204 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 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 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 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 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 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 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 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 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 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.

By changing the operating modes of weather observation sensors, including weather radar, according to the weather phenomenon being predicted, weather phenomena can be predicted with higher accuracy than before. [Solution] The weather forecasting device 3 is a variable-mode weather observation sensor that observes precipitation data and wind direction and wind speed data in an observation range, which is a spatial range to be observed. It includes a weather forecasting unit 5 that receives weather observation data, including precipitation data and wind direction and wind speed data, observed by a weather observation sensor including a weather radar 1 that is positioned so that the observation range encompasses a predetermined weather forecast area, and predicts the weather in the weather forecast area for a predetermined future forecast time range and outputs weather forecast data including precipitation; a sensor data storage unit 4 that stores the installation position, observation range and observation accuracy of each weather observation sensor; and a sensor control unit 6 that receives weather forecast data and controls the weather observation sensor, including changing the operating mode of the variable-mode weather observation sensor.

The invention provides a satellite-borne conical scanning Doppler radar three-dimensional wind field inversion method and device, and the method comprises the steps: dividing a radar observation strip region into a plurality of sub-blocks, enabling the adjacent sub-blocks to have region overlapping, and carrying out the parameterization of a horizontal wind field of each height layer in each sub-block through a segmented continuous change hypothesis; obtaining an optimal horizontal wind field parameter of each height layer in each sub-block by using a variational method, determining a horizontal wind field in each sub-block according to the optimal horizontal wind field parameter, and carrying out linear weighted fusion on the horizontal wind fields at corresponding positions of each sub-block in the overlapping region to obtain a final horizontal wind field; and calculating horizontal divergence according to the optimal horizontal wind field parameter of each height layer in each sub-block, carrying out integration on the horizontal divergence in the height direction by using an atmospheric mass conservation equation to obtain a vertical wind field, and carrying out linear weighted fusion on the vertical wind fields at corresponding positions of each sub-block in the overlapping region to obtain a final vertical wind field. According to the invention, continuous and steady three-dimensional wind field inversion of the satellite-borne conical scanning Doppler radar is realized.

The invention relates to a ground-based radar super-resolution imaging method based on sparse reconstruction and local super-sampling. The ground-based radar super-resolution imaging method comprises the steps of ground-based radar signal modeling and data preprocessing; constructing a measurement model and sparse representation; sparse reconstruction of coarse grids; peak detection and local window selection; local oversampling and fine inversion are carried out; sub-pixel fitting and coordinate refining are carried out; carrying out continuous nuclear least square refining; and imaging fusion and result output are carried out, so that smooth transition among a plurality of overlapped regions is realized. According to the invention, high-precision reconstruction and detail enhancement of ground-based radar observation data are realized, the imaging quality and the target positioning precision can be effectively improved, and the method is suitable for various application scenes such as surface deformation monitoring, slope stability analysis and engineering structure safety detection.

The application discloses a LES super-high-resolution TC atmospheric flow field numerical simulation method and system, and relates to the fields of atmospheric observation, numerical assimilation and high-resolution numerical simulation. The method is characterized in that: three-dimensional dynamic-thermal information is obtained by implementing airship platform radar observation and downward sounding observation in the core area of a tropical cyclone; the radar radial wind and profile observation operators are preprocessed and constructed; a data assimilation strategy is adopted to integrate multi-source observation in a multi-nested grid numerical model to generate a three-dimensional initial / reanalysis field suitable for large eddy simulation; then, a large eddy simulation solver is configured on the innermost grid covering the boundary layer of the tropical cyclone; fine horizontal grids and boundary layer encryption vertical stratification are adopted to explicitly analyze the boundary layer vortex, small-scale convection and turbulent structure; and a super-high-resolution three-dimensional flow field product for fine evaluation of strong wind and disaster risk is output.

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 application discloses a meteorological data processing method, a meteorological data processing device and an electronic equipment, and relates to the technical field of meteorology. The method can reduce the error of meteorological data and improve the accuracy of meteorological data. The method comprises the following steps: acquiring base data observed by each weather radar; performing gridding on the base data to obtain first gridding data of an observation area of each weather radar; determining a blur range of radial velocity; performing velocity de-blurring processing on the radial velocity in the first gridding data according to the blur range; determining a shielding range of a reflectivity factor; performing shielding removal processing on the reflectivity factor in the first gridding data according to the shielding range; performing gridding on the first gridding data after the velocity de-blurring processing and the shielding removal processing to obtain second gridding data; merging the second gridding data of each weather radar to obtain meteorological gridding data of a whole area; and converting the meteorological gridding data into target data corresponding to a weather forecasting system, so that the weather forecasting system can use the target data.

The present invention discloses a method for acquiring vegetation area data using a navigation satellite dual-base InSAR (InSAR) based on multi-factor optimization. The present invention first selects experimental scenarios based on the type of surface vegetation coverage; then calculates radar observation indicators to determine the available experimental points in each scenario; then, by analyzing the seasonal vegetation normalized index of the experimental scenario, the duration of the experiment is determined; finally, based on a multi-objective joint optimization algorithm, the data collection time and satellite combination of each experimental point are designed to obtain the final data collection experimental design scheme. The present invention comprehensively considers factors such as vegetation coverage type, radar observation indicators, and experimental time duration to ultimately determine the data collection experimental design scheme for vegetation scenario research. Data is then collected based on this data and a data set is established for subsequent signal processing and phase analysis, providing a feasible method for accumulating raw data for navigation satellite dual-base InSAR vegetation scenario research.

The application 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. The method comprises the following steps: firstly, performing smoothing and data cleaning processing on original wave height data to construct a fitting model of polynomial and Gaussian global trend analysis. Then, through dynamic fusion of double models, the depth of capturing short-term fluctuations of the polynomial and capturing long-term trends of the Gaussian is combined, and the model generalization ability is improved. Finally, intelligent reference interval positioning is introduced to adapt to the wave height distribution changes in different periods and positions, and a correction factor is introduced to avoid over-correction, so that accurate correction data is obtained, the problem of wave height attenuation with distance is solved, and the efficiency and reliability of the correction processing are significantly improved.

This invention discloses a high-fidelity cooperative jamming method and system for UAV swarms targeting networked radar. The method includes: constructing a UAV dynamics model; constructing a UAV energy consumption model, including UAV propulsion energy consumption and DRFM relay energy consumption; designing false flight paths based on the dynamic characteristics, RCS characteristics, and scene adaptability factors of the false target; calculating the required relay power to simulate RCS fluctuations by calculating the target attitude angle and radar observation angle, combined with three-dimensional RCS data; establishing an optimization model containing UAV dynamics constraints with the goal of minimizing the total energy consumption of the UAV swarm, using the flight heading angle as the optimization variable, and solving the optimization model using a genetic algorithm. This invention improves the credibility of false flight paths, increases the duration of flight path deception, ensures the stability of deception in complex battlefield environments, and enhances the practical adaptability of cooperative jamming.

This paper proposes an aerial target identification method based on the fusion of one-dimensional range profile and pole features. It addresses the problem that changes in the flight attitude, heading, or radar observation angle of an aerial target can significantly alter the number, location, and intensity of the target's scattering centers, leading to decreased range profile stability and consequently affecting target identification accuracy. This application achieves more robust and accurate aerial target identification by fusing resonant region pole features and optical region HRRP features, thus balancing identification stability and target discrimination capability, thereby improving the accuracy of civil airspace monitoring and aerial target detection and identification.

The application discloses a radar photoelectric signal processing method for multi-target detection and belongs to the field of radar multi-target identification. The method solves the problem of poor fusion identification effect of radar signals and photoelectric signals in the multi-target identification process. The technical scheme comprises the following steps: synchronously acquiring an original intermediate frequency signal sequence of a phased array radar and a laser reflection spectrum image sequence of a laser radar, and extracting radar observation data vectors and laser observation data vectors; aligning the laser field of view and the radar field of view, and realizing a three-dimensional common field of view; calculating mutual information values of the radar observation data and the laser observation data; calculating an adaptive adjustment factor, jointly filtering the radar signals, and outputting a radar signal frequency domain response; and calculating the correlation probability between targets, and evaluating whether the targets are the same real target through the correlation probability. The application proposes a probability data correlation model, which is used for more accurately acquiring the number of targets and target trajectories in the field of view and realizing high-precision multi-target detection.

The invention provides a positioning method and device of tunnel construction equipment, equipment and a storage medium, and relates to the field of tunnel positioning. Comprising the following steps: acquiring three-dimensional coordinate information of a plurality of corner reflectors in an area where to-be-positioned tunnel construction equipment is located; obtaining dynamic pixel coordinate information of the corner reflector through a visual module of the tunnel construction equipment to be positioned; determining a conversion relation between a millimeter wave radar coordinate system and an image pixel coordinate system according to the three-dimensional coordinate information and the dynamic pixel coordinate information; determining a radar observation equation of the corner reflector and the millimeter-wave radar module according to a conversion relation between the millimeter-wave radar coordinate system and the image pixel coordinate system; and determining the pose of the tunnel construction equipment to be positioned according to the radar observation equation. The technical problems that the accuracy of an existing tunnel positioning technology is less affected by the dust environment, laser is seriously scattered in the high-dust environment, the effective distance is shortened, and the positioning efficiency of tunnel construction equipment is reduced are solved.

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 invention discloses a satellite position real-time resolving platform and method based on multi-source data fusion, and relates to the technical field of satellite position resolving, the platform comprises a multi-source data access and time reference module, a real-time resolving engine module, a rendering and interaction module and an event bus and auditing module, the multi-source data access and time reference module carries out timestamp labeling and quality rating on two rows of element data, radar observation data, optical observation data and global navigation satellite system broadcast data, and the real-time resolving engine module generates a resolving result containing confidence coefficient parameters and check metadata based on an incremental fusion algorithm. According to the method, unified fusion of multi-source data and verifiability of the resolving process are achieved, and the precision and reliability of satellite position resolving are improved.