18 results about "Rainfall estimation" patented technology

The invention discloses a multi-source data fusion method based on a water conservancy rain measuring radar, and belongs to the technical field of meteorological observation. Historical rainfall inversion, short temporary rainfall forecast and short-term rainfall forecast are realized based on multi-source data fusion, and a space-air-ground integrated rainfall observation and prediction system is constructed. According to the method, innovative technologies such as multi-source data, multi-parameter inversion, AI intelligent optimization and manual correction are fused, the limitation of a traditional rainfall inversion method is broken through, and the precision and service efficiency of quantitative rainfall estimation are remarkably improved; through multi-modal fusion and full-link integration, the method has significant technical advancement and business value in application in the fields of water conservancy and meteorology.

The invention discloses a quantitative rainfall estimation method based on a Hurdle-IMDL framework. The method comprises the following steps: acquiring historical rainfall measurement data and meteorological satellite observation data; constructing a biased quantitative rainfall estimation probability model based on a Hurdle model, correcting a biased rainfall estimation probability sub-model according to an IMDL method, substituting into empirical distribution to construct an empirical quantitative rainfall estimation probability model, and deriving a negative logarithm likelihood function of the empirical quantitative rainfall estimation probability model; constructing an AI model, and optimizing the AI model by taking a negative log-likelihood function as a loss function; and inputting meteorological satellite observation data of a to-be-inverted region into the trained AI model to obtain an estimated value of a parameter of the empirical quantitative precipitation estimation probability model, and estimating the precipitation according to conditional expectation. According to the invention, the Hurdle model is used to solve the zero expansion problem, the IMDL learning method is used to cope with the long tail problem, and the inversion accuracy of extreme rainfall is improved.

The invention relates to the technical field of meteorology and hydrology, and discloses a quantitative rainfall estimation method and system based on multi-source data correction radar echoes, and the method comprises the steps: collecting the multi-source data, such as rainfall station actually measured rainfall data, elevation data, hydrometric station flow, radar reflectivity, soil humidity and environmental attributes, in a target region; a Z-R relation parameter of each radar sampling grid is dynamically calibrated by combining a collaborative Kriging interpolation and physical constraint hybrid neural network model, and a radar reflectivity correction parameter of each grid is calculated by using a difference between rainfall station observation data and radar data; in real-time application, based on the corrected radar reflectivity and the calibrated Z-R parameter, the rainfall intensity of each grid is inverted, and the precision of quantitative rainfall estimation is improved.

The invention discloses a radar quantitative rainfall estimation method based on a space-time attention model, and the method comprises the steps: carrying out the position coding, injecting position information into the embedded representation of sequence elements, explicitly representing the specific coordinates of the sequence elements in a sequence, dividing the codes into two types: fixed coding and learnable coding, and after the position coding is completed, carrying out the estimation of the radar quantitative rainfall. The method comprises the following steps of: calculating a mutual relationship between internal elements of an input sequence to realize a model architecture of information aggregation, establishing endogenous association between the elements of the sequence to realize feature interaction, synchronously calculating and integrating all position information of the sequence, and capturing space-time dependency in meteorological data through a position relationship between independent modeling time and space after calculation and integration; according to the invention, a position characterization mechanism based on three-dimensional space-time relative position coding is introduced, and space-time key features of radar echo data are effectively extracted through multiple attention modules; and designing a space-time position coding strategy capable of self-adaptive learning, and realizing joint feature representation of space-time dimensions.

The invention belongs to the technical field of rainfall observation, and discloses a crowdsourcing video rainfall quantitative calculation method based on a knowledge graph, and the method comprises the following steps: firstly constructing a rainfall video template library, extracting video attributes of crowdsourcing videos in the rainfall video template library, and storing the video attributes into the knowledge graph; secondly, preprocessing the target crowdsourcing video, extracting the video attribute of the target crowdsourcing video, matching the video attribute of the target crowdsourcing video with the video attribute of the crowdsourcing video in the rainfall video template library in sequence, screening out a video set closest to the target crowdsourcing video in the rainfall video template library, and based on the video set, extracting the video attribute of the target crowdsourcing video; similarity calculation is completed based on a rainfall video similarity calculation method; and finally, comparing a preset similarity threshold with the calculated similarity to realize rainfall quantitative calculation of the target crowdsourcing video. According to the method, the crowdsourcing video can be effectively utilized for rainfall estimation, and the accuracy of rainfall estimation is enhanced.

The application discloses a vehicle wiper control method, device, equipment, medium and product. The method comprises the following steps: during the operation of the vehicle, if a wiper control instruction is detected, an image is collected by a vehicle-mounted camera to obtain a target image containing a front windshield of the vehicle; an initial raindrop coverage rate corresponding to the target image is determined, and the target image is iteratively divided based on the initial raindrop coverage rate and an iterative bisection method until a divided region image meets an end recursion condition; a rainfall estimation value is determined according to a target recursion depth at the end of recursion, and the wiper frequency of the vehicle is controlled according to the determined rainfall estimation value. The technical scheme of the application can improve the intelligent level of wiper control and improve driving safety and comfort by dynamically adjusting the wiper frequency through real-time analysis of the raindrop situation on the front windshield.

The application relates to the technical field of radar data processing, and specifically discloses a quantitative rainfall estimation method based on a dual-polarization multi-radar composite technology, which comprises the following steps: obtaining standardized data by preprocessing dual-polarization radar original observation data; calculating and fusing a multi-dimensional quality index: calculating a static environment index and a dynamic physical consistency index, and fusing to obtain a comprehensive quality index; adaptively selecting an optimal rainfall estimation algorithm according to the quality of radar dual-polarization observation data to generate an initial fused rainfall field; dividing multiple quality levels, independently calculating an average field bias correction factor for each quality level, and applying the average field bias correction factor to radar pixels of the corresponding quality level to obtain a final rainfall inversion result. The application realizes source identification and filtering of non-meteorological echoes and abnormal data, avoids the propagation of false information and the amplification of system errors, and improves the reliability and robustness of radar network composition.

The invention relates to the technical field of radar rainfall estimation, and discloses a dual-polarization radar quantitative rainfall estimation method based on deep learning, and the method comprises the steps: collecting dual-polarization radar body scanning basis data, and carrying out the quality control, and obtaining a standardized polarization parameter; constructing and training a hybrid network model based on 3D-CNN + BiLSTM + Attention, and inputting a three-dimensional tensor constructed by a standardized polarization parameter into the trained hybrid network model to obtain an hourly rainfall intensity preliminary estimated value QPE of each radar grid point; positioning target radar grid points, extracting effective data pairs, and calculating a unique correction factor of each radar grid point; and calculating a single scanning height precision estimated value by combining the hourly rainfall intensity initial estimated value QPE of each radar grid point, and performing accumulation according to a preset time length to obtain an accumulated quantitative rainfall estimation product. According to the method, the dual-polarization radar parameters and the deep learning model are fused, correction is carried out in combination with the real-time rain gauge, accurate multi-period regional rainfall products are generated, and reliable support is provided for meteorological monitoring, disaster early warning and the like.

The invention relates to the technical field of deep learning, and particularly relates to an infrared rainfall prediction and model training method and device, and electronic equipment, and the method comprises the steps: obtaining a training set, each sample in the training set comprising a multi-channel brightness temperature feature of a sample region and a corresponding rainfall feature true value; a preset infrared rainfall prediction model is obtained, the infrared rainfall prediction model comprises an input layer, an encoder, a middle layer, a decoder and an output layer, the encoder is additionally provided with an attention mechanism module before down-sampling each time, the middle layer comprises a cavity space pyramid pooling module, the decoder is additionally provided with an attention mechanism module after up-sampling each time, and the output layer comprises a cavity space pyramid pooling module; a weighted mean square error loss function of the model dynamically adjusts an error weight according to precipitation spatial heterogeneity; and training a preset infrared rainfall prediction model by using the training set to obtain a trained infrared rainfall prediction model. According to the technical scheme, infrared rainfall estimation can be carried out more accurately.

The application discloses a mountain torrent disaster early warning method and system, and the method comprises the following steps: acquiring cloud image data; processing the cloud image data based on a pre-trained deep learning model to obtain a predicted rainfall sequence; constructing a hydrological and hydrodynamic model and performing scenario simulation to demarcate a disaster threshold rainfall; and combining the predicted rainfall sequence and the disaster threshold rainfall to perform early warning. The system comprises an image acquisition module, a rainfall prediction module, a threshold determination module and an early warning module. By using the application, mountain torrent disaster early warning can be performed in advance, important disaster relief and risk avoidance time can be obtained for mountain torrent disasters, and the prediction accuracy is well guaranteed due to the combination of the rainfall prediction module and the atmospheric pressure observation and the cloud rainfall estimation method based on deep learning. The application can be widely applied in the field of natural disaster early warning.

The application discloses a microwave link-based rainfall estimation method, which comprises the following steps: obtaining attenuation data through a microwave link; processing the attenuation data by using a pre-processing method to obtain rain-induced attenuation; and inputting the rain-induced attenuation into a pre-established rainfall inversion model to obtain the rainfall intensity of an estimated place. The application discloses a localized rainfall physical inversion model suitable for a microwave link for the first time, solves the defect that the existing method cannot reflect the climate difference of a region, and improves the precision of the rainfall estimation result. The model proposed by the application belongs to a semi-empirical and semi-physical model, and the demand for local historical rainfall data is less compared with a machine learning model. The application perfectly solves the problem that the existing physical model is not suitable for a short link, and improves the applicability of the microwave link rain measurement technology.

To provide a rainfall estimation system capable of appropriately considering an influence on raindrops by the environment until the raindrops fall onto the earth's surface.SOLUTION: A data processing server 71 estimates a rainfall in an optional position on the basis of reception strength data obtained from a radar device 21 according to the intensity of reflection waves obtained by reflection of electric waves radiated from the radar device 21 by raindrops, ground rainfall data obtained from a ground rain gauge 31 for measuring a rainfall in a predetermined point on the earth's surface, and wind direction / wind velocity data obtained from a wind direction anemometer 41 for measuring an influence of the raindrop on the environment until the raindrop reaches the earth's surface.SELECTED DRAWING: Figure 1

The application discloses a kind of quantitative precipitation estimation methods based on Hurdle-IMDL framework, comprising: obtaining historical precipitation measurement data and meteorological satellite observation data;Based on Hurdle model, construct biased quantitative precipitation estimation probability model, revise the biased rainfall estimation probability submodel according to the IMDL method, into experience distribution to construct experience quantitative precipitation estimation probability model and derive its negative log-likelihood function;Build AI model, use negative log-likelihood function as loss function to optimize AI model;Meteorological satellite observation data of the region to be inverted is input into the AI model trained, to obtain the estimated value of the parameter of experience quantitative precipitation estimation probability model, then estimate precipitation according to conditional expectation.The application uses Hurdle model to solve zero inflation problem, and uses IMDL learning method to deal with long tail problem, to improve the inversion accuracy of strong to extreme precipitation.

The invention belongs to the technical field of rainfall estimation, and discloses a rainfall estimation method and device based on radar data, and the method comprises the steps: obtaining minute-level accumulated rainfall information of a ground rainfall observation system, and obtaining a rainfall data source; acquiring radar product data of the weather radar to obtain a radar data source; processing the radar data source according to a preset volume scanning period and a preset format to obtain three-dimensional radar lattice point data; processing the rainfall data source based on a preset body scanning period and an effective detection distance of a weather radar to obtain rainfall label data; obtaining radar precipitation grid point field data according to the three-dimensional radar grid point data and the precipitation label data; training a deep residual network model according to the radar precipitation grid point field data to obtain a precipitation estimation model; and obtaining target three-dimensional radar grid point data and inputting the data into the rainfall estimation model to obtain a rainfall field estimation result. The rainfall can be accurately estimated based on the radar data.

The invention belongs to the technical field of rainfall estimation of rain-measuring radars, and particularly relates to an X-waveband rain-measuring radar rainfall estimation method based on a T matrix and a multi-raindrop spectrum model. The problem of rainfall estimation errors caused by a single raindrop spectrum model in the prior art is solved, and the precision of rainfall estimation is improved by combining the X-band dual-polarization radar measurement data with multiple raindrop spectrum models. The method comprises the following steps: S1, carrying out data acquisition on a target area by using an X-waveband dual-polarization rain measurement radar; s2, carrying out raindrop spectrum model selection according to the rainfall intensity of the target area; s3, calculating polarization parameters based on a T matrix theory; s4, rainfall estimation is carried out in combination with the polarization parameters and the raindrop spectrum model; and S5, correcting and optimizing the raindrop model based on actually measured raindrop spectrum data. In combination with the multi-raindrop spectrum model and the T matrix theory, the rainfall capacity can be estimated more accurately under different rainfall intensities, and particularly, errors caused by raindrop spectrum selection can be remarkably reduced under the condition of heavy rainfall.

The application discloses a high-cold region rainfall method based on a Bayesian optimization SM2RAIN algorithm, and the method comprises the following steps: determining a first batch of to-be-optimized parameters based on the SM2RAIN algorithm and a soil moisture balance equation; determining a second batch of to-be-optimized parameters by considering soil moisture inversion errors; iteratively optimizing the first batch of to-be-optimized parameters and the second batch of to-be-optimized parameters based on a Bayesian algorithm to determine an optimal parameter interval; constructing an estimation model according to the optimal parameter interval; and estimating rainfall information based on the estimation model. By using the application, the rainfall estimation accuracy can be improved. The application can be widely applied to the field of rainfall monitoring as a high-cold region rainfall method based on the Bayesian optimization SM2RAIN algorithm.

The invention discloses a drainage basin space surface rainfall calculation method and device and a medium. The method comprises the steps that the pixel area of each reflectivity unit of the elevation angle of the lowest layer of a weather radar in a set space range above a drainage basin is calculated; calculating a radar lowest layer elevation angle reflectivity space area; calculating the projection area of the elevation reflectivity space area of the lowest layer of the radar to the watershed range; according to the spatial multi-layer all radar reflectivity unit intensity values, calculating spatial multi-layer all radar reflectivity unit rainfall intensity by using a radar quantitative rainfall estimation relational expression; calculating precipitation per unit time; calculating the total amount of spatial precipitation in the T time period; and calculating the drainage basin space surface rainfall in the T time period according to the total space rainfall in the T time period, the projection area of the radar lowest layer elevation angle reflectivity space area to the drainage basin range and the drainage basin geographic area. The method solves the problems of no data area, rainfall station density and the like of the traditional area rainfall, and improves the time precision, the space precision and the rainfall distribution precision of the area rainfall of the watershed.

The invention discloses a radar echo quantitative rainfall estimation method comprising the following steps: obtaining historical echo data, and carrying out denoising and normalization processing on the historical echo data to obtain a training sample; constructing a radar echo quantitative rainfall estimation model based on a Z-R empirical formula, a residual network and a Transform algorithm; training the radar echo quantitative rainfall estimation model through the training sample; and inputting echo data to be predicted into the trained radar echo quantitative rainfall estimation model to obtain rainfall estimation data. According to the method, priori knowledge is provided for the model by using a Z-R empirical formula, and the generalization ability of the model is enhanced. Through introduction of the Transform network, the model can fully mine space-time dynamic characteristics in radar echo data, richer information is provided for weather forecast, and the space-time characteristics are effectively captured. By adopting a replaceable data preprocessing method, a feature fusion mode and a network structure, the method has relatively high adaptability, and the flexibility and adaptability are improved.