87 results about "Cloud radar" patented technology

The invention discloses an airplane ice accretion monitoring method fusing millimeter wave cloud radar and ground-based microwave radiometer data, which comprises the following steps: step 1, taking temperature profile and relative humidity profile data monitored by a ground-based microwave radiometer in a test area in real time as input quantities to obtain airplane ice accretion index values corresponding to all profile data; step 2, obtaining a reflectivity Z profile of liquid water particles in a test area and a total liquid water value LWP on an observation path; step 3, carrying out inversion to obtain a liquid water content LWC profile in the cloud on the observation path; step 4, taking an overlapped area of an area of which the temperature profile data of the foundation microwave radiometer is less than 0 DEG C and an area of which the inverted liquid water content data of the cloud is greater than 0, a supercooled water area in the cloud is formed; and step 5, analyzing whether the ice accumulation area in the step 1 and the supercooled water area in the step 4 are overlapped or not, if yes, representing that ice accumulation occurs in the air, and if not, representing that no ice accumulation occurs in the air. According to the method disclosed by the invention, a millimeter wave cloud radar and ground-based microwave radiometer active and passive combined detection technology is adopted, the total amount of liquid water and a water vapor profile in the airport airspace are detected, the distribution of supercooled liquid water in cloud in the airport airspace is inverted, and the distribution of a potential icing area of an aircraft is monitored in real time.

The invention discloses a composite precipitation cloud layer identification method based on a laser radar-cloud radar, and the method comprises the steps: obtaining the observation data of the laser radar and the cloud radar, and carrying out the data matching of the observation data; processing a signal of the laser radar in a height interval of 18-20 km to obtain a preprocessed laser radar signal; performing laser radar cloud boundary identification based on a VED algorithm, and endowing different laser radar cloud scores according to the signal attenuation rate and the signal gradient; removing earth surface clutters by using a threshold method based on observation of spectral width and Doppler velocity; carrying out cloud radar significant echo recognition based on a threshold method, and determining the cloud score of the cloud radar according to different reflectivity factor signal intensities; determining a light rain area and a heavy rain area according to the Doppler velocity; cloud layer boundary information and a hairy rain area are obtained through cloud layer recognition result fusion, and cloud layer recognition and rainfall area recognition are completed; according to the invention, the accuracy of weather prediction is improved.

The invention discloses a wall surface identification method and device and a storage medium, and relates to the technical field of sensor positioning, and the method comprises the steps: carrying out the fitting processing of a laser radar point cloud collected by a laser point cloud radar, and obtaining a plurality of fitting line segments; screening a target line segment which is greater than a first length threshold and has the longest length from the plurality of fitting line segments; projecting target point clouds associated with the position of the target line segment in wall point clouds and obstacle point clouds collected by the depth camera on the target line segment, and determining at least one target wall surface line segment; and if the length of the target wall surface line segment is greater than a line segment threshold value, outputting the target wall surface line segment under a robot coordinate system to be used by a welting program. According to the invention, after laser point cloud filtering fitting, depth camera wall surface obstacle point cloud projection calibration is fused, the technical problem that the wall surface is difficult to effectively identify is solved, and the wall attaching precision and obstacle avoidance reliability of the cleaning robot in a complex scene are improved.

The invention discloses a visibility inversion method based on millimeter wave cloud radar and laser radar data fusion, and relates to the technical field of meteorology, and the method comprises the steps: guiding a millimeter wave cloud radar and a laser radar to carry out data correction and equivalent relation construction through employing a fog drop particle size distribution function measured by a fog drop spectrometer in a sample scene in advance; and then applying the correction coefficient and the equivalent function relationship to a target scene to perform multi-source data correction and fusion on the millimeter wave cloud radar and the laser radar and invert visibility. The method can fuse complementary advantages of the laser radar and the millimeter wave cloud radar; the introduction of the fogdrop particle size distribution function effectively avoids the distribution hypothesis of fogdrop shapes in the traditional model, can weaken the seasonal drift of the traditional empirical coefficient, significantly improves the accuracy and reliability of visibility inversion, and achieves the high-precision visibility monitoring under various weather conditions from clear sky to dense fog.

The invention relates to the technical field of radar reflectivity factor calibration, and discloses a terahertz cloud measurement radar calibration method based on channel perception, which comprises the following steps: S1, acquiring two types of input data; s2, constructing a multi-scale channel model; s3, constructing a distance error estimation model; s4, constructing a dynamic time correction model; and S5, constructing a calibration module. According to the method, a high-precision physical channel model is constructed to independently predict path attenuation of signals, the concepts of gain deviation and path attenuation factors are put forward, the influence of thermal noise attenuated by radar on radar waves in the atmospheric environment is reflected respectively, and a Kalman filtering algorithm is used to estimate the path attenuation of the signals. And filtering errors generated in the time direction, and finally, calibrating the radar reflectivity factor by using the channel data by means of a calibration formula.

The embodiment of the invention provides a spaceborne radar and ground-based millimeter wave cloud radar reflectivity factor uniformity evaluation method, which is applied to the technical field of atmospheric detection. The method comprises the following steps: acquiring reflectivity factor data of a spaceborne radar and a ground-based millimeter wave cloud radar; establishing reflectivity factor space-time matching data of the spaceborne radar and the ground-based millimeter wave cloud radar according to the reflectivity factor data by taking vertical direction scanning as a reference; preprocessing the time-space matching data of the reflectivity factors; and based on a preset uniformity evaluation algorithm, an evaluation result is calculated according to the preprocessed reflectivity factor space-time matching data. In this way, errors between original data can be better revealed, and a good foundation is laid for product inversion in the next step.

The invention belongs to the technical field of non-cloud echo filtering, and provides a millimeter wave cloud radar non-cloud echo filtering method based on a random forest algorithm, and the method comprises the steps: to-be-classified radar base data collection, space-time texture and vertical gradient calculation, to-be-classified feature vector construction, and random forest classification model classification. The training process of the random forest classification model comprises the steps of data integration and grouping, data marking, label number statistics, echo type determination, training data set extraction, model training and model verification. According to the method, data such as the vertical gradient and the space-time texture are extracted, so that the model training data volume is enriched, and the model accuracy is improved; the reflectivity factors of four adjacent distance libraries are fused with normal cloud echo data, so that the distinguishing capability of the model on regional characteristic differences is improved; through a normal cloud echo training data set and a non-cloud echo training data set, the defect that a traditional method depends on radar power spectrum data is overcome, and the expansibility and replicability of the model are improved.

The invention relates to the field of atmospheric science, and discloses a method for calculating the uniformity degree of a coil-clamping mixing process in cloud based on a remote sensing means, and the method comprises the steps: building a function relation between a parameter b and a uniform mixing degree psi in a radar reflectivity factor and water content function relation (Z-LWC) based on a high-resolution numerical mode; according to the radar reflectivity factor output by the cloud radar and the inverted water content, a Z-LWC relation based on remote sensing observation is established, and the value of a parameter b is obtained; according to the value of the parameter b obtained through remote sensing observation, the function relation between the parameter b and the psi obtained through the numerical mode is combined, and the uniform mixing degree of the coil clamping mixing process is calculated. According to the invention, the observation and research of the clip-coil mixing process can be expanded from expensive and discontinuous aircraft observation data to economic, continuous and widely used remote sensing observation data, the limitation of the conventional research method is broken through, new means for the observation and research of the clip-coil mixing process are added, and the research efficiency of the clip-coil mixing process is improved.

The invention discloses an unmanned ship maritime millimeter wave cloud radar and background field data maneuvering fusion method and device and a medium, and relates to the technical field of meteorological detection. The method comprises the following steps: acquiring a navigation route and radar detection parameters of the unmanned ship; based on the navigation route of the unmanned ship and the radar detection parameters, the unmanned ship is controlled to navigate autonomously according to the set route; wherein a millimeter wave cloud radar system is carried on the unmanned ship; obtaining millimeter wave cloud radar data of the unmanned ship in the sailing process; wherein the millimeter wave cloud radar data are acquired by a millimeter wave cloud radar system based on radar detection parameters; performing cloud cover inversion on the millimeter wave cloud radar data based on a reflectivity and cloud cover formula to obtain cloud cover data in a navigation route process; and interpolating the cloud cover data into the three-dimensional cloud cover data of the background field data to obtain fusion data. According to the invention, accurate fusion of the radar data and the background field data can be realized, so that more comprehensive and more accurate cloud layer information can be obtained.

The invention discloses a cloud phase fine identification and liquid water and ice water quantitative remote sensing device, which comprises a split-phase cloud water quantitative detection laser radar, a novel temperature and humidity remote sensing system and a millimeter wave cloud radar, and is composed of a laser emission module, a telescope receiving module, a microwave radiation module, three laser radar detection modules and a digital signal processing module. The system is composed of a central control processing module and a terminal server. The invention further discloses a remote sensing method of the quantitative remote sensing device. All-day detection and quantitative inversion of liquid water, ice water, water vapor, aerosol and cloud are achieved through the split-phase cloud water quantitative detection laser radar. A novel temperature and humidity remote sensing system is adopted to realize real-time fine detection of a temperature and humidity profile in the cloud. According to the cloud phase state fine identification and liquid water and ice water quantitative remote sensing device and method provided by the invention, the problems that the quantitative detection precision of the liquid water and ice water content in the cloud is insufficient and the mixed phase cloud identification is difficult in the prior art are solved.

The invention provides a raindrop spectrum inversion determination method and device based on a cloud radar and a dual-polarization radar, and relates to the technical field of atmospheric sciences, and the method comprises the steps: obtaining a plurality of first raindrop spectrums corresponding to a target region through the cloud radar; acquiring a second raindrop spectrum corresponding to the target area and a second horizontal reflectivity factor corresponding to the second raindrop spectrum through the dual-polarization radar; and for each first raindrop spectrum, determining a target raindrop spectrum corresponding to the first raindrop spectrum based on all the first raindrop spectrums, the second raindrop spectrum and the second horizontal reflectivity factor. According to the technical scheme, the advantages of the cloud radar and the dual-polarization radar are combined, the limitation of adopting a single radar is broken through, and the accurate target raindrop spectrum can be obtained.

The invention discloses a non-meteorological target identification method based on meteorological feature image clustering and fuzzy fusion. Firstly, millimeter wave cloud radar meteorological data are preprocessed; and then generating a meteorological data parameter feature image, and converting the two-dimensional meteorological data array into a two-dimensional pseudo-color image. According to the method, distribution characteristics of meteorological targets and non-meteorological targets on a data image are analyzed, a meteorological data parameter characteristic image is divided into different potential target areas by using an image segmentation large model, and parameter characteristics in the potential target areas are extracted; and calculating the average radial texture and inter-library change degree of the reflectivity factor, the regional average value and variance of the radial speed, and the regional average value parameter characteristic statistical information of the speed spectrum width. A fuzzy logic fusion algorithm is adopted to determine a fuzzy membership function of statistical information, a fuzzy rule is set to calculate a fuzzy membership degree of each data point, and a weighted summation method is adopted to perform defuzzification to obtain a judgment value compared with a threshold value. According to the invention, the accuracy of identifying the non-meteorological target is improved.

The invention relates to a THI millimeter wave cloud radar clear sky echo filtering method, device and equipment, and the method comprises the steps: carrying out the marking of clear sky echoes or meteorological echoes through employing the dual-mode characteristics of space-time physical information combination for sample data of each channel in a sample data set; marking the sample data of each channel, constructing a corresponding fuzzy logic membership function according to the marked sample data of each channel, obtaining membership degrees of clear sky echoes and meteorological echoes from the base data of different channels in the original THI data obtained through actual measurement by using the corresponding fuzzy logic membership functions, and inputting the membership degrees of the channels into a trained classification identification model, and identifying clear sky echoes and meteorological echoes in the original THI data to realize filtering of the clear sky echoes. By adopting the method, clear sky echoes can be accurately filtered.

The invention discloses a cumulonimbus radar echo image prediction method, and relates to the technical field of image processing. The method comprises the following steps of: constructing an AkEformer model, namely constructing an encoder-decoder backbone network structure on the basis of TrajGRU (TrajGRU); constructing an adjustable kernel convolution layer by adopting an AKConv mechanism, and performing feature extraction; the method comprises the following steps of: integrating an efficient attention mechanism in a Transform framework to generate a feature fusion structure layer based on an Officient Transform; obtaining a radar echo image of cumulonimbus; and inputting the radar echo image into the AkEform model and obtaining the predicted distribution condition of the cumulonage cloud. According to the invention, a new model AkEform is designed; distribution of cumulonage clouds can be predicted according to radar echo signals; physical priori knowledge is incorporated into model design, so that the accuracy of cumulonage cloud prediction is greatly improved, and accurate prediction is realized.

The invention provides a cargo volume estimation method and device, electronic equipment and a storage medium, and relates to the technical field of logistics, and the method comprises the steps: obtaining a structured light 3D point cloud, a radar point cloud and an image of a to-be-estimated cargo; performing registration fusion processing on the structured light 3D point cloud, the radar point cloud and the image to obtain fusion data; determining a target type of the to-be-estimated cargo based on the fusion data; and inputting the fusion data into a target volume estimation model corresponding to the target type to obtain the volume of the to-be-estimated cargo. According to the invention, after the fusion data of the to-be-estimated cargo is obtained, the target type of the to-be-estimated cargo is determined based on the fusion data, and then the fusion data is input into the target volume estimation model corresponding to the target type for volume estimation; and the target volume estimation model corresponding to the target type is obtained by training the sample fusion data of the first sample cargo corresponding to the target type, so that the accuracy of cargo volume estimation can be improved.

The embodiment of the invention discloses a cloud shape classification method based on cloud image and millimeter wave cloud radar data fusion, and the method comprises the steps: constructing a visual sub-network, and obtaining a visual feature based on a training cloud image; constructing a millimeter-wave cloud radar sub-network, and obtaining an index token based on a millimeter-wave cloud radar observation value and an index feature after learning a long-distance dependency relationship; constructing a multi-modal fusion sub-network, and obtaining a fusion feature and a final cloud feature after the features of the two are fused; constructing a loss calculation module, and optimizing a cloud classification model composed of the sub-networks by using a loss value to obtain an optimal cloud classification model; and obtaining a cloud shape classification result by using the optimal cloud shape classification model. According to the method, the advantages of the Transform module are fully utilized, the appearance information and the internal structure of the cloud are learned, finally, the features of the Transform module and the internal structure are fused to represent the cloud, and the classification accuracy of the cloud shape is further improved.

The invention relates to a verification method and system for ground-based millimeter wave cloud radar data, and belongs to the technical field of meteorological analysis. The method comprises the following steps: establishing a clear sky echo identification and filtering method, and generating ground-based millimeter wave cloud radar base data after quality control; classifying the ground-based millimeter wave cloud radar base data after quality control into cloud data and rainfall data; the Cloud Sat satellite and the ground-based millimeter wave cloud radar after quality control have reflectivity factor matching and evaluation in cloud data; and matching and evaluating reflectivity factors in GPM satellite and quality-controlled ground-based millimeter wave cloud radar rainfall data. According to the invention, a ground-based millimeter wave cloud radar quality control and classification method is established, a high-quality refined ground-based millimeter wave cloud radar database can be generated, and a good basis is provided for ground-based millimeter wave cloud radar data application; a matching and evaluation method of spaceborne radar and ground-based millimeter wave cloud radar reflectivity factors under different conditions is established, and reference is provided for ground-based millimeter wave cloud radar calibration correction and satellite-ground radar combined application.

For example, a processor may be configured to process point cloud (PC) radar information comprising radar detection information of a plurality of possible detections, wherein radar detection information corresponding to a possible detection of the plurality of possible detections comprises information of a plurality of radar attributes of the possible detection, wherein the processor is configured to determine a plurality of validity scores corresponding to the plurality of possible detections based on the radar detection information of the plurality of possible detections, a validity score corresponding to the possible radar detection to indicate whether it is more probable that the possible detection is a valid detection or a False-Alarm (FA) detection, wherein the processor is to output radar target information based on the plurality of validity scores corresponding to the plurality of possible detections.

To efficiently retrieve a route for reaching a destination without getting wet on a rainy day.SOLUTION: A system includes a map application system, a rain cloud radar, and AI, and retrieves a route that can reach a destination without getting wet on a rainy day.SELECTED DRAWING: Figure 1

The invention relates to a cloud detection method based on combination of a laser radar and a microwave radar, and belongs to the field of radio. The detection method comprises the steps that a coordinate system where a laser radar radiation antenna is located is used as a reference, and system pointing angle deviation caused by the installation position of a microwave radar extension set is corrected in the mode that angle tracking parameters are calculated according to laser-microwave radar extension set antenna installation differences, and scanning space angle synchronization is guaranteed; time synchronization is guaranteed by controlling the time of a radar terminal issuing a control signal to a laser radar extension set and a microwave radar extension set, so that radar beams arrive at meteorological particle echo information to be consistent; after the radar terminal collects data, characteristic parameter extraction and data fusion are carried out on the composite observation data of the laser radar and the cloud radar. According to the method, the problem of target angle deviation caused by spatial distribution difference of the laser radar and the microwave radar during composite observation of the laser-microwave radar system is solved, and the consistency of observed targets is improved.

This invention relates to a cloud height identification method integrating cloud radar and laser wind radar, belonging to the field of radio. The identification method includes: S1, obtaining the cloud base height and cloud top height by inverting the reflectivity product from the cloud radar in top-view mode; S2, obtaining the cloud base height and cloud top height by inverting the signal-to-noise ratio product from the laser wind radar in DBS mode; S3, performing time matching on the output products of the cloud radar and the laser wind radar, and then fusing the results obtained from the cloud radar and the laser wind radar to obtain the final cloud height identification result. This invention complements the characteristics of the two devices. The laser wind radar can compensate for the detection limitations of millimeter-wave radar in thin clouds and distinguish between cloud layers and aerosol layers, while the cloud radar can supplement the inability of laser wind radar to penetrate thick cloud layers, achieving accurate detection of cloud height across the entire cloud area.

The embodiment of the application discloses a kind of multi-modal cloud classification methods based on visual language model, the method comprises: constructing text generation module, generates the cloud description text containing high-level semantic information;Cloud multi-modal feature extraction and fusion module is constructed, cloud image, cloud description text and millimeter wave cloud radar observation data are carried out feature coding and adaptive fusion, obtain multi-modal feature representation;Classifier module is constructed, and the cloud shape classification result of training cloud image is obtained;Total loss function is constructed, and the multi-modal cloud shape classification model consisting of cloud multi-modal feature extraction and fusion module and classifier module is optimized;Test cloud image, and the cloud description text and millimeter wave cloud radar observation data corresponding to test cloud image are input into multi-modal cloud shape classification model and carry out cloud shape classification.The application is realized by introducing high-level semantic cloud description and designing cloud multi-modal adapter, effective fusion of image, radar and text information, improve the accuracy and robustness of cloud shape classification.

The present application discloses a method and device for identifying and predicting micro-rainfall weather processes, which belongs to the field of environmental monitoring technology. The method collects cloud data from Ka-band cloud radar, precipitation data from X-band rain radar, and data from automatic weather stations. A nonlinear model is established, and the data output by the nonlinear model are grouped according to the time axis and radar type. A fitting curve for the micro-rainfall weather process is established to calculate the coefficient of determination and generate eigenvalues, and the prediction result is determined based on the eigenvalues. The output data of the nonlinear model is corrected based on the data from the automatic weather station and the eigenvalues, and the corrected coefficient of determination and eigenvalues ​​are input into the historical data model library as prediction conditions. The judgment results under the same conditions are screened out and compared with the prediction results. If the comparison result is valid, an alarm information push is generated. The method and device for identifying and predicting micro-rainfall weather processes provided by the present application realize the full-process observation and prediction of water vapor-cloud-precipitation, significantly reducing the false alarm rate.

The invention discloses a millimeter wave cloud radar echo intelligent identification method based on a KAN network. The method comprises the following steps: (1) automatic tag generation; (2) an REC-KAN-MLP network is established in combination with characteristic values and a fuzzy logic algorithm; (3) the model realizes real-time filtering after radar scanning through end-to-end deep learning; according to the method, meteorological echoes and ground clutters in the PPI can be effectively identified without a radar echo power spectrum.

The invention discloses a method for inverting warm cloud microphysical parameters based on a ground-based millimeter wave cloud radar and a microwave radiometer, and the method comprises the steps: 1, obtaining the observation data of the millimeter wave cloud radar, and obtaining a whole-layer liquid water path and a temperature gallery line through the microwave radiometer; 2, identifying warm cloud in millimeter wave cloud radar observation by using a temperature gallery line T; 3, constructing an initial liquid water content profile based on the reflectivity factor and the particle concentration in the warm cloud layer, and calculating an initial liquid water path of the warm cloud layer; 4, the whole-layer liquid water path obtained by the microwave radiometer is used as whole-layer constraint, the initial particle concentration and the initial liquid water content at the vertical height in the warm cloud layer are calibrated according to the proportion, the adjusted warm cloud layer liquid water content and the adjusted warm cloud layer inner particle concentration are obtained, and a physical boundary constraint interval is applied to the particle concentration; and 5, calculating the effective radius of the particles in the warm cloud layer according to the adjusted liquid water content of the warm cloud layer and the particle concentration in the warm cloud layer. According to the method, high-precision warm cloud microphysical parameter vertical distribution can be obtained.

The present invention designs a method for intelligent recognition of non-meteorological targets based on deep learning, and identifies non-meteorological targets existing in millimeter-wave cloud radar data; specifically, by designing a millimeter-wave cloud radar data annotation algorithm and a data iteration algorithm, the corresponding labels are quickly obtained; a data expansion method for expanding millimeter-wave cloud radar data is designed to obtain more diverse training data, and feature extraction and feature restoration are designed to improve the recognition performance of non-meteorological targets; and an excellent recognition model is designed using the encoder-decoder concept. Through the above set of processes, the accuracy of intelligent recognition of non-meteorological targets of millimeter-wave cloud radar is improved. Furthermore, the present invention enhances prompts for specific labels by constructing a prompt enhancement module, thereby improving the sensitivity of the model to specific labels, reducing the computational complexity of the intelligent recognition model, and improving the generalization ability.

The application discloses a millimeter wave cloud radar echo classification method and system based on physical guidance deep learning, comprising: constructing a multi-source fusion dataset, aligning, standardizing and sliding window sampling preprocessing millimeter wave cloud radar base data and co-sited ground observation data; extracting thermodynamic state information from the ground observation data through a physical constraint gating module, generating a clear sky echo probability graph to adaptively weight the radar initial features; based on an encoder-decoder backbone network, the weighted radar features are jointly convolved and extracted, and an ECH regression branch is used to fuse the thermodynamic features and the radar features to predict the effective clutter height; based on the predicted effective clutter height sequence, an adaptive height filtering region is constructed, and morphological opening operation and intersection operation of dynamic height constraint are used to realize fine echo classification post-processing, so that the height balance of meteorological echo retention rate and clear sky echo suppression rate is realized, and the data quality of the millimeter wave cloud radar is effectively improved.

For example, a processor may be configured to process point cloud (PC) radar information comprising radar detection information of a plurality of possible detections, wherein radar detection information corresponding to a possible detection of the plurality of possible detections comprises information of a plurality of radar attributes of the possible detection, wherein the processor is configured to determine a plurality of validity scores corresponding to the plurality of possible detections based on the radar detection information of the plurality of possible detections, a validity score corresponding to the possible radar detection to indicate whether it is more probable that the possible detection is a valid detection or a False-Alarm (FA) detection, wherein the processor is to output radar target information based on the plurality of validity scores corresponding to the plurality of possible detections.

The invention relates to an object recognition method, a storage medium, a program product, electronic equipment and a vehicle. The object identification method comprises the following steps: eliminating an object in a target area in a first image to obtain a second image; and identifying a target object in the target area according to the first image and the second image. According to the embodiment of the invention, the method does not depend on a laser point cloud radar or a plurality of cameras, can achieve the recognition of the obstacle even through the image collected by one monocular camera, does not need the hardware equipment for image collection, and can adapt to different hardware configurations.

The invention provides a millimeter wave cloud radar sidelobe echo filtering method based on a clustering algorithm, and the method comprises the steps: carrying out the quality control of cloud radar data, and obtaining a quality control data set; analyzing the quality control data set through an image neighborhood analysis method to obtain a cloud layer data block; screening the cloud layer data blocks through a gradient detection method to obtain cloud echo data; the cloud echo data comprises side lobe cloud echo data and side lobe-free cloud echo data; calculating the optimal clustering number of the cloud echo data with side lobes; constructing a sidelobe echo recognition model according to the optimal clustering number; and performing data rejection operation on the cloud echo data through the sidelobe echo recognition model to obtain sidelobe echo filtered data. According to the method, filtering of millimeter wave cloud radar sidelobe echoes is realized through a clustering algorithm, and the filtering effect of the cloud radar sidelobe echoes is greatly improved.