35 results about "Rainfall estimation" patented technology

The invention discloses a precipitation estimation method and system based on territorial rainfall multi-data fusion and application, and relates to the technical field of weather forecast, by fusing radar, satellite and ground observation data, the accuracy and reliability of precipitation prediction are remarkably improved, the fusion of multi-source data can make up for the deficiency of a single data source, and the prediction accuracy and reliability are improved. Through space-time registration and a weighted average fusion algorithm, a macroscopic rainfall background is provided by introducing territorial rainfall data, and radar, satellite and ground observation rainfall data are combined, so that rainfall estimation is more comprehensive, ground observation stations are supplemented and fused, effective data of the stations are increased, the real time of rainfall can be reflected more accurately, and the rainfall estimation accuracy is improved. In addition, the fusion of multi-source data can make up for the deficiency of a single data source, and the advantages of different data sources can be effectively integrated through the space-time registration and weighted average fusion algorithm.

The invention relates to the field of meteorological observation, and provides a networking X-band radar full-system differential reflectivity self-adaptive cooperative calibration method and system and a storage medium, and the method comprises the steps: collecting radar base data, and transmitting the radar base data to a data processing center; preprocessing the radar-based data, performing deviation detection on differential reflectivity data of the preprocessed radar-based data, and generating a calibration instruction based on a deviation detection result; based on the calibration instruction, automatically scheduling the radar to be calibrated to execute zenith calibration VPCAL mode scanning; in the scanning process, synchronously collecting VPCAL scanning data and transmitting the VPCAL scanning data to a data processing center; based on VPCAL scanning data, quantitative deviation is calculated and issued to a calibration execution unit; and carrying out automatic parameter adjustment on the X-band radar of which the quantitative deviation exceeds a threshold value, and sending a deviation warning to a user. According to the invention, the monitoring precision, the calibration precision and the quantitative rainfall estimation capability of the weather radar can be improved, and forecasting and early warning services and refined meteorological services are provided.

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 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 an X-band meteorological radar accurate rainfall inversion method based on a backscattering differential phase. The method comprises the following steps: firstly, determining a distribution structure of each rainfall estimation equation of a place according to a raindrop spectrum of the place observed by a raindrop spectrometer; secondly, performing T matrix simulation by utilizing the observed raindrop spectrum, and establishing a rainfall estimation equation of the place; then, a spatial sampling method of radar variables is determined, sampling is carried out, and sampled data is preprocessed; then, the rainfall rate is inverted, and the precision of the rainfall rate is verified; and finally, inverting the one-hour rainfall cumulant and verifying the precision of the one-hour rainfall cumulant. According to the invention, the precision of rainfall inversion of the X-band meteorological radar is improved, and a guarantee is provided for accurate rainfall real-time forecast.

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 discloses a piezoelectric rainfall measurement method based on an optimized depth time sequence prediction network, and the method comprises the steps: obtaining piezoelectric ADC rainfall signal data of a target site, and synchronous wind speed information, temperature and humidity information and rainfall information; extracting a piezoelectric raindrop waveform sequence from the piezoelectric ADC rainfall signal data, and extracting a piezoelectric raindrop waveform sequence caused by raindrop impact by processing the piezoelectric ADC rainfall signal data; constructing a data set containing four types of information including piezoelectric raindrop waveform, wind speed, temperature and humidity and rainfall; constructing and training a rainfall estimation neural network based on the piezoelectric raindrop waveform, the wind speed, the temperature and humidity and the rainfall data set; rainfall data output by the piezoelectric sensor ADC are newly obtained, wind speed and temperature and humidity data are synchronously collected, and a test set is made; and after the data of the test set is processed, inputting the processed data into the trained model so as to carry out actual rainfall prediction. According to the invention, the anti-noise performance of the system is improved, so that the calibration precision is remarkably 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 relates to the field of weather radar rainfall estimation, in particular to a quantitative rainfall estimation method and device based on a dual-polarization radar and a medium, and the method comprises the steps: classifying raindrop spectrum data according to a preset climate state rainfall type, and obtaining a plurality of raindrop spectrum data sets corresponding to different climate state rainfall types, and performing inversion and fitting on each raindrop spectrum data set to obtain a dual-polarization radar precipitation relational expression corresponding to each climate state precipitation type, and performing precipitation estimation on the target region according to each precipitation relational expression and radar data. According to the invention, the precision of quantitative rainfall estimation based on radar is improved, the meteorological monitoring and early warning capability is improved, and the loss is reduced.

The utility model discloses a rainwater flow monitoring device for rainfall estimation, which belongs to the technical field of rainfall monitoring, and comprises a mounting rack and a camera device, the camera device is fixedly connected to the bottom surface of the inner wall of the mounting rack, two sides of the mounting rack are fixedly connected with motors, and the motors are arranged on the mounting rack. A transmission rod is fixedly connected to the output end of the motor, two supporting plates are fixedly connected to the bottom face of the inner wall of the mounting frame, one end of the transmission rod penetrates through one side of the mounting frame and is rotationally connected with one sides of the supporting plates, a first gear is fixedly connected to the exterior of the transmission rod, and a second gear is engaged with the upper portion of the first gear; and a cam is fixedly connected to one side of the second gear and rotationally connected with one side of the inner wall of the mounting frame, an inflating assembly is arranged above the cam, and a cleaning frame is rotationally arranged outside the camera shooting equipment. The camera shooting equipment surface cleaning device can clean the surface of camera shooting equipment and is high in cleaning efficiency.

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 invention relates to the field of data processing, in particular to a monitoring data processing method of a water conservancy rain measuring radar for rainwater condition monitoring and forecasting, and the method comprises the steps: collecting the monitoring data of the water conservancy rain measuring radar; performing terrain complexity assessment according to the feature data of all the radar body scanning units, acquiring a first optimization factor of distance measurement according to the terrain complexity of the monitoring range of the water conservancy rain measurement radar, and performing first Euclidean distance assessment between the radar body scanning units according to the first optimization factor; evaluating a second optimization factor according to the mahalanobis distance between the radar body scanning units, and measuring and evaluating the optimization distance between the radar body scanning units according to the second optimization factor, the first Euclidean distance and the mahalanobis distance; clustering is carried out according to the optimized distance measurement; and performing local self-adaptive Z-R relation fitting evaluation according to a clustering division result. Therefore, the problem that the rainfall estimation precision is low due to the fact that existing distance measurement cannot accurately describe the spatial difference of mountainous radar data is solved.

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 invention discloses a vehicle windshield wiper control method, device and equipment, a medium and a product. The method comprises the steps that in the running process of a vehicle, if a windshield wiper control instruction is detected, image collection is conducted through a vehicle-mounted camera to obtain a target image containing a front windshield of the vehicle; determining an initial raindrop coverage rate corresponding to the target image, and performing iterative division on the target image based on an iterative dichotomy method according to the initial raindrop coverage rate until a divided regional image meets a recursion ending condition; and determining a rainfall estimation value according to the target recursion depth when recursion is ended, and controlling the windshield wiper frequency of the vehicle according to the determined rainfall estimation value. According to the technical scheme, the windshield wiper frequency is dynamically adjusted by analyzing the raindrop condition on the front windshield in real time, the intelligent level of windshield wiper control can be improved, and driving safety and comfort are improved.

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 invention relates to a heavy rainfall nowcasting method in combination with a liquid state ratio. The method comprises the following steps: collecting radar reflectivity data within one hour and rainfall live data of the next hour; converting radar reflectivity data into three-dimensional data under a Cartesian rectangular coordinate system, and converting rainfall actual data into a two-dimensional rainfall lattice point field; building a heavy rainfall nowcasting model; training the model on the training set, optimizing the hyper-parameters on the verification set, and testing the model on the test set; and obtaining rainfall estimation field data. According to the invention, a heavy rainfall nowcasting model is established by using double decoders based on a liquid state ratio and an attention mechanism, an encoder module is adopted to receive echo data after liquid state water content conversion processing and carry out feature coding extraction work, and two parallel decoder modules are adopted to output a heavy rainfall area prediction result and a rainfall prediction result respectively. Quantitative estimation and qualitative classification of a heavy rainfall area are improved by using a comprehensive loss function, and effectiveness tests show that the method can significantly improve the nowcasting accuracy.

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 invention discloses a precipitation estimation method based on multi-source remote sensing data fusion, and the method comprises the following steps: S1, obtaining multi-source remote sensing data, and carrying out the preprocessing of the obtained data; s2, performing feature extraction based on the preprocessed data, and constructing a dimension reduction feature vector; s3, inputting the matrix of the dimensionality reduction feature vector as data into an artificial neural network model, designing a loss function and performing parameter updating; and S4, verifying the model and estimating the precipitation amount. According to the method, data of multiple observation stations are adopted, dynamic adjustment of parameters in a rainfall estimation model according to regional characteristics and climate conditions is achieved, local change differences can be more effectively represented, errors caused by adoption of fixed parameter values are reduced, and the accuracy of rainfall estimation is improved.

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.

The invention provides a rainfall product correction method combining dry and wet season division and weight distribution based on climate adjustment, and the method comprises the steps: calculating an adjustment amount and a scaling factor according to the rainfall data of a region over the years, generating a deviation adjustment value through the adjustment amount AA and the scaling factor SF, applying the deviation adjustment value to a corresponding IMERG rainfall estimation value, and carrying out the correction of a rainfall product. The rainfall estimation value after adjustment and correction is obtained, so that the accuracy of rainfall estimation value correction can be improved, error correction is effectively performed on the satellite rainfall data, and the reliability of the satellite rainfall data is improved.

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

This application belongs to the technical field of rainfall prediction, and relates to a method for estimating rainfall intensity of a geostationary meteorological satellite and related equipment. The method includes: First, preprocess the infrared channel information of the Fengyun-4B satellite and the dual-frequency precipitation radar products, and perform spatio-temporal matching on them due to their spatio-temporal resolutions. Second, establish a rainfall detection model based on the Attention-Unet model by introducing auxiliary information such as longitude, latitude, and elevation to predict whether there is rainfall at each pixel point. Then, extract the spatial structure information of the rainfall cloud clusters according to the rainy pixel points. Finally, introduce the infrared channel information, longitude and latitude information, elevation data, and the spatial structure information of the rain clusters into the Attention-Unet model to construct a rainfall intensity estimation model based on the spatial structure of the rain clusters for rainfall estimation. This application can enhance the ability to extract rainfall areas; at the same time, enhance the ability to capture rainfall details and improve the model efficiency.

The invention discloses an X-band phased array radar minute rainfall estimation method, and belongs to the technical field of meteorology. The method comprises the following steps: acquiring ground rainfall data and X-band radar base data of a target area, and generating a standardized radar data block; splicing the radar data blocks into a time sequence sample according to a time dimension and associating the time sequence sample with a ground minute rainfall label; and constructing a deep learning model based on the CBAM U-Net and the TCN network, extracting spatial features through the CBAM U-Net, dynamically weighting a key region, capturing time sequence association by using causal expansion convolution in combination with the TCN network, and finally outputting a minute-level rainfall estimated value. According to the method, through fusion of a space attention mechanism and time sequence analysis, the problems of insufficient spatial dependency capture, parameter redundancy and weak feature adaptive ability in a traditional method are solved, and the estimation precision and timeliness of short-time heavy rainfall are significantly improved.

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.