Convective gale short-term forecast method and system based on deep learning

Abstract

The present invention relates to the field of meteorological forecasting technology, and specifically to a method and system for short-term forecasting of convective gale based on deep learning. The method comprises: S1, collection and preprocessing of historical meteorological data; S2, construction and training of a deep learning model; S3, real-time monitoring and data collection; S4, output of forecast results; S5, post-processing, extraction and screening of real-time data; the system comprises: a data collection module, a data preprocessing module, a deep learning module, a real-time monitoring module, a forecast module and a real-time data processing module; the present invention is used to collect terrain feature data of a target area, classify and train a deep learning model, and then use the deep learning model to perform prediction and forecast respectively, thereby improving the accuracy of the forecast.

Description

Technical Field

[0001] The present invention relates to the technical field of meteorological forecasting, and in particular to a method and system for short-term forecasting of convective gale based on deep learning. Background Art

[0002] China's meteorological observation regulations define gale as winds reaching or exceeding 17 meters per second (or winds estimated by visual observation to reach or exceed force 8). Gales are one of my country's major weather disasters, causing significant damage to industry, agriculture, transportation, and people's livelihoods. Convective gales are sudden, high-wind events, including those caused by tornadoes, downbursts, and squall lines. They are highly intense, destructive, and require a short defense period. Forecasting the occurrence of convective gales in advance helps residents take protective measures and reduce the harm caused by convective gales.

[0003] In recent years, the application of machine learning methods in meteorology has made significant progress. Experts at home and abroad have conducted extensive research on convective gale forecasting. This has made it possible to predict convective gale formation by learning from historical data. This has also improved numerical forecasts, which are often subject to initial value errors and model errors, significantly enhancing forecast effectiveness. As research on deep learning methods deepens, meteorological scholars are gradually introducing deep learning into convective gale forecasting, integrating artificial intelligence methods with the meteorological industry.

[0004] When applying deep learning models to short-term forecasts of convective gale, the deep learning model is usually trained by only collecting temperature, humidity, and air pressure data as key features related to convective gale, and then setting a threshold of less than level 8 gale (wind speed of 17.2m / s). However, since the terrain conditions that affect the wind speed and movement speed of convective gale during displacement are not taken into account, although the forecast range of convective gale is expanded and the forecast probability is improved, the probability of false alarms is also increased. This method forecasts weather that does not meet the gale standard, reducing the accuracy of the forecast. Therefore, it is necessary to design a method and system for short-term forecasts of convective gale based on deep learning that can improve forecast accuracy and reduce the probability of false alarms. Summary of the Invention

[0005] To solve the above problems, the present invention provides a method and system for short-term forecasting of convective gale based on deep learning, which is used to collect terrain feature data of the target area, classify and train the deep learning model, and then use the deep learning model to make forecasts separately to improve the accuracy of the forecast.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows: a method for short-term forecasting of convective gale based on deep learning, comprising the following steps:

[0007] S1. Collection and preprocessing of historical meteorological data: First, the target area is divided into several test areas, and several prediction points are set in the test areas. Then, the terrain feature data and the location information of the prediction points are collected in each test area. The test areas are grouped according to the terrain feature data. Then, the wind speed, temperature, humidity and air pressure data corresponding to the convective strong winds in each test area are collected. The temperature, humidity and air pressure data are integrated and summarized for data preprocessing.

[0008] S2. Construction and training of a deep learning model: This model uses terrain features, prediction point location information, and historical temperature, humidity, and air pressure as input data, and historical wind speed data collected at each prediction point as output data to build and train the deep learning model.

[0009] S3. Real-time monitoring and data collection: Real-time monitoring of temperature, humidity, and air pressure data in several test areas, and inputting the data into a trained deep learning model. The deep learning model analyzes the data and outputs convective gale forecast results for different locations;

[0010] S4. Output of forecast results: When the deep learning model completes data analysis and determines the presence of convective gale, it outputs the convective gale area and convective gale level;

[0011] S5. Real-time data post-processing and extraction and screening: After completing the convective gale forecast, the collected real-time data will be used as data for updating and training the deep learning model. The deep learning model will be continuously updated and optimized, and the wind speed distribution map of the target area will be extracted to screen out the strong wind areas that need enhanced protection.

[0012] Furthermore, in S1, a plurality of areas to be measured are classified according to terrain feature data, and the classification groups include wind speed reduction type terrain, wind speed enhancement type terrain and wind speed influence neglected type terrain.

[0013] Furthermore, the classification and grouping are carried out according to the following description: after the collection and processing of the terrain feature data, it basically includes canyons and mountain passes, plains, lakes and oceans, hilly basins, urban industrial areas and river valleys and rivers. Canyons and mountain passes and river valleys and rivers are wind speed enhanced terrain, plains and lakes and oceans are wind speed neglected terrain, and urban industrial areas and hilly basins are wind speed slowed terrain.

[0014] Furthermore, in S1, the collection of air pressure data for each region mainly collects air pressure data at the following levels: the earth's surface, 850hPa, 700hPa, and 500hPa levels.

[0015] Furthermore, in S1, data preprocessing specifically includes removing duplicate data and invalid data, filling missing values ​​with interpolation or mean substitution, and individually identifying and correcting outliers.

[0016] Furthermore, in S2, for the construction and training of the deep learning model, the time for convective gales to arrive at any predicted point in a certain test area from historical data and the intensity of convective gales arriving at each area are collected as test and correction data.

[0017] Furthermore, in S3, the short-term forecast of convective gale needs to report the time when the gale arrives in each area. The arrival time of convective gale in each area is calculated by the following formula:

[0018]

[0019] Where x is the number of each area, t x To predict the time required for convective winds to reach each area, S x is the distance between each area and the prediction point, n is the type code of wind speed slowing terrain, wind speed enhancing terrain and wind speed influence ignoring terrain, V n is the displacement speed of convective gale corresponding to each region;

[0020] The convective wind displacement velocity V predicted by the deep learning model is n , calculated using the following formula:

[0021] V n =y*V

[0022] Where V is the convective wind speed predicted by the deep learning model, and y is the increase or decrease ratio of the convective wind speed corresponding to different terrain types. In the terrain area with slowed wind speed, y is 0.96-0.98, in the terrain area with enhanced wind speed, y is 1.02-1.04, and in the terrain area with ignored wind speed, y is 1.00.

[0023] Furthermore, the convective wind displacement velocity V in each region is calculated based on the deep learning model. n , V in wind-slowing terrain areas n If the wind speed is greater than 17.5m / s, a convective gale forecast is required. The wind speed has negligible impact on terrain areas V n If the wind speed is greater than 17.0m / s, a convective gale forecast is required. In the terrain area with enhanced wind speed, V n If the wind speed is greater than 16.5m / s, a convective gale forecast is required.

[0024] Furthermore, in S5, the data is screened specifically to screen out observation data with wind speed greater than or equal to 15 m / s, and in the screened wind speed data, use the threshold segmentation method to extract all connected areas with wind speed greater than the threshold, and mark these areas as potential high wind areas.

[0025] The deep learning-based convective gale short-term forecast system operates based on the convective gale short-term forecast method, including:

[0026] Data collection module, used to collect characteristic data of the target area;

[0027] A data preprocessing module is used to identify several areas to be tested as wind-speed-enhancing terrain, wind-speed-influence-ignoring terrain, and wind-speed-reducing terrain based on the terrain feature data collected by the data collection module, then search and collect wind speed, temperature, humidity, air pressure, and distance from the prediction point corresponding to several categories of areas when encountering strong winds, integrate each data into a data block corresponding to each area, and finally remove or identify and correct duplicate data, invalid data, and erroneous data in the collected data;

[0028] A deep learning module is used to construct and train a deep learning model using terrain features, prediction point location information, and historical temperature, humidity, and air pressure as input data, and historical wind data collected at each prediction point as output data.

[0029] The real-time monitoring module is used to monitor and collect temperature, humidity, and air pressure data from several test areas in real time, and input the data into a trained deep learning model. The deep learning model analyzes the data and outputs convective gale forecasts for different locations. The test areas that are about to be subject to convective gale weather are marked with a "forecast required" signal, which is then transmitted to the forecast module.

[0030] The forecast module is used to receive the data analysis completed by the deep learning model and determine the presence of convective strong wind weather. When the "forecast required" mark is identified, the convective strong wind intensity and the arrival time t of the strong wind in the area will be forecasted in real time. x ;

[0031] The real-time data processing module is used to use the collected real-time data as data for updating and training the deep learning model after completing the forecast of convective strong winds, continuously update and optimize the deep learning model, extract the wind speed distribution map of the target area, and screen out the strong wind areas that need enhanced protection.

[0032] The above scheme has the following beneficial effects:

[0033] 1. This solution first divides the target area into several regions. After collecting terrain feature data for several areas to be tested, the areas are classified based on this data. Since various terrain features can affect the speed and direction of airflow, they are classified according to their different effects on wind speed, including wind-slowing terrain, wind-enhancing terrain, and wind-constant terrain. Based on the different effects of terrain on wind speed, the wind speed of convective gales in different terrain areas can be more accurately predicted. In addition, compared to making separate predictions for different areas, this classification prediction method can greatly improve the efficiency of predictions. Because we can classify areas according to terrain characteristics and then perform unified predictions and analysis for each category, we avoid repeated and tedious prediction work for each area. This not only saves time and effort, but also improves the accuracy and reliability of predictions.

[0034] 2. This solution builds a deep learning model based on collected data on terrain features, temperature, humidity, and air pressure. The model is trained using historical data, and the arrival time of convective winds from the predicted point in each area, as well as the wind intensity upon arrival, are collected as test and correction data. Through training, the deep learning model learns the formation mechanism, propagation patterns, and influencing factors of convective winds, thereby gaining the ability to predict convective winds.

[0035] 3. Under this plan, each region will collect multi-layered air pressure data, including at different levels, such as 850hPa, 700hPa, and 500hPa. This includes not only surface pressure. Since severe weather events like convective gales primarily occur within the troposphere, multi-layered air pressure monitoring within the troposphere can more accurately capture weather changes, thereby improving the accuracy of air pressure data. Furthermore, this accurate data is a crucial foundation for improving the accuracy of weather forecasts.

[0036] 4. In the process of building and training the deep learning model, this solution mainly uses the historical predicted arrival time and wind intensity of each test area when it is hit by convective strong winds, supplemented by the time and wind intensity of convective strong winds arriving at any predicted point in a test area from any predicted point in the adjacent test area, to test and correct the result output of the deep learning model, thereby improving the prediction accuracy of the deep learning model.

[0037] 5. This solution, with its post-processing and extraction and screening design for real-time data, can update and train deep learning models, making them more efficient and accurate. Furthermore, by screening windy areas that require enhanced protection, it can strengthen safety protection and improve the ability to respond to convective strong winds.

[0038] 6. This solution performs corresponding forecast calculations and corresponding forecast methods based on different terrains to improve the targeted nature of the forecast. Compared with the method of making a unified forecast without considering the terrain area, it can reduce the probability of false alarms and misreporting, improve the accuracy of the forecast, reduce the setting of ineffective protection, and save social resources.

[0039] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A schematic diagram of an embodiment of a method and system for short-term convective gale forecasting based on deep learning according to the present invention;

[0041] Figure 2 This is a schematic diagram of regional division of an embodiment of a method and system for short-term convective gale forecasting based on deep learning of the present invention;

[0042] Figure 3 This is a system operation diagram of an embodiment of the method and system for short-term forecasting of convective gale based on deep learning of the present invention. DETAILED DESCRIPTION

[0043] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0044] The following is further described in detail through specific implementation methods:

[0045] Example 1:

[0046] As attached Figure 1 and Figure 2 As shown in the figure: The method for short-term forecast of convective gale based on deep learning includes the following steps:

[0047] S1. Collection and preprocessing of historical meteorological data: First, the target area is divided into several test areas, and several prediction points are set in the test areas. Then, the terrain feature data and location information of the prediction points of the several test areas are collected respectively, and the test areas are grouped according to the terrain feature data. Since various terrain features can affect the speed and direction of airflow, for example, the hilly terrain is undulating, which may cause the airflow to become scattered, the speed to decrease, the direction to change, and the wind speed of convective strong winds to decrease, the classification group design includes wind speed reduction terrain, wind speed enhancement terrain and wind speed influence neglected terrain. After the terrain feature data are collected and processed, they basically include canyons and mountain passes, plains, lakes and oceans, hilly basins, urban industrial areas and river valleys. Canyons and mountain passes and river valleys are wind speed enhancement terrain, plains and lakes and oceans are wind speed influence neglected terrain, and urban industrial areas and hilly basins are wind speed reduction terrain. This is conducive to the subsequent corresponding wind speed forecast calculation for wind speed enhancement terrain, wind speed influence neglected terrain and wind speed reduction terrain. This design classifies wind speed according to the impact of terrain and generates predictions in three categories, which improves forecasting efficiency compared to separate predictions for different regions. After classification, wind speed, temperature, humidity, and air pressure data are collected for several test areas when experiencing convective gales. Pressure data for each region is collected primarily at the Earth's surface, 850hPa, 700hPa, and 500hPa levels. Since convective gales are severe weather conditions that primarily occur in the troposphere, multi-level pressure monitoring within the troposphere can improve the accuracy of pressure data and, consequently, the accuracy of forecasts. After collection, the temperature, humidity, and pressure data are consolidated and summarized for data preprocessing. This preprocessing removes duplicate and invalid data, fills missing values ​​with interpolation or mean substitution, and individually identifies and corrects outliers. This preprocessing makes the temperature, humidity, and pressure data more relevant, providing more reliable data support for the subsequent construction and training of deep learning models.

[0048] S2. Construction and training of deep learning model: A deep learning model is constructed using terrain features as algorithm differentiation criteria, so that the deep learning model has the ability to predict multiple types of terrain, including wind-speed-slowing terrain, wind-speed-enhancing terrain, and wind-speed-influence-ignored terrain. Then, historical temperature, humidity, and air pressure are used as input data, and the historical wind speed data collected at each prediction point is used as output data to train the model. During the training of the deep learning model, the time and wind intensity of convective strong winds from any prediction point in a certain test area to any prediction point in the adjacent test area are collected from the historical data as test and correction data, so that the deep learning model has the function of predicting the intensity and arrival time of convective strong winds, which is beneficial for various regions to take safety precautions against convective strong winds.

[0049] S3. Real-time monitoring and data collection: Real-time monitoring of temperature, humidity, and air pressure data in several test areas. The data is input into the trained deep learning model. The deep learning module predicts convective gale weather. The specific prediction process is to calculate the arrival time of convective gale in each area using the following formula:

[0050]

[0051] Where x is the number of each area, t x To predict the time required for convective winds to reach each area, S x is the distance between each area and the prediction point, n is the type code of wind speed slowing terrain, wind speed enhancing terrain and wind speed influence ignoring terrain, V n is the displacement speed of convective gale corresponding to each region;

[0052] The convective wind displacement velocity V predicted by the deep learning model is n , calculated using the following formula:

[0053] V n =y*V

[0054] Where V is the convective wind speed predicted by the deep learning model, y is the increase or decrease ratio of the convective wind displacement speed corresponding to different terrain types. The y value in the wind speed reduction terrain area is 0.96-0.98, the y value in the wind speed enhancement terrain area is 1.02-1.04, and the y value in the wind speed neglected terrain area is 1.00. After calculating the wind speed data corresponding to each area, combined with the analysis of air pressure, the convective wind displacement speed V of each area is calculated based on the prediction module. n , when V in the wind speed slowing terrain area n If the wind speed is greater than 17.5m / s, a convective gale forecast is required. When the wind speed affects the neglected terrain area V n If the wind speed is greater than 17.0m / s, a convective gale forecast is required. n When the wind speed is greater than 16.5m / s, a convective gale forecast is required. This design helps reduce the probability of false alarms of weather conditions approaching convective gale intensity, while increasing the success rate of forecasting situations where gale forces accelerate to form gale forces during displacement, thereby improving safety.

[0055] S4. Output of forecast results: After the deep learning model predicts the wind speed, it determines whether the intensity corresponding to the wind speed exceeds the set threshold. Combined with the air pressure data analysis, it determines whether the strong wind is convective. If it is determined to be convective, the forecast result for the corresponding area is output;

[0056] S5. Post-processing and extraction and screening of real-time data: After completing the forecast of convective strong winds, the collected real-time data will be used as data for updating and training the deep learning model. The deep learning model will be continuously updated and trained, and the wind speed distribution map of the target area will be extracted to screen out the strong wind areas that need to be strengthened. The specific screening method is to screen out the observation data with wind speed greater than or equal to 15m / s, and use the threshold segmentation method to extract all connected areas with wind speed greater than the threshold in the screened wind speed data, and mark these areas as potential strong wind areas. By marking the strong wind areas that need to be strengthened, it is beneficial to strengthen the safety protection of these strong wind areas when experiencing convective strong winds in the future, thereby reducing the damage rate of convective strong winds to the target areas.

[0057] Example 2:

[0058] As attached Figure 3 As shown: A convective gale short-term forecast system based on deep learning is operated based on the convective gale short-term forecast method described in Example 1, and mainly includes a data collection module, a data preprocessing module, a deep learning module, a real-time monitoring module, a forecast module and a real-time data processing module.

[0059] The data collection module collects characteristic data of the target area and integrates and passes the data to the data preprocessing module.

[0060] In the data preprocessing module, the user first divides the target area into several test areas and sets several test points within the test areas. Then, based on the terrain feature data collected by the receiving data collection module, the test areas are marked as "1", "0" and "-1" respectively based on the characteristics of wind speed-enhancing terrain, wind speed-ignoring terrain and wind speed-reducing terrain. Then, the meteorological historical database is searched for the wind speed, temperature, humidity, air pressure and distance from the forecast point of several categories of areas when encountering strong winds, and each data is integrated into the corresponding data block of each area. Finally, the data is removed or identified and corrected for duplicate data, invalid data and erroneous data in the collected data to improve the reference value and reliability of the data. After data preprocessing, the data is passed to the deep learning module.

[0061] The deep learning module takes terrain features, prediction point location information, historical temperature, humidity, and air pressure as input data, and the historical wind data collected at each prediction point as output data to build and train a deep learning model. After training is completed, the trained deep learning module will be put into forecasting use.

[0062] The real-time monitoring module monitors and collects temperature, humidity and air pressure data of several test areas in real time, and inputs the data into the trained deep learning model. The deep learning model analyzes the data and outputs the convective gale prediction results at different locations. The test areas that are about to be subjected to convective gale weather are marked with a signal representing "forecast required" and transmitted to the forecast module.

[0063] When the forecast module receives the data from the deep learning model and determines that there is convective strong wind weather, after identifying the "forecast required" sign, it will forecast the convective strong wind intensity and arrival time t that the area will suffer in real time. x .

[0064] After completing the forecast of convective strong winds, the real-time data processing module uses the collected real-time data as data for updating and training the deep learning model, continuously updates and optimizes the deep learning model, and extracts the wind speed distribution map of the target area to screen out strong wind areas that require enhanced protection.

[0065] Obviously, the above embodiments are merely examples for clarity of explanation and are not intended to limit the implementation methods. Those skilled in the art will readily appreciate that other variations or modifications based on the above descriptions are possible. It is not necessary and impossible to enumerate all implementation methods here. Obvious variations or modifications arising therefrom remain within the scope of protection of the present invention.

Claims

1. A deep learning-based short-term forecasting method for convective gale, characterized by: The following steps are involved: S1. Collection and preprocessing of historical meteorological data: First, the target area is divided into several test areas, and several prediction points are set in the test areas. Then, the terrain feature data and the location information of the prediction points in each test area are collected respectively. The test areas are grouped according to the terrain feature data. The classification groups include wind speed reduction terrain, wind speed enhancement terrain, and wind speed influence neglected terrain. The terrain feature data include canyons and mountain passes, plains, lakes and oceans, hilly basins, urban industrial areas, and river valleys. Canyons and mountain passes and river valleys are considered wind speed enhancement terrain, plains and lakes and oceans are considered wind speed influence neglected terrain, and urban industrial areas and hilly basins are considered wind speed reduction terrain. After the grouping is completed, the wind speed, temperature, humidity and air pressure data corresponding to each test area when encountering convective strong winds are collected separately, and the temperature, humidity and air pressure data are integrated and summarized for data preprocessing; S2. Construction and training of a deep learning model: This model uses terrain features, prediction point location information, and historical temperature, humidity, and air pressure as input data, and historical wind speed data collected at each prediction point as output data to build and train the deep learning model. S3. Real-time monitoring and data collection: Real-time monitoring of temperature, humidity, and air pressure data in several test areas. The data is input into a trained deep learning model, which analyzes the data and outputs convective gale prediction results for different locations. S4. Forecast result output: When the deep learning model completes the data analysis and determines the presence of convective gale, it outputs the convective gale area and convective gale level; S5. Real-time data post-processing and extraction and screening: After completing the convective gale forecast, the collected real-time data will be used as data for updating and training the deep learning model. The deep learning model will be continuously updated and optimized, and the wind speed distribution map of the target area will be extracted to screen out the strong wind areas that need enhanced protection.

2. The method for short-term convective gale forecasting based on deep learning according to claim 1, characterized in that: In S1, the main levels of air pressure data collection in each region include: 850hPa, 700hPa and 500hPa.

3. The method for short-term convective gale forecasting based on deep learning according to claim 2, characterized in that: In S1, data preprocessing specifically includes removing duplicate data and invalid data, filling missing values ​​with interpolation or mean substitution, and individually identifying and correcting outliers.

4. The method for short-term convective gale forecasting based on deep learning according to claim 3, characterized in that: In S2, for the construction and training of the deep learning model, the time when convective gales arrive at any predicted point in a certain tested area and the gale intensity in the historical data are collected as test and correction data.

5. The method for short-term convective gale forecasting based on deep learning according to claim 4, characterized in that: In S3, the short-term forecast of convective gale needs to report the time when the gale will arrive in each area. The arrival time of convective gale in each area is calculated by the following formula: Where x is the number of each area, t x To predict the time required for convective winds to reach each area, S x is the distance between each area and the prediction point, n is the type code of wind speed slowing terrain, wind speed enhancing terrain and wind speed influence ignoring terrain, V n is the displacement speed of convective gale corresponding to each region; The convective wind displacement velocity V predicted by the deep learning model is n , calculated using the following formula: V n =y * V Where V is the convective wind speed predicted by the deep learning model, and y is the increase or decrease ratio of the convective wind speed corresponding to different terrain types. In the terrain area with slowed wind speed, y is 0.96-0.98, in the terrain area with enhanced wind speed, y is 1.02-1.04, and in the terrain area with ignored wind speed, y is 1.

00.

6. The method for short-term convective gale forecasting based on deep learning according to claim 5, characterized in that: The convective wind displacement velocity V in each region is calculated based on the deep learning model. n , V in wind-slowing terrain areas n If the wind speed is greater than 17.5m / s, a convective gale forecast is required. The wind speed has negligible impact on terrain areas V n If the wind speed is greater than 17.0m / s, a convective gale forecast is required. In the terrain area with enhanced wind speed, V n If the wind speed is greater than 16.5m / s, a convective gale forecast is required.

7. The method for short-term convective gale forecasting based on deep learning according to claim 6, characterized in that: In S5, the data is screened specifically to select observation data with wind speed greater than or equal to 15 m / s, and in the screened wind speed data, use the threshold segmentation method to extract all connected areas with wind speed greater than the threshold, and mark these areas as potential high wind areas.

8. A deep learning-based convective gale short-term forecast system, operating based on the convective gale short-term forecast method according to any one of claims 1 to 7, characterized in that: include: Data collection module, used to collect characteristic data of the target area; A data preprocessing module is used to identify several areas to be tested as wind-speed-enhancing terrain, wind-speed-influence-ignoring terrain, and wind-speed-reducing terrain based on the terrain feature data collected by the data collection module, then search and collect wind speed, temperature, humidity, air pressure, and distance from the prediction point corresponding to several categories of areas when encountering strong winds, integrate each data into a data block corresponding to each area, and finally remove or identify and correct duplicate data, invalid data, and erroneous data in the collected data; A deep learning module is used to construct and train a deep learning model using terrain features, prediction point location information, and historical temperature, humidity, and air pressure as input data, and historical wind data collected at each prediction point as output data. The real-time monitoring module is used to monitor and collect temperature, humidity, and air pressure data from several test areas in real time. This data is then fed into a trained deep learning model, which analyzes the data and outputs convective gale predictions for different locations. Areas expected to be subject to convective gale weather are marked with a "forecast required" signal, which is then transmitted to the forecast module. The forecast module is used to receive the data analysis completed by the deep learning model and determine the presence of convective strong wind weather. When the "forecast required" mark is identified, the convective strong wind intensity and arrival time t that the area will suffer are forecasted in real time. x ; The real-time data processing module is used to use the collected real-time data as data for updating and training the deep learning model after completing the forecast of convective strong winds, continuously update and optimize the deep learning model, extract the wind speed distribution map of the target area, and screen out the strong wind areas that need enhanced protection.

Images