252 results about "Radar observations" patented technology

The invention discloses a sand and dust vertical flux high-precision inversion method based on a laser radar. The method comprises the following steps: acquiring multi-wavelength back scattering and polarization information by using a 355 nm, 532 nm and 1064 nm three-wavelength polarization laser radar system; constructing a five-dimensional optical feature vector space, and combining a support vector machine classifier to realize automatic identification of dust particles; a variational data assimilation technology is adopted to fuse radar observation and numerical forecasting information to invert a three-dimensional wind field; inverting sand and dust mass concentration vertical distribution based on the corrected particle spectrum distribution model; flux calculation and uncertainty quantization are realized through adaptive weighted fusion and a Monte Carlo method; the method has the characteristics of high temporal-spatial resolution, high precision, strong adaptability and the like, and can be widely applied to the fields of weather forecast, environment monitoring, climate research and the like.

The invention belongs to the technical field of aerospace, and relates to an unscented batch processing orbit determination method and system for a continuous low-thrust maneuvering satellite. The method comprises the following steps: acquiring a radar observation vector according to radar measured data or satellite ephemeris data in combination with radar site coordinates; obtaining an initial orbit state at a to-be-estimated moment by adopting a Lambert algorithm according to the radar observation vector; generating an initial expansion state quantity according to the initial orbit state at the to-be-estimated moment; a simplified orbit kinetic equation containing earth non-spherical perturbation, atmospheric resistance perturbation and constant small thrust acceleration under an RTN coordinate system is constructed under the ECI coordinate system; based on the simplified orbit kinetic equation, obtaining an expansion state quantity precision value of the to-be-estimated moment through the initial expansion state quantity, the radar observation vector and an unscented batch processing filtering method; and substituting the precision value of the expansion state quantity of the to-be-estimated moment into the simplified orbit kinetic equation to carry out orbit forecasting at other moments. According to the method, the orbit determination precision is remarkably improved, and the influence of single-point observation errors is reduced.

The invention discloses a slope radar and GNSS three-dimensional deformation data combined observation fusion method and system. The method comprises the following steps: S1, acquiring slope radar data, performing discrete value elimination processing, acquiring GNSS three-dimensional deformation data, and performing discrete value elimination processing and spatial interpolation processing; s2, calculating displacement data of the slope radar data and converting the displacement data to a BLH coordinate system; s3, constructing a combined observation model of the slope radar data and the GNSS three-dimensional deformation data under the same BLH coordinate system; and S4, carrying out data fusion on the slope radar data and the GNSS three-dimensional deformation data according to the weight by taking the iteratively optimized weight of the combined observation model. According to the method, the combined observation model of the multi-source data is jointly constructed based on the slope radar observation equation and the GNSS observation equation, the weighted residual algorithm based on the least square method is introduced, the weighted residual sum is minimized, the high-precision three-dimensional deformation fusion result is generated, and reliable technical support is provided for subsequent slope deformation analysis.

The invention belongs to the technical field of substation inspection, and provides a substation inspection robot multi-sensor fusion obstacle avoidance method and system, and the technical scheme is as follows: obtaining multi-modal sensor data, including laser point cloud data, millimeter wave radar point cloud data, ultrasonic radar observation data and visual image data; preprocessing the obtained multi-modal sensor data to obtain effective observation data, and determining initial obstacle data based on the effective observation data; time synchronization and fusion are carried out on the obtained multiple kinds of initial obstacle data, the fused data serve as obstacle data, scene segmentation is carried out in combination with multiple kinds of sensor data in the fusion process, confidence coefficient decision rules are dynamically set according to different scenes, and obstacle information is determined in combination with the set confidence coefficient decision rules; and generating an optimal obstacle avoidance route based on the determined obstacle information. The problem that a traditional single sensor cannot cope with a complex scene of substation inspection business is solved, and the overall stable operation capacity of the robot is improved.

The invention provides a radar and optical observation segmental arc correlation method and system for space debris. A joint probability density function is obtained through a kernel density estimation algorithm. A radar observation arc section and an optical observation arc section are input. And converting the observation data into a geocentric celestial coordinate system. And acquiring system parameters of the observation equipment. Radar observation data and optical observation data are unified to the same dimension, and a distance measurement value weight and an angle measurement value weight are set. And calculating an initial orbit by the radar observation arc section. And calculating a joint objective function corresponding to the current orbit position speed. And if the convergence condition is met, outputting the minimum value of the joint objective function and the orbit position speed corresponding to the minimum value. Otherwise, updating the track position speed, and repeating until the convergence condition is met. And according to the minimum value of the joint objective function and the joint probability density function, judging whether association is carried out, and outputting confidence during association. The method is easy to implement and can be effectively applied to association of the space debris radar and the optical observation arc section.

The invention discloses a cascading filtering positioning method and device in a complex and severe environment of a well mine and a medium, and achieves a high-precision positioning function of a well mine unmanned vehicle based on a cascading filtering method. According to the method, the IMU, the wheel speed and the wheel rotation angle are subjected to data fusion through adaptive Kalman filtering, the influence of non-Gaussian noise on wheel speed measurement is reduced through non-Gaussian noise feature extraction, and robust prior positioning information is obtained. According to the method, a regular term representing feature degradation is constructed, a laser radar observation model based on a prior map and laser radar data is constructed, and finally, a result of the laser radar observation model and prior positioning information are fused according to the regular term under a regularization Kalman filtering framework to obtain posterior positioning information. The precision and robustness of the method meet the positioning requirements of the mine unmanned vehicle.

The invention discloses a full-field-domain structure high-precision deformation and vibration measurement system based on a microwave interference radar, and the system comprises an observation configuration module which is used for obtaining radar observation parameters and completing the signal transmission and reception; the signal processing module is used for preprocessing the echo signal and constructing an interference phase matrix; the phase unwrapping module is used for generating a continuous phase sequence and calculating the displacement of a reflection point; the frequency spectrum extraction module is used for extracting the frequency component and the vibration amplitude of each reflection point; the response clustering module is used for extracting reflection point clusters with similar frequency responses and constructing a vibration distribution diagram; the modal identification module is used for generating a vibration modal feature vector and marking a difference region; and the track generation module is used for generating a dynamic deformation track diagram of the full field domain structure. According to the invention, high-precision deformation and vibration mode synchronous measurement of a complex structure under full-field-domain and non-contact conditions is realized, and the precision, efficiency and intelligent level of structure monitoring are effectively improved.

The invention discloses an active and passive combined observation carbon dioxide column concentration inversion method, and relates to the technical field of meteorological detection.The method comprises the steps that an active laser radar observation data set is obtained through arrangement according to month information and longitude and latitude information, and differential optical thickness is obtained through calculation based on a radar equation according to priori active laser radar observation data; according to the line-by-line integration principle, a carbon dioxide absorption cross section lookup table is constructed, priori profile data and differential optical thickness are combined, a correction coefficient matrix of a priori profile and corrected priori profile data are obtained through calculation, and according to the optimization algorithm principle, the corrected priori profile data serve as constraint, and the carbon dioxide absorption cross section is obtained. And the concentration of the carbon dioxide column is calculated based on the passive observation data, so that the inversion method provided by the invention not only comprises active laser radar observation, but also comprises the passive observation data, the active observation and the passive observation are effectively combined, more accurate prior profile information is provided, and the inversion precision is improved.

The invention relates to an atmospheric concentration field inversion method and system based on a space-time diagram network. The method comprises the following steps: acquiring atmospheric observation data of different platforms; the method comprises the following steps: standardizing and preprocessing atmospheric observation data of different platforms to generate space-time point cloud data in a unified format; mapping the time-space point cloud data into a graph network structure for describing an atmospheric physical transmission process; constructing a hybrid neural network architecture, taking the graph network structure as the input of the hybrid neural network architecture, and training the hybrid neural network architecture to obtain a prediction model; inputting real-time radar observation data into the prediction model to obtain a three-dimensional particulate matter concentration field prediction result; and analyzing the prediction result of the three-dimensional particulate matter concentration field, and evaluating the pollution cause of the current pollution event. According to the technical scheme, a high-fidelity and time-space continuous concentration field can be generated under the condition that observation data is limited, and the accuracy and reliability of an inversion result are greatly improved.

The invention discloses an I SAR fusion imaging method, system and device based on centralized multi-station networking, and relates to the technical field of radar imaging, and the method comprises the steps: obtaining I SAR frequency domain echo data of each subsystem; performing frequency domain space matching on the frequency domain echo data based on the observation angle difference of each radar; performing frequency domain alignment processing on the matched frequency domain data; and fusion and imaging are carried out. According to the I SAR fusion imaging method, system and device based on the centralized multi-station networking, the imaging resolution is remarkably improved, multi-station frequency domain data are mapped to a unified local frequency space through a coordinate transformation matrix, non-uniform data are subjected to uniform reconstruction in combination with a self-adaptive interpolation algorithm, and the imaging resolution is remarkably improved. The frequency domain integration interval is effectively expanded, compared with a single-station I SAR which only covers a local area of the total rotation angle of the target, the range resolution is further optimized through multi-station bandwidth synthesis in the range direction, and high-fidelity reconstruction of the target in the complex shape is achieved.

The invention discloses a rainfall prediction method based on multi-scale physical Transform, and the method comprises the steps: firstly collecting continuous historical radar observation data from a meteorological radar system, and carrying out the preprocessing of the data; secondly, according to the processed observation data, on the basis of a Transform network, capturing global semantics to obtain aggregation features; and then motion field and intensity residual field prediction is carried out based on the aggregation features to obtain a deterministic prediction sequence. And finally, constructing a generative network based on a multi-scale adapter, obtaining a rainfall probabilistic prediction map according to a deterministic prediction sequence, constructing a multi-scale space-time physical consistency loss function, and carrying out end-to-end training. According to the method, the problems of error accumulation, physical inconsistency, low calculation efficiency and the like in a traditional forecasting method are effectively solved, so that a brand new and accurate technical path is provided for real-time forecasting of the extreme rainfall event.

The invention relates to a wave height parameter correction method and system for low-angle laser radar observation data, and relates to the technical field of ocean observation data processing, and the method comprises the steps: carrying out the smoothing and data cleaning processing of original wave height data, so as to construct a fitting model of polynomial and Gaussian global trend analysis; and then, through double-model dynamic fusion, polynomial short-term fluctuation capture and Gaussian long-term trend capture are deeply combined, so that the generalization ability of the model is improved. And finally, intelligent reference interval positioning is introduced to adapt to wave height distribution changes in different periods and positions, excessive correction is avoided in combination with introduced correction factors, accurate correction data are obtained, the problem that the wave height is attenuated along with the distance is solved, and the efficiency and reliability of correction processing are remarkably improved.

The invention relates to the technical field of satellite navigation and ionosphere detection, in particular to a low-latitude ionosphere disturbance and non-uniform body multi-means collaborative detection and classification method. A multi-network cooperation-multi-source fusion observation mode is adopted, full space-time coverage of low-latitude ionosphere disturbance is achieved, in addition, a data fusion method based on physical constraints is applied, an ionosphere-middle atmosphere-troposphere multi-physics field coupling constraint mechanism is constructed, space-time consistency of data obtained through different observation means is optimized, and the low-latitude ionosphere disturbance is obtained. And finally, key characteristic parameters are extracted by using a multi-scale signal processing method, a disturbance characteristic matrix is constructed in combination with radar observation data (such as drift speed and spectral width), a'three-axis nine-element 'disturbance / non-uniform body classification standard is proposed firstly, then deep learning classification is used, accurate classification of low-latitude ionosphere disturbance is realized, and GNSS signal anomaly recognition capability is improved.

The invention provides a radar signal anti-interference processing method and system based on a space-time attention mechanism. The method comprises the following steps: mapping an original horizontal polarization channel signal and an original vertical polarization channel signal into an interference suppression space, and generating a three-dimensional feature tensor comprising an amplitude component, a polarization phase difference component and an interference suppression residual component; according to the amplitude component, dividing a radar observation area into multiple layers of target blocks in a spatial dimension, and calculating a space-time association weight between adjacent layers of target blocks through a space-time attention mechanism; and according to the space-time correlation weight, screening out a plurality of target blocks meeting a preset continuous motion condition, and decoding a motion mode of the plurality of target blocks by using a long short-term memory network to generate an anti-interference target trajectory coordinate and a velocity vector. According to the method, the polarization characteristics and the space-time attention mechanism are fused, the anti-interference effect of radar signal processing in a strong interference environment is improved, and then the detection precision of the multi-target motion trail is improved.

The invention provides a vertical velocity quantitative estimation method and system based on a dual-polarization radar, and the method employs the multi-polarization parameters of the dual-polarization radar, combines a deep learning technology with radar meteorological physical knowledge, builds a nonlinear relation between the multi-polarization parameters of the dual-polarization radar and the vertical velocity, and achieves the quantitative estimation of the vertical velocity. Quantitative estimation of the three-dimensional vertical velocity profile is realized, and a quantitative analysis product is provided for convective weather forecast and early warning. According to the method, a differential reflectivity column, a differential phase column recognition algorithm and a dual-polarization radar hydrogel classification algorithm are combined to screen an upflow area of convective weather, and a data set of dual-polarization radar observation characteristic data and vertical velocity of the upflow area is established. Transform is used as a prediction model, a space-time attention mechanism is added, and multi-time step information is used as input, so that extraction of time features observed by the dual-polarization radar is facilitated, and the vertical velocity prediction accuracy is improved.

The invention belongs to the field of microwave remote sensing, and relates to a Doppler precipitation radar precipitation and three-dimensional wind field synchronous inversion method and system. The Doppler precipitation radar precipitation and three-dimensional wind field synchronous inversion method comprises the following steps: determining a reflectivity factor of an observation area through echo power of a Doppler precipitation radar; according to the reflectivity factor, determining a rainfall area and calculating the size of rainfall particles; calculating the vertical falling speed of the precipitation particles according to the size of the precipitation particles; calculating the radial velocity of precipitation particles according to the echo phase of the Doppler precipitation radar; removing the vertical falling speed of the precipitation particles to obtain the radial movement speed of the precipitation particles caused by the wind field; and according to the rainfall particle radial motion speed caused by the wind field, a radar observation geometry and a three-dimensional wind field inversion algorithm are combined, and a three-dimensional wind field of the rainfall area is obtained through inversion. By adopting the method provided by the invention, the wind field inversion precision is improved.

The invention discloses a dual-polarization radar data assimilation method and system introducing phase constraint, and belongs to the technical field of numerical weather forecast data assimilation, and the method comprises the steps: obtaining dual-polarization radar observation data; performing data interpolation on the dual-polarization radar data after quality control to obtain dual-polarization radar observation parameters of mode three-dimensional grid points; then calculating spatial distribution information of the hydrogel particles by utilizing a phase state recognition algorithm to obtain fuzzy values of various hydrogel particles on each mode grid point, and obtaining fuzzy values of various background field hydrogels on the mode grid points in one-to-one correspondence with the types of the hydrogels of the mode background field; according to the fuzzy value of the background field hydrogel, the analysis hydrogel and the analysis weight of each mode grid point are determined, the analysis hydrogel and the analysis weight are introduced into a three-dimensional variational assimilation system, the analysis increment source and the increment weight of radar parameters are determined, and assimilation of dual-polarization radar data is completed. According to the method, the pertinence of assimilating and adjusting the distribution of the hydrogel by the data is improved, and the accuracy of forecasting the heavy rainfall is improved.

The invention provides a hectometer-level short temporary rainfall forecasting method based on generative adversarial network downscaling and physical constraint, which belongs to the technical field of weather forecast, and is characterized in that S-band radar jigsaw and high-resolution X-band radar observation data are fused through an adaptive rule to generate a fused radar echo combined reflectivity, and the combined reflectivity is used for forecasting the rainfall of the hectometer-level short temporary rainfall. A super-resolution downscaling model is constructed by using a generative adversarial network, a long-sequence high-resolution radar echo training data set is reconstructed, a deep learning forecasting model is constructed, a high-resolution radar echo sequence is used as input, space-time modeling training is performed by using a composite loss function, and a future radar echo forecasting field is output. The Z-R relation is converted into quantitative rainfall forecast, and finally the quantitative rainfall forecast with the spatial resolution reaching the hectometer level is generated. The interpretability of the model is enhanced through physical constraint, the bottleneck of insufficient hectometer-level resolution training data is effectively solved, and the spatial refinement degree of short temporary rainfall forecasting and the forecasting capacity for a severe convection system are remarkably improved.

The invention discloses a health monitoring and correction method and device based on posture recognition, a medium and a product, and relates to the field of computer vision and health management cross technologies, and the method comprises the steps: obtaining RGB image data, depth map data and radar observation data in real time; respectively carrying out normalization processing on the RGB image data and the depth map data; the RGB image data after normalization processing and the depth map data after normalization processing are fused into RGB-D image data; an HRNet-Spine model is constructed, and the HRNet-Spine The RGB-D image data and the radar observation data are input into an HRNet-Spine model, and a spine key point detection result is obtained; obtaining a health score of the current posture based on a spine key point detection result; a gesture correction suggestion is generated based on the health score for the current gesture. According to the invention, efficient and accurate personnel health monitoring and real-time posture correction can be realized.

The invention discloses a hydrogel phase state identification method and system based on dual-polarization radar networking fusion, and relates to the technical field of hydrogel phase state identification. The method comprises the following specific steps: acquiring dual-polarization radar observation data from a plurality of radar stations; performing data quality evaluation on the dual-polarization radar observation data to obtain qualified data; qualified data are interpolated into a unified grid, networking fusion is performed through an inverse distance weight algorithm, a data set is constructed, and the data set is divided into a training set, a test set and a verification set; constructing a convolutional neural network, training the convolutional neural network by using the training set, and extracting hydrogel particle features in the training set; and after the training set finishes training, verifying the convolutional neural network by using the verification set, carrying out cycle training for multiple times to obtain a phase state identification model, and carrying out hydrogel phase state identification through the phase state identification model. According to the method, a hydrogel classification result with high robustness and accuracy can be obtained, the method does not depend on manually set weight coefficients, and the phase state identification process is objective.

The invention discloses a radar multi-dimensional data dynamic edge weight graph construction method, and relates to the technical field of radar signal processing and graph data modeling, and the method comprises the steps: obtaining a radar observation sequence; the radar observation sequence comprises a plurality of nodes; the nodes correspond to time units in a radar observation sequence; performing feature extraction and feature fusion on the node feature vector of each node to obtain a plurality of fusion feature vectors; based on a plurality of nodes, constructing a dynamic edge and a corresponding dynamic edge weight; the dynamic edge comprises a time sequence adjacent edge, a feature similar edge and a self-loop edge; performing fusion normalization on the dynamic edge weights to obtain a dynamic edge weight matrix after fusion normalization; and constructing a graph structure based on the plurality of fusion feature vectors, the dynamic edge and the dynamic edge weight matrix. According to the method, through time sequence attenuation, a feature similarity dynamic threshold and an edge weight fusion mechanism, the modeling capability of radar target multi-dimensional features is improved.

The invention discloses a data feature enhanced artificial intelligence dual-polarization radar quantitative rainfall estimation method, which comprises the following steps: S1, acquiring dual-polarization radar observation data, and constructing a three-dimensional space grid structure data set; s2, constructing a double-Gaussian probability density function, and performing data feature enhancement on a single variable in the three-dimensional space grid structure data set; s3, constructing a multi-dimensional joint probability density function based on the double-Gaussian probability density function, and performing joint data feature enhancement on all variables in the three-dimensional space grid structure data set; and S4, training an artificial intelligence model reflecting the relation between the radar data and the rainfall data. According to the method, the probability density function characteristics are creatively adopted to describe the rolling change of the dual-polarization radar variable within one hour. By constructing three-dimensional joint probability density function features, not only can the distribution characteristics of dual-polarization radar variables be extracted, but also the space-time evolution rule of precipitation particles can be effectively captured.

The invention discloses a laser wind-finding radar and a data processing and assimilation method thereof. The method comprises the following steps: acquiring observation data of the laser wind-finding radar, atmospheric stability parameters and topographic relief feature data; a three-dimensional space weight distribution function is constructed based on the parameters, and initial influence weights of the observation points on surrounding grids are represented; dynamically adjusting the horizontal influence range and the vertical attenuation characteristic of the function according to the geomorphic stratification degree and the atmospheric vertical stratification change information, and generating dynamic weight distribution matched with the local atmospheric physical and topographic characteristics; the distribution serves as a space adjustment factor of an observation error covariance matrix and is introduced into a cost function, and the wind field analysis field and the dynamic weight distribution are synchronously optimized by minimizing the cost function; and outputting the optimized wind field analysis field for numerical weather forecast. According to the method, accurate and efficient fusion of observation information in three-dimensional variational assimilation is realized, and the authenticity and forecast reliability of wind field analysis under complex conditions are remarkably improved.

The invention belongs to the technical field of weather forecast, and particularly relates to a rainstorm forecast fusion method and device based on dynamic weight, electronic equipment and a storage medium. The method comprises the following steps: acquiring radar data and numerical forecasting data for rainstorm forecasting; obtaining a dynamic weight; and performing data fusion based on the dynamic weight, the radar data and the numerical forecasting data to obtain fused rainstorm forecasting data based on the dynamic weight. According to the method, different characteristics of the radar data and the numerical forecasting data are fully considered, the advantages of the numerical forecasting data and the radar observation data can be fully utilized by dynamically adjusting the weight, and the precision of rainstorm forecasting is improved.

The invention discloses a short-time approaching rainfall prediction method based on multi-source fusion data and an MIM network, and the method comprises the steps: carrying out the space-time matching of radar observation data, Chinese land surface data assimilation system data and site observation data, carrying out the preprocessing of the matched data, including feature factor extraction, missing value processing and the like, building a deep learning model, and carrying out the prediction of the short-time approaching rainfall. The model is trained, and a multi-source fusion rainfall inversion result is output based on real-time radar data; furthermore, errors of a falling area and rainfall intensity of intelligent grid rainfall forecast are identified, a phase correction technology is utilized to correct a position error of an intelligent grid forecast rainfall zone, meanwhile, precision evaluation is performed on a prediction effect of the deep learning model according to the rainfall amount of short-time nowcasting, and intelligent prediction of short-time nowcasting is realized.

The invention relates to the technical field of vehicles, in particular to a method, device and equipment for calibrating the sensing speed of a laser radar through a millimeter wave radar, and the method comprises the steps: building a motion state model of a to-be-observed target; acquiring laser radar observation data and millimeter wave radar observation data of a to-be-observed target, and predicting state estimation of the to-be-observed target at the next moment by using the motion state model; using a first preset Kalman filter to correct the state estimation based on the laser radar observation data to obtain the corrected state estimation, and using a second preset Kalman filter to optimize the corrected state estimation based on the millimeter wave radar observation data to obtain the target state estimation. Therefore, the problem that the speed sensing capability of the laser radar is limited is solved, and the speed tracking capability of the laser radar is improved by fusing the obstacle speed data sensed by the millimeter wave radar into the sensing data of the laser radar.

The invention relates to the technical field of weather three-dimensional simulation, in particular to a three-dimensional dynamic simulation method and system based on multi-modal weather data. The method comprises the following steps: acquiring multi-modal meteorological observation data including ground observation station information, radar observation information and satellite observation information, and calculating a time difference value between observation time of each data source; taking the ratio of the time difference value to the spatial distance between the key observation points as a disturbance propagation speed; establishing a space-time correlation window based on the disturbance propagation speed, calculating a deviation correlation coefficient between the observation sources in the space-time correlation window, and judging an atmospheric state type through the deviation correlation coefficient; and mapping the atmospheric state type into a multi-source coordination parameter. According to the method, a proper range can be set according to propagation characteristics of a weather system through the space-time correlation window, observation data intensive with physical consistency is realized, and the problem of dislocation of different observation source data on timestamps or coverage ranges is effectively avoided.

The invention relates to the technical field of atmospheric wind field inversion, and discloses a low-altitude atmospheric three-dimensional wind field inversion method based on radar observation. The method comprises the steps that radar observation data streams are collected, and a standardized data sequence is generated through integrity check and noise filtering; separating historical and real-time data blocks, and performing time interpolation and space-time gridding space-time alignment on the historical data blocks to form a historical wind field data set; based on the set, extracting a wind field dominant mode by using a mode decomposition algorithm, and constructing a wind field evolution model containing a mode coefficient and an evolution rule; real-time data block features are extracted and then input into a model for assimilation calculation, and a mode coefficient adjustment value is output; and after the model is corrected, low-altitude atmosphere three-dimensional wind field distribution data are simulated and generated. According to the method, through historical data processing, mode modeling and real-time data assimilation, the dynamic evolution characteristics of the wind field can be accurately captured, the inversion precision and real-time performance are improved, and reliable data are provided for related fields.

The invention discloses a large hail identification method and system based on dual-polarization radar three-body scattering characteristics, and relates to the technical field of meteorological identification, and the method comprises the steps: obtaining dual-polarization radar observation data of a historical hail event, and extracting a characteristic threshold value of a hail region dual-polarization radar parameter; acquiring dual-polarization radar base data in real time, retrieving the dual-polarization radar base data according to the characteristic threshold value, and judging whether a hail area exists or not; if the hail region exists, TBSS dual-polarization radar feature recognition is carried out on the hail region of the dual-polarization radar base data, and whether TBSS dual-polarization radar features exist at the hail region is judged; and when TBSS dual-polarization radar characteristics exist, determining that large hail exists at the position. According to the invention, quantitative analysis is carried out on the large hail based on the observation data of the dual-polarization weather radar, and then the large hail is identified, so that the vacancy of dual-polarization radar products is solved, and a powerful means is provided for monitoring and early warning of severe convective storms.

The invention discloses an autonomous berthing method for a double-water-jet-propeller water surface unmanned ship, and the method comprises the steps: defining a state space, an action space, a reward function and a neural network structure element, employing a reinforcement learning method, and according to the current radar observation information of the unmanned ship, the target berth position information, and the current speed information of the unmanned ship; according to the method, the unmanned ship control output information is obtained, the problems that a traditional manual design berthing collision avoidance rule, a physical motion model, a machine learning method, an environment model and the like are greatly influenced by the environment, and intermediate steps are multiple and complex are solved, the calculation efficiency and the one-time planning berthing success rate are effectively improved, and therefore the berthing efficiency is overall improved.