A method and system for establishing an aircraft aerodynamic model database

Abstract

The present invention relates to the technical field of database establishment, and particularly to a method and system for establishing an aircraft aerodynamic model database. The method includes the following steps: obtaining the storage requirements and query requirements of aircraft aerodynamic data and designing a multi-level storage architecture to obtain a basic data layer, a processed data layer, and a high-level model data layer; collecting the original aircraft aerodynamic data sets from wind tunnel experiments, flight tests, and CFD numerical simulations and transmitting and storing them in the basic data layer; analyzing the aerodynamic loads, flow field parameters, and aerodynamic pressure distributions of the original aircraft aerodynamic data sets in the basic data layer and transmitting and storing them in the processed data layer; constructing an aircraft aerodynamic model for the data in the processed data layer and transmitting and storing it in the high-level model data layer, and performing an R-tree multi-dimensional index connection to obtain a multi-level storage database of the aircraft aerodynamic model. The present invention can establish an efficient and intelligent aerodynamic model database.

Description

Technical Field

[0001] The present invention relates to the technical field of database establishment, and particularly to a method and system for establishing an aircraft aerodynamic model database. Background Art

[0002] In aerospace engineering, the accurate prediction and optimization of aircraft aerodynamic performance are crucial for design, safety, and performance improvement. In the prior art, the development of aircraft aerodynamic models mostly relies on a process of individual verification and optimization, which often requires a large amount of experimental data and computing resources. By establishing a comprehensive aerodynamic model database, aerodynamic data of different aircraft under various flight conditions can be systematically collected, stored, and managed. The establishment of such a database involves multiple key steps. First, aerodynamic data under different flight conditions are defined and collected, including speed, angle, airflow characteristics, etc., and these data are analyzed using data mining and machine learning techniques to establish an aerodynamic model with wide applicability. Then, the relevant data of the aerodynamic model are stored and updated through a database management system to ensure the integrity and real-time nature of the aerodynamic data. However, traditional methods for establishing aerodynamic model databases are usually based on a single model or specific flight states, and such models cannot cover all flight conditions and configurations in practical applications, thus limiting the specific process of updating and maintaining the aerodynamic model. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide a method and system for establishing an aircraft aerodynamic model database to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for establishing an aircraft aerodynamic model database includes the following steps:

[0005] Step S1: Obtain the storage requirements and query requirements of aircraft aerodynamic data, and design a multi-level storage architecture according to the storage requirements and query requirements of aircraft aerodynamic data to obtain an aircraft aerodynamic multi-level database storage architecture, where the aircraft aerodynamic multi-level database storage architecture includes a basic data layer, a processed data layer, and a high-level model data layer; collect the original aircraft aerodynamic data sets from wind tunnel experiments, flight tests, and CFD numerical simulations, and transmit and store them in the basic data layer within the aircraft aerodynamic multi-level database storage architecture;

[0006] Step S2: Analyze the original aircraft aerodynamic data sets in the basic data layer for aerodynamic loads, flow field parameters, and aerodynamic pressure distribution to obtain aircraft aerodynamic load data, aircraft aerodynamic flow field parameter data, and aircraft aerodynamic pressure distribution data, and transmit and store them in the processed data layer within the aircraft aerodynamic multi-level database storage architecture;

[0007] Step S3: Construct an aircraft aerodynamic model for the aircraft aerodynamic load data, aircraft aerodynamic flow field parameter data, and aircraft aerodynamic pressure distribution data within the processed data layer to generate an aircraft aerodynamic model dataset, and transmit and store it in the high-level model data layer within the aircraft aerodynamic multi-level database storage architecture;

[0008] Step S4: Perform an R-tree multi-dimensional index connection on the data index relationships within the basic data layer, processed data layer, and high-level model data layer to obtain an aircraft aerodynamic model multi-level storage database.

[0009] Furthermore, Step S1 includes the following steps:

[0010] Step S11: Obtain the aircraft aerodynamic data storage requirements and aircraft aerodynamic data query requirements;

[0011] Step S12: Design a multi-level storage architecture based on the aircraft aerodynamic data storage requirements and aircraft aerodynamic data query requirements to obtain an aircraft aerodynamic multi-level database storage architecture, where the aircraft aerodynamic multi-level database storage architecture includes a basic data layer, a processed data layer, and a high-level model data layer;

[0012] Step S13: Collect the original aircraft wind tunnel experiment dataset, original aircraft flight test dataset, and original aircraft CFD simulation dataset from wind tunnel experiments, flight tests, and CFD numerical simulations;

[0013] Step S14: Perform data integration and analysis on the original aircraft wind tunnel experiment dataset, original aircraft flight test dataset, and original aircraft CFD simulation dataset to obtain an original aircraft aerodynamic dataset, and transmit and store it in the basic data layer within the aircraft aerodynamic multi-level database storage architecture.

[0014] Furthermore, Step S12 includes the following steps:

[0015] Step S121: Conduct a multi-level storage target analysis based on the aircraft aerodynamic data storage requirements and aircraft aerodynamic data query requirements to obtain the aircraft aerodynamic data immediate storage target, aircraft aerodynamic intermediate data processing and storage target, and aircraft aerodynamic model construction storage target;

[0016] Step S122: Design the basic data layer based on the aircraft aerodynamic data immediate storage target to obtain the basic data layer;

[0017] Step S123: Design the processed data layer based on the aircraft aerodynamic intermediate data processing and storage target to obtain the processed data layer;

[0018] Step S124: Design the high-level model data layer according to the storage objectives of the aircraft aerodynamic model to obtain the high-level model data layer;

[0019] Step S125: Integrate the basic data layer, the processed data layer, and the high-level model data layer through multi-level architecture design to obtain the multi-level database storage architecture for aircraft aerodynamics.

[0020] Further, step S2 includes the following steps:

[0021] Step S21: Perform data preprocessing and standardization on the original aircraft aerodynamic data set in the basic data layer to obtain the standardized aircraft aerodynamic data set;

[0022] Step S22: Perform relational database storage formatting conversion on the standardized aircraft aerodynamic data set to obtain the formatted aircraft aerodynamic storage data set;

[0023] Step S23: Perform aerodynamic load analysis on the formatted aircraft aerodynamic storage data set to obtain the aircraft aerodynamic load data;

[0024] Step S24: Perform analysis on the aerodynamic flow field parameters and aerodynamic pressure distribution of the formatted aircraft aerodynamic storage data set to obtain the aircraft aerodynamic flow field parameter data and the aircraft aerodynamic pressure distribution data;

[0025] Step S25: Transmit and store the aircraft aerodynamic load data, the aircraft aerodynamic flow field parameter data, and the aircraft aerodynamic pressure distribution data into the processed data layer in the multi-level database storage architecture for aircraft aerodynamics.

[0026] Further, step S23 includes the following steps:

[0027] Step S231: Extract and process the aerodynamic lift and drag parameters of the formatted aircraft aerodynamic storage data set to obtain the aircraft aerodynamic lift parameter data and the aircraft aerodynamic drag parameter data;

[0028] Step S232: Perform time series synchronization processing on the aircraft aerodynamic lift parameter data and the aircraft aerodynamic drag parameter data to obtain the aircraft aerodynamic lift time series data and the aircraft aerodynamic drag time series data in the same time series dimension;

[0029] Step S233: Perform aerodynamic parametric simulation design on the aircraft aerodynamic lift time series data and the aircraft aerodynamic drag time series data in the same time series dimension to generate the aircraft aerodynamic parametric simulation operation field;

[0030] Step S234: Sample the parameter changes frame by frame for the aircraft aerodynamic parametric simulation operation field to obtain the aerodynamic lift change value and the aerodynamic drag change value at each time frame in the aircraft aerodynamic simulation field; perform frame-by-frame transient aerodynamic load quantization calculation on the aircraft aerodynamic parametric simulation operation field based on the aerodynamic lift change value and the aerodynamic drag change value at each time frame in the aircraft aerodynamic simulation field to obtain the aircraft frame-by-frame transient aerodynamic load data;

[0031] Step S235: Conduct aerodynamic load trend analysis on the aircraft frame-by-frame transient aerodynamic load data to obtain the aircraft aerodynamic load change trend data; perform aerodynamic load dynamic fluctuation analysis on the aircraft frame-by-frame transient aerodynamic load data based on the aircraft aerodynamic load change trend data to obtain the aircraft dynamic aerodynamic load fluctuation change data;

[0032] Step S236: Merge the aircraft frame-by-frame transient aerodynamic load data and the aircraft dynamic aerodynamic load fluctuation change data to obtain the aircraft aerodynamic load data.

[0033] Furthermore, the frame-by-frame transient aerodynamic load quantization calculation in Step S234 is performed through the aircraft frame-by-frame transient aerodynamic load calculation formula, where the aircraft frame-by-frame transient aerodynamic load calculation formula is specifically:

[0034]

[0035] In the formula, ε(t) is the transient aerodynamic load of the aircraft at time frame t, t is the time frame variable parameter, ρ is the gas density, C L (α(t), V(t)) is the aerodynamic lift change value in the aircraft aerodynamic simulation field at time frame t, V(t) is the instantaneous velocity of the aircraft at time frame t, γ is the aircraft speed adjustment coefficient, α(t) is the aircraft aerodynamic lift angle of attack at time frame t, is the maximum aerodynamic lift coefficient of the aircraft, α0 is the aircraft aerodynamic zero-lift angle of attack, α max is the maximum aerodynamic lift angle of attack of the aircraft, C D (β(t), V(t)) is the aerodynamic drag change value in the aircraft aerodynamic simulation field at time frame t, β(t) is the aircraft aerodynamic drag angle of depression at time frame t, is the maximum aerodynamic drag coefficient of the aircraft, β0 is the aircraft aerodynamic zero-drag angle of depression, β max is the maximum aerodynamic drag angle of depression of the aircraft, S is the reference area of the aircraft carrier, and θ is the correction coefficient of the transient aerodynamic load.

[0036] Furthermore, Step S24 includes the following steps:

[0037] Step S241: Analyze the boundary conditions of the aerodynamic flow field of the aircraft's aerodynamic storage formatted dataset to obtain the boundary conditions of the aircraft's aerodynamic flow field;

[0038] Step S242: Based on the boundary conditions of the aircraft's aerodynamic flow field, conduct a fluid dynamics virtual simulation analysis on the aircraft's aerodynamic storage formatted dataset to generate an aerodynamic virtual simulation flow field of the aircraft; conduct an aerodynamic flow field topology analysis on the aerodynamic virtual simulation flow field of the aircraft to obtain a distribution map of the aerodynamic flow field topology structure of the aircraft;

[0039] Step S243: Analyze the parameters of the aerodynamic flow field in the distribution map of the aerodynamic flow field topology structure of the aircraft to obtain the parameter data of the aerodynamic flow field of the aircraft;

[0040] Step S244: Extract the aerodynamic pressure field data from the aircraft's aerodynamic storage formatted dataset to obtain the aerodynamic pressure field data of the aircraft; conduct a pattern recognition analysis on the aerodynamic pressure field data of the aircraft to obtain the distribution pattern data of the aerodynamic pressure field of the aircraft;

[0041] Step S245: Based on the distribution pattern data of the aerodynamic pressure field of the aircraft, conduct a dynamic pressure field distribution correction analysis on the aerodynamic pressure field data of the aircraft to obtain the aerodynamic pressure distribution data of the aircraft.

[0042] Furthermore, step S3 includes the following steps:

[0043] Step S31: Integrate the aerodynamic load data, aerodynamic flow field parameter data, and aerodynamic pressure distribution data of the aircraft in the processed data layer to obtain a comprehensive dataset of the aerodynamic characteristics of the aircraft;

[0044] Step S32: Conduct an aerodynamic flight behavior fitting analysis on the comprehensive dataset of the aerodynamic characteristics of the aircraft to generate an aerodynamic flight behavior fitting pattern; based on the aerodynamic flight behavior fitting pattern, conduct an aerodynamic load response modeling on the aerodynamic load data of the aircraft to generate a sub-model of the aerodynamic behavior fitting load response field;

[0045] Step S33: Conduct an aerodynamic parameter feature point cloud analysis on the aerodynamic flow field parameter data and aerodynamic pressure distribution data of the aircraft in the processed data layer to obtain a cloud of feature points of the aerodynamic flow field parameters of the aircraft and a cloud of feature points of the aerodynamic pressure distribution field of the aircraft;

[0046] Step S34: Conduct an aerodynamic flow field modeling based on the cloud of feature points of the aerodynamic flow field parameters of the aircraft to generate a sub-model of the aerodynamic flow field of the aircraft; conduct an aerodynamic pressure distribution field modeling based on the cloud of feature points of the aerodynamic pressure distribution field of the aircraft to generate a sub-model of the aerodynamic pressure distribution field of the aircraft;

[0047] Step S35: Integrate the aerodynamic model data of the aircraft aerodynamic behavior fitting load response sub-model, the aircraft aerodynamic flow field sub-model, and the aircraft aerodynamic pressure distribution sub-model to generate an aircraft aerodynamic model dataset, and transmit and store it in the high-level model data layer within the aircraft aerodynamic multi-level database storage architecture.

[0048] Further, Step S4 includes the following steps:

[0049] Step S41: Calibrate the aerodynamic multi-dimensional data points of the basic data layer to generate a set of calibrated data points for the basic data layer;

[0050] Step S42: Map the data index relationship of the processing data layer based on the set of calibrated data points of the basic data layer to obtain the aerodynamic data index relationship structure between the basic layer data points and the processing layer;

[0051] Step S43: Dynamically optimize the hierarchical index of the corresponding aircraft aerodynamic model in the high-level model data layer according to the aerodynamic data index relationship structure between the basic layer data points and the processing layer to generate a basic - processing - high-level model dynamic hierarchical data index relationship table;

[0052] Step S44: Perform an R-tree multi-dimensional index connection on the data index relationships in the basic data layer, the processing data layer, and the high-level model data layer based on the basic - processing - high-level model dynamic hierarchical data index relationship table to obtain an aircraft aerodynamic model multi-level storage database.

[0053] Further, the present invention also provides a system for establishing an aircraft aerodynamic model database, which is used to execute the method for establishing an aircraft aerodynamic model database as described above. The system for establishing an aircraft aerodynamic model database includes:

[0054] A storage architecture establishment and basic data layer storage module, which is used to obtain the aircraft aerodynamic data storage requirements and the aircraft aerodynamic data query requirements, and design a multi-level storage architecture according to the aircraft aerodynamic data storage requirements and the aircraft aerodynamic data query requirements to obtain an aircraft aerodynamic multi-level database storage architecture, where the aircraft aerodynamic multi-level database storage architecture includes a basic data layer, a processing data layer, and a high-level model data layer; collect the original aircraft aerodynamic dataset from wind tunnel experiments, flight tests, and CFD numerical simulations, and transmit and store it in the basic data layer within the aircraft aerodynamic multi-level database storage architecture;

[0055] The processing data layer storage module is used to analyze the aerodynamic load, flow field parameters, and aerodynamic pressure distribution of the original aircraft aerodynamic data set in the basic data layer to obtain the aircraft aerodynamic load data, aircraft aerodynamic flow field parameter data, and aircraft aerodynamic pressure distribution data, and transmit and store them in the processing data layer of the aircraft aerodynamic multi-level database storage architecture;

[0056] The high-level model data layer storage module is used to construct an aircraft aerodynamic model for the aircraft aerodynamic load data, aircraft aerodynamic flow field parameter data, and aircraft aerodynamic pressure distribution data in the processing data layer to generate an aircraft aerodynamic model data set, and transmit and store it in the high-level model data layer of the aircraft aerodynamic multi-level database storage architecture;

[0057] The aerodynamic model database index connection establishment module is used to perform R-tree multi-dimensional index connection on the data index relationships in the basic data layer, processing data layer, and high-level model data layer to obtain the aircraft aerodynamic model multi-level storage database.

[0058] The beneficial effects of the present invention:

[0059] 1. The method for establishing an aircraft aerodynamic model database proposed by the present invention, compared with the prior art, the beneficial effect of the present application is that by obtaining the corresponding aircraft aerodynamic data storage requirements and aircraft aerodynamic data query requirements, it can ensure that the database design for storing and querying aircraft aerodynamic data can fully meet the actual requirements. Obtaining detailed storage and query requirements is the basis for establishing an efficient and high-quality data management system. By understanding the storage requirements of aircraft aerodynamic data, the design team can determine the scale of the data volume, data types, storage frequency, and storage duration, so as to select appropriate storage technologies and strategies. For example, whether real-time storage is required, archiving of historical data, data backup, etc. Understanding the data query requirements helps determine the complexity and frequency of queries, whether complex query operations need to be supported, data retrieval speed requirements, and user access permissions, etc. This can ensure that the database design can optimize storage efficiency, improve data access speed, and can handle data requests and analyses of various aerodynamic data types, thereby improving the overall performance and user satisfaction of the aerodynamic model database. By designing a multi-level storage architecture according to the aircraft aerodynamic data storage requirements and aircraft aerodynamic data query requirements, it realizes the efficient management and flexible processing of data through the design of a multi-level database storage architecture. The multi-level storage architecture includes a basic data layer, a processed data layer, and a high-level model data layer. Each layer has its specific functions and roles. The basic data layer is responsible for storing raw data, which comes from different experiments and simulations, so efficient storage and retrieval capabilities are required. The processed data layer processes, cleans, and preliminarily analyzes the data in the basic data layer to provide high-quality data input for the high-level model data layer. The high-level model data layer mainly stores the processed and analyzed data models and their results, which are usually used for further decision-making analysis and model prediction. Through this hierarchical design, efficient storage and management of data can be achieved, avoiding data redundancy and processing bottlenecks, improving the flexibility and scalability of the database establishment process. This hierarchical design can also optimize the data access speed and processing efficiency, and update the corresponding aerodynamic model quickly and conveniently according to the real-time changes of the underlying aerodynamic data.Meanwhile, by collecting datasets from wind tunnel experiments, flight tests, and CFD numerical simulations, a more comprehensive and in-depth understanding of the aerodynamic characteristics of the aircraft can be obtained. The diversity of aerodynamic data and the richness of data sources help improve the reliability and accuracy of the analysis results, and potential errors can be reduced through cross-validation of different data sources. Such a data collection process helps build a more complete aerodynamic database, enabling subsequent data integration and analysis work to be carried out with sufficient data support, thereby being able to capture all flight conditions and configurations of the aircraft and improving the overall performance of the aerodynamic model database establishment process. Also, by storing the integrated data in the basic data layer, the persistence and security of the data can be ensured, and a foundation for further data processing and analysis can be laid. The design of the basic data layer can support efficient data storage and retrieval, ensuring the fluidity and accessibility of the original data throughout the database. Secondly, by performing aerodynamic load analysis on the original aerodynamic datasets of the aircraft in the basic data layer, the key process of the aerodynamic forces exerted on the aircraft under different flight states can be understood. By performing aerodynamic load analysis on the corresponding datasets, the aerodynamic loads of the aircraft can be accurately calculated and predicted, thereby evaluating the pressure and stress it bears during actual flight. This analysis helps identify potential weaknesses and safety hazards in the aircraft design and optimize the structural design of the aircraft to improve its performance and safety. Also, by performing analysis on aerodynamic flow field parameters and aerodynamic pressure distribution on the original aerodynamic datasets of the aircraft, through these two analyses on the stored formatted datasets, detailed flow field characteristics and pressure distribution information can be obtained, helping to identify the key factors of aerodynamic performance. Aerodynamic flow field parameter analysis can reveal the detailed situation of the air flow around the aircraft, including flow velocity, streamline distribution, and vortex structure, which is crucial for understanding the aerodynamic stability and control characteristics of the aircraft. Aerodynamic pressure distribution analysis can show the pressure changes in various regions of the aircraft surface, providing important data on aerodynamic load distribution. These data help optimize the aerodynamic design of the aircraft, increase its lift, reduce drag, and improve flight performance. Accurate aerodynamic pressure distribution data can be used to improve the thermal management system of the aircraft and ensure the structural strength in high-pressure areas. Generally speaking, these analyses can deeply reveal the aerodynamic behavior of the aircraft during actual flight, thereby providing valuable suggestions for design improvement. And by transmitting and storing the aircraft aerodynamic load data, aircraft aerodynamic flow field parameter data, and aircraft aerodynamic pressure distribution data to the processing data layer in the aircraft aerodynamic multi-level database storage architecture, systematic management and efficient processing of the processed data can be achieved. The multi-level database architecture supports hierarchical storage, management, and retrieval of data, making the data access speed faster and the processing ability stronger. This architecture can organize different types of data hierarchically, improving the data management efficiency and query performance, thus meeting the high-performance computing requirements for aircraft aerodynamic data.Finally, by performing R-tree multi-dimensional index connection on the data index relationships within the basic data layer, processed data layer, and high-level model data layer, it is possible to effectively integrate the index relationships of the basic, processed, and high-level model data layers, thereby creating a multi-level storage database. The R-tree index structure can handle the complexity of multi-dimensional data and optimize the data access efficiency. Through this multi-dimensional index connection, efficient storage and query of large-scale aerodynamic data can be achieved, supporting fast data retrieval and processing. This process not only improves the flexibility and efficiency of data management but also supports the dynamic maintenance and update of the corresponding aerodynamic models to adapt to the changing aerodynamic data requirements and model complexity, enabling it to handle complex aerodynamic model data, thereby providing stable and reliable data support, enhancing the processing ability of large-scale aerodynamic data, and providing comprehensive data guarantee for aircraft design and performance analysis.

[0060] 2. The system for establishing an aircraft aerodynamic model database proposed by the present invention is generally composed of a storage architecture establishment and basic data layer storage module, a processed data layer storage module, a high-level model data layer storage module, and an aerodynamic model database index connection establishment module, and can implement any of the methods for establishing an aircraft aerodynamic model database described in the present invention. It is used to jointly realize the method for establishing an aircraft aerodynamic model database through the operations between computer programs running on each module. The internal structure of the system cooperates with each other, which can greatly reduce repetitive work and manpower input, and can quickly and effectively provide a more accurate and efficient process for establishing an aircraft aerodynamic model database, thereby simplifying the operation process of the system for establishing an aircraft aerodynamic model database. Brief Description of the Drawings

[0061] By reading the detailed description of the non-limiting embodiments with reference to the following drawings, other features, objectives, and advantages of the present invention will become more apparent:

[0062] Figure 1 It is a schematic flowchart of the steps of the method for establishing an aircraft aerodynamic model database according to the present invention;

[0063] Figure 2 is Figure 1 a detailed schematic flowchart of step S1 in

[0064] Figure 3 is Figure 2 a detailed schematic flowchart of step S12 in Detailed Embodiments

[0065] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0066] To achieve the above object, please refer to Figures 1 to 3 , the present invention provides a method for establishing an aircraft aerodynamic model database, and the method includes the following steps:

[0067] Step S1: Obtain the storage requirements and query requirements of aircraft aerodynamic data, and perform a multi-level storage architecture design according to the storage requirements and query requirements of aircraft aerodynamic data to obtain an aircraft aerodynamic multi-level database storage architecture, where the aircraft aerodynamic multi-level database storage architecture includes a basic data layer, a processed data layer, and a high-level model data layer; collect the original aircraft aerodynamic data sets from wind tunnel experiments, flight tests, and CFD numerical simulations, and transmit and store them in the basic data layer within the aircraft aerodynamic multi-level database storage architecture;

[0068] Step S2: Analyze the original aircraft aerodynamic data sets in the basic data layer for aerodynamic loads, flow field parameters, and aerodynamic pressure distributions to obtain aircraft aerodynamic load data, aircraft aerodynamic flow field parameter data, and aircraft aerodynamic pressure distribution data, and transmit and store them in the processed data layer within the aircraft aerodynamic multi-level database storage architecture;

[0069] Step S3: Construct an aircraft aerodynamic model for the aircraft aerodynamic load data, aircraft aerodynamic flow field parameter data, and aircraft aerodynamic pressure distribution data in the processed data layer to generate an aircraft aerodynamic model data set, and transmit and store it in the high-level model data layer within the aircraft aerodynamic multi-level database storage architecture;

[0070] Step S4: Perform an R-tree multi-dimensional index connection on the data index relationships in the basic data layer, the processed data layer, and the high-level model data layer to obtain an aircraft aerodynamic model multi-level storage database.

[0071] In the embodiments of the present invention, please refer to Figure 1 As shown, it is a schematic flowchart of the steps of the method for establishing an aircraft aerodynamic model database of the present invention. In this example, the method for establishing an aircraft aerodynamic model database includes the following steps:

[0072] Step S1: Obtain the storage requirements and query requirements of the aircraft aerodynamic data, and design a multi-level storage architecture according to the storage requirements and query requirements of the aircraft aerodynamic data to obtain a multi-level database storage architecture for aircraft aerodynamics, where the multi-level database storage architecture for aircraft aerodynamics includes a basic data layer, a processed data layer, and a high-level model data layer; collect the original aircraft aerodynamic data sets from wind tunnel experiments, flight tests, and CFD numerical simulations, and transmit and store them in the basic data layer within the multi-level database storage architecture for aircraft aerodynamics;

[0073] In an embodiment of the present invention, by collecting the detailed requirements of aircraft aerodynamic data from the aircraft design process and the aerodynamic analysis process, which includes recording the type of aerodynamic data, the data generation frequency, the data volume, the data accuracy, and the data update frequency, and classifying the aerodynamic data into different categories according to the requirements, such as raw data, processed data, model data, etc., each type of data should have different storage requirements and query frequencies. At the same time, determine the data storage requirements such as the storage capacity, storage format (such as relational database, non-relational database, file system, etc.), backup and recovery strategy, etc. required for each type of data, and determine the performance requirements for data query, such as response time, concurrent query quantity, complexity of data retrieval, etc., so as to obtain the storage requirements of aircraft aerodynamic data and the query requirements of aircraft aerodynamic data. And, by combining the storage requirements of aircraft aerodynamic data and the query requirements of aircraft aerodynamic data obtained from the previous analysis, design a multi-level data storage architecture to design and generate the corresponding basic data layer, processed data layer, and high-level model data layer, each layer having specific storage and processing functions, where the basic data layer is designed as the layer for storing raw data, including wind tunnel experiment data, flight test data, and CFD simulation data. This layer should use a high-capacity storage system, such as a distributed file system (such as HDFS) or an object storage system (such as Amazon S3), to meet the large-scale data storage requirements. The processed data layer is used to store the results after processing the raw aerodynamic data, such as data after cleaning, standardization, and aerodynamic analysis. A relational database (such as MySQL or PostgreSQL) or a distributed database (such as Apache Cassandra) can be used to manage the processed data and support efficient data processing and query operations. In addition, the high-level model data layer stores advanced aerodynamic model data and analysis results. This layer uses a data warehouse system (such as Google BigQuery or Amazon Redshift) or a graph database (such as Neo4j) for complex data analysis and model query, thereby designing a multi-level database storage architecture for aircraft aerodynamics.Secondly, by extracting corresponding aerodynamic data from wind tunnel experimental equipment, including measurement data such as pressure, velocity, temperature, etc., which usually exist in the form of experimental records, such as CSV or Excel files, by collecting relevant aerodynamic data from actual flight tests, such as flight parameters recorded by sensors, including velocity, attitude, aerodynamic lift, aerodynamic drag, etc., the data formats include binary files, text files or database records, and by extracting relevant aerodynamic data from computational fluid dynamics (CFD) simulations, such as flow field distribution, pressure field and temperature field, which are usually stored in a dedicated CFD software format, such as CFD data files (such as the result files of OpenFOAM). Then, by using data integration tools (such as Apache Kafka or Apache Spark), the data from different sources are unified in format and merged into a dataset. By cleaning the original data to remove noise and inconsistencies, data transformation operations are performed, such as standardizing the data format, handling missing values, etc., so as to obtain the original aerodynamic dataset of the aircraft. Also, by transmitting and storing the processed original aerodynamic dataset of the aircraft to the basic data layer in the multi-level database storage architecture of aircraft aerodynamics, the data is stored using the interfaces provided by the data storage system (such as the INSERT statement of the database).

[0074] Step S2: Analyze the original aerodynamic dataset of the aircraft in the basic data layer for aerodynamic loads, flow field parameters and aerodynamic pressure distribution to obtain the aircraft aerodynamic load data, aircraft aerodynamic flow field parameter data and aircraft aerodynamic pressure distribution data, and transmit and store them to the processed data layer in the multi-level database storage architecture of aircraft aerodynamics;

[0075] In the embodiments of the present invention, by preprocessing and standardizing the original aircraft aerodynamic data set in the basic data layer, the preprocessing process includes data cleaning and denoising. Data cleaning is achieved by writing a data cleaning algorithm to remove duplicate records and repair missing data. Denoising is performed by applying a smoothing filter, such as a Gaussian filter or a median filter, to reduce measurement errors. Data standardization uses the method of mean and standard deviation to standardize each original aerodynamic data, ensuring that the scales of all data features are consistent, enabling data to be compared and analyzed under the same standard. And by converting the previously processed original aircraft aerodynamic data set into a data format suitable for storage in a relational database, ensuring accurate insertion of data and establishment of relationships between tables. For example, using the INSERT INTO statement to insert data into the corresponding tables one by one and creating necessary indexes to improve query efficiency. At the same time, by performing a quantitative analysis of the aerodynamic load on the previously data-formatted original aircraft aerodynamic data set, where the analysis tools include professional aerodynamic analysis software, such as ANSYS Fluent or OpenFOAM. First, by selecting the aerodynamic load analysis module, importing the aerodynamic data stored in the formatted data set, and setting analysis parameters, such as the motion state of the aircraft, airflow conditions, etc., running the analysis calculation to utilize fluid dynamics theory (such as the Navier-Stokes equation) or empirical formulas to quantitatively calculate the corresponding transient aerodynamic load, and by using statistical and mathematical models (such as regression analysis) to statistically analyze the trend characteristics of the aerodynamic load changing with time to analyze the dynamic fluctuation components in the trend data, thereby obtaining detailed dynamic aerodynamic load fluctuation data. Also, through a data merging algorithm, such as the weighted average method or the interpolation method, combining the transient aerodynamic load and the fluctuation change data, and the combined data should comprehensively cover the aerodynamic load of the aircraft in the simulation environment, including static and dynamic parts, thereby obtaining the aircraft aerodynamic load data. Then, by performing a statistical analysis of the aerodynamic flow field parameters and the pressure field distribution on the previously data-formatted original aircraft aerodynamic data set, using computational fluid dynamics (CFD) software, such as ANSYS Fluent or CFD++, importing the aerodynamic flow field data in the formatted data set, and defining the flow field region and boundary conditions for flow field simulation calculation, thereby obtaining the aircraft aerodynamic flow field parameter data. Also, by using the pressure distribution analysis method to obtain the aerodynamic pressure distribution data, and by drawing visualization results such as contour maps, pressure distribution maps, and streamline maps to show the characteristics of the aerodynamic flow field and the pressure distribution situation, thereby analyzing and obtaining the aircraft aerodynamic pressure distribution data.Finally, by using data transfer tools such as ETL (Extract, Transform, Load) tools, the processed aircraft aerodynamic load data, aircraft aerodynamic flow field parameter data, and aircraft aerodynamic pressure distribution data are exported from the basic data layer. By writing a data upload script, the corresponding aircraft aerodynamic load data, aircraft aerodynamic flow field parameter data, and aircraft aerodynamic pressure distribution data are uploaded to the database in the processed data layer, and the integrity and consistency of the data are confirmed. Finally, the above data is securely stored in the processed data layer of the aircraft aerodynamic multi-level database storage architecture.

[0076] Step S3: Construct an aircraft aerodynamic model for the aircraft aerodynamic load data, aircraft aerodynamic flow field parameter data, and aircraft aerodynamic pressure distribution data in the processed data layer to generate an aircraft aerodynamic model data set, and transmit and store it in the high-level model data layer within the aircraft aerodynamic multi-level database storage architecture;

[0077] In an embodiment of the present invention, by using data processing software, the aircraft aerodynamic load data, aircraft aerodynamic flow field parameter data, and aircraft aerodynamic pressure distribution data in the processed data layer are imported into corresponding data analysis and processing libraries, such as the data analysis libraries of MATLAB or Python. Different aircraft aerodynamic load data, aircraft aerodynamic flow field parameter data, and aircraft aerodynamic pressure distribution data are subjected to fusion analysis of aerodynamic characteristic parameters. By using curve fitting techniques (such as the least squares method or non - linear regression model), the comprehensive aerodynamic characteristic data set obtained after the previous fusion analysis is subjected to fitting analysis of the flight behavior path pattern to classify and statistically analyze the aerodynamic characteristic data according to different flight states and flight phases. At the same time, a flight behavior fitting pattern is generated by using a fitting algorithm. Meanwhile, by combining the previously generated flight behavior fitting pattern, a response model is established for the corresponding aircraft aerodynamic load data, and a modeling tool (such as Simulink of MATLAB or Scikit - learn of Python) is used to construct an aerodynamic load response sub - model for real - time prediction of the aircraft's aerodynamic load response. Secondly, by using a feature point cloud analysis tool (such as CloudCompare or the point cloud toolbox of MATLAB), point cloud analysis of the aerodynamic characteristic parameters of the aircraft aerodynamic flow field parameter data and the aircraft aerodynamic pressure distribution data in the processed data layer obtained from the previous analysis is carried out to convert the aerodynamic flow field parameter data and the aerodynamic pressure distribution data into point cloud formats respectively. By using 3D modeling software (such as ANSYS Fluent or OpenFOAM), an aerodynamic flow field model is established for the previously extracted aircraft aerodynamic flow field parameter feature point cloud set to convert the feature point cloud data into a mesh model, perform mesh optimization and refinement, and calculate parameters such as velocity and pressure in the aerodynamic flow field through numerical simulation methods, thereby establishing and generating an aircraft aerodynamic flow field sub - model. Similarly, based on the aerodynamic pressure distribution field feature point cloud set, an aerodynamic pressure distribution field model is established, also using the same modeling tools and methods, thereby establishing and generating a sub - model of the aerodynamic pressure distribution field. Finally, by using a data integration tool, the previously established aerodynamic load response sub - model, aircraft aerodynamic flow field sub - model, and aircraft aerodynamic pressure distribution field sub - model are integrated with respect to the aerodynamic model data to merge the data of the above - mentioned sub - models, thereby integrating and generating a corresponding aircraft aerodynamic model data set. Finally, the integrated aerodynamic model data set is transmitted and stored in the high - level model data layer of the aircraft aerodynamic multi - level database storage architecture, so as to ensure that all aerodynamic model data is properly managed in the database for subsequent query and analysis.

[0078] Step S4: Perform R - tree multi - dimensional index connection on the data index relationships in the basic data layer, processed data layer, and high - level model data layer to obtain a multi - level storage database of the aircraft aerodynamic model.

[0079] In an embodiment of the present invention, by calibrating data points of aerodynamic data previously stored in the basic data layer, the data includes, but is not limited to, aerodynamic data such as aerodynamic forces, aerodynamic moments, and airflow characteristics at different angles of attack, speeds, and altitudes, and using aerodynamic modeling software to calibrate these data points through an interpolation algorithm, such as using spline interpolation or Kriging interpolation methods, to ensure that each data point has an accurate aerodynamic characteristic description in the basic data layer, thereby calibrating and generating a set of calibrated data points for the basic data layer, and by combining each calibrated data point in the set of calibrated data points of the basic data layer previously generated, using a data mapping algorithm (such as multi-dimensional space mapping or principal component analysis) to perform mapping and matching of the data index relationship for the corresponding aerodynamic processing parameter data in the processing data layer, so as to realize the mapping between the calibrated data points of the basic data layer and the processing data layer. The specific operation includes comparing the aerodynamic characteristic values corresponding to each calibrated data point in the basic data layer with the data in the processing data layer, thereby generating the aerodynamic data index relationship between the data points of the basic data layer and the processing data layer. At the same time, by combining the aerodynamic data index relationship structure between the basic layer data points and the processing layer established through the previous mapping, the corresponding aerodynamic model in the high-level model data layer is optimized for dynamic hierarchical indexing, so as to analyze the relationship between the basic data layer and the processing data layer, and further index and match the corresponding aerodynamic model in the high-level model data layer based on these relationships, that is, how to obtain the corresponding intermediate processing data from the basic data analysis and finally generate the corresponding aerodynamic model, ensuring that the data points of each high-level model are accurately matched with the data points of the basic data layer and the processing data layer. Then, by combining the data point index relationship from the basic layer to the high-level model obtained through the previous matching, the corresponding data index relationships in the basic data layer, the processing data layer, and the high-level model data layer are connected by multi-dimensional indexing of the R-tree to establish a multi-level storage database for the aircraft aerodynamic model, and realize indexing of the aerodynamic data in the basic data layer, the processing data layer, and the high-level model data layer by using the R-tree (or other multi-dimensional space index structures, such as the KD-tree). The specific steps include constructing an R-tree index structure to store the spatial positions and related information of each data point in the basic data layer, the processing data layer, and the high-level model data layer in the R-tree nodes, and through constructing a multi-dimensional index, it is possible to quickly locate the relevant data points during query, thereby connecting and establishing a multi-level storage database, and finally obtaining a multi-level storage database for the aircraft aerodynamic model.

[0080] Further, as an embodiment of the present invention, referring to Figure 2 shown, it is Figure 1 a detailed step flow schematic diagram of step S1 in

[0081] Step S11: Obtain the storage requirements and query requirements of the aircraft aerodynamic data;

[0082] In the embodiment of the present invention, by collecting the detailed requirements of the aircraft aerodynamic data from the aircraft design process and the aerodynamic analysis process, this includes recording the type of aerodynamic data, data generation frequency, data volume, data accuracy, and data update frequency, and classifying the aerodynamic data into different categories according to the requirements, such as raw data, processed data, model data, etc. Each type of data should have different storage requirements and query frequencies. At the same time, determine the data storage requirements such as the storage capacity, storage format (such as relational database, non-relational database, file system, etc.), backup and recovery strategy, etc. required for each type of data, and determine the performance requirements for data query, such as response time, concurrent query quantity, complexity of data retrieval, etc. data query requirements, and finally obtain the storage requirements of the aircraft aerodynamic data and the query requirements of the aircraft aerodynamic data.

[0083] Step S12: Design a multi-level storage architecture according to the storage requirements of the aircraft aerodynamic data and the query requirements of the aircraft aerodynamic data to obtain a multi-level database storage architecture for aircraft aerodynamics, where the multi-level database storage architecture for aircraft aerodynamics includes a basic data layer, a processed data layer, and a high-level model data layer;

[0084] In the embodiments of the present invention, a multi-level data storage architecture is designed by combining the storage requirements and query requirements of aircraft aerodynamic data obtained from previous analyses, so as to design and generate a corresponding basic data layer, processed data layer, and high-level model data layer. Each layer has specific storage and processing functions. The basic data layer is designed to store raw data, including wind tunnel experiment data, flight test data, and CFD simulation data. This layer should use a high-capacity storage system, such as a distributed file system (e.g., HDFS) or an object storage system (such as Amazon S3), to meet the large-scale data storage requirements. The processed data layer is used to store the results after processing the raw aerodynamic data, such as data after cleaning, standardization, and aerodynamic analysis. A relational database (such as MySQL or PostgreSQL) or a distributed database (such as Apache Cassandra) can be used to manage the processed data and support efficient data processing and query operations. In addition, the high-level model data layer stores high-level aerodynamic model data and analysis results. This layer uses a data warehouse system (such as Google BigQuery or Amazon Redshift) or a graph database (such as Neo4j) for complex data analysis and model query. At the same time, by designing the paths for different aerodynamic data to flow between different levels, including the processes of data collection, transmission, storage, and query, ETL (Extract, Transform, Load) tools (such as Apache NiFi or Talend) are used to achieve data flow and transformation, so as to ensure that the efficient storage and query requirements of aerodynamic data can be met, and finally an aircraft aerodynamic multi-level database storage architecture is designed.

[0085] Step S13: Collect the original aircraft wind tunnel experiment dataset, original aircraft flight test dataset, and original aircraft CFD simulation dataset from wind tunnel experiments, flight tests, and CFD numerical simulations;

[0086] In the embodiments of the present invention, the corresponding aerodynamic data is extracted from wind tunnel experiment equipment, including measurement data such as pressure, velocity, and temperature. These data usually exist in the form of experimental records, such as CSV or Excel files, thus obtaining the original aircraft wind tunnel experiment dataset. At the same time, relevant aerodynamic data is collected from actual flight tests, such as flight parameters recorded by sensors, including velocity, attitude, aerodynamic lift, aerodynamic drag, etc. The data formats include binary files, text files, or database records, thus obtaining the original aircraft flight test dataset. Then, relevant aerodynamic data is extracted from computational fluid dynamics (CFD) simulations, such as flow field distribution, pressure field, and temperature field. These data are usually stored in a dedicated CFD software format, such as CFD data files (such as the result files of OpenFOAM), and finally the original aircraft CFD simulation dataset is obtained.

[0087] Step S14: Integrate and analyze the original datasets of aircraft wind tunnel experiments, aircraft flight tests, and aircraft CFD simulations to obtain the original aircraft aerodynamic dataset, and transmit and store it in the basic data layer within the aircraft aerodynamic multi-level database storage architecture.

[0088] In the embodiment of the present invention, by integrating the previously collected original datasets of aircraft wind tunnel experiments, aircraft flight tests, and aircraft CFD simulations into a unified original aircraft aerodynamic dataset, and using data integration tools (such as Apache Kafka or Apache Spark) to unify the data formats from different sources and merge them into one dataset. At the same time, by cleaning the original data to remove noise and inconsistencies, perform data transformation operations such as standardizing the data format and handling missing values. Then, transmit and store the processed original aircraft aerodynamic dataset in the basic data layer of the aircraft aerodynamic multi-level database storage architecture, and use the interface provided by the data storage system (such as the INSERT statement of the database) for data storage.

[0089] Further, as an embodiment of the present invention, refer to Figure 3 shown in Figure 2 is the detailed step flow diagram of step S12 in

[0090] Step S121: Analyze the multi-level storage objectives according to the aircraft aerodynamic data storage requirements and the aircraft aerodynamic data query requirements to obtain the immediate storage objective of aircraft aerodynamic data, the storage objective of aircraft aerodynamic intermediate data processing, and the storage objective of aircraft aerodynamic model construction.

[0091] In the embodiment of the present invention, through a multi-level analysis of the storage requirements of the aircraft aerodynamic data obtained previously and the query requirements of the aircraft aerodynamic data (that is, it is necessary to query the initial aerodynamic data, the aerodynamic intermediate data after analysis and processing, and the aerodynamic model data after modeling, so that the corresponding aerodynamic model can be updated at any time when the aerodynamic data changes), the storage target of the aircraft aerodynamic data is analyzed to evaluate and analyze the immediate storage requirements of the aerodynamic data of the aircraft during real-time simulated flight, and determine which aerodynamic data must be stored in real time to support flight control and real-time monitoring, so as to obtain the immediate storage target of the aircraft aerodynamic data. At the same time, by analyzing the storage target corresponding to the intermediate processing process of the aircraft aerodynamic data, that is, the temporary data storage required during the processing of the aerodynamic data, including data cleaning, conversion, and storage of intermediate calculation results, the storage target for processing the intermediate aerodynamic data of the aircraft is obtained. Then, by analyzing the storage target of the aircraft aerodynamic model data, which involves the long-term storage and processing of the necessary data for the aircraft aerodynamic model to support the optimization and verification of the model, the storage target for constructing the aircraft aerodynamic model is obtained. This analysis can be described hierarchically by creating a detailed data requirement document and using data flow diagrams and requirement analysis tools to clearly identify the data requirements and storage targets at different levels.

[0092] Step S122: Design the basic data layer according to the immediate storage target of the aircraft aerodynamic data to obtain the basic data layer;

[0093] In the embodiment of the present invention, by designing the basic data layer according to the immediate storage target of the aircraft aerodynamic data obtained from the previous analysis, the basic data types to be supported are clarified, including the real-time aerodynamic parameter data of the aircraft, sensor acquisition data, etc., and a high-performance database system is selected, and a suitable database table structure is used to store these original aerodynamic data. The design of the basic data layer should include field definitions of data tables, data type selection, data update frequency, etc. For example, for real-time aerodynamic parameters, a table containing sensor ID, timestamp, data type, and data value can be created, and indexes can be set to speed up data retrieval. The design of the database within the basic data layer should ensure support for high-speed data writing operations and be able to handle high-concurrency data access requests. In addition, to ensure data integrity and consistency, a data verification mechanism and backup strategy are designed to cope with data loss or damage, and finally the basic data layer is designed.

[0094] Step S123: Design the processing data layer according to the storage target for processing the intermediate aerodynamic data of the aircraft to obtain the processing data layer;

[0095] In the embodiments of the present invention, the design of the processing data layer is carried out by processing the storage target according to the aircraft aerodynamic intermediate data obtained from the previous analysis to determine the types and storage requirements of the intermediate processing data. These data usually involve aircraft aerodynamic simulation and calculation results, such as aerodynamic loads, flow field parameters, intermediate results of aerodynamic pressure calculations, etc. During the design process, a database system that supports complex queries and calculations should be selected, and the database table structure of the processing data layer should be defined. For example, in order to store the intermediate results of the flow field simulation, a table can be designed that includes fields such as simulation parameters, calculation steps, and simulation results. In addition, appropriate indexing and data partitioning strategies need to be set, and finally, the processing data layer is designed.

[0096] Step S124: Design the high-level model data layer by constructing a storage target according to the aircraft aerodynamic model to obtain the high-level model data layer;

[0097] In the embodiments of the present invention, similarly, the design of the high-level aerodynamic model data layer is carried out by constructing a storage target according to the aircraft aerodynamic model obtained from the previous analysis to define the data types and storage requirements required during the storage process of the aerodynamic model data. These data include model parameters, training data sets, model training results, etc., and a suitable database system is selected, and the database table structure is designed to support complex data relationships. For example, in order to store the training data of the aerodynamic model, a table is designed that includes fields such as training data samples, feature values, and target values. For model parameters and results, a table can be designed that includes fields such as model name, parameter values, and verification results. In addition, the design of the model data layer should support efficient data reading and updating operations to quickly access and process data during the model construction and verification processes, and finally, the high-level model data layer is obtained.

[0098] Step S125: Integrate the basic data layer, the processing data layer, and the high-level model data layer through a multi-level architecture design to obtain the aircraft aerodynamic multi-level database storage architecture.

[0099] In the embodiments of the present invention, the basic data layer, the processing data layer, and the high-level model data layer generated from the previous design are connected and integrated through a multi-level architecture to integrate and create a unified database architecture, including three-layer data storage structures, corresponding to basic data storage, processing data storage, and model data storage respectively. To achieve effective communication between the data layers, data interfaces and conversion mechanisms are designed to ensure that data can be smoothly transmitted and accessed between layers. During the integration process, data consistency and integrity should be considered, and data synchronization and backup mechanisms are designed to ensure the stability and reliability of the multi-level database, and finally, the aircraft aerodynamic multi-level database storage architecture is obtained.

[0100] Further, step S2 includes the following steps:

[0101] Step S21: Perform data preprocessing and standardization on the original aircraft aerodynamic data set in the basic data layer to obtain the aircraft aerodynamic standardized data set;

[0102] In the embodiment of the present invention, by preprocessing and standardizing the original aircraft aerodynamic data set in the basic data layer, the original data usually includes measured values in various formats, such as aerodynamic loads, flow field parameters, and pressure distributions. The preprocessing process includes data cleaning and denoising. Data cleaning is achieved by writing a data cleaning algorithm to remove duplicate records and repair missing data. Denoising is performed by applying a smoothing filter, such as a Gaussian filter or a median filter, to reduce measurement errors. Data standardization uses the method of mean and standard deviation to standardize each original aerodynamic data, ensuring that the scales of all data features are consistent, enabling data to be compared and analyzed under the same standard, and finally obtaining the aircraft aerodynamic standardized data set.

[0103] Step S22: Perform a relational database storage format conversion on the aircraft aerodynamic standardized data set to obtain the aircraft aerodynamic storage format data set;

[0104] In the embodiment of the present invention, by converting the previously processed aircraft aerodynamic standardized data set into a data format suitable for relational database storage. First, design the table structure of the relational database, including data tables, fields, and data types. For example, create an "aerodynamic load table", a "flow field parameter table", and a "pressure distribution table", define fields such as "timestamp", "position coordinates", and "load value", and use the data import tool in the database management system (DBMS) to import the standardized data set into these tables. By writing SQL scripts, ensure the accurate insertion of data and the establishment of relationships between tables. For example, use the INSERT INTO statement to insert data into the corresponding tables one by one, and create necessary indexes to improve query efficiency. Finally, convert to obtain the aircraft aerodynamic storage format data set, which can effectively support subsequent database query and management operations.

[0105] Step S23: Perform aerodynamic load analysis on the aircraft aerodynamic storage format data set to obtain the aircraft aerodynamic load data;

[0106] In an embodiment of the present invention, a quantitative analysis of the aerodynamic load is performed on the aerodynamic storage formatted data set of the aircraft obtained after previous data formatting. The analysis tools include professional aerodynamic analysis software, such as ANSYS Fluent or OpenFOAM. First, by selecting the aerodynamic load analysis module, the aerodynamic load data in the storage formatted data set is imported, and by setting analysis parameters, such as the motion state of the aircraft, the airflow conditions, etc., the analysis and calculation are carried out to quantify and calculate the corresponding transient aerodynamic load by using fluid dynamics theories (such as the Navier-Stokes equation) or empirical formulas, and the transient aerodynamic load data at each time point is calculated frame by frame. At the same time, by using trend analysis tools (such as the moving average method or fitting algorithm), a statistical analysis of the change trend of the aerodynamic load is carried out on the transient aerodynamic load data obtained from the previous quantitative calculation, so as to statistically analyze the trend characteristics of the aerodynamic load changing with time through statistical and mathematical models (such as regression analysis), and a statistical analysis of the dynamic fluctuation of the corresponding transient aerodynamic load data is carried out by combining the change trend of the aerodynamic load obtained from the previous analysis, so as to analyze the dynamic fluctuation components in the trend data, thereby obtaining detailed dynamic aerodynamic load fluctuation data, which is used to describe the change of the aerodynamic load of the aircraft during actual flight. Then, by merging the transient aerodynamic load data and the dynamic aerodynamic load fluctuation change data obtained from the previous analysis under dynamic and transient conditions, the time axes of the frame-by-frame transient aerodynamic load data and the dynamic fluctuation data are ensured to be aligned, and the transient aerodynamic load and the fluctuation change data are combined through a data merging algorithm, such as the weighted average method or the interpolation method. The merged data should comprehensively cover the aerodynamic load of the aircraft in the simulation environment, including static and dynamic parts, and finally the aerodynamic load data of the aircraft is obtained.

[0107] Step S24: Perform an analysis on the aerodynamic flow field parameters and the aerodynamic pressure distribution of the aerodynamic storage formatted data set of the aircraft to obtain the aerodynamic flow field parameter data and the aerodynamic pressure distribution data of the aircraft.

[0108] In an embodiment of the present invention, a statistical analysis of the aerodynamic flow field parameters and the pressure field distribution is performed on the aerodynamic storage formatted data set of the aircraft obtained after previous data formatting, so as to use computational fluid dynamics (CFD) software, such as ANSYS Fluent or CFD++, to import the aerodynamic flow field data in the formatted data set, and define the flow field region and boundary conditions for flow field simulation calculation, thereby obtaining the aerodynamic flow field parameter data of the aircraft. Then, the aerodynamic pressure distribution data is obtained by using the pressure distribution analysis method, and the characteristics of the aerodynamic flow field and the pressure distribution are presented by visualizing results such as contour plots, pressure distribution plots, and streamline plots, and finally the aerodynamic pressure distribution data of the aircraft is analyzed.

[0109] Step S25: Transmit and store the aircraft aerodynamic load data, aircraft aerodynamic flow field parameter data, and aircraft aerodynamic pressure distribution data into the processing data layer within the aircraft aerodynamic multi-level database storage architecture.

[0110] In an embodiment of the present invention, by using a data transmission tool such as an ETL (Extract, Transform, Load) tool, the processed aircraft aerodynamic load data, aircraft aerodynamic flow field parameter data, and aircraft aerodynamic pressure distribution data are exported from the basic data layer. By writing a data upload script, the corresponding aircraft aerodynamic load data, aircraft aerodynamic flow field parameter data, and aircraft aerodynamic pressure distribution data are uploaded to the database in the processing data layer, and the integrity and consistency of the data are confirmed. For example, the checksum of the data is checked to ensure that the data is not lost or damaged during transmission. Finally, the above data is securely stored in the processing data layer of the aircraft aerodynamic multi-level database storage architecture.

[0111] Further, step S23 includes the following steps:

[0112] Step S231: Perform aerodynamic lift and drag parameter extraction processing on the aircraft aerodynamic storage formatted data set to obtain aircraft aerodynamic lift parameter data and aircraft aerodynamic drag parameter data;

[0113] In an embodiment of the present invention, by extracting the parameters related to aerodynamic lift and drag from the previously formatted aircraft aerodynamic storage formatted data set, which usually contains the originally measured aerodynamic data obtained through sensors, simulators, or wind tunnel tests. The extraction process involves the following specific operations. By using data processing software (such as MATLAB or the NumPy library in Python) to read the formatted data set, and processing the original data in the data set through algorithms to separate the parameters related to aerodynamic lift (such as lift coefficient Cl, air flow velocity, angle of attack, etc.) and the parameters related to aerodynamic drag (such as drag coefficient Cd, air flow velocity, angle of attack, etc.). And by using data screening and filtering techniques to ensure that the extracted lift and drag parameters are accurate and meet the requirements of the experimental design. At the same time, by using data processing tools to clean the selected parameter data, removing noise and outliers, to ensure the quality and reliability of the extracted aerodynamic lift and drag parameters. Finally, the aircraft aerodynamic lift parameter data and aircraft aerodynamic drag parameter data are obtained.

[0114] Step S232: Perform time series synchronization processing on the aircraft aerodynamic lift parameter data and aircraft aerodynamic drag parameter data to obtain aircraft aerodynamic lift time series data and aircraft aerodynamic drag time series data under the same time series dimension;

[0115] In an embodiment of the present invention, the time-series synchronization processing method is used to synchronize the time series of the previously extracted aircraft aerodynamic lift parameter data and the aircraft aerodynamic drag parameter data to ensure their alignment in the same time series. First, determine the timestamps or time markers in the aerodynamic lift and drag parameter data, and use time-series data processing tools (such as the Pandas library in Python) to align the time of these two data sets to ensure that the lift data and drag data at each time point correspond at the same moment. And through interpolation techniques (such as linear interpolation or spline interpolation), fill in the missing data points in the time series to ensure the integrity of the time series. Finally, obtain the aircraft aerodynamic lift time-series data and the aircraft aerodynamic drag time-series data in the same time-series dimension.

[0116] Step S233: Perform aerodynamic parametric simulation design on the aircraft aerodynamic lift time-series data and the aircraft aerodynamic drag time-series data in the same time-series dimension to generate an aircraft aerodynamic parametric simulation operation field;

[0117] In an embodiment of the present invention, the time-series synchronized aircraft aerodynamic lift time-series data and the aircraft aerodynamic drag time-series data are used for aerodynamic parametric simulation design. In the specific implementation process, first select an aerodynamic simulation software (such as ANSYS Fluent or OpenFOAM), create an aerodynamic model of the aircraft in the software, import the synchronized lift and drag data, and parameterize the model by using aerodynamic parametric techniques (such as statistics-based methods or machine learning models). Finally, simulate and generate an aircraft aerodynamic parametric simulation operation field, which includes defining the simulation range, setting boundary conditions, and determining the accuracy and resolution of the simulation grid. The aerodynamic parametric simulation operation field will be based on time-series data to simulate the aerodynamic behavior of the aircraft under different flight conditions.

[0118] Step S234: Perform frame-by-frame parameter change sampling on the aircraft aerodynamic parametric simulation operation field to obtain the aerodynamic lift change value and the aerodynamic drag change value at each time frame in the aircraft aerodynamic simulation field; Based on the aerodynamic lift change value and the aerodynamic drag change value at each time frame in the aircraft aerodynamic simulation field, perform frame-by-frame transient aerodynamic load quantization calculation on the aircraft aerodynamic parametric simulation operation field to obtain the aircraft frame-by-frame transient aerodynamic load data;

[0119] In an embodiment of the present invention, by setting frame-by-frame sampling parameters in a simulation software to select an appropriate time interval, so as to extract the aerodynamic data of each time frame from the simulation operation field, and by using a data acquisition tool (such as the data acquisition toolbox of MATLAB or the SciPy library of Python) to sample the aerodynamic lift and drag of each frame, recording the change values of the aerodynamic lift and the change values of the aerodynamic drag of each time frame, so as to obtain the change values of the aerodynamic lift and the change values of the aerodynamic drag at each time frame in the aerodynamic simulation field of the aircraft. At the same time, by combining the change values of the aerodynamic lift, the change values of the aerodynamic drag, the instantaneous speed of the aircraft, the aircraft speed adjustment coefficient, the aerodynamic lift angle of attack of the aircraft, the maximum aerodynamic lift coefficient of the aircraft, the zero-lift aerodynamic angle of attack of the aircraft, the maximum-lift aerodynamic angle of attack of the aircraft, the aerodynamic drag depression angle of the aircraft, the maximum aerodynamic drag coefficient of the aircraft, the zero-drag aerodynamic depression angle of the aircraft, the maximum-drag aerodynamic depression angle of the aircraft, the reference area of the aircraft carrier and related parameters obtained from the previous frame-by-frame sampling, a suitable aerodynamic load calculation formula is constructed to perform quantitative calculation of the aerodynamic load under transient conditions, so as to use fluid dynamics theory (such as the Navier-Stokes equation) or empirical formula to quantitatively calculate the corresponding transient aerodynamic load, and calculate the transient aerodynamic load data at each time point frame by frame, and finally obtain the frame-by-frame transient aerodynamic load data of the aircraft.

[0120] Step S235: Perform aerodynamic load trend analysis on the frame-by-frame transient aerodynamic load data of the aircraft to obtain the aircraft aerodynamic load change trend data; based on the aircraft aerodynamic load change trend data, perform aerodynamic load dynamic fluctuation analysis on the frame-by-frame transient aerodynamic load data of the aircraft to obtain the aircraft dynamic aerodynamic load fluctuation change data;

[0121] In an embodiment of the present invention, by using a trend analysis tool (such as the moving average method or the fitting algorithm), statistical analysis of the changing trend of the aerodynamic load is performed on the frame-by-frame transient aerodynamic load data of the aircraft obtained by previous quantization calculations, so as to statistically analyze the trend characteristics of the aerodynamic load changing with time through statistical and mathematical models (such as regression analysis). These trend characteristics may include the growth or decay trend of lift and drag, periodic fluctuations, etc. The trend analysis results show the overall changing law of the aerodynamic load of the aircraft under different flight conditions, thereby obtaining the aerodynamic load change trend data of the aircraft. At the same time, by combining the aerodynamic load change trend data of the aircraft obtained by previous analysis, statistical analysis of the dynamic fluctuations of the aerodynamic load is performed on the corresponding frame-by-frame transient aerodynamic load data of the aircraft, so as to analyze the dynamic fluctuation components in the trend data, and identify the periodic or non-periodic fluctuations of the aerodynamic load. By using frequency domain analysis methods, such as Fourier transform, the frequency and amplitude characteristics of the fluctuations are extracted, and combined with time domain data, the influencing factors of the load fluctuations, such as airflow instability, aircraft attitude change, etc., are evaluated, thereby obtaining detailed dynamic aerodynamic load fluctuation data, which describes the aerodynamic load change of the aircraft during actual flight, and finally obtains the dynamic aerodynamic load fluctuation change data of the aircraft.

[0122] Step S236: Merge the frame-by-frame transient aerodynamic load data of the aircraft and the dynamic aerodynamic load fluctuation change data of the aircraft to obtain the aerodynamic load data of the aircraft.

[0123] In an embodiment of the invention, by merging the frame-by-frame transient aerodynamic load data of the aircraft and the dynamic aerodynamic load fluctuation change data of the aircraft obtained by previous analysis under dynamic and transient conditions, the time axes of the frame-by-frame transient aerodynamic load data and the dynamic fluctuation data are aligned to ensure the synchronization of the aerodynamic load data, and through a data merging algorithm, such as the weighted average method or the interpolation method, the transient aerodynamic load and the fluctuation change data are combined. The merged data should comprehensively cover the aerodynamic load of the aircraft in the simulation environment, including static and dynamic parts, and finally obtain the aerodynamic load data of the aircraft.

[0124] Further, the frame-by-frame transient aerodynamic load quantization calculation in step S234 is performed through the frame-by-frame transient aerodynamic load calculation formula of the aircraft, where the frame-by-frame transient aerodynamic load calculation formula of the aircraft is specifically:

[0125]

[0126] In the formula, ε(t) is the transient aerodynamic load of the aircraft at time frame t, t is the time frame variable parameter, ρ is the gas density, C L$(α(t), V(t))$ is the value of the change in aerodynamic lift in the aircraft aerodynamic simulation field at time frame $t$, $V(t)$ is the instantaneous velocity of the aircraft at time frame $t$, $\gamma$ is the aircraft velocity adjustment coefficient, and $\alpha(t)$ is the angle of attack of the aircraft aerodynamic lift at time frame $t$. is the maximum aerodynamic lift coefficient of the aircraft, $\alpha_0$ is the angle of attack of the aircraft aerodynamic zero lift, $\alpha$ max is the angle of attack of the aircraft aerodynamic maximum lift, $C$ D $(β(t), V(t))$ is the value of the change in aerodynamic drag in the aircraft aerodynamic simulation field at time frame $t$, and $\beta(t)$ is the angle of depression of the aircraft aerodynamic drag at time frame $t$. is the maximum aerodynamic drag coefficient of the aircraft, $\beta_0$ is the angle of depression of the aircraft aerodynamic zero drag, $\beta$ max is the angle of depression of the aircraft aerodynamic maximum drag, $S$ is the reference area of the aircraft carrier, and $\theta$ is the correction coefficient of the transient aerodynamic load.

[0127] In the present invention, a frame-by-frame transient aerodynamic load calculation formula for an aircraft is obtained through the use of a specific mathematical model and verification, which is used to perform frame-by-frame transient aerodynamic load quantification calculation on the aircraft aerodynamic parametric simulation operation field. This frame-by-frame transient aerodynamic load calculation formula for the aircraft can provide transient aerodynamic load data for the aircraft at each time frame by integrating the changes in aerodynamic lift and aerodynamic drag. Such accurate transient load assessment is crucial for analyzing the aerodynamic behavior of the aircraft under different flight conditions. By calculating the aerodynamic load frame by frame, the dynamic aerodynamic response of the aircraft in various flight states can be captured, which helps to identify rapid changes in aerodynamic load and potential aerodynamic instabilities. Secondly, the aerodynamic lift and drag coefficients in the formula involve the non-linear changes in the angle of attack and angle of depression of the aircraft, as well as the velocity adjustment coefficient, considering the complex aerodynamic effects in actual flight. This comprehensive consideration makes the calculation results closer to the actual flight situation. Moreover, by analyzing the frame-by-frame transient aerodynamic load data, the aerodynamic design of the aircraft can be optimized, the flight performance can be improved, and existing structural problems can be prevented, thereby enhancing the safety and efficiency of the aircraft. The frame-by-frame transient load data can be used to predict the performance of the aircraft under various operating conditions, including the aerodynamic pressure and load at different flight stages, which helps to perform more accurate performance prediction and verification during the design stage. Additionally, the introduction of the correction coefficient enables the load calculation to be adjusted according to the actual situation, further improving the accuracy and practicality of the calculation results, thus ensuring that the analysis results can reflect the actual flight conditions. In summary, the formula provides the ability to perform refined calculation and in-depth analysis of the aircraft aerodynamic load, which helps to improve aircraft design, optimize performance, and enhance flight safety. To sum up, the formula fully considers the transient aerodynamic load $\varepsilon(t)$ of the aircraft at time frame $t$, the time frame variable parameter $t$, the gas density $\rho$, and the change in aerodynamic lift $C$ in the aircraft aerodynamic simulation field at time frame $t$.L (α(t), V(t)), the instantaneous velocity V(t) of the aircraft at time frame t, the aircraft velocity adjustment coefficient γ, the aerodynamic lift angle of attack α(t) of the aircraft at time frame t, the maximum aerodynamic lift coefficient of the aircraft The aerodynamic zero-lift angle of attack α0 of the aircraft, the maximum aerodynamic lift angle of attack α of the aircraft max , the change value C of the aerodynamic drag in the aerodynamic simulation field of the aircraft at time frame t D (β(t), V(t)), the aerodynamic drag depression angle β(t) of the aircraft at time frame t, the maximum aerodynamic drag coefficient of the aircraft The aerodynamic zero-drag depression angle β0 of the aircraft, the maximum aerodynamic drag depression angle β of the aircraft max , the reference area S of the aircraft carrier, the correction coefficient θ of the transient aerodynamic load, where, through the time frame variable parameter t, the instantaneous velocity V(t) of the aircraft at time frame t, the aircraft velocity adjustment coefficient γ, the aerodynamic lift angle of attack α(t) of the aircraft at time frame t, the maximum aerodynamic lift coefficient of the aircraft The aerodynamic zero-lift angle of attack α0 of the aircraft and the maximum aerodynamic lift angle of attack α of the aircraft max constitute a change value C of the aerodynamic lift in the aerodynamic simulation field of the aircraft at time frame t L The functional relationship of (α(t), V(t)) Meanwhile, by combining the time frame variable parameter t, the instantaneous velocity V(t) of the aircraft at time frame t, the aircraft velocity adjustment coefficient γ, β(t) is the aerodynamic drag depression angle of the aircraft at time frame t, is the maximum aerodynamic drag coefficient of the aircraft, β0 is the aerodynamic zero-drag depression angle of the aircraft and β max is the maximum aerodynamic drag depression angle of the aircraft, which constitutes a change value C of the aerodynamic drag in the aerodynamic simulation field of the aircraft at time frame t D The functional relationship of (β(t), V(t)) According to the mutual correlation relationship between the transient aerodynamic load ε(t) of the aircraft at time frame t and the above parameters, a functional relationship is formed This formula can realize the frame-by-frame transient aerodynamic load quantization calculation process of the aircraft aerodynamic parameterized simulation operation field. Meanwhile, by introducing the correction coefficient θ of the transient aerodynamic load, it can be adjusted according to the error situation in the calculation process, thereby improving the accuracy and applicability of the frame-by-frame transient aerodynamic load calculation formula of the aircraft.

[0128] Furthermore, step S24 includes the following steps:

[0129] Step S241: Analyze the aerodynamic flow field boundary conditions of the aerodynamic storage formatted data set of the aircraft to obtain the aerodynamic flow field boundary conditions of the aircraft

[0130] In an embodiment of the present invention, the aerodynamic flow field boundary conditions are analyzed by analyzing the aerodynamic storage formatted data set of the aircraft obtained after previously formatting, so as to first import the formatted data set into the aerodynamic simulation software by using a special aerodynamic analysis tool (such as ANSYS Fluent or OpenFOAM), and then define the geometric shape and boundary conditions of the aircraft, including the body surface, aerodynamic shape and existing environmental influences (such as airflow direction and speed), and implement detailed settings of the boundaries around the aircraft, such as setting flow velocity and temperature boundary conditions at the airflow inlet, setting pressure boundary conditions at the airflow outlet, and setting relevant physical properties and calculation conditions according to the actual flight state of the aircraft, and finally obtaining the aerodynamic flow field boundary conditions of the aircraft.

[0131] Step S242: performing fluid dynamics virtual simulation analysis on the aircraft aerodynamic storage formatted data set based on the aircraft aerodynamic flow field boundary conditions to generate an aircraft aerodynamic virtual simulation flow field; performing aerodynamic flow field topology analysis on the aircraft aerodynamic virtual simulation flow field to obtain an aircraft aerodynamic flow field topology structure distribution map;

[0132] In an embodiment of the present invention, a fluid dynamics simulation analysis is performed on a corresponding aircraft aerodynamic storage formatted data set by using a fluid dynamics simulation tool (such as ANSYS Fluent or OpenFOAM) in combination with the aircraft aerodynamic flow field boundary conditions obtained by the previous analysis, so that the previously obtained boundary conditions are applied to the aerodynamic data set, and numerical simulation is performed to simulate and calculate the velocity, pressure and temperature distribution of the aerodynamic flow field, thereby generating an aircraft aerodynamic virtual simulation flow field. During the simulation process, the accuracy of the simulation results is ensured by selecting a suitable grid division method (such as a structured grid or an unstructured grid) and performing a convergence test. At the same time, after obtaining the aircraft aerodynamic virtual simulation flow field, a topological analysis of the aerodynamic flow field is performed to analyze the main structural features and flow paths of the aerodynamic flow field using a topological analysis tool (such as a topological optimization tool in MATLAB), and finally a topological structure distribution diagram of the aircraft aerodynamic flow field is generated to display the overall picture and main flow patterns of the aerodynamic flow field.

[0133] Step S243: performing aerodynamic flow field parameter analysis on the aircraft aerodynamic flow field topological structure distribution diagram to obtain aircraft aerodynamic flow field parameter data;

[0134] In an embodiment of the present invention, by using an aerodynamic data analysis tool (such as the aerodynamic data processing toolbox of MATLAB), key aerodynamic flow field parameters are extracted from the aerodynamic flow field topology structure distribution map obtained through previous topology analysis, including velocity distribution, pressure distribution, and turbulence intensity, etc., and parametric processing and statistical analysis are performed to calculate the aerodynamic parameter values of each point in the flow field. Through comparison and fitting analysis, the main aerodynamic flow field parameter data are determined. These data reflect the aerodynamic characteristics of the aircraft under different flight states, and finally the aerodynamic flow field parameter data of the aircraft are obtained.

[0135] Step S244: Extract aerodynamic pressure field data from the aerodynamic storage formatted data set of the aircraft to obtain the aerodynamic pressure field data of the aircraft; perform recognition analysis on the pressure field distribution pattern of the aerodynamic pressure field data of the aircraft to obtain the aerodynamic pressure field distribution pattern data of the aircraft.

[0136] In an embodiment of the present invention, through the extraction process of aerodynamic pressure field related data from the aerodynamic storage formatted data set of the aircraft obtained after previous formatting, the aerodynamic storage formatted data set is imported into a data processing tool (such as the data analysis library of MATLAB or Python), aerodynamic pressure field data is extracted from it, and data preprocessing is performed, such as noise removal and data smoothing, to ensure the accuracy of the data. At the same time, by using a pattern recognition algorithm (such as K-means clustering or principal component analysis), recognition analysis on the distribution pattern of the aerodynamic pressure field data of the aircraft is performed to identify the main patterns and characteristics of the aerodynamic pressure field, and finally the aerodynamic pressure field distribution pattern data of the aircraft are obtained, enabling it to reflect the pressure distribution law of the aircraft under different flight conditions.

[0137] Step S245: Perform dynamic pressure field distribution correction analysis on the aerodynamic pressure field data of the aircraft based on the aerodynamic pressure field distribution pattern data of the aircraft to obtain the aerodynamic pressure distribution data of the aircraft.

[0138] In an embodiment of the present invention, by combining the aerodynamic pressure field distribution pattern data obtained from previous analysis and using a pressure field data correction tool (such as the optimization toolbox of MATLAB or the Scipy library of Python), the identified pressure field distribution pattern is applied to the original aerodynamic pressure field data of the aircraft, and by adjusting the model parameters and correction algorithm, the inconsistencies and errors in the original pressure field data are corrected. At the same time, iterative optimization is performed to ensure that the corrected pressure field data conforms to the aerodynamic pressure field of the actual aircraft. Finally, the aerodynamic pressure distribution data of the aircraft are obtained. These corrected data can be used for further aerodynamic performance evaluation and design optimization.

[0139] Furthermore, step S3 includes the following steps:

[0140] Step S31: Integrate the aerodynamic load data, aerodynamic flow field parameter data, and aerodynamic pressure distribution data of the aircraft in the processed data layer to obtain an integrated dataset of the aircraft's aerodynamic characteristics;

[0141] In the embodiment of the present invention, by using data processing software, the aerodynamic load data, aerodynamic flow field parameter data, and aerodynamic pressure distribution data of the aircraft in the processed data layer are imported into the corresponding data analysis and processing libraries, such as the data analysis libraries of MATLAB or Python. Then, through data cleaning and preprocessing, outliers are removed and the data is standardized. Subsequently, statistical analysis methods, such as principal component analysis (PCA) or factor analysis, are applied to perform a fusion analysis of the aerodynamic characteristic parameters of different aerodynamic load data, aerodynamic flow field parameter data, and aerodynamic pressure distribution data of the aircraft. Finally, an integrated dataset of the aircraft's aerodynamic characteristics is obtained. This dataset includes multi-dimensional aerodynamic characteristic parameters and is used for subsequent analysis and modeling.

[0142] Step S32: Perform an aerodynamic flight behavior fitting analysis on the integrated dataset of the aircraft's aerodynamic characteristics to generate an aerodynamic flight behavior fitting pattern of the aircraft; Based on the aerodynamic flight behavior fitting pattern of the aircraft, perform an aerodynamic load response modeling on the aerodynamic load data of the aircraft to generate a sub-model of the aerodynamic behavior fitting load response field of the aircraft;

[0143] In the embodiment of the present invention, by using curve fitting techniques (such as the least squares method or non-linear regression model), perform a fitting analysis of the flight behavior path pattern on the integrated dataset of the aircraft's aerodynamic characteristics obtained after the previous fusion analysis, so as to classify and statistically analyze the aerodynamic characteristic data according to different flight states and flight phases. At the same time, use the fitting algorithm to establish a relationship model between the aerodynamic behavior and the flight state, and optimize the parameters of the model through the cross-validation method, thereby fitting and generating the aerodynamic flight behavior fitting pattern of the aircraft. At the same time, by combining the previously generated aerodynamic flight behavior fitting pattern of the aircraft, perform a response modeling on the corresponding aerodynamic load data of the aircraft, so as to construct an aerodynamic load response model by using a modeling tool (such as Simulink of MATLAB or Scikit-learn of Python). First, extract the key features from the aerodynamic flight behavior fitting pattern of the aircraft, construct the input parameters of the response model, and use these feature data to train the aerodynamic load of the aircraft, thereby generating an aerodynamic load response model. During the modeling process, use model validation techniques (such as K-fold cross-validation) to evaluate the accuracy and stability of the model, and finally simulate and generate a sub-model of the aerodynamic behavior fitting load response field of the aircraft for real-time prediction of the aerodynamic load response of the aircraft.

[0144] Step S33: Perform aerodynamic parameter feature point cloud analysis on the aircraft aerodynamic flow field parameter data and the aircraft aerodynamic pressure distribution data in the processed data layer to obtain the aircraft aerodynamic flow field parameter feature point cloud set and the aircraft aerodynamic pressure distribution field feature point cloud set;

[0145] In the embodiment of the present invention, by using a feature point cloud analysis tool (such as CloudCompare or the point cloud toolbox of MATLAB), perform point cloud analysis on the aircraft aerodynamic flow field parameter data and the aircraft aerodynamic pressure distribution data in the processed data layer obtained from the previous analysis, so as to convert the aerodynamic flow field parameter data and the aerodynamic pressure distribution data into point cloud format respectively, and perform denoising processing. At the same time, apply a point cloud clustering algorithm (such as DBSCAN or K-means) to extract feature points, and finally obtain the aircraft aerodynamic flow field parameter feature point cloud set and the aircraft aerodynamic pressure distribution field feature point cloud set.

[0146] Step S34: Perform aerodynamic flow field modeling based on the aircraft aerodynamic flow field parameter feature point cloud set to generate an aircraft aerodynamic flow field sub-model; perform aerodynamic pressure distribution field modeling based on the aircraft aerodynamic pressure distribution field feature point cloud set to generate an aircraft aerodynamic pressure distribution field sub-model;

[0147] In the embodiment of the present invention, by using a three-dimensional modeling software (such as ANSYS Fluent or OpenFOAM), perform aerodynamic flow field modeling on the aircraft aerodynamic flow field parameter feature point cloud set extracted previously, so as to convert the feature point cloud data into a grid model, perform grid optimization and refinement, and calculate parameters such as velocity and pressure in the aerodynamic flow field through numerical simulation methods, thereby establishing and generating an aircraft aerodynamic flow field sub-model. Similarly, perform aerodynamic pressure distribution field modeling based on the aircraft aerodynamic pressure distribution field feature point cloud set, also using the same modeling tools and methods, thereby establishing and generating a sub-model of the aerodynamic pressure distribution field, and ensuring the accuracy and reliability of the sub-model through simulation and verification, and finally generating an aircraft aerodynamic pressure distribution field sub-model.

[0148] Step S35: Integrate the aerodynamic model data of the aircraft aerodynamic behavior fitting load response field sub-model, the aircraft aerodynamic flow field sub-model, and the aircraft aerodynamic pressure distribution field sub-model to generate an aircraft aerodynamic model data set, and transmit and store it in the high-level model data layer of the aircraft aerodynamic multi-level database storage architecture.

[0149] In an embodiment of the present invention, by using a data integration tool, the aerodynamic behavior fitting load response sub-model, the aerodynamic flow field sub-model, and the aerodynamic pressure distribution sub-model of the aircraft established previously are integrated for the aerodynamic model data, so as to merge the data of the above-mentioned sub-models, thereby integrating and generating a corresponding aircraft aerodynamic model data set. Finally, the integrated aerodynamic model data set is transmitted and stored in the high-level model data layer of the aircraft aerodynamic multi-level database storage architecture, so as to ensure that all aerodynamic model data are properly managed in the database for subsequent query and analysis.

[0150] Further, step S4 includes the following steps:

[0151] Step S41: Calibrate the aerodynamic multi-dimensional data points of the basic data layer to generate a set of calibrated data points for the basic data layer;

[0152] In an embodiment of the present invention, by calibrating the data points of the aerodynamic data previously stored in the basic data layer, these data include, but are not limited to, aerodynamic data such as aerodynamic forces, aerodynamic moments, and airflow characteristics at different angles of attack, speeds, and altitudes. The data acquisition system is used to record these experimental data and perform preliminary cleaning and normalization processing to ensure the accuracy and consistency of the data. The aerodynamic modeling software is used to calibrate these data points through an interpolation algorithm, such as using the spline interpolation or Kriging interpolation method, to ensure that each data point has an accurate aerodynamic characteristic description in the basic data layer. Finally, a set of calibrated data points for the basic data layer is generated, and this set will contain the aerodynamic characteristic values corresponding to each type of aerodynamic data.

[0153] Step S42: Based on the set of calibrated data points for the basic data layer, map the data index relationship of the processing data layer to obtain the aerodynamic data index relationship structure between the basic layer data points and the processing layer;

[0154] In the embodiment of the present invention, by combining each calibration data point in the calibration data point set of the basic data layer generated by previous calibration, the data mapping algorithm (such as multi-dimensional space mapping or principal component analysis) is used to perform mapping matching of the data index relationship on the corresponding pneumatic processing parameter data in the processing data layer, so as to realize the mapping between the calibration data points in the basic data layer and the processing data layer. The specific operation includes comparing the pneumatic characteristic value corresponding to each calibration data point in the basic data layer with the data in the processing data layer, and calculating the relationship between the two layers of data by linear regression or the least square method. At the same time, an index relationship structure is generated by using the calculation result to record the pneumatic data index relationship between the data points in the basic data layer and the processing data layer. The establishment of this relationship structure ensures that the corresponding data points in the basic data layer can be quickly retrieved in the processing data layer, thereby realizing the effective connection between the data layers and optimizing the data retrieval, and finally obtaining the pneumatic data index relationship structure between the basic layer data points and the processing layer.

[0155] Step S43: Dynamically optimize the hierarchical index of the corresponding aircraft aerodynamic model in the high-level model data layer according to the pneumatic data index relationship structure between the basic layer data points and the processing layer, so as to generate a basic-processing-high-level model dynamic hierarchical data index relationship table;

[0156] In the embodiment of the present invention, by combining the pneumatic data index relationship structure between the basic layer data points and the processing layer established by previous mapping, the hierarchical index of the corresponding aerodynamic model in the high-level model data layer is dynamically optimized, so as to analyze the relationship between the basic data layer and the processing data layer, and further index and match the corresponding aerodynamic models in the high-level model data layer based on these relationships. That is, how to obtain the corresponding intermediate processing data from the basic data analysis and finally generate the corresponding aerodynamic model, so as to realize the hierarchical index link of the basic data points to the high-level model data layer through the mapping relationship of the processing data layer. Specifically, the hierarchical optimization technology (such as tree structure or graph structure optimization algorithm) is used to dynamically adjust the high-level model data to ensure that each data point in the high-level model accurately matches the data points in the basic data layer and the processing data layer, and finally connect and optimize to generate a basic-processing-high-level model dynamic hierarchical data index relationship table, which records the data point relationship from the basic layer to the high-level model, so as to improve the efficiency of pneumatic data processing and query.

[0157] Step S44: Perform R-tree multi-dimensional index connection on the data index relationships in the basic data layer, the processing data layer, and the high-level model data layer based on the basic-processing-high-level model dynamic hierarchical data index relationship table, so as to obtain a multi-level storage database of the aircraft aerodynamic model.

[0158] In an embodiment of the present invention, by combining the basic - processing - high - level model dynamic hierarchical data index relationship table obtained through previous dynamic connection optimization, a multi - dimensional index connection of the corresponding data index relationships in the basic data layer, the processing data layer, and the high - level model data layer is performed using an R - tree, so as to establish a multi - level storage database for the aircraft aerodynamic model, and to implement indexing of the aerodynamic data in the basic data layer, the processing data layer, and the high - level model data layer by using an R - tree (or other multi - dimensional space index structures, such as KD - tree). The specific steps include constructing an R - tree index structure to store the spatial positions and related information of each data point in the basic data layer, the processing data layer, and the high - level model data layer in the R - tree nodes, and through constructing a multi - dimensional index, it is possible to quickly locate the relevant data points during query. Then, the index structure is applied to the data points in the basic - processing - high - level model dynamic hierarchical data index relationship table to achieve multi - level aerodynamic data association, thereby connecting to establish a multi - level storage database to store the index structure and the actual aerodynamic model data in the database, realizing efficient data management and retrieval, and finally obtaining a multi - level storage database for the aircraft aerodynamic model.

[0159] Furthermore, the present invention also provides a system for establishing an aircraft aerodynamic model database, which is used to execute the method for establishing an aircraft aerodynamic model database as described above. The system for establishing an aircraft aerodynamic model database includes:

[0160] A storage architecture establishment and basic data layer storage module, which is used to obtain the storage requirements and query requirements of aircraft aerodynamic data, and perform a multi - level storage architecture design according to the storage requirements and query requirements of aircraft aerodynamic data to obtain an aircraft aerodynamic multi - level database storage architecture, where the aircraft aerodynamic multi - level database storage architecture includes a basic data layer, a processing data layer, and a high - level model data layer; by collecting the original aircraft aerodynamic data sets from wind tunnel experiments, flight tests, and CFD numerical simulations, and transmitting and storing them in the basic data layer within the aircraft aerodynamic multi - level database storage architecture;

[0161] A processing data layer storage module, which is used to analyze the original aircraft aerodynamic data sets in the basic data layer for aerodynamic loads, flow field parameters, and aerodynamic pressure distribution to obtain aircraft aerodynamic load data, aircraft aerodynamic flow field parameter data, and aircraft aerodynamic pressure distribution data, and transmit and store them in the processing data layer within the aircraft aerodynamic multi - level database storage architecture;

[0162] The high-level model data layer storage module is used to construct an aircraft aerodynamic model for the aircraft aerodynamic load data, aircraft aerodynamic flow field parameter data, and aircraft aerodynamic pressure distribution data within the processed data layer, so as to generate an aircraft aerodynamic model data set, and transmit and store it in the high-level model data layer of the aircraft aerodynamic multi-level database storage architecture;

[0163] The aerodynamic model database index connection establishment module is used to perform R-tree multi-dimensional index connection on the data index relationships within the basic data layer, processed data layer, and high-level model data layer to obtain an aircraft aerodynamic model multi-level storage database.

[0164] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for establishing an aircraft aerodynamic model database, characterized in that It includes the following steps: Step S1: Obtain the aircraft aerodynamic data storage requirements and the aircraft aerodynamic data query requirements, and design a multi-level storage architecture according to the aircraft aerodynamic data storage requirements and the aircraft aerodynamic data query requirements to obtain the aircraft aerodynamic multi-level database storage architecture, where the aircraft aerodynamic multi-level database storage architecture includes a basic data layer, a processed data layer, and a high-level model data layer; collect the original aircraft aerodynamic data sets from wind tunnel experiments, flight tests, and CFD numerical simulations, and transmit and store them in the basic data layer within the aircraft aerodynamic multi-level database storage architecture; Step S2: Analyze the original aircraft aerodynamic data sets in the basic data layer for aerodynamic loads, flow field parameters, and aerodynamic pressure distributions to obtain aircraft aerodynamic load data, aircraft aerodynamic flow field parameter data, and aircraft aerodynamic pressure distribution data, and transmit and store them in the processed data layer within the aircraft aerodynamic multi-level database storage architecture; Step S3: Construct an aircraft aerodynamic model for the aircraft aerodynamic load data, aircraft aerodynamic flow field parameter data, and aircraft aerodynamic pressure distribution data in the processed data layer to generate an aircraft aerodynamic model data set, and transmit and store it in the high-level model data layer within the aircraft aerodynamic multi-level database storage architecture; among them, Step S3 includes the following steps: Step S31: Integrate the aerodynamic characteristic parameters of the aircraft aerodynamic load data, aircraft aerodynamic flow field parameter data, and aircraft aerodynamic pressure distribution data in the processed data layer to obtain an integrated aircraft aerodynamic characteristic data set; Step S32: Conduct aerodynamic flight behavior fitting analysis on the integrated aircraft aerodynamic characteristic data set to generate an aerodynamic flight behavior fitting mode for the aircraft; based on the aerodynamic flight behavior fitting mode, perform aerodynamic load response modeling on the aircraft aerodynamic load data to generate an aerodynamic behavior fitting load response sub-model for the aircraft; Step S33: Conduct aerodynamic parameter feature point cloud analysis on the aircraft aerodynamic flow field parameter data and the aircraft aerodynamic pressure distribution data in the processed data layer to obtain an aircraft aerodynamic flow field parameter feature point cloud set and an aircraft aerodynamic pressure distribution field feature point cloud set; Step S34: Conduct aerodynamic flow field modeling based on the aircraft aerodynamic flow field parameter feature point cloud set to generate an aircraft aerodynamic flow field sub-model; conduct aerodynamic pressure distribution field modeling based on the aircraft aerodynamic pressure distribution field feature point cloud set to generate an aircraft aerodynamic pressure distribution field sub-model; Step S35: Integrate the aerodynamic model data of the aircraft aerodynamic behavior fitting load response sub-model, the aircraft aerodynamic flow field sub-model, and the aircraft aerodynamic pressure distribution field sub-model to generate an aircraft aerodynamic model data set, and transmit and store it in the high-level model data layer within the aircraft aerodynamic multi-level database storage architecture; Step S4: Perform R-tree multi-dimensional index connection on the data index relationships in the basic data layer, the processed data layer, and the high-level model data layer to obtain an aircraft aerodynamic model multi-level storage database.

2. The method for establishing an aircraft aerodynamic model database according to claim 1, wherein Step S1 includes the following steps: Step S11: Obtain the aircraft aerodynamic data storage requirements and the aircraft aerodynamic data query requirements; Step S12: Design a multi-level storage architecture according to the aircraft aerodynamic data storage requirements and the aircraft aerodynamic data query requirements to obtain an aircraft aerodynamic multi-level database storage architecture, where the aircraft aerodynamic multi-level database storage architecture includes a basic data layer, a processed data layer, and a high-level model data layer; Step S13: Collect the original aircraft wind tunnel experiment dataset, the original aircraft flight test dataset, and the original aircraft CFD simulation dataset from wind tunnel experiments, flight tests, and CFD numerical simulations; Step S14: Perform data integration and analysis on the original aircraft wind tunnel experiment dataset, the original aircraft flight test dataset, and the original aircraft CFD simulation dataset to obtain the original aircraft aerodynamic dataset, and transmit and store it in the basic data layer within the aircraft aerodynamic multi-level database storage architecture.

3. The method for establishing an aircraft aerodynamic model database according to claim 2, characterized in that Step S12 includes the following steps: Step S121: Conduct a multi-level storage target analysis according to the aircraft aerodynamic data storage requirements and the aircraft aerodynamic data query requirements to obtain the aircraft aerodynamic immediate storage target, the aircraft aerodynamic intermediate data processing and storage target, and the aircraft aerodynamic model construction storage target; Step S122: Design the basic data layer according to the aircraft aerodynamic immediate storage target to obtain the basic data layer; Step S123: Design the processed data layer according to the aircraft aerodynamic intermediate data processing and storage target to obtain the processed data layer; Step S124: Design the high-level model data layer according to the aircraft aerodynamic model construction storage target to obtain the high-level model data layer; Step S125: Integrate the multi-level architecture designs of the basic data layer, the processed data layer, and the high-level model data layer to obtain the aircraft aerodynamic multi-level database storage architecture.

4. The method for establishing an aircraft aerodynamic model database according to claim 1, wherein Step S2 includes the following steps: Step S21: Perform data preprocessing and standardization on the original aircraft aerodynamic dataset in the basic data layer to obtain the aircraft aerodynamic standardized dataset; Step S22: Perform a relational database storage format conversion on the aircraft aerodynamic standardized dataset to obtain the aircraft aerodynamic storage format dataset; Step S23: Conduct an aerodynamic load analysis on the aircraft aerodynamic storage format dataset to obtain the aircraft aerodynamic load data; Step S24: Conduct an analysis of aerodynamic flow field parameters and aerodynamic pressure distribution on the aircraft aerodynamic storage format dataset to obtain the aircraft aerodynamic flow field parameter data and the aircraft aerodynamic pressure distribution data; Step S25: Transmit and store the aircraft aerodynamic load data, the aircraft aerodynamic flow field parameter data, and the aircraft aerodynamic pressure distribution data in the processed data layer within the aircraft aerodynamic multi-level database storage architecture.

5. The method for establishing an aircraft aerodynamic model database according to claim 4, wherein Step S23 includes the following steps: Step S231: Extract and process the aerodynamic lift and drag parameters from the aircraft aerodynamic storage format dataset to obtain the aircraft aerodynamic lift parameter data and the aircraft aerodynamic drag parameter data; Step S232: Perform time series synchronization processing on the aircraft aerodynamic lift parameter data and the aircraft aerodynamic drag parameter data to obtain the aircraft aerodynamic lift time series data and the aircraft aerodynamic drag time series data in the same time series dimension; Step S233: Perform aerodynamic parameterization simulation design on the aircraft aerodynamic lift time series data and the aircraft aerodynamic drag time series data in the same time series dimension to generate an aircraft aerodynamic parameterization simulation operation field; Step S234: Perform frame-by-frame parameter change sampling on the aircraft aerodynamic parameterization simulation operation field to obtain the aerodynamic lift change value and the aerodynamic drag change value at each time frame in the aircraft aerodynamic simulation field; Based on the aerodynamic lift change value and the aerodynamic drag change value at each time frame in the aircraft aerodynamic simulation field, perform frame-by-frame transient aerodynamic load quantization calculation on the aircraft aerodynamic parameterization simulation operation field to obtain the aircraft frame-by-frame transient aerodynamic load data; Step S235: Perform aerodynamic load trend analysis on the aircraft frame-by-frame transient aerodynamic load data to obtain the aircraft aerodynamic load change trend data; Based on the aircraft aerodynamic load change trend data, perform aerodynamic load dynamic fluctuation analysis on the aircraft frame-by-frame transient aerodynamic load data to obtain the aircraft dynamic aerodynamic load fluctuation change data; Step S236: Merge the aircraft frame-by-frame transient aerodynamic load data and the aircraft dynamic aerodynamic load fluctuation change data to obtain the aircraft aerodynamic load data.

6. The method for establishing an aircraft aerodynamic model database according to claim 5, wherein The frame-by-frame transient aerodynamic load quantization calculation in Step S234 is calculated through the aircraft frame-by-frame transient aerodynamic load calculation formula, where the specific formula of the aircraft frame-by-frame transient aerodynamic load calculation formula is: where ε(t) is the transient aerodynamic load of the aircraft at time frame t, t is the variable parameter of the time frame, ρ is the gas density, C L (α(t), V(t)) is the change value of the aerodynamic lift in the aircraft aerodynamic simulation field at time frame t, V(t) is the instantaneous velocity of the aircraft at time frame t, γ is the aircraft speed adjustment coefficient, α(t) is the angle of attack of the aircraft aerodynamic lift at time frame t, is the maximum aerodynamic lift coefficient of the aircraft, α0 is the zero-lift angle of attack of the aircraft aerodynamics, α max is the maximum lift angle of attack of the aircraft aerodynamics, C D (β(t), V(t)) is the change value of the aerodynamic drag in the aircraft aerodynamic simulation field at time frame t, β(t) is the angle of depression of the aircraft aerodynamic drag at time frame t, is the maximum aerodynamic drag coefficient of the aircraft, β0 is the zero-drag angle of depression of the aircraft aerodynamics, β max is the maximum drag angle of depression of the aircraft aerodynamics, S is the reference area of the aircraft carrier, and θ is the correction coefficient of the transient aerodynamic load.

7. The method for establishing an aircraft aerodynamic model database according to claim 4, characterized in that Step S24 includes the following steps: Step S241: Perform aerodynamic flow field boundary condition analysis on the aircraft aerodynamic storage formatted data set to obtain the aircraft aerodynamic flow field boundary conditions; Step S242: Based on the aircraft aerodynamic flow field boundary conditions, perform fluid dynamics virtual simulation analysis on the aircraft aerodynamic storage formatted data set to generate an aircraft aerodynamic virtual simulation flow field; Perform aerodynamic flow field topology analysis on the aircraft aerodynamic virtual simulation flow field to obtain the aircraft aerodynamic flow field topology structure distribution map; Step S243: Perform aerodynamic flow field parameter analysis on the aircraft aerodynamic flow field topology structure distribution map to obtain the aircraft aerodynamic flow field parameter data; Step S244: Extract the aerodynamic pressure field data from the aircraft aerodynamic storage formatted data set to obtain the aircraft aerodynamic pressure field data; Perform pressure field distribution pattern recognition analysis on the aircraft aerodynamic pressure field data to obtain the aircraft aerodynamic pressure field distribution pattern data; Step S245: Based on the aircraft aerodynamic pressure field distribution pattern data, perform dynamic pressure field distribution correction analysis on the aircraft aerodynamic pressure field data to obtain the aircraft aerodynamic pressure distribution data.

8. The method for establishing an aircraft aerodynamic model database according to claim 1, wherein Step S4 includes the following steps: Step S41: Perform aerodynamic multi-dimensional data point calibration on the basic data layer to generate a basic data layer calibration data point set; Step S42: Based on the calibration data point set in the basic data layer, perform data index relationship mapping on the processed data layer to obtain the aerodynamic data index relationship structure between the basic layer data points and the processed layer; Step S43: According to the aerodynamic data index relationship structure between the basic layer data points and the processed layer, perform dynamic hierarchical index optimization on the corresponding aircraft aerodynamic model in the high-level model data layer to generate a basic-processed-high-level model dynamic hierarchical data index relationship table; Step S44: Based on the basic-processed-high-level model dynamic hierarchical data index relationship table, perform R-tree multi-dimensional index connection on the data index relationships in the basic data layer, the processed data layer, and the high-level model data layer to obtain a multi-level storage database for the aircraft aerodynamic model.

9. A system for establishing an aircraft aerodynamic model database, characterized in that For implementing the method for establishing an aircraft aerodynamic model database as described in claim 1, the system for establishing an aircraft aerodynamic model database includes: A storage architecture establishment and basic data layer storage module, configured to obtain the aircraft aerodynamic data storage requirement and the aircraft aerodynamic data query requirement, and perform multi-level storage architecture design according to the aircraft aerodynamic data storage requirement and the aircraft aerodynamic data query requirement to obtain an aircraft aerodynamic multi-level database storage architecture, where the aircraft aerodynamic multi-level database storage architecture includes a basic data layer, a processed data layer, and a high-level model data layer; collect the original aircraft aerodynamic data sets from wind tunnel experiments, flight tests, and CFD numerical simulations, and transmit and store them in the basic data layer within the aircraft aerodynamic multi-level database storage architecture; A processed data layer storage module, configured to perform analysis on the original aircraft aerodynamic data sets in the basic data layer for aerodynamic loads, flow field parameters, and aerodynamic pressure distribution to obtain aircraft aerodynamic load data, aircraft aerodynamic flow field parameter data, and aircraft aerodynamic pressure distribution data, and transmit and store them in the processed data layer within the aircraft aerodynamic multi-level database storage architecture; A high-level model data layer storage module, configured to construct an aircraft aerodynamic model for the aircraft aerodynamic load data, aircraft aerodynamic flow field parameter data, and aircraft aerodynamic pressure distribution data in the processed data layer to generate an aircraft aerodynamic model data set, and transmit and store them in the high-level model data layer within the aircraft aerodynamic multi-level database storage architecture; An aerodynamic model database index connection establishment module, configured to perform R-tree multi-dimensional index connection on the data index relationships in the basic data layer, the processed data layer, and the high-level model data layer to obtain a multi-level storage database for the aircraft aerodynamic model.

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