A rainfall prediction method and system for the Beijing-Tianjin-Hebei region

Abstract

The present invention discloses a rainfall prediction method and system for the Beijing-Tianjin-Hebei region, which relates to the technical field of rainfall prediction. It includes obtaining multi-source data, preprocessing the data, extracting the core characteristics of the data, dynamically modeling rainfall according to the core characteristics of the data, and simulating the rainfall evolution path; constructing a rainfall prediction model, predicting the rainfall intensity based on the rainfall evolution path, giving early warnings for extreme weather based on the prediction results, and generating a rainfall distribution map of high-risk areas. Through the fusion of multi-source data, the present invention uses a generative adversarial network and spectral analysis to improve the spatial resolution of the data and the prediction ability of precipitation evolution. Combining dynamic rainfall modeling, fractional differential equations, and multiple regression models, it improves the real-time performance and accuracy of rainfall prediction, can effectively cope with the challenges of rainfall prediction under complex weather conditions, and realizes accurate prediction of rainfall intensity and extreme weather events.

Description

Technical Field

[0001] The present invention relates to the technical field of rainfall prediction, and particularly to a rainfall prediction method and system for the Beijing-Tianjin-Hebei region. Background Art

[0002] In recent years, with the frequent occurrence of climate change and extreme weather events, how to accurately predict and respond to rainfall and its secondary disasters has become an important topic in meteorological research and applications. Traditional rainfall prediction methods mainly rely on the observation data of meteorological stations and combine numerical weather prediction models for precipitation prediction. However, when facing complex terrains and changing climate conditions, traditional methods often have problems such as large prediction errors, poor real-time performance, and low spatial resolution. In order to improve the accuracy and timeliness of rainfall prediction, in recent years, the application of multi-source data fusion and machine learning methods has gradually become an important direction for improving rainfall prediction accuracy. The diversification and real-time update of meteorological data provide richer input conditions for prediction models. At the same time, rainfall prediction methods based on big data analysis and artificial intelligence technology have gradually been studied and applied. Nevertheless, existing rainfall prediction technologies still face several challenges, especially in the application of large-scale regions. Existing technologies rely on traditional weather forecasting models and often struggle to effectively improve in terms of spatial resolution, prediction accuracy, and real-time performance. The spatial coverage of meteorological station data is limited, and there are significant gaps in the spatial resolution between GNSS data and global meteorological data such as ERA5, which cannot provide sufficiently detailed prediction information. In addition, the dynamic evolution path and complexity of rainfall often lead to inaccuracies in traditional methods for predicting rainfall intensity and regional rainfall. Especially in complex climate regions such as the Beijing-Tianjin-Hebei region, local precipitation and heavy rainfall events are difficult to be effectively warned and accurately predicted, thus affecting disaster prevention and emergency response. Summary of the Invention

[0003] In view of the above existing problems, the present invention is proposed.

[0004] Therefore, the present invention provides a rainfall prediction method and system for the Beijing-Tianjin-Hebei region, which solves the problem that in complex climate regions such as the Beijing-Tianjin-Hebei region, local precipitation and heavy rainfall events are difficult to be effectively warned and accurately predicted, thus affecting disaster prevention and emergency response.

[0005] To solve the above technical problems, the present invention provides the following technical solutions:

[0006] In the first aspect, the present invention provides a rainfall prediction method for the Beijing-Tianjin-Hebei region, which includes,

[0007] Obtaining multi-source data, preprocessing the data, and then extracting the core characteristics of the data, and performing dynamic rainfall modeling according to the core characteristics of the data to simulate the rainfall evolution path;

[0008] Build a rainfall prediction model, predict rainfall intensity based on the rainfall evolution path, issue extreme weather warnings based on the prediction results, and generate a rainfall distribution map for high-risk areas;

[0009] Visualize and store the real-time rainfall prediction and warning information in a database.

[0010] As a preferred embodiment of the rainfall prediction method for the Beijing-Tianjin-Hebei region according to the present invention, wherein: the step of obtaining multi-source data, preprocessing the data, and extracting the core characteristics of the data refers to collecting meteorological station data, GNSS data, and ERA5 data, cleaning and standardizing the collected data, and using a spatial interpolation method to spatially align sparse observation data;

[0011] Use the generative adversarial network algorithm to improve the spatial resolution of ERA5 data to the kilometer level;

[0012] Use the spectral analysis method to extract the time series of precipitable water vapor data in GNSS data. Use the fast Fourier transform to convert the time-domain signal into a frequency-domain signal, calculate the frequency components, obtain the amplitude and phase information of the frequency components, use the frequency points with amplitudes greater than the average amplitude as the core periodic characteristics, and calculate the data change rate based on the extracted precipitable water vapor data.

[0013] As a preferred embodiment of the rainfall prediction method for the Beijing-Tianjin-Hebei region according to the present invention, wherein: the step of performing dynamic rainfall modeling according to the core characteristics of the data and simulating the rainfall evolution path refers to connecting to the ERA5 data interface, selecting the Beijing-Tianjin-Hebei region and the time range, extracting humidity, temperature, and wind speed component data, integrating the real-time temperature and humidity observation data of multiple meteorological stations in the region, and storing the data as a CSV file with a unified format. Each record contains a timestamp, longitude and latitude, temperature, and humidity;

[0014] Traverse the collected data, mark the missing values, use the time series linear interpolation method to fill in the missing values, align the ERA5 data and meteorological station data in time, and normalize the humidity, temperature, and wind speed components;

[0015] Obtain the humidity and temperature at the current time point as the initial conditions, set the initial rainfall to zero and set the time step;

[0016] Fit the evaporation constant from historical meteorological station data through a nonlinear fitting method and the condensation constant , calculate the evaporation rate and the condensation efficiency at the first time step using the initial conditions;

[0017] Use the calculation result as the starting point for the next time step to iteratively calculate the evaporation rate and condensation efficiency to obtain the final evaporation rate and condensation efficiency ;

[0018] Based on the calculated evaporation rate and condensation efficiency, establish a fractional differential equation to describe the rainfall change:

[0019] ;

[0020] In the formula, is the fractional change rate of rainfall, is the fractional order, which is set by experimental optimization;

[0021] Integrate the humidity, temperature, evaporation rate, condensation efficiency, and rainfall at time t into the state variable matrix X(t);

[0022] Define the modulation function and use the modulation function method to fit the model parameters:

[0023] ;

[0024] In the formula, are the optimized model parameters, is the composite modulation function, is the state variable matrix, is the fractional derivative of the modulation function;

[0025] Initialize the basis function of the Chebyshev polynomial, project the dynamic equation into the Chebyshev basis function space, and calculate the polynomial coefficients ;

[0026] Use the Chebyshev polynomial method to numerically solve the fractional differential equation to obtain the time series solution of rainfall ;

[0027] Set the rainfall threshold Q, traverse the time series of rainfall. If the current rainfall is greater than the threshold Q, mark the current time as the rainfall start time. If the current rainfall is less than or equal to the threshold Q and meets the condition for n hours, mark the current time as the rainfall stop time. Obtain the rainfall time window based on the rainfall start time and stop time and record the initial coordinates of the current rainfall event;

[0028] Update the coordinates of the rainfall event according to the wind speed components provided by ERA5 and the initial coordinates of the rainfall event:

[0029] ,

[0030] ;

[0031] In the formula, and are the coordinates of the current rainfall area, and are the coordinates of the rainfall area at the next time step, and are the wind speed components, is the time step;

[0032] Recording the rainfall coordinates at each time step forms a dynamic rainfall path.

[0033] As a preferred solution of the rainfall prediction method for the Beijing-Tianjin-Hebei region described in the present invention, wherein: constructing the rainfall prediction model and predicting the rainfall intensity based on the rainfall evolution path means constructing the rainfall prediction model through a multiple linear regression model, using the state variable matrix data as training data and inputting it into the multiple linear regression model for iterative training, defining a loss function and an Adam optimizer for iterative optimization of the model parameters, and stopping the iteration and outputting the model parameters to update the multiple linear regression model when the loss of the multiple linear regression model no longer decreases significantly during continuous iteration;

[0034] Inputting the real-time data within the coverage of the rainfall evolution path into the multiple linear regression model to obtain the predicted value of the regional rainfall intensity, and integrating the predicted value of the rainfall intensity according to the rainfall time window range to obtain the predicted value of the regional rainfall amount.

[0035] As a preferred solution of the rainfall prediction method for the Beijing-Tianjin-Hebei region described in the present invention, wherein: based on the prediction result, issuing an extreme weather warning and generating a rainfall distribution map of high-risk areas means setting rainfall thresholds A and B, and A > B. If the predicted rainfall is greater than or equal to threshold A, it is extreme rainfall, and an alarm is immediately issued and emergency measures are taken. If the predicted rainfall is less than threshold A and greater than or equal to threshold B, it is moderate rainfall, with a medium impact, and it is recommended to take preventive measures. If the predicted rainfall is less than threshold B, it is light rainfall, with no major risk;

[0036] Using GIS technology to generate a distribution map of high-risk areas, using red, yellow, and blue to mark extreme risk areas, moderate risk areas, and light risk areas in turn, and overlaying the administrative boundaries of the areas to clarify the affected cities and regions.

[0037] As a preferred solution of the rainfall prediction method for the Beijing-Tianjin-Hebei region described in the present invention, wherein: visually displaying the real-time rainfall prediction and warning information means mapping the hierarchical warning information onto the generated distribution map of high-risk areas, and plotting a curve graph of the rainfall changing with time, overlaying the rainfall intensity distributions at different time points, and displaying the distribution map of high-risk areas and the curve graph in real time through a visual interface.

[0038] As a preferred embodiment of the rainfall prediction method for the Beijing-Tianjin-Hebei region of the present invention, the following steps are included: storing the data in the database means sorting the real-time rainfall prediction and warning information in chronological order and storing it in the central database. The database regularly detects the integrity and security of the stored data and uploads it to the cloud for backup.

[0039] In a second aspect, the present invention provides a rainfall prediction system for the Beijing-Tianjin-Hebei region, including:

[0040] A data acquisition module for collecting multi-source data, performing data cleaning and standardization, improving the spatial resolution of ERA5 data, and extracting the core features from GNSS data;

[0041] A rainfall modeling module for constructing the rainfall evolution path and simulating the time window and spatial variation of rainfall events;

[0042] A rainfall prediction module for predicting the rainfall intensity and regional rainfall based on the rainfall evolution path;

[0043] A warning module for grading the risk based on the predicted rainfall and generating a high-risk area distribution map;

[0044] A display and storage module for generating a high-risk area distribution map in real time, storing the real-time rainfall prediction and warning information, and regularly backing it up to the cloud.

[0045] In a third aspect, the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program. When the computer program is executed by the processor, any step of the rainfall prediction method for the Beijing-Tianjin-Hebei region described in the first aspect of the present invention is implemented.

[0046] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by the processor, any step of the rainfall prediction method for the Beijing-Tianjin-Hebei region described in the first aspect of the present invention is implemented.

[0047] The beneficial effects of the present invention are as follows: Through the fusion of multi-source data, the present invention uses generative adversarial networks and spectral analysis to improve the spatial resolution of the data and the prediction ability of precipitation evolution. Combining dynamic rainfall modeling, fractional differential equations, and multiple regression models, the real-time performance and accuracy of rainfall prediction are improved, effectively coping with the challenges of rainfall prediction under complex weather conditions and achieving accurate prediction of rainfall intensity and extreme weather events. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0049] Figure 1 It is a flowchart of the rainfall prediction method for the Beijing-Tianjin-Hebei region in Embodiment 1.

[0050] Figure 2 It is a structural diagram of the rainfall prediction system for the Beijing-Tianjin-Hebei region in Embodiment 1. Detailed implementation manners

[0051] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will make a detailed description of the specific implementation manners of the present invention with reference to the accompanying drawings of the specification.

[0052] In the following description, many specific details are set forth to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0053] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it an individual or alternative embodiment that is mutually exclusive with other embodiments.

[0054] Embodiment 1, referring to Figure 1 and Figure 2 , is the first embodiment of the present invention. This embodiment provides a rainfall prediction method for the Beijing-Tianjin-Hebei region, including the following steps:

[0055] S1. Obtain multi-source data, extract the core characteristics of the data after preprocessing the data, perform dynamic rainfall modeling based on the core characteristics of the data, and simulate the rainfall evolution path;

[0056] Specifically, obtaining multi-source data and extracting the core characteristics of the data after preprocessing the data means collecting meteorological station data, GNSS data, and ERA5 data, cleaning and standardizing the collected data, and using spatial interpolation methods to spatially align sparse observation data;

[0057] Using the generative adversarial network algorithm to improve the spatial resolution of ERA5 data to the kilometer level;

[0058] Using the spectral analysis method to extract the time series of precipitable water vapor data in GNSS data, the time-domain signal is converted into a frequency-domain signal using the fast Fourier transform, the frequency components are calculated, the amplitude and phase information of the frequency components are obtained, the frequency points with amplitudes greater than the average amplitude are used as the core periodic features, and the data change rate is calculated based on the extracted precipitable water vapor data;

[0059] The meteorological station data includes temperature, humidity, and rainfall data. The GNSS data includes zenith wet delay and precipitable water vapor data. The ERA5 data includes humidity, temperature gradient, and wind speed data.

[0060] By obtaining multi-source data (meteorological stations, GNSS, ERA5) and extracting the core characteristics after preprocessing them, the present invention can effectively improve the accuracy and spatial precision of rainfall prediction. First, data cleaning and standardization ensure the consistency and quality of data from different sources, avoiding the influence of noise and inconsistencies in the data on subsequent analysis. The spatial interpolation method is used to spatially align sparse observation data, effectively filling the gaps between meteorological stations and GNSS observation points, making the meteorological information in the entire prediction area more complete and continuous. Especially in areas with a sparse meteorological observation network, the interpolation process greatly improves the spatial coverage of the data. The application of the generative adversarial network (GAN) algorithm significantly improves the spatial details of low-resolution data by enhancing the spatial resolution of ERA5 data to the kilometer level, thereby enhancing the model's ability to capture local precipitation changes and making rainfall prediction more accurate and reliable. The combination of spectral analysis and fast Fourier transform can extract the periodic change characteristics of atmospheric water vapor from GNSS data. By calculating the amplitude and phase information of the frequency components, the key periodic factors affecting precipitation are found, and the data change rate of the precipitable water vapor data is calculated based on this, thus providing more detailed spatio-temporal characteristics for rainfall intensity prediction.

[0061] Furthermore, dynamic rainfall modeling is carried out according to the core characteristics of the data. Simulating the rainfall evolution path means connecting to the ERA5 data interface, selecting the Beijing-Tianjin-Hebei region and the time range, extracting the humidity, temperature, and wind speed component data, integrating the real-time temperature and humidity observation data of multiple meteorological stations in the region, and storing the data as a CSV file with a unified format. Each record contains a timestamp, longitude and latitude, temperature, and humidity;

[0062] Traverse the collected data, mark the missing values, use the time series linear interpolation method to fill in the missing values, align the ERA5 data and the meteorological station data in time, and normalize the humidity, temperature, and wind speed components;

[0063] By cleaning, standardizing, and spatially interpolating meteorological station data, GNSS data, and ERA5 data, the noise and missing problems in the data can be effectively eliminated, improving the quality and accuracy of the data. Meanwhile, using spectral analysis to extract the precipitable water vapor (PWV) characteristics in GNSS data can identify the periodic characteristics of rainfall events, providing precise basic data for subsequent modeling;

[0064] Obtain the humidity and temperature at the current time point as initial conditions, set the initial rainfall to zero, and set the time step;

[0065] Fit the evaporation constant and the condensation constant from historical meteorological station data through a non-linear fitting method, and calculate the evaporation rate and the condensation efficiency using the initial conditions in the first time step:

[0066] ,

[0067] ;

[0068] where h is the humidity, T is the temperature, is the reference temperature, obtained through statistical analysis of historical data;

[0069] Take the calculation results as the starting point for the next time step to iteratively calculate the evaporation rate and the condensation efficiency ;

[0070] Based on the calculated evaporation rate and condensation efficiency, establish a fractional differential equation to describe the change in rainfall:

[0071] ;

[0072] where is the fractional change rate of rainfall, is the fractional order, set by experimental tuning;

[0073] Through dynamic modeling and rainfall evolution path simulation, the time window and spatial distribution of rainfall events can be accurately captured. The prediction of the dynamic path enables the model to track the development of rainfall events in real time, accurately predict the start and end times and scope of rainfall, and provide more accurate rainfall prediction results;

[0074] Integrate the humidity, temperature, evaporation rate, condensation efficiency, and rainfall at time t into the state variable matrix X(t);

[0075] Define the modulation function and use the modulation function method to fit the model parameters:

[0076] ,

[0077] ,

[0078] ;

[0079] In the formula, are the optimized model parameters, is the composite modulation function, is the state variable matrix, is the fractional derivative of the modulation function, Y is the total time span, is the Gamma function, is the historical time variable, is the fractional order, is the integer derivative of the modulation function, and i is the index of the modulation function;

[0080] The non - linear fitting method enables the model to better fit non - linear parameters such as the evaporation constant and the condensation constant, improving the simulation accuracy. The fractional - order differential equation provides a more flexible and accurate mathematical description for rainfall variation, especially suitable for dealing with the non - linear characteristics of rainfall changing over time;

[0081] Initialize the basis functions of the Chebyshev polynomial:

[0082] ;

[0083] In the formula, is the k - th order Chebyshev polynomial, and are the first two - order Chebyshev polynomials respectively;

[0084] Project the dynamic equation into the Chebyshev basis function space and calculate the polynomial coefficients :

[0085] ;

[0086] In the formula, is the rainfall rate data point at the m - th time point, obtained by subtracting the condensation efficiency from the evaporation rate, and M is the total number of time points;

[0087] Use the Chebyshev polynomial method to numerically solve the fractional - order differential equation to obtain the time - series solution of rainfall :

[0088] ;

[0089] In the formula, are the coefficients of the Chebyshev polynomial, is the k-th order Chebyshev polynomial, and N is the highest order of the polynomial;

[0090] The use of the Chebyshev polynomial can improve the accuracy and computational efficiency of the numerical solution, avoid numerical instability in the solution of high-order differential equations, and reduce the computational time and resource consumption;

[0091] Set the rainfall threshold Q, traverse the time series of rainfall. If the current rainfall is greater than the threshold Q, mark the current time as the start time of rainfall. If the current rainfall is less than or equal to the threshold Q and continues to meet the condition for n hours, mark the current time as the stop time of rainfall. Obtain the rainfall time window based on the start time and stop time of rainfall and record the initial coordinates of the current rainfall event;

[0092] Update the coordinates of the rainfall event according to the wind speed components provided by ERA5 and the initial coordinates of the rainfall event:

[0093] ,

[0094] ;

[0095] In the formula, and are the coordinates of the current rainfall area, and are the coordinates of the rainfall area at the next time step, and are the wind speed components, is the time step;

[0096] Record the rainfall coordinates at each time step to form a dynamic rainfall path.

[0097] Through multi-source data fusion, core feature extraction and dynamic modeling, combined with the innovative application of nonlinear fitting and fractional differential equations, the accuracy and timeliness of rainfall prediction have been significantly improved. By capturing the rainfall evolution path in real time, accurately predicting the rainfall intensity and spatial distribution, it can provide accurate decision-making basis for extreme weather warnings. In addition, the Chebyshev polynomial method is used to optimize the numerical solution and spatial interpolation technology, effectively solving the spatio-temporal inconsistency problem of data and improving the reliability and computational efficiency of prediction. Finally, this method provides scientific, timely and accurate support for meteorological warnings, disaster management and flood control and drainage, significantly enhancing the response ability and emergency handling ability of regional rainfall prediction.

[0098] S2. Build a rainfall prediction model, predict the rainfall intensity based on the rainfall evolution path, issue extreme weather warnings based on the prediction results, and generate a rainfall distribution map of high-risk areas;

[0099] Specifically, to construct a rainfall prediction model and predict rainfall intensity based on the rainfall evolution path means to construct a rainfall prediction model through a multiple linear regression model. Use the state variable matrix data as training data and input it into the multiple linear regression model for iterative training. Define a loss function and an Adam optimizer to iteratively optimize the model parameters. When the loss of the multiple linear regression model no longer decreases significantly during continuous iteration, stop the iteration, output the model parameters, and update the multiple linear regression model.

[0100] Input the real-time data (weather station, GNSS, ERA5 data) within the coverage of the rainfall evolution path into the multiple linear regression model to obtain the regional rainfall intensity prediction value. According to the rainfall time window range, perform integral calculation on the rainfall intensity prediction value to obtain the regional rainfall amount prediction value.

[0101] By constructing a multiple linear regression model and combining multi-dimensional meteorological data, and integrating data through the state variable matrix, the present invention can greatly improve the accuracy and reliability of rainfall intensity prediction. In addition, the use of the Adam optimizer accelerates the training process, and the regional rainfall amount prediction value is obtained through integral calculation. Through these technical means, the present invention realizes the accurate prediction of rainfall intensity in the Beijing-Tianjin-Hebei region and can provide effective support for extreme weather warning and disaster management, having important application value.

[0102] Furthermore, to conduct extreme weather warning based on the prediction results and generate a rainfall distribution map of high-risk areas means to set rainfall thresholds A and B through statistical analysis of historical rainfall data, and A > B. If the predicted rainfall is greater than or equal to threshold A, it is extreme rainfall, and an alarm is immediately issued and emergency measures are taken. If the predicted rainfall is less than threshold A and greater than or equal to threshold B, it is moderate rainfall, with medium impact, and it is recommended to take preventive measures. If the predicted rainfall is less than threshold B, it is light rainfall, with no major risk.

[0103] Use GIS technology to generate a high-risk area distribution map, and use red, yellow, and blue to mark extreme risk areas, moderate risk areas, and light risk areas in turn, and overlay the regional administrative boundaries to clarify the affected cities and regions.

[0104] By combining rainfall prediction, threshold setting, and the application of GIS technology, not only the accuracy and timeliness of rainfall warning are improved, but also the visual display of rainfall amount and the timely identification of high-risk areas are realized, providing accurate data support for various disaster emergency responses. Through the visual display and the regional rainfall distribution map, the public can clearly see the intensity distribution of the current rainfall event and understand the risk level of their respective regions. This not only helps to enhance the public's disaster warning awareness but also promotes their disaster prevention preparations in daily life. For example, according to the alarm information of high-risk areas, flood prevention and drainage, material reserves and other preparations are made in advance.

[0105] S3. Visualize and store the real-time rainfall prediction and warning information in a database;

[0106] Specifically, visualizing the real-time rainfall prediction and warning information means mapping the graded warning information onto the generated high-risk area distribution map, plotting the curve of rainfall changing over time, overlaying the rainfall intensity distribution at different time points, and displaying the high-risk area distribution map and the curve through a visualization interface in real time.

[0107] By combining multi-dimensional rainfall prediction information (rainfall amount, rainfall intensity, time variation, risk area), it provides more comprehensive data support for the meteorological prediction system, further improving the accuracy of the meteorological prediction system. In addition, by integrating different meteorological data sources (weather stations, GNSS, ERA5, etc.), it can provide more reliable real-time data support for meteorological researchers, promoting the further development of meteorological prediction technology. By visually displaying the real-time rainfall prediction and warning information, accurate warning information can be provided to the government, disaster emergency management departments, and the public within a very short time. Decision-makers can quickly judge which areas are at greater risk and take corresponding emergency measures (such as traffic control, flood prevention, emergency shelter, etc.). This real-time and high-efficiency greatly improve the speed and accuracy of emergency response and reduce the losses caused by extreme weather.

[0108] Further, storing in the database means sorting the real-time rainfall prediction and warning information in chronological order and storing it in the central database. The database regularly detects the integrity and security of the stored data and uploads it to the cloud for backup.

[0109] By storing the real-time rainfall prediction and warning information in the central database, conducting regular integrity and security detections, and combining cloud backup technology, the efficiency of data storage and management is significantly improved. Through this method, real-time update and reliable storage of meteorological warning data can be achieved, providing an efficient data foundation for decision support and ensuring the stability and reliability of the meteorological warning system.

[0110] This embodiment also provides a rainfall prediction system for the Beijing-Tianjin-Hebei region, including:

[0111] A data acquisition module for collecting multi-source data, performing data cleaning and standardization, improving the spatial resolution of ERA5 data, and extracting the core features from GNSS data;

[0112] A rainfall modeling module for constructing a rainfall evolution path and simulating the time window and spatial variation of rainfall events;

[0113] A rainfall prediction module for predicting rainfall intensity and regional rainfall based on the rainfall evolution path;

[0114] An early warning module for grading risks based on the predicted rainfall and generating a distribution map of high-risk areas;

[0115] A display and storage module for generating a distribution map of high-risk areas in real time, storing real-time rainfall prediction and early warning information, and regularly backing it up to the cloud.

[0116] This embodiment also provides a computer device applicable to the rainfall prediction method in the Beijing-Tianjin-Hebei region, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the rainfall prediction method for the Beijing-Tianjin-Hebei region proposed in the above embodiment.

[0117] The computer device can be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0118] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the rainfall prediction method for the Beijing-Tianjin-Hebei region as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, abbreviated as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, abbreviated as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, abbreviated as EPROM), programmable read-only memory (Programmable Red-Only Memory, abbreviated as PROM), read-only memory (Read-Only Memory, abbreviated as ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0119] In summary, through the fusion of multi-source data, the present invention adopts a generative adversarial network and spectral analysis to improve the spatial resolution of data and the prediction ability of precipitation evolution. Combining dynamic rainfall modeling, fractional differential equations and multiple regression models, it improves the real-time performance and accuracy of rainfall prediction, can effectively cope with the challenges of rainfall prediction under complex weather conditions, and realizes accurate prediction of rainfall intensity and extreme weather events.

[0120] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A rainfall prediction method for the Beijing-Tianjin-Hebei region, characterized by: include, Acquire multi-source data, pre-process the data, extract the core characteristics of the data, conduct dynamic rainfall modeling based on the core characteristics of the data, and simulate the rainfall evolution path; Build a rainfall prediction model, predict rainfall intensity based on the rainfall evolution path, issue extreme weather warnings based on the prediction results, and generate rainfall distribution maps for high-risk areas; Visualize and store real-time rainfall forecast and warning information in a database; The dynamic rainfall modeling based on the core characteristics of the data and simulating the rainfall evolution path refers to connecting the ERA5 data interface, selecting the Beijing-Tianjin-Hebei region and time range, extracting humidity, temperature and wind speed component data, integrating the real-time temperature and humidity observation data of multiple meteorological stations in the region, and storing the data as a CSV file with a unified format, where each record contains a timestamp, longitude and latitude, temperature and humidity; We traverse the collected data, mark missing values, fill in missing values ​​using time series linear interpolation, time-align the ERA5 data with the weather station data, and normalize the humidity, temperature, and wind speed components; Get the humidity and temperature at the current time point as initial conditions, set the initial rainfall to zero and set the time step; Fitting the evaporation constant from historical weather station data by nonlinear fitting method and the condensation constant , calculate the evaporation rate using the initial conditions at the first time step and condensation efficiency ; The calculated results are used as the starting point of the next time step to iterate the evaporation rate and condensation efficiency to obtain the final evaporation rate. and condensation efficiency ; Based on the calculated evaporation rate and condensation efficiency, a fractional differential equation is established to describe the change in rainfall: ； In the formula, is the fractional rate of change of rainfall, is the fractional order, which is set by experimental tuning, h is the humidity, and T is the temperature; Integrate the humidity, temperature, evaporation rate, condensation efficiency and rainfall at time t into the state variable matrix X(t); Define the modulation function and use the modulation function method to fit the model parameters: ; In the formula, are the optimized model parameters, is the composite modulation function, is the state variable matrix, is the fractional derivative of the modulation function; Initialize the basis functions of the Chebyshev polynomial, project the dynamic equations into the Chebyshev basis function space, and calculate the polynomial coefficients ; The fractional differential equation is numerically solved using the Chebyshev polynomial method to obtain the rainfall time series solution. ; Set the rainfall threshold Q, traverse the rainfall time series, if the current rainfall is greater than the threshold Q, mark the current time as the rainfall start time, if the current rainfall is less than or equal to the threshold Q and continues for n hours to meet the conditions, mark the current time as the rainfall stop time, get the rainfall time window according to the rainfall start time and stop time and record the initial coordinates of the current rainfall event; Update the coordinates of the rainfall event based on the wind speed components and the initial coordinates of the rainfall event provided by ERA5: ; In the formula, and are the coordinates of the current rainfall area, and are the coordinates of the rainfall area at the next time step, and is the wind speed component, is the time step; The rainfall coordinates at each time step are recorded to form a dynamic rainfall path.

2. The method for predicting rainfall in the Beijing-Tianjin-Hebei region according to claim 1, characterized in that: The acquiring of multi-source data and extracting the core characteristics of the data after preprocessing the data refers to collecting weather station data, GNSS data and ERA5 data, cleaning and standardizing the collected data, and using a spatial interpolation method to spatially align the sparse observation data; Use a generative adversarial network algorithm to increase the spatial resolution of ERA5 data to the kilometer level; The spectrum analysis method is used to extract the time series of atmospheric precipitable water data in GNSS data. The time domain signal is converted into a frequency domain signal using fast Fourier transform, the frequency component is calculated, the amplitude and phase information of the frequency component is obtained, and the frequency point with an amplitude greater than the average amplitude is taken as the core periodic feature. The data change rate is calculated based on the extracted atmospheric precipitable water data.

3. The method for predicting rainfall in the Beijing-Tianjin-Hebei region according to claim 2, characterized in that: The construction of the rainfall prediction model and the prediction of rainfall intensity based on the rainfall evolution path refers to constructing the rainfall prediction model through a multivariate linear regression model, using the state variable matrix data as training data to input into the multivariate linear regression model for iterative training, defining the loss function and the Adam optimizer to iteratively optimize the model parameters, and stopping the iteration to output the model parameters and update the multivariate linear regression model when the loss of the multivariate linear regression model no longer decreases significantly during the continuous iteration process; The real-time data within the coverage of the rainfall evolution path is input into the multivariate linear regression model to obtain the regional rainfall intensity forecast value. According to the rainfall time window range, the rainfall intensity forecast value is integrated and calculated to obtain the regional rainfall forecast value.

4. The method for predicting rainfall in the Beijing-Tianjin-Hebei region according to claim 3, characterized in that: The extreme weather warning based on the forecast results and the generation of a rainfall distribution map for high-risk areas refer to setting rainfall thresholds A and B, and A>B. If the forecast rainfall is greater than or equal to threshold A, it is extreme rainfall, an alarm is immediately issued, and emergency measures are taken. If the forecast rainfall is less than threshold A and greater than or equal to threshold B, it is moderate rainfall with moderate impact, and preventive measures are recommended. If the forecast rainfall is less than threshold B, it is light rainfall with no major risk. Use GIS technology to generate a distribution map of high-risk areas, use red, yellow and blue to mark extreme risk areas, moderate risk areas and mild risk areas in sequence, superimpose regional administrative boundaries, and identify the affected cities and regions.

5. The method for predicting rainfall in the Beijing-Tianjin-Hebei region according to claim 4, characterized in that: The visual display of real-time rainfall forecast and warning information refers to mapping the graded warning information onto the generated high-risk area distribution map, drawing a curve graph of rainfall changes over time, superimposing the rainfall intensity distribution at different time points, and displaying the high-risk area distribution map and curve graph in real time through a visual interface.

6. The method for predicting rainfall in the Beijing-Tianjin-Hebei region as claimed in claim 5, characterized in that: The storage in the database refers to sorting the real-time rainfall forecast and warning information in chronological order and storing it in a central database. The database regularly performs integrity and security checks on the stored data and uploads it to the cloud for backup.

7. A rainfall prediction system for the Beijing-Tianjin-Hebei region, based on the rainfall prediction method for the Beijing-Tianjin-Hebei region according to any one of claims 1 to 6, characterized in that: include, Data acquisition module, used to collect multi-source data and perform data cleaning and standardization, improve the spatial resolution of ERA5 data, and extract the core features of GNSS data; The rainfall modeling module is used to construct the rainfall evolution path and simulate the time window and spatial variation of rainfall events; Rainfall prediction module, used to predict rainfall intensity and regional rainfall based on rainfall evolution path; Early warning module, used to classify risks based on predicted rainfall and generate distribution maps of high-risk areas; The display storage module is used to generate real-time distribution maps of high-risk areas, store real-time rainfall forecasts and warning information, and regularly back up to the cloud.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the rainfall prediction method for the Beijing-Tianjin-Hebei region described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the rainfall prediction method for the Beijing-Tianjin-Hebei region described in any one of claims 1 to 6 are implemented.

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