A dual linear polarization Doppler weather radar simulation practice data acquisition and analysis method

Abstract

The application provides a kind of dual linear polarization Doppler weather radar simulation practice data acquisition and analysis method.The dual linear polarization Doppler weather radar simulation practice data acquisition and analysis method includes a.data source identification: identify and register the diversified data sources from different types of learning, training, assessment and simulation, b.data acquisition: data acquisition from multiple data sources in a timely or real-time manner, ensuring data integrity and consistency, the data acquisition step includes: data extraction from different sensors and data sources in a timely or real-time manner;Support processing large amount of data extraction.The dual linear polarization Doppler weather radar simulation practice data acquisition and analysis method solves the problem of radar signal interference by environmental noise and blind area or dead angle.Through accurate data acquisition and processing, environmental noise can be effectively filtered out, the quality and accuracy of the signal are improved, thereby avoiding the common blind area and signal attenuation problem in traditional radar simulation.

Description

Technical Field

[0001] This invention relates to the field of weather radar simulation technology, specifically to a method for data acquisition and analysis in dual-linear polarization Doppler weather radar simulation practice. Background Technology

[0002] Dual-polarization Doppler weather radar is a radar system capable of simultaneously transmitting and receiving radar signals in two polarization directions, primarily used for weather observation and meteorological analysis. Compared to traditional single-polarization radar, dual-polarization radar provides more information about the morphology, size, density, and distribution of precipitation particles, thus helping to more accurately predict weather and monitor weather phenomena. Data acquisition for dual-polarization Doppler radar mainly includes the following steps: signal transmission and reception, echo signal processing, Doppler effect analysis, and data fusion and processing.

[0003] Although dual-polarization Doppler weather radar offers high accuracy in meteorological monitoring, it still faces some challenges and limitations in practical applications: radar signals can be affected by environmental noise and electronic interference, resulting in poor data quality. Due to structural and installation limitations, radar systems may have blind spots or dead zones, preventing the comprehensive acquisition of weather information from all directions. Furthermore, the high-frequency, large-volume radar data requires substantial storage space and high-speed transmission channels, which traditional data storage and transmission methods may struggle to meet real-time processing demands. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method for acquiring and analyzing simulation training data from a dual-polarization Doppler weather radar, which solves the problems of radar signals being affected by environmental noise, having blind spots or dead zones, and the difficulty of traditional data storage and transmission methods meeting real-time processing requirements.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for acquiring and analyzing simulation training data from a dual-linear polarization Doppler weather radar, comprising: a. Data source identification: Identify and register diverse data sources from different types of learning, training, assessment, and simulation; b. Data Acquisition: Timely or real-time data acquisition from multiple data sources to ensure data integrity and consistency; c. Data preprocessing: Clean, format, and standardize the collected raw data, and apply data encryption and user authentication mechanisms to ensure the security of data transmission; d. Data Loading: Loads preprocessed data into the specified storage system, supporting multiple data storage formats, including relational databases, NoSQL databases, and data warehouses; e. Data Management: Clean and integrate the loaded data, and ensure data consistency across different data sources. Ensure the efficiency of data query, modification, deletion, and export functions through unified operation management. f. Data Analysis: Applying statistical analysis techniques to conduct in-depth analysis of stored data, identifying patterns and trends within the data; g. Results evaluation: Evaluate the analytical results to verify their accuracy and reliability, and ensure the validity of the analytical results; h. Insight Extraction: Extracting valuable insights and suggestions from the analysis results to support teaching and decision-making; i. Data visualization: Visualizing analysis results through charts and dashboards to help decision-makers understand the meaning of the data; j. Decision support: Data-driven insights support decision-making in analyzing trainee training results and predicting behavior; k. Action Execution: Implement optimization plans based on the analysis results, such as adjusting training plans and raising assessment requirements; l. Feedback mechanism: Feedback is provided to the data collection stage based on the data analysis results to improve data collection strategies and methods, forming a closed loop of data processing and optimization.

[0006] Preferably, the data acquisition step includes: Extract data from different sensors and data sources at regular intervals or in real time; It supports the extraction of large amounts of data and ensures data consistency. Data security is ensured by encrypting the transmitted data using a proprietary encryption algorithm.

[0007] Preferably, the data management steps include: Data cleaning: removing duplicate and erroneous information, filling in missing values, and ensuring data quality; Data integration: Merging data from different sources to create a consistent data view; Data storage: Data is stored using a storage system that supports fast access.

[0008] Preferably, the data analysis steps include: Statistical analysis methods are used to analyze data and discover potential trends and patterns. Machine learning algorithms can be used to further uncover patterns in the data and optimize training and assessment methods.

[0009] Preferably, the data visualization step includes: Presenting data analysis results in the form of charts and dashboards makes it easier for instructors to make decisions. The data visualization displays support interactive operations, allowing users to customize report and data display styles.

[0010] Preferably, the feedback mechanism includes: Optimize data collection strategies based on analysis results to further improve data quality and accuracy; Regularly evaluate data collection and analysis methods to improve the efficiency of data processing workflows.

[0011] Preferably, the data management module includes: Data storage module: responsible for storing the cleaned and integrated data in a designated database or data warehouse; Data query module: Supports efficient query operations, allowing users to retrieve, modify, or export data according to their needs.

[0012] Preferably, the data preprocessing module includes: Data formatting: Ensure that the data is in a consistent format to suit subsequent analysis operations; Data standardization: Converting data from different sources into a unified standard format.

[0013] Preferably, the data loading module supports relational databases, NoSQL databases, and data warehouses, and the data acquisition module supports real-time data stream processing to ensure real-time performance and data timeliness.

[0014] Preferably, the training data evaluation module includes: Training performance evaluation: Evaluate trainee performance based on changes in their training results; Chart Display: The training progress and results of trainees are displayed through various chart formats.

[0015] This invention provides a method for acquiring and analyzing simulated weather radar data from a dual-polarization Doppler weather radar training exercise. It offers the following advantages: This dual-polarization Doppler weather radar simulation training data acquisition and analysis method solves the problems of radar signal interference from environmental noise and blind spots or dead zones. Through precise data acquisition and processing, environmental noise can be effectively filtered out, improving signal quality and accuracy, thereby avoiding the blind spots and signal attenuation problems common in traditional radar simulations. Furthermore, traditional data storage and transmission methods often fail to meet real-time data processing requirements. This invention employs technology that supports large-volume data extraction and real-time data stream processing, enabling efficient processing and storage of massive amounts of acquired data. This ensures that the system can perform data analysis efficiently and in real-time, greatly improving the processing capabilities of simulation training data.

[0016] It provides powerful decision support capabilities. By applying statistical analysis techniques and machine learning algorithms, this invention can deeply uncover potential patterns in data and generate visual reports based on the analysis results, helping instructors to evaluate trainees' training progress and achievements in real time. Through charts and dashboards, decision-makers can intuitively understand the meaning of the data and make more scientific and personalized adjustments to training and assessment. Furthermore, the data feedback mechanism can dynamically adjust the data collection strategy based on the analysis results, forming a continuous optimization loop that ensures continuous improvement in data quality and system efficiency. Simultaneously, encrypted data transmission and user authentication mechanisms guarantee data security and legitimacy, effectively preventing the risks of data leakage and illegal use. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the structure of the present invention; Figure 2 This is a schematic diagram of the data flow of the present invention; Figure 3 This is a schematic diagram of the system principle of the present invention; Figure 4 This is a flowchart of the present invention; Figure 5 This is a flowchart of the data management process for the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] like Figure 1-5 As shown, this embodiment of the invention provides a method for acquiring and analyzing simulation training data from a dual-polarization Doppler weather radar, including: a. Data source identification: identifying and registering diverse data sources from different types of learning, training, assessment, and simulation.

[0020] b. Data Acquisition: Data is collected periodically or in real-time from multiple data sources to ensure data integrity and consistency. Data acquisition steps include: Extract data from different sensors and data sources at regular intervals or in real time; It supports the extraction of large amounts of data and ensures data consistency. Data security is ensured by encrypting the transmitted data using a proprietary encryption algorithm.

[0021] c. Data Preprocessing: The collected raw data is cleaned, formatted, and standardized. Data encryption and user authentication mechanisms are applied to ensure the security of data transmission. The data preprocessing module includes: Data formatting: Ensure that the data is in a consistent format to suit subsequent analysis operations; Data standardization: Converting data from different sources into a unified standard format.

[0022] d. Data Loading: Loads preprocessed data into the specified storage system, supporting multiple data storage formats, including relational databases, NoSQL databases, and data warehouses. The data loading module supports relational databases, NoSQL databases, and data warehouses, while the data acquisition module supports real-time data stream processing to ensure real-time performance and data timeliness.

[0023] e. Data Management: Clean and integrate the loaded data, ensuring data consistency across different data sources. Through unified operation management, ensure the efficiency of data query, modification, deletion, and export functions. Data management steps include: Data cleaning: removing duplicate and erroneous information, filling in missing values, and ensuring data quality; Data integration: Merging data from different sources to create a consistent data view; Data storage: Data is stored using a storage system that supports fast access.

[0024] The data management module includes: Data storage module: responsible for storing the cleaned and integrated data in a designated database or data warehouse; Data query module: Supports efficient query operations, allowing users to retrieve, modify, or export data according to their needs.

[0025] f. Data Analysis: Applying statistical analysis techniques to conduct in-depth analysis of stored data, identifying patterns and trends within the data. Data analysis steps include: Statistical analysis methods are used to analyze data and discover potential trends and patterns. Machine learning algorithms can be used to further uncover patterns in the data and optimize training and assessment methods.

[0026] g. Results Evaluation: Evaluate the analysis results to verify their accuracy and reliability and ensure their validity. This includes the internship data collection module, the internship data management module, and the internship data evaluation module.

[0027] h. Insight Extraction: Extract valuable insights and suggestions from the analysis results to support teaching and decision-making.

[0028] i. Data Visualization: Visualizing analysis results through charts and dashboards to help decision-makers understand the meaning of the data. Data visualization steps include: Presenting data analysis results in the form of charts and dashboards makes it easier for instructors to make decisions. The data visualization displays support interactive operations, allowing users to customize report and data display styles.

[0029] j. Decision support: Data-driven insights support decision-making in analyzing trainee training results and predicting behavior.

[0030] k. Action Execution: Implement optimization plans based on the analysis results, such as adjusting training plans and raising assessment requirements.

[0031] 1. Feedback Mechanism: Based on the data analysis results, feedback is sent to the data collection stage to improve data collection strategies and methods, forming a closed loop of data processing and optimization. The feedback mechanism includes: Optimize data collection strategies based on analysis results to further improve data quality and accuracy; Regularly evaluate data collection and analysis methods to improve the efficiency of data processing workflows.

[0032] The training data evaluation module includes: Training performance evaluation: Evaluate trainee performance based on changes in their training results; Chart Display: The training progress and results of trainees are displayed through various chart formats.

[0033] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for acquiring and analyzing simulation training data from a dual-linear polarization Doppler weather radar, characterized in that, include: a. Data source identification: Identify and register diverse data sources from different types of learning, training, assessment, and simulation; b. Data Acquisition: Timely or real-time data acquisition from multiple data sources to ensure data integrity and consistency; c. Data preprocessing: Clean, format, and standardize the collected raw data, and apply data encryption and user authentication mechanisms to ensure the security of data transmission; d. Data Loading: Loads preprocessed data into the specified storage system, supporting multiple data storage formats, including relational databases, NoSQL databases, and data warehouses; e. Data Management: Clean and integrate the loaded data, and ensure data consistency across different data sources. Ensure the efficiency of data query, modification, deletion, and export functions through unified operation management. f. Data Analysis: Applying statistical analysis techniques to conduct in-depth analysis of stored data, identifying patterns and trends within the data; g. Results Evaluation: Evaluate the analysis results to verify their accuracy and reliability and ensure their validity. This includes the internship data collection module, the internship data management module, and the internship data evaluation module. h. Insight Extraction: Extracting valuable insights and suggestions from the analysis results to support teaching and decision-making; i. Data visualization: Visualizing analysis results through charts and dashboards to help decision-makers understand the meaning of the data; j. Decision support: Data-driven insights support decision-making in analyzing trainee training results and predicting behavior; k. Action Execution: Implement optimization plans based on the analysis results, such as adjusting training plans and raising assessment requirements; l. Feedback mechanism: Feedback is provided to the data collection stage based on the data analysis results to improve data collection strategies and methods, forming a closed loop of data processing and optimization.

2. The method for acquiring and analyzing simulated weather radar data according to claim 1, characterized in that: The data acquisition steps include: Extract data from different sensors and data sources at regular intervals or in real time; It supports the extraction of large amounts of data and ensures data consistency. Data security is ensured by encrypting the transmitted data using a proprietary encryption algorithm.

3. The method for acquiring and analyzing simulation training data from a dual-linear polarization Doppler weather radar according to claim 1, characterized in that: The data management steps include: Data cleaning: removing duplicate and erroneous information, filling in missing values, and ensuring data quality; Data integration: Merging data from different sources to create a consistent data view; Data storage: Data is stored using a storage system that supports fast access.

4. The method for acquiring and analyzing simulation training data from a dual-linear polarization Doppler weather radar according to claim 1, characterized in that: The data analysis steps include: Statistical analysis methods are used to analyze data and discover potential trends and patterns. Machine learning algorithms can be used to further uncover patterns in the data and optimize training and assessment methods.

5. The method for acquiring and analyzing simulated weather radar data according to claim 1, characterized in that: The data visualization steps include: Presenting data analysis results in the form of charts and dashboards makes it easier for instructors to make decisions. The data visualization displays support interactive operations, allowing users to customize report and data display styles.

6. The method for acquiring and analyzing simulation training data from a dual-linear polarization Doppler weather radar according to claim 1, characterized in that: The feedback mechanism includes: Optimize data collection strategies based on analysis results to further improve data quality and accuracy; Regularly evaluate data collection and analysis methods to improve the efficiency of data processing workflows.

7. The method for acquiring and analyzing simulation training data from a dual-linear polarization Doppler weather radar according to claim 1, characterized in that: The data management module includes: Data storage module: responsible for storing the cleaned and integrated data in a designated database or data warehouse; Data query module: Supports efficient query operations, allowing users to retrieve, modify, or export data according to their needs.

8. The method for acquiring and analyzing simulated weather radar data according to claim 1, characterized in that: The data preprocessing module includes: Data formatting: Ensure that the data is in a consistent format to suit subsequent analysis operations; Data standardization: Converting data from different sources into a unified standard format.

9. The method for acquiring and analyzing simulation training data from a dual-linear polarization Doppler weather radar according to claim 1, characterized in that: The data loading module supports relational databases, NoSQL databases, and data warehouses, while the data acquisition module supports real-time data stream processing to ensure real-time performance and data timeliness.

10. The method for acquiring and analyzing simulation training data from a dual-linear polarization Doppler weather radar according to claim 1, characterized in that: The training data evaluation module includes: Training performance evaluation: Evaluate trainee performance based on changes in their training results; Chart Display: The training progress and results of trainees are displayed through various chart formats.

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