267 results about "Echo intensity" patented technology

The invention discloses an aircraft cold cloud precipitation enhancement quantitative evaluation method based on radar echo enhancement, and belongs to the technical field of disaster prevention and reduction. The method comprises the following steps: acquiring to-be-processed radar echo data and to-be-processed ground raindrop spectrometer data; performing separation processing on the radar echo data to be processed to obtain rainfall enhancement influence echoes and natural rainfall echoes; according to the to-be-processed ground raindrop spectrograph data, the rainfall enhancement influence echo and the natural rainfall echo, establishing a Z-I relationship between the rainfall enhancement influence echo intensity Z of the near-ground layer and the rainfall intensity I; and according to the Z-I relation, the rainfall enhancement influence echo and the natural rainfall echo, calculating to obtain the total rainfall enhancement amount. According to the invention, the rainfall area with clear boundary generated by artificial rainfall enhancement can be separated, the natural precipitation can be identified, and the airplane operation rainfall increment can be quantitatively estimated by using radar data.

Provided is an automatic hail identification method based on radar image spatial mapping, including: obtaining a base reflectivity radar image; grading radar echoes using intensity values represented by different colors, and detecting radar center coordinates; performing spatial mapping, and establishing a dictionary for radar echo intensity in a new radar image space; after expanding the dictionary, transforming V-shaped notch and TBSS characteristics behind an original radar strong echo area into a standard rectangle and a triangle behind a strong echo in the new radar image space; determining whether there is a V-shaped notch and TBSS according to whether there is a standard rectangle or a triangle behind the strong echo; and if so, marking in the original radar image and calculating a position where hail occurs. A rapid and accurate method is provided for automation of hail monitoring and early warning and elimination of continuous high-intensity analysis pressure of forecasters.

The embodiment of the invention provides a radar echo extrapolation prediction method and system based on a test time training strategy. According to the embodiment of the invention, the method comprises the steps: carrying out the time axis calibration and space grid alignment processing of a radar echo historical collection sequence collected by an atmosphere detection system, and generating radar echo sequence data with time-space consistency; constructing an echo evolution relevance representation model based on the data, and generating an echo evolution dependency graph and a multi-step extrapolation initial condition set; then introducing a test time training strategy to carry out adaptive prediction processing, and adjusting edge weight parameters in real time to obtain a radar echo multi-step extrapolation prediction sequence with time dynamic characteristics; and finally, according to the sequence, extracting an echo intensity change characteristic and a space movement track rule, and combining historical climate cycle evolution data to generate a climate evolution trend description set containing an echo intensity trend curve and a path offset vector so as to realize more accurate climate evolution prediction and weather forecast.

The invention relates to a lightning potential forecasting method and system based on a random forest. The method comprises the following steps: (1) collecting and preprocessing multi-source meteorological data; (2) feature extraction: cloud picture texture and shape features are extracted from satellite data, and echo intensity, top height and vertical development speed features are extracted from radar data; (3) constructing and optimizing a model; and (4) early warning thunder and lightning. The method has the advantages that the random forest serves as a typical representation of the ensemble learning algorithm, and the core of the random forest is to integrate prediction results of a plurality of decision trees. According to the integration mode based on Bagging, multi-dimensional feature information can be fully integrated, and the overfitting problem which is likely to happen to a single decision tree is effectively restrained. In a thunder and lightning potential forecasting scene, the algorithm can accurately capture a complex nonlinear relationship between meteorological elements and thunder and lightning potential by adaptively learning association between a large amount of historical meteorological data and thunder and lightning events, so that the forecasting accuracy is remarkably improved.

The invention discloses a B-ultrasonic auxiliary diagnosis system based on artificial intelligence. The B-ultrasonic auxiliary diagnosis system comprises a data acquisition and preprocessing module used for acquiring B-ultrasonic images and performing standardization processing; the physical characteristic extraction module is used for extracting physical characteristics of acoustic impedance, attenuation coefficients, echo intensity and scattering and reflection modes and providing embedded data for the feature fusion module; the feature fusion and representation module is used for deeply combining physical characteristics and spatial features through a multi-modal feature fusion and adaptive graph convolution method to generate high-dimensional feature representation; the deep learning diagnosis model module is used for realizing joint optimization of feature learning and diagnosis tasks on the basis of a multi-task deep neural network framework guided by physical semantics; and the visualization and auxiliary diagnosis module is used for presenting diagnosis results and providing decision support for doctors through thermodynamic diagrams, multi-modal analysis and intelligent explanation. According to the method, the limitation of a traditional deep learning model under the challenges of low contrast, noise interference, data shortage and the like can be solved.

The invention relates to the technical field of ultrasonic detection, and discloses an ultrasonic-based weld defect detection method and system.The method comprises the steps that a historical database is established, ultrasonic echo signals are traversed in the historical database, a reuse defect strategy or an ultrasonic verification strategy is determined according to the traversal result, and when the reuse defect strategy or the ultrasonic verification strategy is determined, the ultrasonic verification strategy is determined; determining a first ultrasonic result based on the echo delay and the echo intensity, determining a second ultrasonic result based on the ultrasonic echo signal and the ultrasonic feature model, determining whether the weld joint to be detected has defects or not according to the first ultrasonic result and the second ultrasonic result, and performing Fourier transform on the ultrasonic echo signal to obtain the weld joint to be detected. The defect type of the weld joint to be detected is determined based on the ultrasonic characteristic spectrogram, the attenuation result is determined based on the three-dimensional model and the metal sample and written into the simulation database of the simulation environment, the defect type is verified based on the relation between the ultrasonic characteristic spectrogram and the simulation spectrum curve, and the reliability and stability of ultrasonic detection are ensured.

The invention discloses an acoustic Doppler flow velocity measurement method and system, and the method comprises the steps: generating an emission parameter combination adaptive to a current hydrological environment according to the depth characteristics and precision requirements of a target water area; original echo signals are collected based on the emission parameter combination, and a three-dimensional flow velocity vector of temperature compensation is output; inputting the three-dimensional flow velocity vector into the dynamic segmentation network to generate a layered flow velocity distribution model; calling a layered flow velocity distribution model, fusing fluxgate compass azimuth data to carry out flow direction compensation, triggering a deviation correction algorithm when equipment inclination exceeds a limit, and outputting absolute flow velocity distribution of pose calibration; and according to absolute flow velocity distribution and echo intensity dynamic range constraint, calculating an error interval of each depth unit, and outputting a final flow velocity profile with confidence identification. According to the embodiment of the invention, the environmental adaptability and data reliability of flow velocity measurement can be improved.

The invention provides a forest resource dynamic supervision method based on a multi-temporal laser radar, and the method comprises the steps: carrying out the data collection of a target forest in different periods, and obtaining multi-temporal point cloud data, which is used for monitoring the change of the target forest in a time sequence; performing hierarchical processing on point cloud of each period in the multi-temporal point cloud data by taking an echo sequence and echo intensity as constraint conditions to obtain canopy layer point cloud data and ground layer point cloud data, calculating a crown change index and a ground change index, setting a crown change threshold value and a ground change threshold value, and calculating a crown change threshold value and a ground change threshold value according to the crown change index and the ground change index; the method comprises the following steps: identifying regions with significant change of crowns and grounds, realizing classification of crown and ground changes, obtaining crown and ground change analysis results, and drawing a forest resource change distribution map. According to the method, quantitative analysis is carried out by constructing a plurality of change indexes, automatic extraction, classification and mapping of forest crown and ground change information are completed in combination with change threshold segmentation, and forest resource changes are dynamically monitored.

The invention discloses a weather radar echo intensity online calibration method, and relates to the technical field of weather radar detection. The radar collects and processes an environment signal to obtain effective clutter data; determining a relative calibration offset of the weather radar according to the effective clutter data and the relative calibration baseline; and calibrating the weather radar based on the relative calibration offset. According to the method, the weather radar is calibrated online in real time by using the ground clutter creatively, and the dependence on external equipment is reduced. And the precision and timeliness of radar echo intensity measurement are obviously improved. The maintenance cost and the operation difficulty are reduced, and the calibration inconsistency caused by manual operation errors is reduced. The calibration cost of the weather radar is remarkably reduced, continuous high-quality output of data is ensured, the accuracy and reliability of data acquired by the weather radar are remarkably improved, higher requirements of modern meteorological observation and forecasting are met, and powerful support is provided for meteorological observation and early warning and forecasting. And the long-term consistency of the data is ensured. And the method has wide adaptability.

The present disclosure provides a method and system for evaluating anti-skid performance of airport pavement based on spaceborne SAR. the method obtains an echo intensity plot by processing SAR images; Establish a FNN model using machine learning algorithms that input objective conditions, surface texture parameters, measured friction coefficients, and output SAR echo intensity; Finally, input the SAR images to be predicted and the assumed friction coefficient into the FNN model, to compare and predict the anti-skid performance. The present disclosure is based on the properties of SAR echoes and establishes a correlation between SAR echo intensity and pavement friction coefficient in both temporal and spatial dimensions, enabling uninterrupted remote sensing monitoring of regional anti-skid performance over a long period and large spatial distribution.

An underwater detection device (10) is provided with a data generation module (11a) which forms a plurality of reception beams (RB1) in a scanning range based on electrical signals outputted from a plurality of ultrasonic oscillators (13a) during a reception period after a transmission timing and generates data relating to echo intensities at respective positions in the scanning range and a fish school tracking module (11b) which updates the tracking area (A0) of the fish school based on data and navigation data. The fish school tracking module (11b) determines whether to use data based on fish school echoes in the tracking area (A0) for updating the tracking area (A0) based on a determination as to whether an index value indicating how large the echo intensity of the tracking area (A0) is relative to the echo intensity around the tracking area (A0) is equal to or greater than a threshold value.

The invention discloses an artificial fish reef pile distribution characteristic analysis method based on echo detection, and relates to the technical field of ecological environment monitoring, and the method comprises the following steps: data collection and preprocessing: selecting a multi-frequency echo detection device, and transmitting and receiving sound waves of different frequencies at the same time to adapt to detection demands of different water depths and fish reef types; all-directional cruise detection is carried out in a target sea area, it is ensured that all areas where artificial fish reefs possibly exist are covered, and echo signal data under different frequencies are recorded; according to the invention, an advanced signal processing algorithm is adopted, a multi-angle detection data synthesis technology is combined, a high-resolution three-dimensional model of the fish reef pile is constructed, the model is optimized and refined, and the characteristic parameters of the fish reef pile, such as echo intensity, reflection area and height difference, are extracted, so that the high-resolution three-dimensional model of the fish reef pile is obtained. The spatial distribution pattern of the fish reef pile, the relation between the fish reef pile and the marine environment and the dynamic change condition along with time can be deeply analyzed.

The invention discloses a pipeline detection device and method based on phased array ultrasonic waves, and relates to the technical field of pipeline detection. The pipeline detection device based on phased array ultrasonic waves comprises a shell with an opening in the lower portion and a phased array ultrasonic detector installed in the shell, an arc-shaped filter plate is fixedly connected to the upper portion of a strip-shaped discharging groove in an embedded mode, and a scraping and collecting mechanism used for scraping and collecting a coated coupling agent in time after detection is completed is arranged on the right portion of an inner cavity of the shell. According to the invention, the coupling agent is uniformly coated on the surface of the pipeline in a thickness-constant manner through the self-extrusion coating mechanism, the consistency of acoustic impedance in a welding seam or a detection area is ensured, the stability of ultrasonic echo intensity is improved, and a detection blind area caused by coating missing is avoided, so that the detection accuracy and reliability are improved; couplant can be scraped and collected in time after detection, the retention time of the couplant on the surface of the pipeline is shortened, solidification residues are effectively prevented, and the surface quality of the pipeline is protected.

The invention relates to the technical field of surveying and mapping remote sensing, and discloses a sparse airborne laser radar data single tree identification method based on a Transform network, and the method comprises the following steps: S1, collecting airborne laser radar point cloud data of a forest; s2, preprocessing the laser point cloud data to obtain a single tree instance point cloud data sample; and S3, constructing a double-branch Transform network, training the model by using the training set, selecting an optimal model, and testing. According to the method, a Transformer model of a double-branch structure is designed to process geometric information corresponding to three-dimensional coordinate information of point cloud data and other attribute information such as a scanning angle, an echo number and echo intensity so as to obtain a high-precision classification result.

The present invention discloses a radar monitoring and early warning method for open-pit mine slope stability, which relates to the technical field of geological disaster monitoring and early warning. It is used to solve the problem that existing slope radar monitoring is greatly affected by electromagnetic interference and is difficult to achieve accurate and stable real-time early warning. By obtaining the position information of the radar and the mine edge to be measured and generating a monitoring distance, the echo intensity is measured to calculate the initial reflectivity, and the radar deployment position is dynamically adjusted according to the required echo intensity. After updating the position information, multiple time periods are set to send radar signals of different frequencies. At the same time, the number of wireless devices and power supply fluctuations in the area are monitored, and the voltage spikes are extracted by using the power fluctuations of the wireless devices. The electromagnetic interference intensity is comprehensively analyzed in combination with the number of devices. The reflection change score is integrated based on the multi-frequency echo signal. The current reflectivity is calculated based on the radar echo according to the score screening standard, and compared with the initial reflectivity to determine whether an alarm is issued, thereby significantly improving the real-time and reliability of the slope instability early warning.

The invention provides a monitoring method and system for the conveying state of grain capable of being washed based on multi-mode sensing. Multi-axis laser speed measurement probes with dust covers are arranged on the two sides of the conveying belt, so that interference of dust to a light path is eliminated. Laser Doppler frequency shift signals of surface particles and echo intensity data of deep particles are synchronously collected, and a hierarchical control volume model is established; and utilizing the echo intensity to compensate the scattering signal, and generating a flow velocity distribution profile map through self-correlation processing. A particle motion vector field is further constructed by adopting an optical flow method, and a collision energy value is calculated through vector field energy distribution. When the energy value exceeds a preset threshold value, a positioning early warning signal is generated, and real-time monitoring and accurate positioning of particle collision abnormity are achieved. According to the technical scheme, full-dimension monitoring of the particle motion state in the grain conveying process can be achieved in the high-dust environment, collision abnormity is effectively recognized, the fault area is accurately positioned, and the grain conveying quality is guaranteed.

The first aspect of this application proposes a method for fusing laser point cloud and visible light image, including: S1, performing spatial dimensionality reduction on the point cloud data to obtain two-dimensional point cloud data; S2, extracting feature points of the two-dimensional point cloud data and feature points of the visible light image data through the Scale-Invariant Feature Transform (SIFT) algorithm; S3, determining the projection plane, projecting the point cloud data through the projection model to generate a depth-echo intensity image, and constructing a 3D-2D coordinate mapping relationship between the point cloud data and the depth-echo intensity image; S4, mapping the feature points of the visible light image data to the point cloud data according to the 3D-2D coordinate mapping relationship. This solution can achieve a good image registration and fusion effect, can be applied to various platforms, and has a wide range of applicable scenarios. The second aspect of this application proposes a system for fusing laser point cloud and visible light image.

The invention discloses a target detection method, device and equipment based on millimeter wave radar, a storage medium and a program product. Determining a distance-Doppler map based on the echo signal received by the millimeter wave radar; determining the angle of each target based on the phase echo signal of each target; filtering the targets based on a first set threshold value and the echo intensity of each target to obtain candidate targets; determining second detection thresholds corresponding to different angles and / or different speeds; and filtering the candidate targets based on the second detection threshold, the echo intensity of each candidate target and the angle and / or speed of each candidate target to obtain a final detection target. According to the target detection method based on the millimeter wave radar provided by the embodiment of the invention, weak targets, false noise targets and sidelobe false targets caused by strong targets can be effectively filtered out, so that the accuracy of target detection is improved, and calculation resources for performing angle estimation on the targets subsequently are further saved.

The present application provides a method and device for window occlusion detection, which belongs to the field of sensor technology and can be used for assisted driving and autonomous driving. The method includes: obtaining a first window echo intensity and a second window echo intensity, the first window echo intensity represents the reference window echo intensity, and the second window echo intensity represents the window echo intensity at the current moment; obtaining the detection parameters of the target point at the first moment and the detection parameters at the second moment, the detection parameters including the reflectivity and / or position coordinates of the target point; according to the first window echo intensity, the second window echo intensity, the detection parameters at the first moment and the detection parameters at the second moment, obtaining a window occlusion parameter, the window occlusion parameter is used to indicate the window occlusion state. This method improves the accuracy of lidar window occlusion detection, so as to improve the terminal's advanced driving assistance system (ADAS) capabilities in autonomous driving or assisted driving.

The invention relates to the technical field of laser ranging, in particular to a novel railway tunnel deformation real-time surveying and mapping system based on a three-dimensional scanning technology, which comprises a laser sampling module, an echo evaluation module, a space repositioning module, a residual evolution module and a deformation surveying and mapping module. According to the method, after continuous distance information of a target position is obtained, measuring point sequence data is constructed based on a time sequence, a signal abnormal area is recognized through echo intensity fluctuation, the signal abnormal area is further converted into a space coordinate structure, track differences are extracted, vector data are generated, deviation characteristics are analyzed in combination with the tunnel space position, and the deformation amplitude is quantified. Compared with a periodic observation mode depending on manual distribution of measuring points, the method has the advantages that fine coordinate difference data can be continuously output, the deformation monitoring frequency and the space coverage range can be improved, manual interference errors can be reduced, and the dynamic mastering capability of the tunnel structure evolution process can be enhanced.

The invention relates to the field of bridge damage detection, in particular to a municipal bridge damage defect detection method and system. The method comprises the following steps: carrying out edge detection and connected domain extraction on an acquired bridge surface grey-scale map to obtain a plurality of edge pixel points and a plurality of connected domains, and calculating the regularity of a target area; constructing a gray scale run-length matrix of the target rectangle, and calculating the texture confusion degree of the target area according to the gray scale run-length matrix; carrying out weighted summation on the regularity and the texture confusion degree, carrying out normalization, and taking a normalization result as the initial abnormal degree of the bridge; ultrasonic pulses are transmitted to the target area to obtain echo time and echo intensity, the echo time and the echo intensity serve as correction factors to correct the initial anomaly degree of the bridge to obtain a bridge damage index, and defect detection is completed. According to the technical scheme, the bridge defect detection efficiency and accuracy can be improved.

The invention provides a suspended sediment concentration inversion method capable of resisting particle size interference, which comprises the following steps of: standardizing the original acoustic echo intensity of each observation grid to obtain the standardized volume back scattering intensity; establishing a sonar equation considering the sound wave attenuation of the sediment particles, and performing explicit processing on the sonar equation considering the sound wave attenuation of the sediment particles to obtain an explicit inversion model which resists particle size interference and considers the on-way change of the sediment particle size; and performing model parameter calibration on the explicit inversion model, and performing suspended sediment concentration inversion on the river observation section based on the suspended sediment concentration inversion value of each observation grid. According to the method, the dependence of ADCP inversion of the suspended sediment concentration on suspended sediment particle size distribution is effectively eliminated, and the stability and accuracy of suspended sediment concentration inversion are improved. The particle size interference resistant explicit method can realize continuous and real-time suspended sediment concentration monitoring, and is suitable for various water environments such as rivers, lakes, reservoirs and the like.

The invention provides a metasurface structure and a metasurface structure preparation method, and relates to the technical field of electromagnetic wave regulation and control. The metasurface structure comprises a frequency selection layer, an isolation layer and a reconfigurable gradient impedance layer, the frequency selection layer is located above the isolation layer; wherein the frequency selection layer is used for receiving a first electromagnetic wave incident at a first angle and inhibiting the echo intensity of scattering of the first electromagnetic wave; the reconfigurable gradient impedance layer is located below the isolation layer; wherein the reconfigurable gradient impedance layer is used for receiving a second electromagnetic wave incident at a second angle and inhibiting the echo intensity of scattering of the second electromagnetic wave; wherein the first angle is smaller than the second angle. According to the application, the echo intensity of mirror scattering is suppressed through the frequency selection layer, and the echo intensity of non-mirror scattering is suppressed through the reconfigurable gradient impedance layer, so that electromagnetic scattering suppression of a wide angular domain is realized.

The embodiment of the invention provides a data processing method and device based on a laser radar. The method comprises the steps that signal characteristic parameters are acquired; and determining an extended echo intensity value of the target echo signal according to the signal characteristic parameters. According to the embodiment of the invention, the method achieves the extension of the echo intensity of the target echo signal through the laser radar, improves the discrimination degree of a close-range target, reduces the crosstalk phenomenon of a sensor in the laser radar, and enables a real target to be effectively distinguished from a false target with the crosstalk phenomenon.

The invention belongs to the technical field of feature recognition of underwater complex targets, and relates to a boundary element-based multi-bright-spot cluster target interference feature estimation method, which comprises the steps of geometric model construction and introduction, numerical calculation based on multi-physics field coupling, echo intensity representation by adopting an angle-frequency spectrum, and analysis of resonance generated by multi-body interference. According to the method, a numerical model for solving a three-dimensional double-gas-cylinder omnidirectional scattering sound field is established based on a boundary element method, and omnidirectional and broadband scattering characteristics of a target in horizontal and vertical attitudes are calculated; a multi-bright-spot dual-target strong scattering coupling mechanism is mainly analyzed, and a multi-body configuration and single target scattering interference fringe generation mechanism is disclosed; according to the method, the inherent attribute characteristics of the single target can still be effectively identified in a cluster target scattering field; the mapping relation among the geometric configuration, the spatial arrangement mode and the frequency response is obtained, and a theoretical formula suitable for double-target sound scattering forecast is deduced.

The invention relates to the technical field of signal transmission, in particular to a polarization signal intelligent transmission analysis method and system based on a detector. Acquiring echo energy of polarized signals of all array elements under an antenna plane with a preset inclination angle in a preset weather state; obtaining an initial clustering center through a K-means algorithm according to the data features of the echo energy, and carrying out clustering to obtain an echo energy cluster; according to the data characteristics of the echo energy cluster, obtaining the credibility of data in the cluster and an echo energy representative cluster; and obtaining an echo intensity characteristic value according to the quantity characteristic of the echo energy cluster, the data volume ratio in the echo energy representative cluster and the intra-cluster data credibility of the echo energy representative cluster. The optimal inclination angle corresponding to the preset weather state is obtained according to the echo intensity characteristic value; and the polarization signal is transmitted according to the array element under the antenna plane with the optimal inclination angle in the preset weather state, so that the accuracy of collecting the landform information through the polarization signal in the windy and rainy weather is improved.

The invention relates to the technical field of safety monitoring and health management in the service period of a diversion tunnel in water conservancy and hydropower engineering, in particular to a diversion tunnel inner surface acoustic echo point extraction method which comprises the following steps: (1) an omnidirectional sonar actively emits sound waves to irradiate the inner surface of the tunnel, and meanwhile, periodic sampling is performed to form a series of discrete echo data points; (2) filtering direct waves, multipath echoes and noise data points in the discrete echo data points, and reserving actual echo data points on the inner surface of the tunnel; (3) one frame of data in the actual echo data points is used as a processing unit, and the intensity value change rate R between the adjacent data points is calculated one by one; (4) distance information and intensity information represented by acoustic echo points on the inner surface of the diversion tunnel should be optimal values, the acoustic echo points should be distributed at positions with large reflection echo intensity values and dense reflection echo intensity values in a short-distance spherical wave propagation mode, and the distance and intensity information is considered as a whole; the next data point with the maximum intensity value change rate between the adjacent data points is taken as an acoustic echo point P of the current frame data; (5) acoustic echo point extraction is carried out on the discrete echo data points of the next frame according to the steps (3) and (4) until acoustic echo points of all data frames are extracted and stored; according to the method, the problem of multiple data points formed by echo periodic sampling can be effectively solved, tunnel inner surface acoustic echo points giving consideration to strength information and position information are extracted, and reliable data support is provided for reconstruction and disease detection and recognition of the diversion tunnel inner surface.

The invention provides a method and a system for identifying environment abnormity in a direct-current battery swap station. The method comprises the following steps: fusing multi-source images acquired by the direct-current battery swap station; determining at least one suspected abnormal region based on the fused image; constructing a three-dimensional structure model of an object in the suspected abnormal area based on reflection deformation information of structured light projected into the suspected abnormal area for multiple times; determining reflection change information of the object in the suspected abnormal area based on reflection echo intensity and reflection echo time of the obtained ultrasonic wave on the surface of the object in the suspected abnormal area; and performing logic correlation analysis based on the three-dimensional structure model and the reflection change information, and determining a target abnormal region in the to-be-identified direct-current battery swap station. According to the method and the system, the capability of identifying tiny and hidden abnormal objects of the direct-current battery swap station is greatly improved, missed judgment is effectively reduced, the accuracy and the reliability of environment abnormity identification in the direct-current battery swap station are improved, and stable operation of the battery swap station is guaranteed.

The invention provides a ground penetrating radar clutter suppression method and system and a medium, the method highlights a texture change area through first-order difference, can accurately capture change information of echo intensity and suppress background fluctuation and noise, meanwhile, introduces graph regularization to ensure similarity of adjacent pixels, suppresses isolated noise points, and improves noise suppression efficiency. And the stability of the texture structure is enhanced. By means of the combination, clutters can be effectively removed when the method is used for processing complex backgrounds, and meanwhile key structure information is reserved. Complex echo texture data are projected to a singular value feature space through singular value decomposition, singular values with a large change rate are screened out for reconstruction, redundant interference can be effectively removed, and stable echo textures are reserved. According to the mechanism, when the method is used for processing multi-target, dispersed and irregular echo signals, target signals and clutters can be effectively separated, and the detection precision is influenced.

The invention provides an oil-gas-water three-phase slug flow measurement method based on pulse wave ultrasonic Doppler, which comprises the following steps of: obliquely mounting a pulse wave ultrasonic Doppler sensor at the bottom of a pipeline, and ensuring that an included angle between a sound wave and a fluid flowing direction is theta; based on the pulse echo intensity principle and the Doppler principle, echo intensity profile data reflecting phase interface distribution characteristics and flow velocity profile data reflecting flow field distribution characteristics are synchronously obtained through the pulse wave ultrasonic Doppler sensor; according to the dynamic response of the test data to the flow process, establishing a signal processing algorithm to realize unit structure division of the slug flow, and further extracting related information of proportions of different areas of the slug flow, liquid film thickness and flow velocity of a gas bomb area, gas content of the gas bomb area and flow velocity of a liquid bomb area; finally, various kinds of information are fused through a physical model, and measurement of the total volume flow is achieved.