17 results about "Cloud particle" patented technology

The application discloses a cloud particle spectrum optimization method and system based on a mask autoencoder, a medium and equipment, and the method comprises the following steps: obtaining a cloud particle shadow image to be completed, performing pretreatment, and obtaining a pretreated image; based on a trained cloud particle image completion model, a reconstruction patch is performed to obtain a completed image; the completed image is evaluated and screened, the cloud particle geometric scale statistics and particle size grading of the completed image passing the evaluation are performed, the flight sweeping distance and the effective sampling width corresponding to each particle size grading are combined, the grading sampling volume and the grading concentration are calculated, and finally the cloud particle spectrum is obtained. The overall process of the method does not depend on cloud particle level artificial labeling, and only the original image is needed to complete the training and application, thereby reducing the data preparation cost, being suitable for popularization and application in large-scale airborne observation data, and increasing the number of available large cloud particle samples entering the cloud particle spectrum statistical link without increasing the hardware complexity.

The application discloses an ice wind tunnel non-uniform particle diameter multi-particle cloud simulation method, device, equipment and medium, and the method comprises the following steps: S1, grid division is performed on a calculation region in the ice wind tunnel; S2, air flow field calculation is performed by using the grid and convergence is calculated; S3, on the basis of the flow field calculation convergence, a discrete phase model is introduced, combined with a multi-nozzle incident source position, boundary characteristics of the non-uniform particle diameter multi-particle incidence, non-uniform particle diameter cloud multi-particle non-steady state motion heat transfer simulation is completed; S4, cloud particles are updated once when the flow field calculation in the iteration process is set to a step number; and S5, when the number of incident particles and the number of particles leaving the calculation domain reach a dynamic balance, if the evaluation of the particle diameter distribution and the cloud uniformity at the outlet section does not meet the requirements, the nozzle incident source position and the cloud particle boundary setting are redesigned, and the coupling iteration calculation of the cloud non-steady state is performed. The application can obtain more accurate distribution results of the cloud at each section position.

The present application relates to the technical field of liquid water content detection in air, and particularly relates to a method and device for detecting liquid water content in air by using a hot-wire water content meter. The method comprises obtaining first data related to the hot-wire water content meter and second data related to a cloud particle probe, wherein the hot-wire water content meter and the cloud particle probe are on the same detection platform; establishing a data set of physical parameters changing with time according to the first data and the second data; setting a cloud entering standard and calculating liquid water content according to a liquid water content calculation formula; calculating the liquid water content of each sub-class according to the sub-class of the cloud particle probe, and selecting the optimal value of the liquid water content. The present application solves the problems of error overlap caused by multi-parameter combination calculation in the laboratory empirical formula of the prior art, and the error caused by human operation.

The invention discloses a spherical particle size measurement method and device based on deep learning numerical prediction, and belongs to the field of particle measurement and image processing. The method comprises the following steps: acquiring interference fringe image data of spherical particles with different sizes, marking a corresponding particle size label for each image, and dividing the images into a training set, a verification set and a test set; the training set is used to train a pre-constructed neural network model, the verification set monitors the training process and stores the optimal model weight, and the test set tests the prediction precision of the model; the pre-constructed neural network model is based on an original UNet + + network and comprises a plurality of residual blocks and a space attention module, and a full-connection output head is added to realize end-to-end mapping from an image to a size value; and finally, inputting an interference fringe image of a to-be-measured particle into the trained network, and directly outputting a particle size prediction value. According to the method, high-precision, real-time and end-to-end prediction of the spherical particle size is realized, and the method is suitable for scenes such as cloud particle field on-line monitoring.

This invention relates to the field of airborne cloud particle image processing technology, and provides a method and system for intelligent assessment and screening of airborne cloud particle image quality. The method extracts comprehensive quality features through multi-scale spatial and frequency domain joint analysis, employs a multi-task learning architecture to calculate image quality scores and defect types, determines a quality screening threshold based on score distribution characteristics and environmental parameters, retains valid images above the threshold and removes invalid images, and updates the learning architecture parameters based on the distribution of quality features of valid images and the clustering results of defect features of invalid images. This invention improves the accuracy and adaptability of cloud particle image screening.

The ice wind tunnel cloud and mist particle measuring device is suitable for testing an incoming flow cloud and mist field in a wind tunnel test section, a first hole and a second hole are formed in corresponding positions of two side faces in the length direction of the wind tunnel test section, and the ice wind tunnel cloud and mist particle measuring device comprises a first shell, a second shell and a controller, a first cylinder is arranged in the center area of the first shell, a second cylinder is arranged in the center area of the second shell, the first cylinder and the second cylinder are each of a hollow structure with the two ends open, laser beams are scattered after irradiating an incoming flow cloud field, water drops of the irradiated part in the cloud field shield laser to form projection, and a projection image is shot by the camera and fed back to the controller; and the detection is completed by the controller, so that the detection or test efficiency is improved.

The utility model provides an airborne holographic cloud particle measuring instrument, which comprises a main control part and a particle monitoring and collecting part, and is characterized in that the main control part comprises a cylindrical pod, a conical pod rear cover fixed at the end part of the rear end of the cylindrical pod and a spherical front cover fixed at the end part of the front end of the cylindrical pod; an optical terminal unit and a main control circuit board are assembled in the rear end of the cylindrical pod; the particle monitoring and collecting part comprises a strip-shaped ellipsoidal shell; the strip-shaped ellipsoidal shell is suspended at the front end of the cylindrical pod through a transition connecting rod. The particle screening device is simple in structure and convenient to use and maintain, particularly, the overall modeling is streamline, the phenomena of wind resistance and particle breaking are greatly reduced, and the particle screening device has high timeliness, accuracy and reliability.

This invention provides a method and apparatus for hail cloud attenuation based on lidar detection feedback, belonging to the field of meteorological control technology. The method includes: registering and correcting joint detection data from weather radar and optical lidar; identifying the spatial distribution characteristics of hail-containing layers based on the registered and corrected joint detection data to generate spatial positioning data for laser control; acquiring the charge density distribution of the target cloud, establishing a safety window constraint matrix corresponding to the charge density gradient, and defining the laser path and laser power parameters; performing laser irradiation operation according to the laser path and laser power parameters, and updating the echo time difference compensation model and the safety window constraint matrix based on the lidar feedback signal. This invention achieves adaptive adjustment of laser irradiation parameters according to changes in cloud particle size and charge, thereby safely and accurately attenuating hail layers.

The invention discloses a cloud particle spectrum optimization method and system based on a mask auto-encoder, a medium and equipment, and the method comprises the steps: obtaining a to-be-complemented cloud particle shadow image, and carrying out the preprocessing, and obtaining a preprocessed image; performing patch reconstruction based on the trained cloud particle image completion model to obtain a completed image; and evaluating and screening the complemented image, carrying out cloud particle geometric scale statistics and particle size grading on the evaluated complemented image, and calculating grading sampling volume and grading concentration in combination with the flight scanning distance and the effective sampling width corresponding to each particle size grading to obtain a final cloud particle spectrum. The whole process of the method does not depend on cloud particle level manual annotation, training and application can be completed only through an original image, the data preparation cost is reduced, the method is suitable for application and popularization in large-scale airborne observation data, and the number of available large cloud particle samples entering a cloud particle spectrum statistical link is increased on the premise that the hardware complexity is not increased.

The embodiment of the invention discloses a customer management method, device and equipment. The method comprises the following steps: receiving a customer management request for a target customer; in response to the client management request, determining a candidate operation strategy corresponding to the target client based on the client value contribution, the client value change and the business stickiness of the target client; determining a model by using a pre-trained strategy, and selecting a target operation strategy corresponding to the target customer from the candidate operation strategies; wherein the strategy determination model is constructed based on a preset cloud drift algorithm, and the cloud drift algorithm is used for searching a global optimal solution and a local optimal solution in a preset search space by using cloud particles related to the candidate operation strategy based on a preset fitness function, and determining the candidate operation strategy based on the global optimal solution and the local optimal solution. Determining the target operation strategy; and performing customer management on the target customer based on the target operation strategy.

The application discloses a mountainous power transmission line icing type identification method and system, and aims to solve the problems of limited deployment quantity of existing icing online monitoring devices and difficulty in obtaining effective data under extreme conditions. The application screens a mountainous tower point with an icing image as a sample tower point, constructs a first characteristic index (air temperature) and a second characteristic index (cloud particle) by interpolating meteorological reanalysis data to the sample tower point and icing observation data of the tower point, interpolates the meteorological reanalysis data to a target tower point, screens icing times according to the first characteristic index, identifies an icing type according to the second characteristic index, and forms an hourly icing type sequence of the target tower point. The application realizes efficient calculation by using decoupled meteorological indexes, breaks through the limitation of mountainous terrain, and provides a reliable reference for power grid icing disaster prevention.

The invention discloses a cloud body visualization method based on weather radar data, a medium, equipment and a product. The method comprises the following steps: calculating a cloud particle diameter and a cloud particle distribution density according to the weather radar data; calculating the cloud thickness, the cloud aggregation degree and the edge index according to the cloud particle distribution density; constructing a first cloud visual density model according to the cloud particle diameter, the cloud thickness and the cloud aggregation degree; generating normalized three-dimensional noise distribution according to the cloud gathering center, and adding the normalized three-dimensional noise distribution to the first cloud vision density model to obtain a second cloud vision density model; calculating a Berlin noise weight according to the cloud aggregation degree and the cloud edge index, and adding the Berlin noise to the second cloud vision density model according to the weight to obtain a third cloud vision density model; light stepping is carried out in the pixel observation direction, the light transmittance and brightness values of pixels are calculated, and cloud body visual image expression is carried out by using a volume rendering technology. The obtained cloud distribution has the three-dimensional body structure information of the cloud, and the morphological structure characteristics of the cloud are highlighted.

The invention relates to the technical field of particle imaging, in particular to a particle morphology analysis system based on a high-resolution airborne cloud particle imager. Comprising a particle motion state sensing unit; a multi-frame imaging trigger acquisition unit; the particle motion-form coupling reconstruction unit adopts an embedded processor, integrates the acceleration data output by the IMU to obtain the relative speed of the particles, and restores the motion path of the particles in combination with multiple frames of images; and a morphological data integration output unit. According to the invention, through the attitude compensation module, non-spherical particle types such as ice crystals and aragonium are identified based on contour differences of real morphological characteristics, then a core morphological axis is extracted as a correction reference, and finally directional rotation correction is performed according to the particle motion direction, so that the adaptability of the non-spherical particle morphology and the motion attitude is improved; the problem of form misjudgment caused by lack of a non-spherical particle attitude adaptation correction mechanism is solved.

The invention provides an airborne cloud particle data processing method, device, device and system based on collaborative feature mapping. Carrying out time sequence semantic anchoring binding on particle original data collected by airborne cloud particle detection equipment, flight state data collected by an airborne flight state sensor and airflow monitoring data collected by an airborne airflow monitoring sensor to generate a multi-dimensional time sequence association data set, and carrying out association path traversal mining on the multi-dimensional time sequence association data set; generating a coupling association path data set, executing dynamic node mapping of airborne flight working conditions and particle original data, generating a working condition adaptive mapping content set, performing one-to-one link binding on the working condition adaptive mapping content set and the particle original data subjected to false target removal based on sample time sequence attributes, and generating a link-bound particle data set; in combination with the working condition adaptation mapping content set, cloud micro physical parameter calculation and working condition adaptation correction are carried out, and standardized liquid water content, number concentration and spectral distribution data are generated. According to the invention, the accuracy and reliability of airborne cloud particle data processing can be improved.

The invention discloses an in-cloud particle velocity inversion method based on EarthCard 1B, and aims to solve the problems of insufficient error suppression, poor scene adaptability, disjunction of processing flow and single verification system in the prior art when the in-cloud particle velocity is inversed by EC-CPR data of an EarthCard satellite. A full-link collaborative optimization process of data preprocessing-mask generation-velocity inversion-result output is constructed, invalid value replacement in a preprocessing stage, velocity calibration and a subsequent inversion step are in deep linkage, and mask rules are uniformly applied to Doppler velocity and settling velocity calculation. High-precision inversion of the in-cloud particle velocity in a full-cloud scene is realized, and reliable data support is provided for cloud microphysical process analysis and climate model parameterization optimization.

The present application relates to the technical field of particle imaging, in particular to a particle morphology analysis system based on a high-resolution airborne cloud particle imager, comprising a particle motion state sensing unit, a multi-frame imaging trigger acquisition unit, a particle motion-morphology coupling reconstruction unit, the particle motion-morphology coupling reconstruction unit adopts an embedded processor, integrates acceleration data output by the IMU to obtain particle relative speed, restores particle motion path in combination with multi-frame images, and a morphology data integration output unit.The present application identifies non-spherical particle types such as ice crystals and graupel based on the profile difference of real morphology characteristics through a posture compensation module, extracts a core morphology axis as a correction reference, and finally performs directional rotation correction according to the particle motion direction, thereby improving the adaptability of non-spherical particle morphology and motion posture and solving the morphology misjudgment problem caused by the lack of non-spherical particle posture adaptation correction mechanism.

This invention discloses a device and method for synchronously measuring cloud particle turbulence based on digital holography and particle image velocimetry. The device includes a housing with a laser emitting arm and a laser receiving arm mounted on it, forming a sampling area between the two arms. Inside the housing are a dual-optical-path holographic measurement unit, a PIV turbulence measurement unit, and a control unit. The dual-optical-path holographic measurement unit generates two laser beams: one beam travels along its original path and exits through the laser emitting arm, while the other beam is redirected and then emitted through the same arm. The two laser beams cross each other in the sampling area and are incident on the laser receiving arm, where they are imaged onto two cameras to obtain holographic images. The PIV turbulence measurement unit generates one laser beam to form a sheet light source, which exits from the laser emitting arm and illuminates the flow field between the sampling areas, acquiring scattering images of the particles. This invention can simultaneously achieve holographic cloud particle measurement and turbulence measurement, increasing the sampling volume while also improving measurement accuracy.