649 results about "Weather radar" patented technology

The invention relates to the technical field of meteorological detection, in particular to a tornado collaborative observation method based on detection equipment. The method comprises the following steps: carrying out real-time monitoring on regional micro-pressure and meteorological echoes of a suspicious target region through a micromanometer and an S-band new-generation weather radar, and carrying out severe convection occurrence matching prediction to obtain a severe convection occurrence prediction target region; performing solar radio calibration processing on the corresponding radar detection equipment and triggering a tornado detection strategy cooperation instruction to perform severe convection target three-dimensional cooperation observation and target strong correlation area fusion division so as to generate a strong convection target and other strong correlation area radar field; performing equal-intensity convection characteristic analysis on the radar field of the equal-intensity correlation area of the severe convection target to generate a tornado equal-intensity convection characteristic data set; and obtaining a preset tornado feature library and carrying out tornado identification analysis to generate a tornado occurrence susceptible lattice point region. According to the invention, the monitoring precision and real-time performance of the tornado detection process can be improved.

The invention discloses an intelligent discharge port dynamic regulation and control system and method based on multi-source time sequence fusion, and relates to the technical field of intelligent water affairs, and the system comprises a data collection module which is used for obtaining the multi-source data of a target area in real time; the space-time fusion module is used for mapping the meteorological radar data to the topological space of the drainage pipe network through a space-time alignment encoder, dynamically fusing the meteorological radar data, the surface runoff sensor data and the historical drainage data by adopting a multi-head attention mechanism, and generating a space-time fusion tensor; the prediction model module is used for constructing a TCN-LSTM hybrid model, taking the space-time fusion tensor as input and taking future drainage flow as output; and the dynamic regulation and control module is used for optimizing the gate opening degree and the pump station power control sequence in a rolling manner based on a model predictive control algorithm, and performing dual-target optimization by combining real-time electricity price data and overflow risks. According to the system, the prediction precision of the drainage flow can be improved, and double-target balance of the overflow risk and the energy consumption cost can be achieved.

The invention discloses a method and system for estimating irrigation water consumption and water utilization efficiency, and relates to the technical field of water resource management, and the method comprises the steps: collecting the plant stem flow rate, canopy temperature and soil profile water content data of a target area through a multi-source sensing device, and obtaining a gradient and permeability distribution diagram of a geographic information system; dividing the farmland of the target area into sub-areas with different irrigation priorities based on a soil conductivity mutation point detection algorithm; constructing a dynamic water demand model fused with the plant transpiration hysteresis effect, performing space-time correlation analysis on the stem flow rate data and the soil moisture content gradient, and calculating the real-time water demand intensity of each sub-region; the method comprises the following steps: accessing short-time rainfall prediction data of a meteorological radar, dynamically correcting a future irrigation decision by adopting a sliding time window mechanism, and triggering a gradient delay irrigation instruction when the predicted rainfall amount exceeds an irrigation threshold value; and calculating the actual moisture utilization efficiency based on the inversion data of the moisture content of the irrigated soil and the unmanned aerial vehicle multispectral image.

The invention relates to a rainfall kinetic energy space-time prediction method based on dual-polarization weather radar observation parameters, and the method comprises the steps: firstly processing minute-resolution raindrop spectrometer data, calculating raindrop diameter distribution, and calibrating the data of a rain gauge; secondly, dual-polarization radar parameters (ZH, ZDR and KDP) are calculated based on a physical formula, rainfall kinetic energy KE is calculated in combination with micro-physical characteristics, and rainfall types are marked; and then, after atmospheric field variables are extracted and physical normalization is carried out, a bidirectional time series kinetic energy prediction network (BTKE-Net) is constructed, and rainfall kinetic energy is accurately predicted through BiLSTM and a deep regression network. And finally, after attenuation and offset correction, inputting the data into the trained BTKE-Net model, and carrying out real-time space-time prediction. The innovation of the invention lies in that the application potential of the dual-polarization weather radar in the water environment field is excavated, large-range high-precision rainfall kinetic energy space-time prediction is realized, and more accurate scientific basis and decision support are provided for water and soil conservation and meteorological disaster prevention and control.

The invention belongs to the technical field of weather forecast, and discloses an information fusion dual-polarization radar urban heavy rainfall short-term and imminent forecast deep learning method and system. The invention provides a novel urban heavy rainfall short-term and imminent forecasting method based on meteorological radar data through a research thought of'space-time-space-time fusion 'on the basis of a deep learning technology in combination with a physical mechanism of heavy rainfall. According to the method, heavy rainfall is accurately forecasted by analyzing spatial and temporal distribution characteristics of heavy rainfall, extracting high-dimensional spatial characteristics, developing a super-resolution heavy rainfall spatial characteristic extraction model, combining time evolution characteristics of heavy rainfall and adopting an LSTM and spatial and temporal characteristic fusion model, and the accuracy and timeliness of urban heavy rainfall forecasting are improved.

The invention provides a multi-band radar data space downscaling method based on data fusion. The method comprises the following steps: step 1, acquiring S-band and X-band observation data from a weather radar; step 2, carrying out data preprocessing on the obtained observation data; step 3, constructing a spatial downscaling model; 4, performing training verification and testing on the spatial downscaling model through the observation data after data preprocessing; step 5, according to the trained spatial downscaling model, performing spatial downscaling of the S-band observation data; and step 6, judging whether the generated data is close to a true value or not. By means of the deep learning neural network, the model can extract and fuse the characteristics of the S-band observation data and the X-band observation data, the original low-resolution S-band data is converted into high-resolution data, the requirement for high resolution of radar data in practical application is met, and dependence on hardware facilities is reduced.

The invention discloses a statistical result-based X-band phased array weather radar multi-beam scanning mode observation error calibration method and system. The method comprises the following steps of: S1, acquiring observation base data of an S-band dual-polarization Doppler weather radar and an X-band dual-polarization phased array weather radar as initial data; s2, performing data quality control on the radar-based data to obtain preprocessed data; and S3, by adopting a different radar time and space sampling volume coordinate matching method and taking an observation value of the S-band radar as a reference, counting a matching difference of the preprocessed data to obtain an observation error of the X-band radar and correcting the observation error.

The invention discloses a method and a system suitable for identifying bird echoes by a Chinese S-band dual-polarization weather radar. The method comprises the following steps: preprocessing observation parameter characteristics of Chinese S-band dual-polarization weather radar base data; establishing a membership function and a weight parameter of each parameter of the weather radar to form a fuzzy logic model; and through comprehensive judgment of statistical bird flock membership function aggregation values, accurate distinguishing of bird flock and rainfall data is realized. Based on four observation parameters and three derivations of the dual-polarization radar, probability density distribution function characteristics of bird flock and rainfall data are analyzed, a fuzzy logic algorithm process and weight parameters suitable for S-band dual-polarization radar bird flock recognition in China are constructed, the accuracy of bird flock and rainfall echo distinguishing is improved, and the probability density distribution function characteristics of the bird flock and rainfall data are analyzed. Through individual verification under different weather conditions such as bird separation, rainfall and clear sky, the effectiveness of the method is verified, and a new technical path is provided for future weather radar biological echo recognition and ecological monitoring.

The invention relates to an X-band two-dimensional phased array radar which comprises a microstrip antenna transmitting and receiving antenna subarray, a power distribution / synthesis network, a digital T / R assembly, a frequency source, a wave control unit, a servo turntable, a terminal server and the like. A sub-array modular design is adopted for receiving and transmitting of a radar microstrip antenna, a 4 * 4 array is used as a basic sub-array unit for array expansion to achieve a 32 * 32 array, and the total number of channels is 1024. The detection capability of an original phased array weather radar on a local meteorological target can be improved, the problem that a traditional phased array radar is large in size and difficult to maintain is solved, electric scanning can be conducted in the azimuth direction and the pitching direction, meanwhile, the mechanical scanning capability is achieved, high-precision rainfall monitoring of the scene can be achieved through two-dimensional electric scanning of the vertical headspace, and the application range is wide. Therefore, the detection efficiency of the phased array radar is greatly improved, and real-time monitoring and scanning of a specified target are realized.

The invention discloses a networking X-band weather radar cooperative adaptive control method, and relates to the technical field of weather radars. The problems that in the prior art, real-time state data and historical data cannot be fully utilized to construct an accurate radar performance model, and deep analysis on environment characteristics is lacked when control parameters are dynamically adjusted are solved. According to the method, the operation state and historical data of the radar system are collected and analyzed, the radar performance model is established, and the control parameters are dynamically optimized, so that the observation performance and the resource utilization efficiency of the radar system are remarkably improved, state information sharing and cooperative adjustment among the radar systems are realized based on a cooperative control mechanism of a communication link, and the reliability of the system is improved. The adaptability and reliability of the system are effectively enhanced, main factors influencing the system performance can be identified by constructing an intelligent performance evaluation module, model parameters are dynamically adjusted, constraint conditions are optimized, and long-term efficient operation of the radar network under complex environmental conditions is ensured.

The invention relates to the technical field of meteorological analysis, and more specifically relates to a satellite-borne and multiband ground-based radar reflectivity factor joint verification method and a satellite-borne and multiband ground-based radar reflectivity factor joint verification system. The scheme comprises the following steps: setting data sources of a spaceborne radar and a ground-based weather radar; performing data quality control according to the data sources of the spaceborne radar and the ground-based weather radar to form a corrected data source; matching and comparing the reflectivity factors of the spaceborne radar and the ground-based weather radar according to the corrected data source to form optimal pairing data; establishing a fused networking radar consistency evaluation algorithm to determine an alarm list, and forming a problem level; standard comparison is carried out on the ground-based radar reflectivity factors to form a matching result of the standard database and the novel spaceborne radar FY3-G / PMR; evaluation classification is set, and evaluation results under different classifications of the matching result of the standard database and the novel spaceborne radar FY3-G / PMR are formed and stored.

The embodiment of the invention provides a simulated target reflectivity factor quantitative generation method for weather radar far-field region calibration, and is applied to the technical field of weather radar calibration. The method comprises the following steps: acquiring radar antenna and emission parameters, a distance between a target simulator erection point and a radar, a simulation target distance, and a transmit-receive radiance ratio of a target simulator; according to a pre-constructed simulation target equivalent sectional area quantitative model, based on the radar antenna and emission parameters, the distance between the target simulator erection point and the radar, the transmit-receive radiance ratio of the target simulator and the simulation target distance, calculating to obtain a simulation target equivalent sectional area; and calculating to obtain a radar reflectivity factor expected value according to the equivalent sectional area of the simulation target, the radar antenna and emission parameters and a weather radar equation. In this way, a simulated target reflectivity factor for weather radar far-field region calibration can be quantitatively generated.

The invention discloses a multivariate progressive rainstorm-mountain torrent chain disaster comprehensive risk evaluation method and system, and the method comprises the steps: constructing a multiband weather radar networking multi-target optimization model, and coupling a radar rain measurement simulation model to obtain an optimization networking model; taking the collected scheme set as input to solve and obtain an optimal networking scheme and rainfall forecast; constructing a flood forecast model, extracting rainfall forecast as model input to obtain initial flood forecast, correcting and optimizing to obtain an optimized flood forecast model, and simulating to obtain flood forecast; screening initial mountain torrent risk factors to construct a mountain torrent risk factor set, carrying out dimension reduction on the mountain torrent risk factors to obtain a rainstorm-mountain torrent chain type comprehensive risk index, calculating a comprehensive risk index value based on flood forecasting, inputting the comprehensive risk index value into a mountain torrent risk evaluation model constructed by adopting an XGBoost model, and solving the model to obtain comprehensive risk forecasting. According to the invention, the scientificity and timeliness of disaster emergency decision making are enhanced, and the early warning capability and risk management level of rainstorm-mountain torrent disasters are improved.

The invention discloses a multi-mode radar echo extrapolation method based on a Doppler dual-polarization weather radar, and belongs to the technical field of meteorology. The method specifically comprises the following steps: firstly, acquiring Doppler dual-polarization weather-based data, and converting the Doppler dual-polarization weather-based data into multi-modal physical parameter data; then, constructing a multi-modal radar echo extrapolation model comprising a multi-modal encoder, a depth feature extraction module and a decoder; the encoder is composed of a double-branch structure, performs preliminary feature extraction on original multi-modal information, inputs the extracted features to the depth feature extraction module, further extracts depth spatial-temporal features through a self-attention mechanism, and finally, completes layer-by-layer decoding of the depth spatial-temporal features through the decoder, and introduces preliminary features to strengthen transmission of shallow feature information; and inputting newly observed radar information into the trained echo extrapolation model, and predicting a radar echo image at a future moment. According to the invention, powerful technical support is provided for real-time meteorological monitoring, rainfall forecast and extreme weather early warning.

The invention provides a lightning frequency nowcasting method and system, and relates to the technical field of lightning nowcasting, and the method comprises the steps: obtaining the actual measurement data of severe convective weather of a target region in a current time period, the severe convective weather actual measurement data at least comprise weather radar echo actual measurement data, lightning frequency actual measurement data and stationary satellite multichannel actual measurement infrared brightness temperature; and based on a preset time forecasting interval, inputting the severe convective weather measured data into a multi-branch coding lightning frequency forecasting model to obtain weather radar echo forecasting data and lightning frequency forecasting data of the target area in the next time period output by the multi-branch coding lightning frequency forecasting model, the multi-branch coding lightning frequency forecasting model is obtained through training based on severe convection weather sample data marked with severe convection weather labels. According to the invention, lightning frequency forecasting is realized, and the accuracy of lightning forecasting is improved.

The invention relates to the technical field of meteorology and hydrology, and discloses a short and temporary rainfall forecasting method and system based on a raindrop spectrometer and a meteorological radar, and the method comprises the steps: carrying out the preprocessing of radar echo image data and raindrop spectrometer data; quantitatively estimating a radar reflectivity factor according to the raindrop spectrometer data; extrapolating radar echo image data based on an optical flow method and a UNet network model, and extrapolating a radar reflectivity factor based on an LSTM network model; performing dynamic regression on the extrapolated radar echo image data to obtain the radar echo image data of the required prediction period; carrying out global correction on the radar echo image data of the required predicted period according to the radar reflectivity factor of the required predicted period obtained by extrapolation; and forecasting rainfall according to the radar echo image data after global correction. According to the method, radar echo image data can be closer to near-surface rainfall information, and the accuracy of rainfall forecast and the adaptability to complex and changeable meteorological data are improved.

The invention discloses a multi-source data and physical information fused tornado space-time intelligent prediction method, which comprises the steps of collecting observation data of a Doppler weather radar and reanalysis data of a mid-term weather forecast center, performing projection conversion, space alignment, time interpolation and the like of space-time dimensions, and fusing key physical characteristics of the tornado; establishing a space-time convolution model network suitable for tornado multi-scale feature extraction, inputting fusion features and corresponding coordinate information, and outputting a prediction control quantity of tornado space-time evolution; a physical control equation of the atmospheric motion and thermodynamic process is converted into a penalty term, a space-time convolution model loss function is embedded, and physical information constraint is carried out on network parameters; and obtaining tornado prediction control quantities of different time windows in the future, and generating a probability graph of the potential development area of the tornado. Through fusion of multi-source data and a physical mechanism, future tornado event probability is quantified, and reliable data support and scientific basis are provided for meteorological prediction and emergency response.

The invention provides a large-scale insect and bird migratory flight monitoring system based on a weather radar net, and the system employs a data collection module to obtain S, C and X wave band weather radar monitoring data of the national weather radar net, and a quality control module carries out the quality control of the radar-based data in the monitoring data, and further provides the radar-based data to an echo recognition module to construct recognition features. A classification recognition result is obtained by adopting a recognition model corresponding to the wave band; the inversion quantification module inverts the insect and bird density, the insect and bird speed and the vertical distribution of the movement direction based on the classified and identified insect and bird echoes to obtain an inversion quantification result; and the classification identification result and the inversion quantification result of the single wave band form a local monitoring result. And the visualization module displays a single-band local monitoring result or a multi-band fusion monitoring result. According to the invention, multi-band data can be collected, monitoring results are fused, real-time and efficient monitoring of migration of insects and birds is realized, and the monitoring range is expanded.

The invention discloses an intelligent ship formation navigation control method, and relates to the technical field of ship navigation control, and the control method comprises the specific steps: S100, multi-source data collection: installing various sensors and communication equipment on a ship, according to the invention, a ship AIS sensor, a meteorological radar echo sensor, an underwater sonar detection sensor, a satellite communication device and a VHF communication device are installed on a ship, so that comprehensive acquisition of various environmental information and states of the ship in the ship navigation process is realized; the multi-source heterogeneous data is subjected to cleaning, conversion and normalization processing of a data center and then is efficiently fused by a deep hybrid network model H-Net, and deep feature extraction is performed by using a convolution attention module and an improved gating cycle unit iGRU, so that the efficiency and accuracy of data processing are greatly improved, and the data processing efficiency and accuracy are greatly improved. And the key information in the navigation environment can be captured more accurately.

The invention discloses a rainfall forecast correction method based on CNN-BiLSTM-At and ST-CFormer. The rainfall forecast correction method comprises the following steps: acquiring a meteorological radar echo sequence image and meteorological station observation data, and preprocessing the meteorological radar echo sequence image and the meteorological station observation data; constructing a CNN-BiLSTM-At rainfall forecast model structure, training the structure by using the preprocessed meteorological radar echo sequence image, optimizing hyper-parameters by using an adaptive genetic algorithm, and obtaining a trained rainfall forecast target model; the method comprises the following steps: constructing an ST-CFormer rainfall forecast deviation correction model structure, training the ST-CFormer rainfall forecast deviation correction model structure by using data obtained by fusing a rainfall forecast image and observation data of a meteorological station, and optimizing hyper-parameters by using an NSGA-II algorithm to obtain a trained rainfall forecast deviation correction model. According to the method, secondary correction is carried out on the forecast result by utilizing meteorological station observation data and the global modeling capability of the Swindow-transformer, so that the rainfall forecast precision is effectively improved.

The invention relates to a thunderstorm and gale identification method based on multi-dimensional characteristic parameter fusion, and belongs to the field of radio, and the method comprises the steps: employing a neighborhood value method to remove the electromagnetic interference of dual-line polarization Doppler weather radar data, carrying out the rollback fuzzy processing, and then carrying out the meshing; dual-polarization radar data, wind profile radar data, sounding data and ground automatic station data are respectively obtained, and respective characteristic parameters are extracted from the data; carrying out normalization processing on the characteristic parameters, and obtaining corresponding parameters; constructing a multi-parameter integrated early warning model based on the plurality of fusion parameters, and training and verifying the model; and inputting parameters into the trained model for operation, and outputting the thunderstorm gale probability P. According to the thunderstorm gale identification method, through combination of multi-source data fusion and multiple physical mechanism fusion and integration of innovation of a machine learning model, the technology of thunderstorm gale identification from single index dependence to multi-dimensional collaboration and from static threshold value to dynamic self-adaption is realized.

The invention discloses a phased array weather radar calibration method based on a generalized metal ball calibration theory. Comprising the following steps: in a microwave anechoic chamber, collecting two-dimensional antenna pattern data of a phased array weather radar in at least one working mode and pointing to a plurality of different beams; performing numerical integration on the two-dimensional antenna pattern data, calculating a combined beam main lobe solid angle of the transmitting antenna and the receiving antenna, and calculating an equivalent beam width product; calculating a calibration theoretical reflectivity factor of the metal ball based on the product of the theoretical radar scattering sectional area and the equivalent beam width of the metal ball; actual detection echo data of the phased array radar on the metal ball under different working modes and beam pointing is acquired, and an actual reflectivity factor is calculated; and comparing the actual reflectivity factor with the calibration theoretical reflectivity factor to complete calibration of the phased array weather radar. Aiming at the non-Gaussian working mode of the phased array weather radar, the reflectivity factor of the phased array weather radar can be quantitatively evaluated, and the radar detection performance is improved.

The invention relates to the technical field of artificial precipitation enhancement, and discloses a method for identifying the artificial precipitation enhancement operation time and the precipitation enhancement potential area of laminated mixed cloud, and the method comprises the steps: drawing a radar combination reflectivity parameter distribution diagram at each time through obtaining the weather radar base data of the one-time laminated mixed cloud precipitation process of a location; secondly, calculating a cumulus cloud development index of the rainfall process moment by moment by using a radar combined reflectivity parameter; according to the ratio of the adjacent cumulus cloud development indexes, judging the time for carrying out artificial precipitation enhancement operation in the laminated mixed cloud development time period; and finally, according to the radar combined reflectivity parameter suitable for the rainfall enhancement operation time, judging a rainfall enhancement potential area. According to the method, the single physical quantity of the combined reflectivity factor in the weather radar base data can be used for accurately calculating and reflecting the development or extinction stage of the laminated hybrid cloud system, and the artificial precipitation enhancement operation time and the precipitation enhancement potential area are identified, so that the scientificity and timeliness of the design and implementation of the artificial precipitation enhancement operation scheme are improved; and the scientificity and the accuracy of the airplane and ground precipitation enhancement operation are improved.

The invention discloses a microwave link-weather radar combined rainfall inversion method based on Z-R relation adaptive adjustment, and the method comprises the steps: defining a related parameter set of microwave link rainfall estimation and weather radar reflectivity measurement in observation time, and carrying out the matching and space-time averaging processing, and obtaining rainfall intensity-radar reflectivity characteristics; fitting a radar reflectivity-rainfall intensity relation within observation time by using a least square method based on the rainfall intensity-radar reflectivity characteristics; according to the radar reflectivity-rainfall intensity relation sample of the known position, calculating the spatial distribution of the radar reflectivity-rainfall intensity relation in the research area by using an inverse distance weighted interpolation method; and according to the spatial distribution of the radar reflectivity-rainfall intensity relationship within the observation time and the average reflectivity factor measured by the radar, calculating to obtain a spatial distribution result of the accumulated rainfall within the observation time. According to the invention, the accuracy of regional rainfall monitoring is effectively improved.

The invention is suitable for the technical field of smart civil aviation, and provides a flight plan dynamic generation method and system in severe weather, and the method comprises the following steps: obtaining data, including origin-destination and plan time of a flight, an air route network, and aircraft performance of the flight; on the basis of meteorological radar data, according to the risk threshold value, the future communication condition of the air route network is predicted; constructing a flight plan dynamic generation model library based on different scheduling priorities; and according to control conditions, a solver is used for carrying out serialization solution on each model, and the most adaptive model and the corresponding optimal strategy are determined by comparing the closeness of positive and negative ideal distances between a model result and the best worst scheme. According to the invention, flight plan dynamic generation model libraries with different scheduling priorities are established, a multi-scale scheme evaluation recommendation technology is discussed, and technical support is provided for daily dispatchers to carry out flight plan intelligent management.

The invention relates to a weather radar beam shielding rate calculation method based on temporal-spatial resolution improvement, belongs to the field of radio, realizes adaptive precise temporal-spatial matching of meteorological unmanned aerial vehicle sounding data and ECMWF forecast data under different weather echo types, realizes calculation of spatial distance in grids in an expansion field, and improves the weather radar beam shielding rate. The method realizes preliminary extraction of spatial-temporal characteristics, constructs a self-attention mechanism model fused with physical perception, successfully generates a high-spatial-resolution atmospheric temperature, atmospheric relative humidity and atmospheric pressure data set, realizes generation of the high-spatial-temporal-resolution atmospheric temperature, atmospheric relative humidity and atmospheric pressure data set, and improves the spatial-temporal-resolution atmospheric temperature, atmospheric relative humidity and atmospheric pressure data set. Accurate calculation of the beam ray propagation path of the high-temporal-spatial-resolution radar is achieved, calculation of the shielding orientation and the maximum elevation angle of a shielding object in each orientation ray beam block is achieved, finally, a beam shielding power integral calculation model is introduced, and accurate calculation of the weather radar beam shielding rate is successfully achieved.

The invention discloses an X-band weather radar reflectivity data quality control method based on deep learning, and belongs to the technical field of meteorological data processing. The deep learning technology is introduced, the convolutional neural network is used for extracting the space and time characteristics of the original reflectivity data, high-precision data quality evaluation and classification are achieved, noise, low-quality and high-quality data can be efficiently recognized under the complex meteorological condition, and through real-time monitoring and a periodic optimization mechanism, the real-time performance of the system is improved. Model parameters are dynamically adjusted to adapt to data input changing in real time, the accuracy and reliability of data processing are guaranteed, through modular integration of a deep learning model, the deep learning model can be embedded into an existing meteorological system, automatic and intelligent application of data quality control is achieved, the data processing efficiency and quality control precision are remarkably improved, and the data processing efficiency is improved. And technical support is provided for the fields of weather prediction, climate research, disaster early warning and the like.

The invention relates to an improved weather radar echo short-time nowcasting method based on a diffusion model, and the method comprises the steps: obtaining a to-be-measured radar echo image, combining the to-be-measured radar echo image with time sequence information, inputting the to-be-measured radar echo image into a radar echo extrapolation model, and obtaining a denoised radar echo image; the radar echo extrapolation model is obtained by using a training set to train a diffusion model; the training set is a combined reflectivity data set; and processing the image blocks through a core denoising module in the radar echo extrapolation model, and predicting noise. According to the invention, the precision and anti-interference capability of short temporary rainfall prediction can be improved.

The invention discloses a multi-mode coordinated ship main and auxiliary power automatic switching system, which relates to the technical field of ship power systems and comprises a monitoring system, a diagnosis decision system, an execution system, a switching verification system, a maintenance management system and a data communication system. According to the invention, the airborne weather radar, the ship weather instrument and the weather fax receiver are used for monitoring the current weather condition of navigation and predicting the weather condition of a future navigation route, so that a prediction basis is provided for a power system operation mode selected by future ship navigation; meanwhile, the ship weather instrument monitors surrounding wind speed, wind direction, air temperature and humidity data in real time when the ship sails, the current data is stored in the data storage, a database is formed, and a follow-up maintenance management system can predict potential risks according to historical data conveniently.

The invention discloses a weather radar echo intensity online calibration method, and relates to the technical field of weather radar detection. The radar collects and processes an environment signal to obtain effective clutter data; determining a relative calibration offset of the weather radar according to the effective clutter data and the relative calibration baseline; and calibrating the weather radar based on the relative calibration offset. According to the method, the weather radar is calibrated online in real time by using the ground clutter creatively, and the dependence on external equipment is reduced. And the precision and timeliness of radar echo intensity measurement are obviously improved. The maintenance cost and the operation difficulty are reduced, and the calibration inconsistency caused by manual operation errors is reduced. The calibration cost of the weather radar is remarkably reduced, continuous high-quality output of data is ensured, the accuracy and reliability of data acquired by the weather radar are remarkably improved, higher requirements of modern meteorological observation and forecasting are met, and powerful support is provided for meteorological observation and early warning and forecasting. And the long-term consistency of the data is ensured. And the method has wide adaptability.