High-precision detection and identification system for atmospheric gravity waves in South China Sea region

Abstract

The invention relates to the field of atmospheric gravity wave detection, in particular to a high-precision detection and recognition system for atmospheric gravity waves in a South China Sea region. The technical problems that in the prior art, an atmospheric gravity wave detection and recognition system is often difficult to accurately recognize weak signals of atmospheric gravity waves and is prone to being affected by noise and other interference factors, and when large-scale data processing is conducted, the processing efficiency is low, and consumed time is long often exist are solved. According to the technical scheme, the high-precision detection and identification system for the atmospheric gravity waves in the South China Sea region comprises a data acquisition module, an identification analysis module, a real-time feedback module and a visual display module, according to the method, artificial intelligence technologies such as deep learning and machine learning are utilized, the characteristics of the atmospheric gravity waves can be identified from complex data more accurately, misjudgment and missed judgment are reduced, data can be processed and analyzed immediately after being generated through real-time online data processing, and timely support is provided for forecasting and early warning.

Description

Technical Field

[0001] The present invention relates to the field of atmospheric gravity wave detection, and in particular to a high-precision atmospheric gravity wave detection and identification system in the South China Sea region. Background Art

[0002] The high-precision detection and identification system for atmospheric gravity waves in the South China Sea region is a comprehensive system that integrates a variety of detection methods and data processing technologies. It aims to achieve high-precision detection, identification and analysis of gravity waves in the atmosphere of the South China Sea region. The system may include ground observation stations, high-altitude detection equipment (such as lidar, weather balloons, etc.), satellite remote sensing data receiving and processing platforms and other components. It integrates a variety of advanced detection methods and data processing technologies, and provides strong support for atmospheric science research, weather forecasts, climate forecasts and marine meteorological services in the South China Sea region. However, the atmospheric gravity wave detection and identification system under the existing technology often finds it difficult to accurately identify the weak signals of atmospheric gravity waves, is easily affected by noise and other interference factors, and often has problems of low processing efficiency and long time consumption when performing large-scale data processing. Summary of the invention

[0003] In order to overcome the problems that the atmospheric gravity wave detection and identification systems under existing technologies often find it difficult to accurately identify the weak signals of atmospheric gravity waves, are easily affected by noise and other interference factors, and often have low processing efficiency and long time consumption when performing large-scale data processing.

[0004] The technical solution of the present invention is: a high-precision detection and identification system for atmospheric gravity waves in the South China Sea region, comprising:

[0005] The data acquisition module is responsible for receiving the raw data from various detection devices and performing data cleaning, data compression and time synchronization processing on the raw data;

[0006] The recognition and analysis module is used to apply deep learning models and machine learning algorithms to perform feature extraction, atmospheric gravity wave recognition, classification, and parameter estimation on preprocessed data;

[0007] Real-time feedback module, used to realize real-time data processing and instant feedback of results, including real-time monitoring of atmospheric gravity waves, generation and release of early warning information;

[0008] The visualization module is used to display the analysis results in the form of intuitive charts and images, supports multi-perspective and multi-level data exploration, and provides an interactive interface for researchers and decision makers.

[0009] Preferably, the data acquisition module is responsible for receiving the raw data from various detection devices, and performing data cleaning, data compression and time synchronization processing on the raw data; the recognition and analysis module applies deep learning models and machine learning algorithms to perform feature extraction, atmospheric gravity wave recognition, classification and parameter estimation on the pre-processed data; the real-time feedback module realizes real-time data processing and instant feedback of results, including real-time monitoring of atmospheric gravity waves, generation and release of early warning information; the visualization display module displays the analysis results in the form of intuitive charts and images, supports multi-perspective and multi-level data exploration, and provides an interactive interface for researchers and decision makers.

[0010] Preferably, the data acquisition module includes a detection equipment group and a preprocessing unit; the detection equipment group includes a lidar, a satellite remote sensing device and an environmental sensor, and the lidar, the satellite remote sensing device and the environmental sensor are used to collect various environmental data, including raw data of atmospheric temperature, humidity, wind speed and wind direction meteorological information; the preprocessing unit uses data cleaning to remove noise and outliers from the raw data, uses data compression technology to reduce the burden of data storage and transmission, and uses time synchronization technology to ensure the time consistency of multi-source data. The preprocessing unit integrates an adaptive filtering algorithm, dynamically adjusts the filtering parameters according to the data characteristics, and optimizes the preprocessing effect.

[0011] Preferably, the data acquisition module comprises the following steps when working:

[0012] S101: The data acquisition module receives raw data streams from laser radar, satellite remote sensing equipment and environmental sensors;

[0013] S102: Perform preliminary verification on the received data to check the integrity, format correctness and validity of the timestamp of the data;

[0014] S103: setting initial parameters of the filter, including filter order and cutoff frequency; determining parameter adjustment strategy, including gradient descent method and least square method;

[0015] S104: inputting the raw data to be processed into the filter;

[0016] S105: Apply the filter to denoise the data, use the parameter adjustment strategy to calculate new filter parameters, and update the filter state. The filter parameters will be dynamically adjusted according to the characteristics of the input data;

[0017] S106: Outputting filtered data, i.e., data with noise and outliers removed;

[0018] S107: Selecting the LZ4 compression algorithm to compress the cleaned data to reduce the requirements of storage space and transmission bandwidth;

[0019] S108: using a timestamp calibration technology to align data from different devices to the same time reference, and performing time synchronization processing on data from different detection devices;

[0020] S109: Use a distributed database to store data.

[0021] Preferably, the recognition and analysis module uses a convolutional neural network model and a cluster analysis algorithm to automatically extract features, identify atmospheric gravity waves, classify and estimate parameters of the preprocessed data. Through transfer learning technology, a pre-trained model containing known atmospheric gravity wave events is used to accelerate the convergence speed of the atmospheric gravity wave recognition model.

[0022] Preferably, when the recognition and analysis module is working, it includes the following steps:

[0023] S201: receiving cleaned, compressed and time-synchronized multivariate meteorological data from a data acquisition module;

[0024] S202: Convert meteorological data into a format suitable for CNN model processing;

[0025] S203: Construct a CNN architecture according to task requirements, including an input layer, multiple convolutional layers, a fully connected layer, and an output layer;

[0026] S204: Perform sliding window operation on the input data through multiple convolution kernels to extract local features; each convolution layer is followed by a nonlinear activation function ReLU and a pooling layer;

[0027] S205: After multiple convolution and pooling layers, a feature map containing high-level abstract features is generated;

[0028] S206: Select and further extract features from the original data or the feature map extracted by CNN to construct a feature set for clustering;

[0029] S207: Standardize or normalize the selected features;

[0030] S208: Use known atmospheric gravity wave events to conduct supervised training of the CNN model, and adjust the model parameters through the back-propagation algorithm to minimize the prediction error;

[0031] S209: Under unsupervised or weakly supervised conditions, apply the clustering algorithm K-means to perform cluster analysis on the feature set to identify different atmospheric gravity wave patterns or categories;

[0032] S210: Optimizing the performance of CNN models and clustering algorithms through cross-validation and hyperparameter tuning;

[0033] S211: Automatically extract and classify input data using the trained CNN model to identify atmospheric gravity wave events;

[0034] S212: For the identified atmospheric gravity wave events, estimate their wave velocity, wavelength, and amplitude parameters based on the results of cluster analysis;

[0035] S213: Output recognition and analysis results.

[0036] As a preferred option, the real-time feedback module adopts the stream processing technology Apache Kafka to ensure the real-time data processing, adopts the parallel computing framework to improve the processing speed, and builds a distributed processing architecture; through the intelligent early warning algorithm, historical data and current trends are combined to predict the impact range and intensity of atmospheric gravity waves, and realize real-time data processing and instant feedback of results, including real-time monitoring of atmospheric gravity waves, generation and release of early warning information.

[0037] Preferably, the real-time feedback module comprises the following steps when working:

[0038] S301: Using Kafka as a message queue, data is received through the Apache Kafka stream processing platform in real time from various detection devices.

[0039] S302: Load the pre-trained deep learning model, use GPU acceleration to forward propagate the input data, and obtain the recognition results and parameter estimation values ​​of atmospheric gravity waves; use parallel processing algorithms to process data in parallel using multiple GPUs or multiple CPU resources;

[0040] S303: Integrate the output data on atmospheric gravity waves with relevant data in the historical database;

[0041] S304: Write Python scripts or use data preprocessing libraries to extract and construct features; use Pandas' groupby and transform methods to calculate seasonal change features, and use NumPy's array operations to calculate similar events, seasonal changes, and geographic location features in historical data;

[0042] S305: extracting similar event features, seasonal variation features, and geographic location features in historical data useful for predicting future trends of atmospheric gravity waves from the integrated data;

[0043] S306: Select the GBDT implementation library GradientBoostingRegressor, set its initial parameters, and use the training set data to call the model's fit method for training;

[0044] S307: Use the test set data to call the predict method of the model for prediction and calculate the prediction error; adjust the model parameters according to the evaluation results, increase the number of trees, and adjust the learning rate;

[0045] S308: Using the GBDT model to analyze the influence of features on the prediction results;

[0046] S309: Setting warning thresholds for atmospheric gravity waves based on predicted values ​​of amplitude, frequency, and propagation speed parameters. When the predicted results exceed these thresholds, the system will trigger a warning mechanism.

[0047] S310: When the prediction result of the GBDT model triggers the warning condition, the system generates warning information using the prediction result of the GBDT model and the warning threshold as input according to the preset format and template;

[0048] S311: Use the communication interface to send the warning information to the target user.

[0049] Preferably, the visualization display module includes a visualization unit and an interaction unit; the visualization unit is used to display the analysis results in the form of intuitive charts and images, supporting multi-perspective and multi-level data exploration; the interaction unit provides an interactive interface for researchers and decision makers; augmented reality and virtual reality technologies are used to provide researchers with data analysis experience; and user queries are responded to through intelligent question and answer through natural language processing technology to perform information retrieval and knowledge transfer.

[0050] Beneficial effects of the present invention:

[0051] Compared with the existing atmospheric gravity wave detection and identification systems, which often find it difficult to accurately identify the weak signals of atmospheric gravity waves and are easily affected by noise and other interference factors, and often have problems of low processing efficiency and long time consumption when performing large-scale data processing; this system uses artificial intelligence technologies such as deep learning and machine learning to more accurately identify the characteristics of atmospheric gravity waves from complex data, reduce misjudgments and missed judgments, and through real-time online data processing, it can process and analyze data immediately after it is generated, providing timely support for forecasts and warnings. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 What is shown is a schematic diagram of the working process of the high-precision detection and identification system of atmospheric gravity waves in the South China Sea region of the present invention;

[0053] Figure 2 What is shown is a schematic diagram of the working steps of the identification and analysis module of the South China Sea region atmospheric gravity wave high-precision detection and identification system of the present invention. DETAILED DESCRIPTION

[0054] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0055] See also Figure 1-2 The present invention provides an embodiment: a high-precision detection and identification system for atmospheric gravity waves in the South China Sea region, comprising:

[0056] The data acquisition module is responsible for receiving the raw data from various detection devices and performing data cleaning, data compression and time synchronization processing on the raw data;

[0057] The recognition and analysis module is used to apply deep learning models and machine learning algorithms to perform feature extraction, atmospheric gravity wave recognition, classification, and parameter estimation on preprocessed data;

[0058] Real-time feedback module, used to realize real-time data processing and instant feedback of results, including real-time monitoring of atmospheric gravity waves, generation and release of early warning information;

[0059] The visualization module is used to display the analysis results in the form of intuitive charts and images, supports multi-perspective and multi-level data exploration, and provides an interactive interface for researchers and decision makers.

[0060] Preferably, the data acquisition module is responsible for receiving the raw data from various detection devices, and performing data cleaning, data compression and time synchronization processing on the raw data; the recognition and analysis module applies deep learning models and machine learning algorithms to perform feature extraction, atmospheric gravity wave recognition, classification and parameter estimation on the pre-processed data; the real-time feedback module realizes real-time data processing and instant feedback of results, including real-time monitoring of atmospheric gravity waves, generation and release of early warning information; the visualization display module displays the analysis results in the form of intuitive charts and images, supports multi-perspective and multi-level data exploration, and provides an interactive interface for researchers and decision makers.

[0061] Preferably, the data acquisition module includes a detection equipment group and a preprocessing unit; the detection equipment group includes a lidar, a satellite remote sensing device and an environmental sensor, and the lidar, the satellite remote sensing device and the environmental sensor are used to collect various environmental data, including raw data of atmospheric temperature, humidity, wind speed and wind direction meteorological information; the preprocessing unit uses data cleaning to remove noise and outliers from the raw data, uses data compression technology to reduce the burden of data storage and transmission, and uses time synchronization technology to ensure the time consistency of multi-source data. The preprocessing unit integrates an adaptive filtering algorithm, dynamically adjusts the filtering parameters according to the data characteristics, and optimizes the preprocessing effect.

[0062] Preferably, the data acquisition module comprises the following steps when working:

[0063] S101: The data acquisition module receives raw data streams from laser radar, satellite remote sensing equipment and environmental sensors;

[0064] S102: Perform preliminary verification on the received data to check the integrity, format correctness and validity of the timestamp of the data;

[0065] S103: setting initial parameters of the filter, including filter order and cutoff frequency; determining parameter adjustment strategy, including gradient descent method and least square method;

[0066] S104: inputting the raw data to be processed into the filter;

[0067] S105: Apply the filter to denoise the data, use the parameter adjustment strategy to calculate new filter parameters, and update the filter state. The filter parameters will be dynamically adjusted according to the characteristics of the input data;

[0068] S106: Outputting filtered data, i.e., data with noise and outliers removed;

[0069] S107: Selecting the LZ4 compression algorithm to compress the cleaned data to reduce the requirements of storage space and transmission bandwidth;

[0070] S108: using a timestamp calibration technology to align data from different devices to the same time reference, and performing time synchronization processing on data from different detection devices;

[0071] S109: Use a distributed database to store data.

[0072] Preferably, the recognition and analysis module uses a convolutional neural network model and a cluster analysis algorithm to automatically extract features, identify atmospheric gravity waves, classify and estimate parameters of the preprocessed data. Through transfer learning technology, a pre-trained model containing known atmospheric gravity wave events is used to accelerate the convergence speed of the atmospheric gravity wave recognition model.

[0073] Preferably, when the recognition and analysis module is working, it includes the following steps:

[0074] S201: receiving cleaned, compressed and time-synchronized multivariate meteorological data from a data acquisition module;

[0075] S202: Convert meteorological data into a format suitable for CNN model processing;

[0076] S203: Construct a CNN architecture according to task requirements, including an input layer, multiple convolutional layers, a fully connected layer, and an output layer;

[0077] S204: Perform sliding window operation on the input data through multiple convolution kernels to extract local features; each convolution layer is followed by a nonlinear activation function ReLU and a pooling layer;

[0078] S205: After multiple convolution and pooling layers, a feature map containing high-level abstract features is generated;

[0079] S206: Select and further extract features from the original data or the feature map extracted by CNN to construct a feature set for clustering;

[0080] S207: Standardize or normalize the selected features;

[0081] S208: Use known atmospheric gravity wave events to conduct supervised training of the CNN model, and adjust the model parameters through the back-propagation algorithm to minimize the prediction error;

[0082] S209: Under unsupervised or weakly supervised conditions, apply the clustering algorithm K-means to perform cluster analysis on the feature set to identify different atmospheric gravity wave patterns or categories;

[0083] S210: Optimizing the performance of CNN models and clustering algorithms through cross-validation and hyperparameter tuning;

[0084] S211: Automatically extract and classify input data using the trained CNN model to identify atmospheric gravity wave events;

[0085] S212: For the identified atmospheric gravity wave events, estimate their wave velocity, wavelength, and amplitude parameters based on the results of cluster analysis;

[0086] S213: Output recognition and analysis results.

[0087] As a preferred option, the real-time feedback module adopts the stream processing technology Apache Kafka to ensure the real-time data processing, adopts the parallel computing framework to improve the processing speed, and builds a distributed processing architecture; through the intelligent early warning algorithm, historical data and current trends are combined to predict the impact range and intensity of atmospheric gravity waves, and realize real-time data processing and instant feedback of results, including real-time monitoring of atmospheric gravity waves, generation and release of early warning information.

[0088] Preferably, the real-time feedback module comprises the following steps when working:

[0089] S301: Using Kafka as a message queue, data is received through the Apache Kafka stream processing platform in real time from various detection devices.

[0090] S302: Load the pre-trained deep learning model, use GPU acceleration to forward propagate the input data, and obtain the recognition results and parameter estimation values ​​of atmospheric gravity waves; use parallel processing algorithms to process data in parallel using multiple GPUs or multiple CPU resources;

[0091] S303: Integrate the output data on atmospheric gravity waves with relevant data in the historical database;

[0092] S304: Write Python scripts or use data preprocessing libraries to extract and construct features; use Pandas' groupby and transform methods to calculate seasonal change features, and use NumPy's array operations to calculate similar events, seasonal changes, and geographic location features in historical data;

[0093] S305: extracting similar event features, seasonal variation features, and geographic location features in historical data useful for predicting future trends of atmospheric gravity waves from the integrated data;

[0094] S306: Select the GBDT implementation library GradientBoostingRegressor, set its initial parameters, and use the training set data to call the model's fit method for training;

[0095] S307: Use the test set data to call the predict method of the model for prediction and calculate the prediction error; adjust the model parameters according to the evaluation results, increase the number of trees, and adjust the learning rate;

[0096] S308: Using the GBDT model to analyze the influence of features on the prediction results;

[0097] S309: Setting warning thresholds for atmospheric gravity waves based on predicted values ​​of amplitude, frequency, and propagation speed parameters. When the predicted results exceed these thresholds, the system will trigger a warning mechanism.

[0098] S310: When the prediction result of the GBDT model triggers the warning condition, the system generates warning information using the prediction result of the GBDT model and the warning threshold as input according to the preset format and template;

[0099] S311: Use the communication interface to send the warning information to the target user.

[0100] Preferably, the visualization display module includes a visualization unit and an interaction unit; the visualization unit is used to display the analysis results in the form of intuitive charts and images, supporting multi-perspective and multi-level data exploration; the interaction unit provides an interactive interface for researchers and decision makers; augmented reality and virtual reality technologies are used to provide researchers with data analysis experience; and user queries are responded to through intelligent question and answer through natural language processing technology to perform information retrieval and knowledge transfer.

[0101] Preferably, when the visual display module is working, it includes the following steps:

[0102] S401: receiving data processed by the intelligent recognition and analysis module, including recognition results and parameter estimation of atmospheric gravity waves;

[0103] S402: Check whether the received data is complete and in a correct format, and convert the received data into a format suitable for visual display, such as JSON or CSV;

[0104] S404: Design a visualization scheme based on data characteristics and select appropriate chart types and layouts;

[0105] S405: Analyze the statistical characteristics of the data, such as maximum value, minimum value, average value, etc., to determine the details of the visualization solution;

[0106] S406: Select an appropriate chart type such as a line chart, a scatter chart, or a heat map according to the data type and display requirements;

[0107] S407: Use the data visualization library D3.js to encode the designed visualization solution;

[0108] S408: Initialize the visualization canvas according to the browser window size or the specified container size;

[0109] S409: Binding the processed data to chart elements, such as coordinate axes and data points;

[0110] S410: Add interactive elements such as buttons, sliders, and selection boxes, and write event processing functions to respond to user operations.

[0111] Preferably, when the visual display module is working, it also includes the following steps:

[0112] S411: Use web front-end technologies HTML5, CSS3 and JavaScript to develop user interactive interfaces and integrate visual charts into the interfaces;

[0113] S412: Build the overall layout of the page, including the navigation bar, sidebar, and content area;

[0114] S413: Integrate the visualization chart as a component into the page content area;

[0115] S414: Write the jump logic between pages and the interaction logic between components to ensure smooth user operation;

[0116] S415: Use the performance testing tool LoadRunner to perform stress testing on the interaction unit;

[0117] S416: Analyze performance bottlenecks such as network delay and data processing speed based on the test results;

[0118] S417: Collect user feedback, and iteratively optimize the visualization display module based on the user feedback.

[0119] Example

[0120] Optionally, when performing data recognition and analysis, a new set of South China Sea regional meteorological data is received, which has been preprocessed and converted into a format suitable for CNN model input; the input data is as follows:

[0121] Time frame: one week;

[0122] Spatial scope: a specific area in the South China Sea;

[0123] Data dimension: four-dimensional tensor (time, altitude, longitude, latitude), each dimension contains temperature, humidity, wind speed (u component, v component), and air pressure parameters;

[0124] The data is fed into the trained CNN model, which automatically performs multi-layer convolution and pooling operations to extract features related to atmospheric gravity waves. Features may include but are not limited to: areas with significant temperature gradient changes, stratification with sudden increases in wind speed, and pressure fluctuation patterns;

[0125] The fully connected layer and the Softmax layer convert the extracted features into probability distribution, output the probability that each data point belongs to a different atmospheric gravity wave category, set a threshold, and when the probability of a category exceeds the threshold, the data point is considered to belong to that type of atmospheric gravity wave event;

[0126] From the features extracted by the CNN model, parameters such as wave speed, wavelength, and amplitude are selected to estimate the temperature gradient and periodic variation characteristics of wind speed in a specific layer; based on the results of K-means clustering analysis, the identified atmospheric gravity wave events are grouped, and each group represents atmospheric gravity waves generated by different sources or with different propagation characteristics;

[0127] Combined with wind speed data and gravity wave propagation direction information, the wave velocity (V) is calculated by measuring the distance that atmospheric gravity waves propagate in a specific direction within a fixed time period; the wavelength (λ) is estimated by using the distance between spatially adjacent wave crests or troughs; the amplitude (A) is estimated by calculating half of the difference between the maximum and minimum values ​​of the wave parameters; for each identified atmospheric gravity wave event, its wave velocity, wavelength, and amplitude parameters are estimated, and the association between these parameters and the clustering results is recorded;

[0128] The identification and analysis results are output in a structured form, including: the type of atmospheric gravity waves (such as terrain waves, convection waves, etc., based on clustering results), occurrence location (latitude and longitude, altitude), occurrence time (specific time point or time period), estimated wave speed, wavelength, and amplitude parameters.

[0129] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge scope of those skilled in the art without departing from the spirit of the present invention.

Claims

1. A high-precision detection and identification system for atmospheric gravity waves in the South China Sea region, characterized by: Included are: The data acquisition module is responsible for receiving the raw data from various detection devices and performing data cleaning, data compression and time synchronization processing on the raw data; The recognition and analysis module is used to apply deep learning models and machine learning algorithms to perform feature extraction, atmospheric gravity wave recognition, classification, and parameter estimation on preprocessed data; Real-time feedback module, used to realize real-time data processing and instant feedback of results, including real-time monitoring of atmospheric gravity waves, generation and release of early warning information; The visualization display module is used to display the analysis results in the form of intuitive charts and images, supports multi-perspective and multi-level data exploration, and provides an interactive interface for researchers and decision makers.

2. The high-precision detection and identification system for atmospheric gravity waves in the South China Sea region according to claim 1 is characterized in that: The data acquisition module includes a detection equipment group and a preprocessing unit; the detection equipment group includes lidar, satellite remote sensing equipment and environmental sensors, which are used to collect various environmental data, including raw data of atmospheric temperature, humidity, wind speed and wind direction meteorological information; the preprocessing unit uses data cleaning to remove noise and outliers from the raw data, uses data compression technology to reduce the burden of data storage and transmission, and uses time synchronization technology to ensure the time consistency of multi-source data. The preprocessing unit integrates an adaptive filtering algorithm, dynamically adjusts the filtering parameters according to the data characteristics, and optimizes the preprocessing effect.

3. The high-precision detection and identification system for atmospheric gravity waves in the South China Sea region according to claim 2 is characterized in that: When the data acquisition module is working, it includes the following steps: S101: The data acquisition module receives raw data streams from laser radar, satellite remote sensing equipment and environmental sensors; S102: Perform preliminary verification on the received data to check the integrity, format correctness and validity of the timestamp of the data; S103: setting initial parameters of the filter, including filter order and cutoff frequency; determining parameter adjustment strategy, including gradient descent method and least square method; S104: inputting the raw data to be processed into the filter; S105: Apply the filter to denoise the data, use the parameter adjustment strategy to calculate new filter parameters, and update the filter state. The filter parameters will be dynamically adjusted according to the characteristics of the input data; S106: Output the filtered data; S107: Select LZ4 compression algorithm to compress the cleaned data; S108: using a timestamp calibration technology to align data from different devices to the same time reference, and performing time synchronization processing on data from different detection devices; S109: Use a distributed database to store data.

4. The high-precision detection and identification system for atmospheric gravity waves in the South China Sea region according to claim 3 is characterized by: The recognition and analysis module uses a convolutional neural network model and cluster analysis algorithm to automatically extract features, identify atmospheric gravity waves, classify and estimate parameters of preprocessed data. Through transfer learning technology, a pre-trained model containing known atmospheric gravity wave events is used to accelerate the convergence speed of the atmospheric gravity wave recognition model.

5. The high-precision detection and identification system for atmospheric gravity waves in the South China Sea region according to claim 4 is characterized in that: When the recognition and analysis module is working, the following steps are included: S201: receiving cleaned, compressed and time-synchronized multivariate meteorological data from a data acquisition module; S202: Convert meteorological data into a format suitable for CNN model processing; S203: Construct a CNN architecture according to task requirements, including an input layer, multiple convolutional layers, a fully connected layer, and an output layer; S204: Perform sliding window operation on the input data through multiple convolution kernels to extract local features; each convolution layer is followed by a nonlinear activation function ReLU and a pooling layer; S205: After multiple convolution and pooling layers, a feature map containing high-level abstract features is generated; S206: Select and further extract features from the original data or the feature map extracted by CNN to construct a feature set for clustering; S207: Standardize or normalize the selected features; S208: Use known atmospheric gravity wave events to conduct supervised training of the CNN model, and adjust the model parameters through the back-propagation algorithm to minimize the prediction error; S209: Under unsupervised or weakly supervised conditions, apply the clustering algorithm K-means to perform cluster analysis on the feature set to identify different atmospheric gravity wave patterns or categories; S210: Optimizing the performance of CNN models and clustering algorithms through cross-validation and hyperparameter tuning; S211: Automatically extract and classify input data using the trained CNN model to identify atmospheric gravity wave events; S212: For the identified atmospheric gravity wave events, estimate their wave velocity, wavelength, and amplitude parameters based on the results of cluster analysis; S213: Output recognition and analysis results.

6. The high-precision detection and identification system for atmospheric gravity waves in the South China Sea region according to claim 5 is characterized in that: The real-time feedback module uses stream processing technology Apache Kafka to ensure real-time data processing, uses a parallel computing framework to increase processing speed, and builds a distributed processing architecture; Through intelligent early warning algorithms, historical data and current trends are combined to predict the impact range and intensity of atmospheric gravity waves, and realize real-time data processing and instant feedback of results, including real-time monitoring of atmospheric gravity waves, generation and release of early warning information.

7. The high-precision detection and identification system for atmospheric gravity waves in the South China Sea region according to claim 6 is characterized by: When the real-time feedback module is working, it includes the following steps: S301: Using Kafka as a message queue, data is received through the Apache Kafka stream processing platform in real time from various detection devices. S302: Load the pre-trained deep learning model, use GPU acceleration to forward propagate the input data, and obtain the recognition results and parameter estimation values ​​of atmospheric gravity waves; use parallel processing algorithms to process data in parallel using multiple GPUs or multiple CPU resources; S303: Integrate the output data on atmospheric gravity waves with relevant data in the historical database; S304: Write Python scripts or use data preprocessing libraries to extract and construct features; use Pandas' groupby and transform methods to calculate seasonal change features, and use NumPy's array operations to calculate similar events, seasonal changes, and geographic location features in historical data; S305: extracting similar event features, seasonal variation features, and geographic location features in historical data useful for predicting future trends of atmospheric gravity waves from the integrated data; S306: Select the GBDT implementation library GradientBoostingRegressor, set its initial parameters, and use the training set data to call the model's fit method for training; S307: Use the test set data to call the predict method of the model for prediction and calculate the prediction error; adjust the model parameters according to the evaluation results, increase the number of trees, and adjust the learning rate; S308: Using the GBDT model to analyze the influence of features on the prediction results; S309: Setting warning thresholds for atmospheric gravity waves based on predicted values ​​of amplitude, frequency, and propagation speed parameters. When the predicted results exceed these thresholds, the system will trigger a warning mechanism. S310: When the prediction result of the GBDT model triggers the warning condition, the system generates warning information using the prediction result of the GBDT model and the warning threshold as input according to the preset format and template; S311: Use the communication interface to send the warning information to the target user.

8. The high-precision detection and identification system for atmospheric gravity waves in the South China Sea region according to claim 7 is characterized in that: The visualization display module includes a visualization unit and an interaction unit; the visualization unit is used to display the analysis results in the form of intuitive charts and images, supporting multi-perspective and multi-level data exploration; the interaction unit provides an interactive interface for researchers and decision makers; it uses augmented reality and virtual reality technologies to provide researchers with data analysis experience; it responds to user queries through intelligent question and answer through natural language processing technology, and conducts information retrieval and knowledge transfer.

9. The high-precision detection and identification system for atmospheric gravity waves in the South China Sea region according to claim 8, characterized in that: When the visualization module is working, it includes the following steps: S401: receiving data processed by the recognition and analysis module, including recognition results and parameter estimation of atmospheric gravity waves; S402: Check whether the received data is complete and in a correct format, and convert the received data into a JSON or CSV format suitable for visual display; S404: Design a visualization scheme based on data characteristics and select appropriate chart types and layouts; S405: Analyze the statistical characteristics of the data, including maximum value, minimum value and average value; S406: Select a chart type, such as line chart, scatter chart, or heat map, according to the data type and display requirements; S407: Use the data visualization library D3.js to encode the designed visualization solution; S408: Initialize the visualization canvas according to the browser window size or the specified container size; S409: Binding the processed data to the coordinate axes and data points on the chart elements; S410: Add interactive elements such as buttons, sliders, and selection boxes, and write event processing functions to respond to user operations.

10. The high-precision detection and identification system for atmospheric gravity waves in the South China Sea region according to claim 9, characterized in that: When the visualization module is working, the following steps are also included: S411: Use web front-end technologies HTML5, CSS3 and JavaScript to develop user interactive interfaces and integrate visual charts into the interfaces; S412: Build the overall layout of the page, including the navigation bar, sidebar, and content area; S413: Integrate the visualization chart as a component into the page content area; S414: Write the jump logic between pages and the interaction logic between components to ensure smooth user operation; S415: Use the performance testing tool LoadRunner to perform stress testing on the interaction unit; S416: Analyze network delay and data processing speed according to the test results; S417: Collect user feedback, and iteratively optimize the visualization display module based on the user feedback.

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