Artificial intelligence meteorological data processing system and method

Abstract

The invention discloses an artificial intelligence meteorological data processing system and method, and aims to improve the accuracy and real-time performance of meteorological prediction. The system comprises a data acquisition module, a data preprocessing module, a data storage module, an artificial intelligence analysis module, a prediction output module, a feedback learning module and an anomaly detection module. Through real-time acquisition and fusion of multi-source data, the system performs cleaning, noise reduction, interpolation and format conversion on meteorological data, and performs modeling and prediction by using a long-short-term memory network. The system adopts a Bayesian optimization and adaptive learning rate adjustment mechanism to ensure the precision and stability of the model. And the anomaly detection module is used for identifying extreme weather events and continuously optimizing model parameters through a feedback learning mechanism. The prediction result is visualized in a graphical manner, and an alarm is generated when an abnormality is detected. The method is suitable for the fields of weather forecast, agriculture, traffic and the like, and has wide application value.

Description

Technical Field

[0001] The present invention relates to the technical field of meteorological data processing, and in particular to an artificial intelligence meteorological data processing system and method. Background Art

[0002] With the intensification of global climate change and the frequent occurrence of extreme weather events, weather forecasting has become increasingly important. Accurate weather forecasts are not only crucial to daily life, but also have a significant impact on industries such as agriculture, aviation, and energy. However, traditional weather forecasting methods have many limitations and cannot meet the requirements of modern society for high accuracy and real-time performance.

[0003] Traditional meteorological data processing methods mainly rely on numerical weather prediction (NWP) models, which are based on atmospheric physics laws, such as fluid mechanics equations and thermodynamics equations, combined with observational data to make weather forecasts. Although these models have made great progress in the past few decades, they still have the following major problems:

[0004] Limited data sources: Traditional weather forecasts usually rely on fixed weather stations and satellite data, which have limited coverage and spatial and temporal resolution and cannot fully capture subtle changes in the atmosphere. Moreover, data collection and processing often take a long time and are difficult to update in real time.

[0005] Complexity and high computing resource requirements: Numerical weather forecast models involve solving complex partial differential equations, which require very high computing resources, especially for long-term or large-scale weather forecasts. This high computational effort limits the frequency and accuracy of forecasts, especially in short-term weather forecasts, where the real-time nature of forecast results is severely affected.

[0006] Model error accumulation: Traditional numerical weather forecast models are prone to error accumulation in practical applications. Since the model is very sensitive to initial conditions, any small error in the observed data will be magnified during the forecasting process, resulting in a gradual increase in the deviation of the forecast results.

[0007] Unable to fully utilize big data and multi-source data: With the development of science and technology, the sources of meteorological data have become increasingly diverse. In addition to traditional weather station and satellite data, they also include radar data, ocean monitoring data, drone data, and meteorological information in social media. However, traditional meteorological data processing systems are often unable to effectively integrate and utilize these multi-source data, resulting in a waste of data resources.

[0008] In response to the above problems, in recent years, artificial intelligence (AI) technology, especially deep learning technology, has attracted widespread attention in the application of meteorological data processing and forecasting. AI technology can automatically learn complex nonlinear relationships from large-scale data and update the forecast model in real time, which has significant advantages:

[0009] Multi-source data fusion and processing: AI systems can process and fuse meteorological data from different sources and in different formats, improve data utilization, and process massive amounts of data in a short time.

[0010] Automatic modeling and real-time prediction: AI technology can automatically build prediction models and continuously optimize model parameters through adaptive learning to achieve high-precision, real-time weather forecasts.

[0011] Anomaly detection and feedback mechanism: The AI ​​system can automatically detect anomalies in the forecast and adjust the model through a feedback mechanism to improve forecast accuracy and the ability to respond to extreme weather events.

[0012] Based on the above background, the present invention proposes a meteorological data processing system and method based on artificial intelligence technology, aiming to solve the problems existing in traditional meteorological data processing methods, make full use of modern data processing technology, and improve the accuracy, real-time and comprehensiveness of meteorological forecasts. By integrating multi-source data, applying deep learning models and real-time feedback mechanisms, the present invention can significantly improve the accuracy of meteorological forecasts and the ability to respond to extreme weather events, and has broad application prospects. Summary of the invention

[0013] The purpose of the present invention is to provide an artificial intelligence meteorological data processing system and method to solve the problems raised by the above background technology.

[0014] To achieve the above object, the present invention provides the following technical solution: an artificial intelligence meteorological data processing method, comprising the following steps:

[0015] Data collection: The data collection module collects meteorological data in real time from multiple meteorological data sources (including satellite data, ground meteorological station data, meteorological radar data, etc.);

[0016] Data preprocessing: The data preprocessing module is used to clean, reduce noise and interpolate the collected meteorological data. The characteristic of the data preprocessing module is that the data interpolation adopts the following algorithm:

[0017] When x i When missing:

[0018]

[0019] Among them, x i is the interpolated data, x i-1 and xi+1 are the data values ​​at adjacent time points. This step ensures the continuity and integrity of the data and provides a complete data set for subsequent modeling;

[0020] Data storage: The preprocessed meteorological data is stored in the distributed data storage module, using a storage strategy based on hash distribution. The storage formula is:

[0021] H(k)=kmodN

[0022] Among them, H(k) is the hash function, k is the data key value, and N is the number of storage nodes; this step ensures efficient access to large-scale data.

[0023] As a preferred technical solution of the present invention, the following steps are further included: Data fusion: After the data collection step, a multi-source data fusion algorithm is used to fuse data from different meteorological data sources, and the fusion formula is:

[0024]

[0025] Among them, D f is the fused data set, D i is the data of the ith data source, w i is the weight coefficient of the corresponding data source, satisfying

[0026] As a preferred technical solution of the present invention, the method further comprises the following steps to predict meteorological data:

[0027] Artificial intelligence modeling and prediction: Based on the preprocessed and fused meteorological data, the long short-term memory (LSTM) network is used to model the meteorological time series data. The prediction formula is as follows:

[0028]

[0029] in, is the predicted meteorological data value, σ is the activation function, W h and W x are the weight matrices of the network, h t is the current hidden state, x t is the input data, and b is the bias term. This step captures the patterns and trends in the time series through the LSTM model.

[0030] As a preferred technical solution of the present invention, the prediction step also includes:

[0031] Model optimization: A model adjustment method based on Bayesian optimization is used, and its optimization objective function is:

[0032]

[0033] Among them, L(θ) is the loss function, y t is the actual observed data, is the model prediction data, θ is the model parameter, and λ is the regularization coefficient. This step improves the prediction accuracy by adjusting the model parameters.

[0034] As a preferred technical solution of the present invention, the following steps are further included: Adaptive learning rate adjustment: During the model optimization process, the learning rate is adaptively adjusted, and the adjustment formula is:

[0035]

[0036] Among them, α t is the learning rate of the tth iteration, β is the learning rate decay factor, and t is the current iteration number.

[0037] As a preferred technical solution of the present invention, the following steps are further included:

[0038] Anomaly detection: After the meteorological data forecast results are generated, the anomaly detection module is used to detect anomalies in the forecast results. The detection formula is:

[0039]

[0040] Among them, E t is the abnormal score at the tth time point, σ y is the standard deviation of the actual observed data, if E t If the threshold is exceeded, it is considered abnormal. This step is used to identify potential extreme weather conditions.

[0041] As a preferred technical solution of the present invention, the following steps are further included:

[0042] Feedback learning: According to the anomaly detection results, the artificial intelligence model is adjusted through the feedback learning module to reduce the error in future predictions. The reinforcement learning strategy is adopted, and its strategy function is:

[0043]

[0044] Among them, π(a|s) is the probability of selecting action a in state s, and Q(s,a) is the Q value of state s and action a, which represents the long-term reward of performing action a in state s. Feedback learning enables the model to continuously adapt to new data and improve prediction performance.

[0045] As a preferred technical solution of the present invention, the following steps are further included: Alarm generation: After abnormality detection, when an abnormality is detected, the system automatically generates alarm information and notifies through the user terminal. The judgment condition for alarm triggering is:

[0046]

[0047] Among them, Threshold is the preset abnormal threshold, E t is the abnormal score, if E t If the threshold is exceeded, an alarm is generated. The visualization and alarm functions provide users with intuitive prediction information and warnings of extreme situations.

[0048] The present invention also provides an artificial intelligence meteorological data processing system, comprising a data acquisition module, a data preprocessing module, a data storage module, a data fusion module, an artificial intelligence analysis module, a feedback learning module, an anomaly detection module, a prediction output module and an alarm module, characterized in that:

[0049] The data acquisition module is used to collect meteorological data from multiple meteorological data sources in real time;

[0050] The data preprocessing module is used to clean, reduce noise, interpolate and convert the format of the collected meteorological data to ensure the integrity and continuity of the data;

[0051] The data storage module adopts distributed storage technology to store pre-processed meteorological data and supports efficient access operations;

[0052] The data fusion module is used to fuse meteorological data from different sources into a unified data set to improve the overall quality of the data;

[0053] The AI ​​analysis module uses a long short-term memory (LSTM) network to model and predict meteorological data, and uses Bayesian optimization and adaptive learning rate methods to improve the model's prediction accuracy;

[0054] The feedback learning module combines the anomaly detection results and uses a reinforcement learning strategy to dynamically adjust the model parameters;

[0055] The anomaly detection module is used to identify anomalies in the prediction results;

[0056] The forecast output module is used to visualize the weather forecast results and output them to the user terminal;

[0057] The alarm module generates alarm information and notifies the user when an abnormality is detected.

[0058] As a preferred technical solution of the present invention, the system further comprises:

[0059] Adaptive learning module, which is used to dynamically adjust the learning rate of the model according to the feedback learning results to improve the training efficiency and prediction performance of the model;

[0060] The graphics generation unit is used to generate various forms of forecast visualization charts, such as line charts, heat maps, etc., and provide a user-friendly interface for users to query and analyze.

[0061] Compared with the prior art, the beneficial effects of the present invention are as follows: the meteorological data processing system and method based on artificial intelligence technology provided by the present invention have significant beneficial effects. First, by introducing multi-source data fusion technology, the system can integrate multi-dimensional information from different meteorological data sources, such as satellite data, ground meteorological station data, and radar data. This fusion effectively improves the integrity and spatiotemporal coverage of the data, providing richer and more accurate inputs for the prediction model. At the same time, the system adopts advanced deep learning algorithms, especially long short-term memory (LSTM) networks, which can automatically learn complex time series patterns from a large amount of historical meteorological data, greatly improving the accuracy of weather forecasts. Bayesian optimization and adaptive learning rate adjustment mechanisms ensure that the model maintains stability and efficiency during continuous updating and optimization.

[0062] Secondly, the present invention realizes early warning of extreme weather events and dynamic adjustment of the model through feedback learning and anomaly detection mechanisms. The anomaly detection module can automatically identify abnormal conditions in the prediction results, such as heavy rain, heat waves, etc., and timely adjust the model parameters through the feedback learning mechanism to further improve the accuracy of the prediction and the ability to cope with extreme weather. In addition, the prediction results are intuitively displayed through the visualization module, and an alarm message is generated when an anomaly is detected to help users take timely countermeasures. The combination of these functions makes the present invention have a wide range of application value and significant practicality in the fields of weather forecasting, agriculture, transportation and disaster warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is a method flow chart of the artificial intelligence meteorological data processing system and method proposed by the present invention. DETAILED DESCRIPTION

[0064] 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.

[0065] Example 1: Meteorological data collection and preprocessing

[0066] Data collection:

[0067] In this embodiment, the system collects meteorological data from multiple data sources, including satellite remote sensing data, ground weather station data, ocean monitoring data, and weather radar data. These data sources provide multi-dimensional meteorological information such as temperature, humidity, wind speed, wind direction, rainfall, and air pressure.

[0068] The data acquisition module connects to various data sources through an API interface, captures the latest meteorological data at regular intervals (such as once an hour), and synchronizes data from different data sources to ensure that data at the same time point can be effectively integrated.

[0069] Data preprocessing:

[0070] In the data preprocessing stage, the collected data are first cleaned to remove redundant information and obviously erroneous data points (such as values ​​that exceed physical possibility).

[0071] Noise filtering is then performed, and the data is smoothed using methods such as moving average filtering or Kalman filtering to reduce the impact of random fluctuations on the prediction model.

[0072] For missing data points, the system uses linear interpolation to fill them. The specific formula is as follows:

[0073]

[0074] This maintains data continuity and provides a complete data set for subsequent modeling.

[0075] Example 2: Data storage and integration

[0076] Data Storage:

[0077] After processing the meteorological data, the system uses a distributed database (such as Hadoop HDFS or Amazon S3) to store it. In order to ensure fast access and high availability of data, the system uses a hash function for data distribution. The specific formula is as follows:

[0078] H(k)=kmodN

[0079] Where k is the data key value and N is the number of storage nodes. This strategy ensures balanced distribution of large-scale data and avoids overloading of a single node.

[0080] Data Fusion:

[0081] In this embodiment, the system fuses data from multiple meteorological data sources. The weighted average method is used to integrate data from different sources to reduce the deviation that may be introduced by a single data source. The fusion formula is as follows:

[0082]

[0083] Among them, D i is the data of the ith data source, w i is the weight coefficient. The weight can be dynamically adjusted based on the historical accuracy or reliability of the data source to improve the overall accuracy of the data.

[0084] Example 3: Artificial Intelligence Modeling and Prediction

[0085] LSTM model training:

[0086] In this embodiment, the preprocessed and fused data is input into the LSTM (Long Short-Term Memory) network for training. The LSTM network can effectively capture the time series characteristics in the meteorological data and predict future meteorological conditions. The system divides the historical weather data into a training set and a validation set, performs model training on the training set, and optimizes the performance of the model by adjusting the hyperparameters of the LSTM network (such as the number of layers and the number of hidden units).

[0087] The prediction formula is as follows:

[0088]

[0089] in, is the predicted value at the future moment, σ is the activation function, W h and W x are the weight matrices for hidden states and inputs, respectively.

[0090] Model optimization and adaptive learning:

[0091] The system uses the Bayesian optimization method to adjust the parameters of the model to minimize the prediction error. The optimization objective function is:

[0092]

[0093] Among them, L(θ) is the loss function and λ is the regularization term. The system also applies an adaptive learning rate adjustment mechanism to ensure that the model can converge stably during the training process. The specific formula is:

[0094]

[0095] Among them, β is the learning rate decay factor.

[0096] Example 4: Anomaly Detection and Feedback Learning

[0097] Anomaly Detection:

[0098] In this embodiment, after generating the forecast results, the system will use the anomaly detection module to detect anomalies. The main purpose of anomaly detection is to identify possible extreme weather events, such as heavy rain or heat waves. The system uses standardized residuals to measure anomalies. The specific formula is as follows:

[0099]

[0100] Among them, E t is the abnormality score, σ y is the standard deviation of the actual observed data. If the anomaly score exceeds the preset threshold, the system will mark the prediction as abnormal.

[0101] Feedback Learning:

[0102] If an anomaly is detected, the system triggers a feedback learning mechanism to adjust the model through a reinforcement learning strategy to reduce errors in future predictions. The system's reinforcement learning strategy is based on a soft max function, and the strategy function is:

[0103]

[0104] Where π(a|s) is the probability of selecting action a in state s, and Q(s,a) is the corresponding action value function. Feedback learning continuously adjusts the model to adapt to changing weather patterns.

[0105] Example 5: Visualization of prediction results and generation of alarms

[0106] Visualization of prediction results:

[0107] Alarm generation:

[0108] The weather forecast results generated by the system can be displayed in the form of charts through the graphics generation unit, such as line charts, bar charts, heat maps, etc. Users can view weather change trends in different time ranges through the user interface to facilitate decision-making.

[0109] During the visualization process, the system also provides interactive functions, allowing users to select different data dimensions for analysis, such as temperature, rainfall, etc.

[0110] When the system detects an anomaly, the alarm module generates an alarm message and notifies the user through channels such as SMS, email or mobile application. The alarm is triggered based on the following conditions:

[0111]

[0112] Among them, Threshold is the preset abnormal threshold. This function can timely warn relevant departments or individuals to take countermeasures before extreme weather events occur.

[0113] The above embodiments demonstrate the specific implementation of the meteorological data processing system and method based on artificial intelligence technology. Through these embodiments, the system can efficiently collect, process and analyze multi-source meteorological data, generate high-precision weather forecasts, and warn of potential extreme weather events. These embodiments are highly practical and extensible and can be widely used in weather forecasting, agriculture, transportation and other fields.

[0114] Through these embodiments, the practical application and superiority of the deep peak-shaving system and method of the heating unit coupled with molten salt energy storage in different application scenarios can be fully demonstrated. Each embodiment describes in detail the composition, operation principle and control method of the system to ensure that readers can clearly understand the practical application and technical advantages of the present invention.

[0115] The above embodiments are specific implementations of the present invention, and those skilled in the relevant technical field can make different changes and modifications based on the content of the present invention without departing from the spirit and scope of the present invention. Therefore, all equivalent implementations that do not depart from the spirit and scope of the present invention should be included in the protection scope of the present invention.

[0116] The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in the field. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An artificial intelligence meteorological data processing method, characterized in that: The following steps are involved: Data collection: The data collection module collects meteorological data from multiple meteorological data sources in real time; Multiple meteorological data sources include satellite data, ground weather station data, and weather radar data; Data preprocessing: Use the data preprocessing module to clean, reduce noise and interpolate the collected meteorological data. The data interpolation uses the following algorithm: When x i When missing: Among them, x i is the interpolated data, x i-1 and x i+1 are the data values ​​at adjacent time points respectively; Data storage: The preprocessed meteorological data is stored in the distributed data storage module, using a storage strategy based on hash distribution. The storage formula is: H(k)=k mod N Among them, H(k) is the hash function, k is the data key value, and N is the number of storage nodes.

2. The artificial intelligence meteorological data processing method according to claim 1, characterized in that: Further comprising the steps of: Data fusion: After the data collection step, the multi-source data fusion algorithm is used to fuse the data from different meteorological data sources. The fusion formula is: Among them, D f is the fused data set, D i is the data of the ith data source, w i is the weight coefficient of the corresponding data source, satisfying 3. The artificial intelligence meteorological data processing method according to claim 1, characterized in that: Further comprising, using the following steps to predict meteorological data: Artificial intelligence modeling and prediction: Based on the preprocessed and fused meteorological data, the meteorological time series data is modeled using a long short-term memory network. The prediction formula is as follows: in, is the predicted meteorological data value, σ is the activation function, W h and W x are the weight matrices of the network, h t is the current hidden state, x t is the input data, and b is the bias term.

4. The artificial intelligence meteorological data processing method according to claim 3 is characterized in that: The prediction step also includes: Model optimization: The model adjustment method based on Bayesian optimization is adopted, and its optimization objective function is: Among them, L(θ) is the loss function, y t is the actual observation data, is the model prediction data, θ is the model parameter, and λ is the regularization coefficient.

5. The artificial intelligence meteorological data processing method according to claim 4, characterized in that: Further comprising the steps of: Adaptive learning rate adjustment: During the model optimization process, the learning rate is adaptively adjusted. The adjustment formula is: Among them, α t is the learning rate of the tth iteration, β is the learning rate attenuation factor, and t is the current iteration number.

6. The artificial intelligence meteorological data processing method according to claim 3 is characterized in that: Further comprising the steps of: Anomaly detection: After the meteorological data forecast results are generated, the anomaly detection module is used to detect anomalies in the forecast results. The detection formula is: Among them, E t is the abnormal score at the tth time point, σ y is the standard deviation of the actual observed data, if E t If it exceeds the set threshold, it is judged as abnormal.

7. The artificial intelligence meteorological data processing method according to claim 6, characterized in that: Further comprising the steps of: Feedback learning: Based on the anomaly detection results, the artificial intelligence model is adjusted through the feedback learning module to reduce the error in future predictions. The reinforcement learning strategy is adopted, and its strategy function is: Among them, π(a|s) is the probability of selecting action a in state s, and Q(s,a) is the Q value of state s and action a, which represents the long-term reward of performing action a in state s.

8. The artificial intelligence meteorological data processing method according to claim 6, characterized in that: Further comprising the following steps: Alarm generation: After anomaly detection, when an anomaly is detected, the system automatically generates an alarm message and notifies through the user terminal. The judgment conditions for alarm triggering are: Among them, Threshold is the preset abnormal threshold, E t is the abnormal score, if E t If the threshold is exceeded, an alarm is generated.

9. An artificial intelligence meteorological data processing system, comprising a data acquisition module, a data preprocessing module, a data storage module, a data fusion module, an artificial intelligence analysis module, a feedback learning module, an anomaly detection module, a prediction output module and an alarm module, characterized in that: The data acquisition module is used to collect meteorological data from multiple meteorological data sources in real time; The data preprocessing module is used to clean, reduce noise, interpolate and convert the format of the collected meteorological data to ensure the integrity and continuity of the data; The data storage module adopts distributed storage technology to store pre-processed meteorological data and supports efficient access operations; The data fusion module is used to fuse meteorological data from different sources into a unified data set to improve the overall quality of the data; The artificial intelligence analysis module uses long short-term memory networks to model and predict meteorological data, and uses Bayesian optimization and adaptive learning rate methods to improve the prediction accuracy of the model; The feedback learning module combines the anomaly detection results and uses a reinforcement learning strategy to dynamically adjust the model parameters; The anomaly detection module is used to identify anomalies in the prediction results; The forecast output module is used to visualize the weather forecast results and output them to the user terminal; The alarm module generates alarm information and notifies the user when an abnormality is detected.

10. The artificial intelligence meteorological data processing system according to claim 9, characterized in that: The system further comprises: Adaptive learning module, which is used to dynamically adjust the learning rate of the model according to the feedback learning results to improve the training efficiency and prediction performance of the model; The graphics generation unit is used to generate various forms of forecast visualization charts, such as line charts and heat maps, and provide a user-friendly interface for user query and analysis.

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