31 results about "Precipitation particle" patented technology

The invention discloses a three-dimensional Gaussian splash modeling method and system based on scene prior in complex weather. The method comprises the following steps of: 1, acquiring a group of low-illumination original image sequences, corresponding camera parameters and a pose relation between viewpoints; 2, removing rainfall particle shielding in the original image sequence to obtain a clear image sequence; obtaining a corresponding mask by adopting a segmentation large model; 3, extracting scene geometric priori information with illumination invariance, wherein the scene geometric priori information comprises a depth priori image and an edge gradient priori image; 4, constructing a three-dimensional Gaussian splashing model containing edge gradient, depth and illumination attributes, and rendering a prediction result by using a microrasterization method; and constructing a total loss function in combination with the mask, and optimizing the three-dimensional Gaussian splash model by optimizing the total loss function. According to the method, all-around elimination of rainfall particle and lens rainwater interference is realized, a robust scene geometry priori acquisition mechanism is innovatively constructed, and a multi-attribute constrained three-dimensional Gaussian splash model is established.

The application discloses a high-precision precipitation particle phase state identification method and system, and relates to the technical field of atmospheric detection.The application solves the problems of difficult precipitation particle tracking, many misjudgments of phase state identification and no accurate basis for repairing misjudgment points in the prior art.The application comprises the following steps: synchronously sampling continuous frames of precipitation particles, generating a unique identification of the particles through double-feature coupling, constructing and completing integrity verification of a space-time trajectory; based on the qualified trajectory, carrying out consistency verification in time and space dimensions, and marking suspicious misjudgment points and effective sampling points; constructing an exclusive traceability sample set for the misjudgment points, and deducing and repairing the real phase state according to the phase state correlation type of the sample set.The application realizes unique tracking of the particles in the whole process, accurately identifies and repairs the misjudgment points, improves the overall precision of the precipitation particle phase state identification, and guarantees the continuity of the phase state evolution.

The invention relates to the technical field of rainfall inversion, in particular to a multi-source data fused rainfall radar high-precision rainfall inversion method, which comprises the steps of S1, defining an inversion target and selecting multi-source data; s2, multi-source data preprocessing and cloud layer influence pre-judgment; s3, fusion algorithm type selection and correction model construction; s4, assimilating historical data; s5, dynamic weight optimization is carried out; the judgment logic can effectively distinguish distorted data and effective data which are shielded by clouds, blind use of shielded rainfall particle detection data for inversion is avoided, false rainfall areas or missed judgment areas are reduced, rainfall particle information which is originally large in deviation due to signal reflection and absorption of the clouds is closer to the real situation, and the accuracy of rainfall particle detection is improved. The accuracy of rainfall intensity inversion is remarkably improved, short-term historical data processed by a physical model and a long-term climate background field are fused into a real-time inversion process, a smoother, more stable and more reasonable rainfall inversion result can be provided, and the accuracy of rainfall products in short-term and imminent forecasting is greatly enhanced.

The invention discloses a raindrop spectrum parameter profile inversion method and a raindrop spectrum parameter profile inversion system, which aim at the problem that the existing rainfall micro-physical parameterization scheme is insufficient in characterization of raindrop spectrum regional difference and vertical evolution characteristics, and optimize the raindrop spectrum parameter profile inversion process by fusing rainfall particle phase state classification. And the precision and reliability of an inversion result are improved. According to the method, collaborative inversion is carried out through multi-source vertical detection data by using a micro-rain radar (high-sensitivity small particle detection) and a laser raindrop spectrometer (ground drop spectrum verification), the vertical gradient change rule of raindrop spectrum parameters is quantified, the mu-lambda relation of different heights is constructed, and raindrop spectrum parameter profile inversion of rainfall particle phase state classification is realized.

This manufacturing method is for manufacturing an observation device (110) for observing precipitation particles such as raindrops. The manufacturing method comprises: a step for preparing an antenna module (30) that transmits electromagnetic waves (E) and receives electromagnetic waves (E) reflected by a storage container (20) and electromagnetic waves (E) reflected by precipitation particles; a step for outputting information relating to a placement position for placing the antenna module (30) in the storage container (20) on the basis of the value of the dielectric constant of the wall (21) of the storage container (20), the value of the thickness of the wall (21), and a theoretical formula for converting a function indicating the relationship between the electromagnetic waves (E) transmitted from the antenna module (30) and the electromagnetic waves (E) reflected by the storage container (20) into a function having, as variables, the dielectric constant of the wall (21), the thickness of the wall (21), and the placement position; and a step for storing the antenna module (30) in the storage container (20) and placing the antenna module (30) at the placement position.

The utility model provides an airborne holographic cloud precipitation particle imager. The airborne holographic cloud precipitation particle imager comprises a cylindrical pod, a conical pod cover and a strip-shaped ellipsoid end cover, the two ends of the rear side face of the strip-shaped ellipsoid end cover are each provided with a laser module installation shell and a camera assembly installation shell. The first far-away end of the laser module is provided with a first reflective mirror which reflects emergent light of the laser module, and the emergent light penetrates through a first light hole in the inner side of the first far-away end and then is vertically emitted to the camera assembly mounting shell. A second far end of the camera assembly mounting shell is provided with a second reflective mirror, and the inner side of the second far end is provided with a second light hole directly facing the first light hole; and an optical transceiver unit is fixed in the front end of the cylindrical pod. The device is simple in structure and convenient to use and maintain, particularly, the whole shape is streamline, the phenomena of wind resistance and particle breaking are greatly reduced, and the device has high timeliness, accuracy and reliability.

The invention discloses a method for realizing dual-polarization phased array weather radar target simulation by using a digital receiver, and relates to the technical field of radar signal processing. According to the method for realizing dual-polarization phased array weather radar target simulation by utilizing the digital receiver, a weather target micro-physical parameter model is established, abundant FPGA resources in the phased array radar digital receiver are combined, and a similar direct digital frequency synthesis (DDS) technology is utilized, so that a high-fidelity digital intermediate frequency echo signal is generated; dynamic target simulation is realized in an active array digital receiver of a phased array radar. Controllable and repeatable simulation of various weather phenomena (rain, snow, hail and the like) can be realized in a laboratory environment. Under the condition of not depending on external hardware, echo characteristics of weather targets (such as rainfall particles), including parameters such as distance, speed, amplitude and dual-polarization parameter quantity, are simulated, and debugging, performance verification and algorithm testing of a radar system are effectively supported.

The application relates to the technical field of signal processing, and discloses an intelligent anti-interference signal processing method and system for a portable laser wind measurement radar, the method comprising the following steps: constructing a physical-inspired feature network, outputting a feature vector and mapping the feature vector to a low-dimensional physical parameter vector, constructing a composite loss function according to the low-dimensional physical parameter vector; constructing a raindrop particle size distribution according to the low-dimensional physical parameter vector, calculating the backscattering cross section and the terminal falling velocity of each particle, obtaining a Doppler frequency shift according to the terminal falling velocity, and obtaining the spectrum of an interference signal according to the backscattering cross section and the Doppler frequency shift; obtaining a pure wind field signal according to the spectrum of the interference signal and a mixed spectrum, and obtaining a horizontal wind vector according to the pure wind field signal. The application realizes high-precision wind speed inversion.

The present application relates to the technical field of vehicles and discloses a wiper control method, a wiper control device and a vehicle.The wiper control method comprises the following steps: acquiring visual form features and impact pulse features of precipitation particles in a front windshield area of a vehicle, wherein the visual form features are used to represent the overall form and distribution law of the precipitation particles in the spatial and motion levels, and the impact pulse features are used to reflect the vibration change trend caused by the impact of the precipitation particles on the front windshield; determining a precipitation type based on the visual form features, the impact pulse features and real-time temperature features, wherein the real-time temperature features are used to describe the temperature influence of a driving environment on the front windshield area; and controlling the vehicle to execute a corresponding wiper control strategy based on the precipitation type.The present application can reduce the situation of over-frequent or untimely wiping, improve the visual clarity of the front windshield in bad weather, and thus enhance the driving safety and driving comfort.

The invention relates to the technical field of raindrop spectrum inversion, in particular to a method for inverting an aerial raindrop spectrum by using characteristic parameters of a dual-polarization radar, and the method comprises the steps of data collection, quality control, calculation and fusion. Modeling and counting; the statistics specifically comprises the statistics of a radar and rainfall type relationship and the statistics of a typing and raindrop spectrum type fitting relationship; and the business application process comprises a four-dimensional raindrop spectrum product and a rainfall estimation product. The research of the invention can bring significant improvements in three aspects: in the aspect of monitoring capability, a radar secondary product system can be enriched, and visual visualization of spatial precipitation particle distribution characteristics is realized; in the aspect of early warning and forecasting, the heavy rainfall falling area can be researched and judged in advance through the distribution characteristics of particles in the air, and the disastrous weather early warning timeliness is effectively prolonged; in the aspect of artificial influence weather, refined rainfall micro-physical structure information is provided, and reliable technical support is provided for operation condition identification and effect evaluation.

This invention discloses a method for inverting atmospheric precipitation particle spectra, belonging to the field of inversion technology. It addresses the limitations of existing technologies in accurately obtaining inversion parameters for ground and atmospheric microphysical parameters, thus hindering understanding of the formation, development, and variation mechanisms of atmospheric cloud precipitation and consequently preventing accurate prediction and interpretation of atmospheric processes and meteorological disasters. The method includes: acquiring the observed power spectrum using C-band frequency-modulated continuous wave radar and employing denoising methods to remove noise levels; constructing the atmospheric power spectrum based on horizontal wind data acquired from wind profiler radar; constructing the raindrop spectrum using ground raindrop data acquired from a ground raindrop spectrometer and a standardized gamma distribution; and performing ground and atmospheric microphysical parameter inversion based on the above results. This invention is used for inverting and obtaining ground and atmospheric microphysical parameters.

The present application relates to the field of radar technology, and more particularly to a precipitation particle phase state recognition method for an X-band dual-polarization micro-weather radar, which comprises the following steps: Step 1, scanning at a set pitch angle between horizontal and vertical, obtaining spectrum reflectivity, spectrum differential reflectivity and spectrum correlation coefficient along Doppler velocity distribution for each range bin; Step 2, determining a separation boundary according to a valley value formed by first decreasing and then increasing the spectrum correlation coefficient, dividing the range bin along Doppler velocity into a plurality of spectrum components corresponding to single phase states respectively; Step 3, obtaining component differential reflectivity, component correlation coefficient and relative order along fall velocity for each spectrum component, and obtaining phase state spatial distribution by summarizing and splicing. The present application can separate and recognize multiple coexisting phase states in a single resolution volume, and improves the accuracy of phase state recognition.

This invention discloses a high-precision precipitation particle detection method and system. It relates to the field of optical and meteorological measurement equipment technology, solving the problems of poor accuracy and insufficient adaptability in precipitation particle parameter measurement in existing technologies. This invention corrects the distortion of precipitation particle slice contours caused by these movements through horizontal and vertical velocities, thereby accurately identifying the shape, size, flatness, three-dimensional orientation, and type of precipitation particles during their descent. Based on the degree of change in the upper and lower sets of precipitation particle contours, the rotation rate of the precipitation particles is estimated. An artificial intelligence recognition algorithm is used to train and analyze the precipitation particle contours to identify the type of precipitation particles. A photoelectric detection unit detects the difference in scattering intensity signals of precipitation particles to two light sources, and a threshold is used to determine the phase state of the precipitation particles, thus improving the ability to identify precipitation particle types.

The present application relates to the technical field of rainfall inversion, in particular to a multi-source data fusion rain measuring radar high-precision rainfall inversion method, comprising S1: inversion target definition and multi-source data selection; S2: multi-source data preprocessing and cloud layer influence prediction; S3: fusion algorithm selection and correction model construction; S4: history data assimilation; S5: dynamic weight optimization; The judgment logic can effectively distinguish the distorted data and the effective data shielded by the cloud layer, avoid blindly using the shielded precipitation particle detection data for inversion, reduce the appearance of false rainfall area or missed area, make the originally large-deviation precipitation particle information due to cloud layer reflection and absorption signal closer to the real situation, significantly improve the accuracy of rain intensity inversion, and the short-term historical data processed through the physical model and the long-term climate background field are integrated into the real-time inversion process, so that more smooth, stable and reasonable rainfall inversion results can be provided, and the accuracy of the short-term forecast of the rainfall product is greatly improved.

1. The name of the design product: holographic precipitation weather phenomenon detection sensor. 2. The use of the design product: mainly used in the field of meteorological observation, through holographic imaging technology, real-time monitoring and analysis of precipitation particle (raindrop, snowflake, hail, etc.) data, providing high-precision observation data support for weather forecasting, disaster warning, climate research and artificial weather modification. 3. The design points of the design product: in the perspective drawing. 4. The picture or photo that best indicates the design points: perspective drawing.

The invention provides a channel modeling method, a model application method, electronic equipment and a storage medium. The channel modeling method comprises the steps that a rainfall type factor and a rainfall rate in a current environment are obtained, the value of the rainfall type factor is larger than or equal to a first preset value and smaller than or equal to a second preset value, the first preset value represents that the rainfall type in the current environment is rain, and the second preset value represents that the rainfall type in the current environment is snow; according to the rainfall rate, determining a rainfall intensity grade in the current environment; and generating a signal attenuation model of the wireless communication channel in the current environment based on the rainfall type factor and the rainfall intensity grade. According to the embodiment of the invention, the rainfall type factor and the rainfall intensity grade are adopted to represent the rain and snow channel attenuation in a unified manner, and the unified channel model based on the equivalent rainfall particles is constructed to describe the mixed rainfall scene in a unified manner, improve the model precision and reduce the error of the signal attenuation prediction result.

This invention discloses a method and system for geographic mapping information acquisition based on big data, belonging to the field of geographic mapping information acquisition technology. By establishing an environmental disturbance data acquisition module, it actively collects non-topographic meteorological interference factors such as wind speed (Wv), relative humidity (Hr), precipitation particle size (Dp), and air pressure (Pa). Furthermore, it introduces a quantification mechanism for disturbance intensity values, breaking the limitation of traditional mapping systems that "only rely on laser body signals to identify anomalies." This enables the system to perceive and understand natural disturbance backgrounds, maintaining the stability of mapping signal judgment even under strong disturbance weather conditions. Through clustering and probabilistic modeling to identify anomalous samples, a noise identification function that can be used for real-time matching is constructed. This allows the system to make accurate judgments based on pattern similarity when facing nonlinear interference such as signal drift and echo abrupt changes, improving the reliability and intelligence level of noise identification.

Provided are a device and a method for multi-angle stereoscopic imaging measurement of precipitation particles, including an optical illumination unit used for illuminating a sampling space; a multi-angle imaging unit used for performing multi-angle imaging on the precipitation particles; a data acquisition control unit used for controlling the optical illumination unit and the multi-angle imaging unit; a data processing unit used for processing and displaying image data of the precipitation particles; and a protection unit used for protecting the optical illumination unit, the multi-angle imaging unit and the data acquisition control unit.

The invention discloses a self-adaptive segmented differential propagation phase shift processing method and device, and belongs to the technical field of weather radar quality control, and the method comprises the steps: firstly, employing the dual-polarization parameters of a weather radar, carrying out the self-adaptive recognition of the change position of rainfall particles, and dividing the whole radial data into a plurality of independent segments with relatively consistent features; secondly, linear programming processing with monotonicity constraint is executed on each divided section, so that the differential propagation phase shift in each section is monotonous and not reduced; and finally, by setting an overlapping region, the adjacent sections are in smooth transition during splicing, so that the difference propagation phase shift after splicing is kept continuous and monotonous in a global range.

The present application relates to the field of vehicle control, and particularly relates to a glass surface cleaning method in a snowfall scene and a vehicle. The present application can directly determine whether it is a snowfall scene based on the actual sensing condition of the windshield surface by acquiring the first sensing data of the precipitation particles acting on the windshield, instead of relying on the traditional vehicle-mounted rain light sensor, thereby avoiding the problem that the existing sensor cannot accurately identify snow, sleet and ice layer due to problems such as light refraction and wavelength adaptation. Secondly, the windshield is heated after confirming the snowfall scene, then the second sensing data is continuously collected and the surface adhesion state of the precipitation particles is analyzed during the heating process, realizing the sensing of different stages of snowfall, thereby supporting adaptive cleaning operation, and solving the problem that the prior art lacks adaptive cleaning mechanism.

The invention discloses a method for judging freezing rain and snowfall in winter, and relates to the technical field of meteorology. The method comprises the following steps: acquiring research samples of observation data of a ground automatic weather station, a sounding radar and a wind profile radar in winter, preprocessing the observation data of the ground automatic weather station, the sounding radar and the wind profile radar, and extracting physical quantity parameters; statistical analysis is carried out on the ground air temperature of 2 meters, the air temperature of 700 hundred Pa, the air temperature of 500 hundred Pa, the potential thickness between 500 hundred Pa and 700 hundred Pa, the height of a 0-degree layer, the cloud top temperature, and the vertical speed, the horizontal wind speed and the horizontal wind direction distribution characteristics at five heights (150 meters, 390 meters, 750 meters, 1350 meters and 2430 meters), the temperature of a specific layer is combined with the vertical speed of rainfall particles, and the optimal freezing rain and snowfall judgment indexes are established. And the capability of discriminating freezing rain and snowfall can be improved.

To provide a method for manufacturing an observation device that can observe precipitation particles with greater accuracy. [Solution] The manufacturing method is a method for manufacturing an observation device 110 for observing precipitation particles (e.g., raindrops RD). The manufacturing method includes the steps of: preparing an antenna module 30 that emits electromagnetic waves E and receives electromagnetic waves E reflected by a housing container 20 and electromagnetic waves E reflected by precipitation particles; converting a function showing the relationship between electromagnetic waves E emitted from the antenna module 30 and electromagnetic waves E reflected by the housing container 20 into a theoretical formula having the dielectric constant of the wall portion 21 of the housing container 20, the thickness of the wall portion 21, and the installation position in the housing container 20 as variables; outputting information regarding the installation position based on the value of the dielectric constant of the wall portion 21 and the value of the thickness of the wall portion 21; and housing the antenna module 30 in the housing container 20 and installing the antenna module 30 at the installation position.

The application discloses a raindrop spectrum parameter profile inversion method and system, and aims at the problem that an existing precipitation microphysical parameterization scheme is insufficient in representing regional differences and vertical evolution characteristics of a raindrop spectrum, and the method is characterized in that the inversion process of the raindrop spectrum parameter profile is optimized by fusing precipitation particle phase classification, and the precision and reliability of the inversion result are improved. According to the method, multi-source vertical detection data are used, a small-particle detection micro rain radar and a laser raindrop spectrometer are used for cooperative inversion, the vertical gradient change rule of the raindrop spectrum parameter is quantified, the mu-Lambda relationship at different heights is constructed, and the raindrop spectrum parameter profile inversion of the precipitation particle phase classification is realized.

The invention provides a two-step quality control method for raindrop spectrum data of a DSG1 type rainfall phenomenon instrument, relates to the technical field of meteorological observation data processing, and aims to solve the problems that original raindrop spectrum data is poor in quality due to interference of non-rainfall particles, instrument errors and special weather, and an existing control method is insufficient in adaptability. The method comprises the following steps: S1, data acquisition: switching seasons by adopting a dual-mode rain gauge, and screening effective data of which the hourly rain intensity is greater than or equal to 0.1 mm of the rain gauge; s2, data preprocessing: rejecting suspected data through triple screening of particle number, particle size range and rain intensity deviation; s3, performing quality control by a double-speed threshold method, dynamically selecting optimal positive and negative thresholds, and removing speed feature abnormal data; and S4, quality evaluation: taking rain gauge data as a true value, verifying an effect through a correlation coefficient and a relative error, and supplementing false data processing in strong wind and snowfall weather. The method is high in universality, and reliable data support can be provided for rainfall microphysical research and numerical weather forecast.

The invention discloses a high-precision precipitation particle phase state identification method and system, relates to the technical field of atmospheric exploration, and solves the problems that in the prior art, precipitation particles are difficult to track, the number of phase state identification misjudgments is large, and misjudgment point repair has no accurate basis. Generating a particle unique identifier through double-feature coupling, and constructing and completing integrity verification of the space-time trajectory; carrying out time and space two-dimensional coherence verification based on the qualified trajectory, and marking suspicious misjudgment points and effective sampling points; and constructing an exclusive traceability sample set for the misjudgment points, and deducing and repairing the real phase state according to the phase state association type of the sample set. According to the method, the particle whole-flow unique tracking is realized, misjudgment points are accurately discriminated and repaired, and the continuity of phase evolution is guaranteed while the overall precision of precipitation particle phase identification is improved.

This invention provides a method and system for jointly inverting precipitation microphysical parameter profiles based on active radar and passive microwave. The method uses multi-source observation data from active radar and passive microwave to constrain particle microphysical parameters and establish a dual-optimization inversion model. The observation operators of the inversion model include the changes in microphysical properties of different types of particles and the electromagnetic wave attenuation effect, which can reduce the uncertainty of inversion caused by particle microphysical assumptions and attenuation effects. Thus, the optimal state variables of different types of precipitation particles on the profile can be obtained by inversion, improving the accuracy of precipitation microphysical parameter profile inversion.

The invention discloses a method for inverting rainfall particle size distribution by using a dual-polarization radar, and belongs to the technical field of meteorological remote sensing and data inversion. According to the method, multi-source observation data is introduced, a physical model and a machine learning technology are combined, a particle size distribution inversion model is established, physical consistency constraints are coupled in the inversion process, and high-precision inversion of radar rainfall particle size distribution is achieved. Meanwhile, based on a statistical analysis and uncertainty quantification method, the uncertainty of an inversion result is quantitatively evaluated. According to the method, the accuracy and reliability of radar rainfall particle size distribution inversion are improved, the physical interpretability of the model is enhanced, and the method is suitable for the fields of quantitative rainfall estimation and hydrometeorological analysis in a complex meteorological environment and has a wide application prospect.

The application provides an X-band weather radar double-channel multifunctional receiving assembly, and relates to the technical field of communication, which comprises: parallel receiving of horizontal and vertical polarization echo signals, twice frequency down-conversion by using first and second local oscillator signals, two digital intermediate frequency signals obtained through a digital intermediate frequency processing module; meanwhile, an up-conversion excitation signal is input into a transmitting chain; a function calibration module calibrates the noise coefficient of a receiving channel, calibrates the delay of a transmission path, and feeds a coupled sample of a transmitting signal into a receiving channel for power monitoring to determine a link gain compensation coefficient; the local oscillator signal and the digital intermediate frequency signal are led out to an external interface for monitoring the frequency source performance and amplitude and phase consistency; two signals are calibrated according to the compensation coefficient, differential reflectivity and differential phase are extracted, precipitation particle phase state structure is analyzed, strong convective weather core areas are determined and circled, and the automation level and early warning timeliness of the X-band weather radar in short-term early warning business are significantly improved.

The invention discloses a rainfall analysis method and system based on micro-rain radar data, and belongs to the field of meteorological radar signal processing, and the method comprises the steps: carrying out the vertical detection of a target airspace through a micro-rain radar, and obtaining a radar echo signal; performing noise filtering on the original Doppler power spectrum density matrix to obtain a cleaned observation Doppler power spectrum; performing feature extraction on the observation Doppler power spectrum by using a pre-trained neural network model, and outputting prior probability distribution about rainfall types; constructing and solving a precipitation state unified inversion model to obtain a globally optimal unified precipitation state vector; and based on the unified rainfall state vector, calculating rainfall particle number density distribution of each height layer, and carrying out integration to obtain an instantaneous rainfall rate of each height layer. According to the method, the problem of attenuation correction error accumulation in a traditional method is solved, and accurate early warning of rainfall dynamic risks is realized.

The invention discloses a hail particle characteristic data extraction method suitable for a laser raindrop spectrometer, and belongs to the technical field of meteorological measurement. According to the method, the relation between the falling speed of precipitation particles and the particle size and the corresponding threshold value are comprehensively utilized, and broken particle artifacts entering an instrument sampling area after particles are broken due to mechanical collision between the particles and an instrument shell in the measurement process, edge-wiped particles and particles with the particle falling speed changed due to strong wind can be recognized; and then classifying and identifying the raindrops and the hail by using the relationship between the raindrop falling speed and the particle size and the relationship between the hail falling speed and the particle size, and further separating and extracting the hail particle characteristic parameter information. According to the invention, the hail particles in the data measured by the laser raindrop spectrometer can be effectively identified and extracted, and the method is of great significance in further utilizing the hail particle characteristic data of the laser raindrop spectrometer to research the cloud precipitation micro-physical process.