Meteorological information prediction processing platform using artificial intelligence

Abstract

The invention relates to the technical field of meteorological information processing and prediction, and discloses a meteorological information prediction processing platform using artificial intelligence, and the platform comprises a data collection module which is used for collecting meteorological data from a plurality of data sources; the data preprocessing module is used for cleaning, formatting and integrating the collected meteorological data; the feature engineering module is used for extracting and constructing features from the preprocessed data; the model training module is used for training by utilizing machine learning and deep learning models; the model verification and evaluation module is used for verifying and evaluating the performance of the training model; the real-time prediction module is used for performing real-time weather prediction by utilizing the trained model; and the result display and early warning module is used for displaying the prediction result and sending out early warning information. Through collection and fusion of multi-source data, including meteorological station data, satellite remote sensing data, Internet of Things sensor data and social media data, more comprehensive and meticulous meteorological information can be provided.

Description

Technical Field

[0001] The present invention relates to the technical field of meteorological information processing and forecasting, and in particular to a meteorological information forecasting and processing platform using artificial intelligence. Background Art

[0002] Current meteorological information prediction and processing platforms mainly rely on traditional meteorological observation data and physical models for weather forecasting. These platforms usually collect data from a single or small number of data sources such as ground meteorological stations, satellite remote sensing and meteorological radar, and then make predictions through numerical weather prediction models. Although these methods can provide weather forecasts to a certain extent, there are still many limitations and defects, resulting in insufficient accuracy and timeliness of forecasts.

[0003] The coverage and accuracy of traditional meteorological data are limited, especially in areas with complex terrain or remote sea areas, where the density of meteorological stations is insufficient, making it difficult to provide detailed and accurate meteorological data. In addition, although meteorological radar and satellite remote sensing data can provide observation information over a wide range, their real-time performance is poor due to the lag in data processing and transmission. Numerical weather forecast models are complex and computationally demanding, and usually require a lot of computing resources and time, which limits the update frequency of real-time forecasts. With climate change and the frequent occurrence of extreme weather events, traditional methods are unable to meet the needs of rapid response and accurate forecasting.

[0004] Existing technologies also lack the ability to comprehensively utilize multi-source data. In recent years, with the development of Internet of Things and big data technologies, the ways to obtain meteorological data have become more diverse. For example, widely distributed Internet of Things sensors can collect local meteorological data in real time, and weather-related information posted by users on social media platforms can also serve as a valuable data source. However, existing meteorological forecasting platforms are rarely able to effectively integrate and utilize these multi-source data, thus failing to fully tap the potential of these data in improving forecast accuracy and timeliness.

[0005] In addition, the traditional weather forecast model has a long update cycle, and the model training and update process relies on expert experience and manual adjustments, making it difficult to quickly adapt to changes in the weather environment. The lack of continuous learning and adaptive optimization capabilities may cause the model performance to decline when faced with new data and new weather phenomena. Summary of the invention

[0006] In view of the shortcomings of the existing technology, the present invention provides a meteorological information forecasting and processing platform using artificial intelligence, which solves the problem that the coverage and accuracy of traditional meteorological data are limited, especially in areas with complex terrain or remote sea areas, where the density of meteorological stations is insufficient and it is difficult to provide detailed and accurate meteorological data.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a meteorological information prediction and processing platform using artificial intelligence, comprising:

[0008] A data collection module for collecting meteorological data from multiple data sources;

[0009] Data preprocessing module, used to clean, format and integrate the collected meteorological data;

[0010] Feature engineering module, used to extract and construct features from preprocessed data;

[0011] Model training module, used for training using machine learning and deep learning models;

[0012] Model validation and evaluation module, used to validate and evaluate the performance of the training model;

[0013] Real-time prediction module, used to make real-time weather predictions using trained models;

[0014] Result display and warning module, used to display prediction results and issue warning information;

[0015] The continuous learning and model updating module is used to regularly update the model and adjust the model parameters to maintain the prediction accuracy.

[0016] Preferably, the data collection module includes:

[0017] Weather station data collection unit, used to collect temperature, humidity, wind speed, and precipitation data from ground weather stations;

[0018] Satellite remote sensing data collection unit, used to collect remote sensing images and meteorological data from satellites, such as cloud images and surface temperature;

[0019] IoT sensor data collection unit, used to collect IoT sensor data distributed in various places;

[0020] The social media data collection unit is used to collect information and pictures posted by users related to meteorology from social media.

[0021] Preferably, the data preprocessing module includes:

[0022] Data cleaning unit, used to process missing values, outliers, and remove noise data;

[0023] Data formatting unit, used to uniformly format data from different sources;

[0024] The data integration unit is used to integrate and align data from different data sources.

[0025] Preferably, the feature engineering module includes:

[0026] Feature extraction unit: used to extract the features of temperature change rate and accumulated precipitation from the preprocessed data;

[0027] Feature construction unit: used to construct new features and improve the predictive ability of the model.

[0028] Preferably, the model training module includes:

[0029] Model selection unit, used to select suitable machine learning and deep learning models;

[0030] Model optimization unit: used to optimize model hyperparameters to improve model performance;

[0031] Model training unit: used to train the selected model using multi-source data.

[0032] Preferably, the model verification and evaluation module includes:

[0033] Cross-validation unit, used to perform cross-validation and evaluate the generalization ability of the model;

[0034] Performance evaluation unit: used to calculate and evaluate the performance indicators of model accuracy, precision, and recall.

[0035] Preferably, the real-time prediction module includes:

[0036] A real-time data input unit is used to input the meteorological data collected in real time into the trained model for prediction;

[0037] Real-time prediction calculation unit: used to perform real-time prediction calculation and generate prediction results.

[0038] Preferably, the result display and early warning module includes:

[0039] Data visualization unit, used to display the prediction results in the form of charts and maps;

[0040] Warning generation unit: used to generate extreme weather warning information based on forecast results;

[0041] User interface unit: used to provide a user-friendly interface to display forecast results and warning information.

[0042] Preferably, the continuous learning and model updating module includes:

[0043] Data update unit, used to collect and integrate the latest meteorological data to ensure the timeliness and comprehensiveness of model training data;

[0044] Model retraining unit, which retrains the existing model using the latest data to update the model parameters and structure;

[0045] Model performance monitoring unit, which is used to continuously monitor the prediction performance of the model in actual applications and record the accuracy, precision, and recall indicators of the model;

[0046] User feedback collection unit, used to collect user feedback on prediction results, analyze user needs and model performance differences as a reference for model updates;

[0047] The model version management unit is used to manage different versions of models, save and record the model version information of each update, so as to facilitate the backtracking and comparative analysis of the model.

[0048] Preferably, the meteorological information forecasting and processing method using artificial intelligence comprises the following steps:

[0049] Step 1: collect meteorological data from multiple data sources through a data collection module, and pre-process the collected meteorological data through a data pre-processing module;

[0050] Step 2: The feature engineering module extracts and constructs features from the preprocessed data;

[0051] Step 3: Use machine learning and deep learning models to train the extracted and constructed features through the model training module;

[0052] Step 4: Verify and evaluate the performance of the training model through the model verification and evaluation module;

[0053] Step 5: Input the meteorological data collected in real time into the trained model through the real-time prediction module to perform real-time weather prediction, and display the prediction results through the data visualization unit through the result display and warning module, and generate extreme weather warning information through the warning generation unit, and provide the user interface unit to display it to the user;

[0054] Step 6. Finally, use the continuous learning and model updating module to regularly update the model and adjust the model parameters to maintain the accuracy of the prediction.

[0055] The present invention provides a weather information forecasting and processing platform using artificial intelligence.

[0056] Beneficial effects:

[0057] 1. The present invention can provide more comprehensive and detailed meteorological information by collecting and integrating multi-source data, including weather station data, satellite remote sensing data, IoT sensor data, and social media data. Combined with machine learning and deep learning models, the prediction accuracy of the model is improved through sophisticated data preprocessing and feature engineering. In addition, the real-time prediction module can process the latest meteorological data, provide timely weather forecasts and warning information, and enable users to take countermeasures earlier.

[0058] 2. The present invention can timely discover and predict extreme weather events through the result display and warning module, data visualization and automatic warning generation unit. The generated warning information can be notified to relevant departments and the public in a timely manner through the user interface unit to help them take necessary protective measures and reduce the losses caused by disasters. At the same time, the application of the user feedback collection unit also improves the effectiveness and reliability of the warning system.

[0059] 3. The present invention realizes regular updating and optimization of the model through continuous learning and model updating modules through data updating units, model retraining units, model performance monitoring units, etc. By collecting and integrating the latest data, continuously monitoring and evaluating model performance, and combining user feedback, the model parameters and structure are continuously adjusted and optimized to ensure that the model maintains efficient and stable prediction capabilities in the long term. The introduction of the model version management unit also ensures the traceability and comparability of the model, providing strong support for model iteration and performance improvement. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a module block diagram of the present invention;

[0061] Figure 2 is an architecture diagram of the data collection module of the present invention;

[0062] Figure 3 is an architecture diagram of a data preprocessing module of the present invention;

[0063] Figure 4 This is an architecture diagram of the feature engineering module of the present invention;

[0064] Figure 5 This is an architecture diagram of the model training module of the present invention;

[0065] Figure 6 is an architecture diagram of the model verification and evaluation module of the present invention;

[0066] Figure 7 is an architecture diagram of the real-time prediction module of the present invention;

[0067] Figure 8 This is an architecture diagram of the result display and early warning module of the present invention;

[0068] Fig. 9 The architecture diagram of the continuous learning and model updating module of the present invention;

[0069] Fig.10 The present invention is a flowchart of the method for weather information forecasting and processing. DETAILED DESCRIPTION

[0070] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0071] Embodiment 1:

[0072] Please see attached Figure 1-Figure 10 The embodiment of the present invention provides a meteorological information prediction and processing platform using artificial intelligence, including:

[0073] A data collection module for collecting meteorological data from multiple data sources;

[0074] Weather station data collection unit: automatically collects temperature, humidity, wind speed, precipitation and other data through the ground weather station network and transmits them to the data center.

[0075] Satellite remote sensing data collection unit: obtains a wide range of meteorological data, such as cloud maps and surface temperature, through satellite remote sensing technology, and transmits the data to the platform.

[0076] IoT sensor data collection unit: Use IoT sensors distributed in various places to collect environmental data in real time, such as temperature, humidity, air quality, etc.

[0077] Social Media Data Collection Unit: Obtaining real-time weather-related information by analyzing information and pictures posted by users on social media platforms.

[0078] Through multi-source data collection, information from different sources can be integrated to form a more comprehensive meteorological data set. These data can verify and complement each other, improving the accuracy and coverage of the data. Enhance the timeliness and diversity of the data. Improve data coverage, especially for areas that are difficult to monitor. Use social media data to increase the real-time monitoring capabilities of sudden weather events.

[0079] Data preprocessing module, used to clean, format and integrate the collected meteorological data;

[0080] Data cleaning unit: Use statistical analysis and machine learning techniques to automatically detect and repair missing values, outliers, and remove noise data.

[0081] Data formatting unit: Convert data from different sources into a unified format to facilitate subsequent processing and analysis.

[0082] Data integration unit: Integrate and align the cleaned and formatted data to form a unified spatiotemporal dataset.

[0083] Through data preprocessing, we can ensure the quality and consistency of data and provide a reliable data foundation for subsequent feature engineering and model training. We can improve the integrity and consistency of data, reduce data noise and improve data reliability. We can form a unified spatiotemporal data set to facilitate subsequent analysis and processing.

[0084] Feature engineering module, used to extract and construct features from preprocessed data;

[0085] Feature extraction unit: Extract key features from the preprocessed data, such as temperature change rate, accumulated precipitation, etc., and use statistical methods and machine learning techniques to identify important features.

[0086] Feature construction unit: constructs new composite features based on existing features to improve the prediction ability of the model.

[0087] Through feature extraction and construction, raw data can be converted into more meaningful features, helping the model to better understand and predict meteorological changes.

[0088] Beneficial effects: Improve the predictive performance of the model. Reduce the dimension of the data and improve computational efficiency. Provide more explanatory features for easier analysis and understanding.

[0089] Model training module, used for training using machine learning and deep learning models;

[0090] Model selection unit: Select appropriate machine learning and deep learning models, such as random forest, support vector machine, convolutional neural network, long short-term memory network, etc., according to data characteristics and prediction requirements.

[0091] By integrating multi-source data, adopting a variety of machine learning and deep learning models such as random forests, support vector machines, convolutional neural networks, long short-term memory networks, and combining comprehensive data preprocessing and feature engineering, we can achieve high-precision and timely weather forecasts. Through real-time forecasting, result display and early warning, continuous learning and model update mechanisms, we can provide accurate and personalized meteorological services, significantly improving the reliability and effectiveness of the forecast.

[0092] Model optimization unit: Use hyperparameter optimization techniques (such as grid search and Bayesian optimization) to optimize model parameters and improve model performance.

[0093] Model training unit: Use multi-source data to train the selected model and use the training data set to adjust and learn the model parameters.

[0094] By selecting the appropriate model and optimizing model parameters, the model's predictive and generalization capabilities can be improved, and the model's predictive accuracy and stability can be improved. It can adapt to different types of data and improve the model's generalization capabilities. Through optimization, the model's performance can be improved and the risk of overfitting can be reduced.

[0095] Model validation and evaluation module, used to validate and evaluate the performance of the training model;

[0096] Cross-validation unit: Use cross-validation technology to evaluate the generalization ability of the model and ensure the stability of the model's performance on different data sets.

[0097] Performance evaluation unit: calculates and evaluates the model's accuracy, precision, recall and other performance indicators, and comprehensively evaluates the model's prediction effect.

[0098] Through cross-validation and performance evaluation, we ensure that the model performs consistently on both training data and new data, avoiding overfitting and underfitting problems. We improve the robustness and stability of the model. We provide quantitative performance indicators to facilitate model selection and optimization. We ensure the effectiveness of the model in practical applications.

[0099] Real-time prediction module, used to make real-time weather predictions using trained models;

[0100] Real-time data input unit: inputs the meteorological data collected in real time into the trained model for real-time prediction.

[0101] Real-time prediction calculation unit: performs real-time prediction calculations, generates prediction results, and transmits the results to the result display and early warning module.

[0102] Through real-time data input and forecast calculation, it can realize real-time monitoring and forecasting of meteorological changes, and provide the latest weather forecast information in time. It can provide real-time meteorological forecasts and improve the timeliness of forecasts. It can timely discover and warn extreme weather events to reduce disaster losses. It can also improve the ability to respond to sudden weather changes.

[0103] Result display and warning module, used to display prediction results and issue warning information;

[0104] Data visualization unit: displays forecast results in the form of charts, maps, etc., providing intuitive weather information.

[0105] Warning generation unit: Generates extreme weather warning information based on forecast results and automatically issues warning notifications.

[0106] User interface unit: provides a user-friendly interface, displays forecast results and warning information, and supports user-defined settings and queries.

[0107] Through data visualization and warning generation, users can intuitively understand weather forecast information and take preventive measures in time. Provide intuitive weather information display for easy understanding and use by users. Automatically generate and send warning information to improve the timeliness and accuracy of warnings. Provide personalized meteorological services to meet the needs of different users.

[0108] The continuous learning and model updating module is used to regularly update the model and adjust the model parameters to maintain the prediction accuracy.

[0109] Data update unit: collects and integrates the latest meteorological data to ensure the timeliness and comprehensiveness of model training data.

[0110] Model retraining unit: Retrain the existing model using the latest data to update the model parameters and structure.

[0111] Model performance monitoring unit: Continuously monitor the prediction performance of the model in actual applications and record indicators such as model accuracy, precision, and recall.

[0112] Model selection and optimization unit: Based on the performance monitoring results, select the optimal model or adjust the model structure and parameters to improve the prediction performance.

[0113] Automatic deployment unit: Automatically deploy the updated model to the real-time prediction module to ensure timely update of the prediction results.

[0114] User feedback collection unit: collects user feedback on prediction results, analyzes differences in user needs and model performance, and uses this as a reference for model updates.

[0115] Model version management unit: manage different versions of models, save and record the model version information of each update, so as to facilitate the backtracking and comparative analysis of the model.

[0116] Through continuous learning and model updating, we ensure that the model always maintains high accuracy and adaptability, and timely reflects the latest meteorological changes. We improve the long-term prediction performance of the model and reduce the risk of performance degradation. We adapt to the ever-changing meteorological environment and maintain the real-time and accuracy of the model. We provide model version management to facilitate model iteration and optimization.

[0117] Embodiment 2:

[0118] Please see attached Figure 1-Figure 10 , a meteorological information forecasting and processing method using artificial intelligence comprises the following steps:

[0119] Step 1: collect meteorological data from multiple data sources through a data collection module, and pre-process the collected meteorological data through a data pre-processing module;

[0120] The data collection module collects meteorological data from multiple data sources, and the data preprocessing module preprocesses the collected meteorological data to ensure the quality and consistency of the data.

[0121] Step 2: The feature engineering module extracts and constructs features from the preprocessed data;

[0122] The feature engineering module extracts and constructs features from preprocessed data to improve the expressiveness of the data and the predictive performance of the model.

[0123] Step 3: Use machine learning and deep learning models to train the extracted and constructed features through the model training module;

[0124] The model training module uses machine learning and deep learning models to train the extracted and constructed features and optimize the model parameters.

[0125] Step 4: Verify and evaluate the performance of the training model through the model verification and evaluation module;

[0126] The model validation and evaluation module verifies and evaluates the performance of the training model to ensure the stability of the model on different data sets.

[0127] Step 5: Input the meteorological data collected in real time into the trained model through the real-time prediction module to perform real-time weather prediction, and display the prediction results through the data visualization unit through the result display and warning module, and generate extreme weather warning information through the warning generation unit, and provide the user interface unit to display it to the user;

[0128] The real-time forecasting module inputs the meteorological data collected in real time into the trained model for real-time weather forecasting.

[0129] The forecast results are displayed through the result display and warning module, and extreme weather warning information is generated, which is then displayed to the user through a user interface unit.

[0130] Step 6. Finally, use the continuous learning and model updating module to regularly update the model and adjust the model parameters to maintain the accuracy of the prediction.

[0131] Use the continuous learning and model update modules to regularly update the model and adjust model parameters to maintain prediction accuracy and ensure that the model is always in the best state.

[0132] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A weather information forecasting and processing platform using artificial intelligence, characterized in that: include: A data collection module for collecting meteorological data from multiple data sources; Data preprocessing module, used to clean, format and integrate the collected meteorological data; Feature engineering module, used to extract and construct features from preprocessed data; Model training module, used for training using machine learning and deep learning models; Model validation and evaluation module, used to validate and evaluate the performance of the training model; Real-time prediction module, used to make real-time weather predictions using trained models; Result display and warning module, used to display prediction results and issue warning information; The continuous learning and model updating module is used to regularly update the model and adjust the model parameters to maintain the prediction accuracy.

2. The meteorological information forecasting and processing platform using artificial intelligence according to claim 1, characterized in that: The data collection module comprises: Weather station data collection unit, used to collect temperature, humidity, wind speed, and precipitation data from ground weather stations; Satellite remote sensing data collection unit, used to collect remote sensing images and meteorological data from satellites, such as cloud images and surface temperature; IoT sensor data collection unit, used to collect IoT sensor data distributed in various places; The social media data collection unit is used to collect information and pictures posted by users related to meteorology from social media.

3. The meteorological information forecasting and processing platform using artificial intelligence according to claim 1, characterized in that: The data preprocessing module comprises: Data cleaning unit, used to process missing values, outliers, and remove noise data; Data formatting unit, used to uniformly format data from different sources; The data integration unit is used to integrate and align data from different data sources.

4. The meteorological information forecasting and processing platform using artificial intelligence according to claim 1, characterized in that: The feature engineering module includes: Feature extraction unit: used to extract the features of temperature change rate and accumulated precipitation from the preprocessed data; Feature construction unit: used to construct new features and improve the predictive ability of the model.

5. The meteorological information forecasting and processing platform using artificial intelligence according to claim 1, characterized in that: The model training module includes: Model selection unit, used to select suitable machine learning and deep learning models; Model optimization unit: used to optimize model hyperparameters to improve model performance; Model training unit: used to train the selected model using multi-source data.

6. The meteorological information forecasting and processing platform using artificial intelligence according to claim 1, characterized in that: The model verification and evaluation module includes: Cross-validation unit, used to perform cross-validation and evaluate the generalization ability of the model; Performance evaluation unit: used to calculate and evaluate the performance indicators of model accuracy, precision, and recall.

7. The meteorological information forecasting and processing platform using artificial intelligence according to claim 1, characterized in that: The real-time prediction module comprises: A real-time data input unit is used to input the meteorological data collected in real time into the trained model for prediction; Real-time prediction calculation unit: used to perform real-time prediction calculation and generate prediction results.

8. The meteorological information forecasting and processing platform using artificial intelligence according to claim 1, characterized in that: The result display and early warning module includes: Data visualization unit, used to display the prediction results in the form of charts and maps; Warning generation unit: used to generate extreme weather warning information based on forecast results; User interface unit: used to provide a user-friendly interface to display forecast results and warning information.

9. The meteorological information forecasting and processing platform using artificial intelligence according to claim 1, characterized in that: The continuous learning and model updating module includes: Data update unit, used to collect and integrate the latest meteorological data to ensure the timeliness and comprehensiveness of model training data; Model retraining unit, which retrains the existing model using the latest data to update the model parameters and structure; Model performance monitoring unit, which is used to continuously monitor the prediction performance of the model in actual applications and record the accuracy, precision, and recall indicators of the model; User feedback collection unit, used to collect user feedback on prediction results, analyze user needs and model performance differences as a reference for model updates; The model version management unit is used to manage different versions of models, save and record the model version information of each update, so as to facilitate the backtracking and comparative analysis of the model.

10. A method for forecasting and processing meteorological information using artificial intelligence, according to a meteorological information forecasting and processing platform using artificial intelligence according to any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: collect meteorological data from multiple data sources through a data collection module, and pre-process the collected meteorological data through a data pre-processing module; Step 2: The feature engineering module extracts and constructs features from the preprocessed data; Step 3: Use machine learning and deep learning models to train the extracted and constructed features through the model training module; Step 4: Verify and evaluate the performance of the training model through the model verification and evaluation module; Step 5: Input the meteorological data collected in real time into the trained model through the real-time prediction module to perform real-time weather prediction, and display the prediction results through the data visualization unit through the result display and warning module, and generate extreme weather warning information through the warning generation unit, and provide the user interface unit to display it to the user; Step 6. Finally, use the continuous learning and model updating module to regularly update the model and adjust the model parameters to maintain the accuracy of the prediction.

Images