of the defect. A two-dimensional image is preprocessed through a synthetic aperture focusing technology, a combined image is reprocessed through a phase coherence technology, the quality and accuracy of the image are further improved, and a high-resolution three-dimensional image is constructed in combination with an imaging synthetic aperture focusing technology. The ultrasonic phased array technology, the SAFT technology, the phase coherence technology and the synthetic aperture focusing technology are combined, the imaging resolution is effectively improved, the method has remarkable advantages especially when defect parts in complex structures such as cable lead seals are recognized, the imaging quality is optimized, a two-dimensional image is converted into a high-resolution three-dimensional image, and finally an accurate defect recognition result is obtained.

The invention discloses an atmosphere laser radar data analysis method based on deep learning, and the method comprises the following steps: S1, collecting and preprocessing atmosphere laser radar echo signal data, and generating a time series data set; s2, constructing a long sequence prediction model based on Transform, and forming a preliminary prediction result sequence; s3, optimizing model structure parameters and training parameters by using a particle filtering algorithm to obtain an optimized model; s4, using the optimized model to predict current and future moment data, and generating a final prediction sequence; s5, performing residual analysis on the final prediction sequence and the real-time observation data, and identifying abnormal points; and S6, sudden disturbance is detected, particle filtering optimization is restarted, and the prediction model is dynamically updated. According to the invention, by fusing the deep learning long sequence prediction model and the particle filter optimization algorithm, high-precision prediction and abnormal dynamic identification of atmospheric laser radar data are realized.

The invention provides a life sleep sign monitoring method, and relates to the technical field of sign data monitoring, and the method comprises the following steps: collecting an original echo signal of a monitoring area, and extracting a respiration signal, a heartbeat signal and a body movement signal corresponding to a body movement event from the original echo signal; obtaining relevance characteristics of the respiratory signal and the heartbeat signal and adjusting characteristics of the body movement event on the respiratory signal and / or the heartbeat signal; according to the relevance features and the adjustment features, biological features representing individual physiological differences are generated; based on the biological characteristics, target identity probability distribution is obtained; and performing interference suppression processing on the original echo signal according to the target identity probability distribution so as to separate a target respiration waveform and a target heartbeat waveform of the target individual from the original echo signal. The method is suitable for practical application scenes such as multi-person same-bed monitoring and family bedroom monitoring, and the technical bottleneck that the monitoring precision is reduced along with the increase of the target number in the prior art is broken through.