Establishment method and system based on aircraft aerodynamic model database

Abstract

The invention relates to the technical field of database establishment, in particular to an establishment method and system based on an aircraft aerodynamic model database. The method comprises the following steps: obtaining an aircraft pneumatic data storage demand and an aircraft pneumatic data query demand, and carrying out multi-level storage architecture design to obtain a basic data layer, a processing data layer and a high-level model data layer; the method comprises the following steps: collecting aircraft original aerodynamic data sets of wind tunnel experiments, flight tests and CFD numerical simulation, and transmitting and storing the aircraft original aerodynamic data sets to a basic data layer; carrying out aerodynamic load, flow field parameter and aerodynamic pressure distribution analysis on the original aerodynamic data set of the aircraft in the basic data layer, and transmitting and storing the aerodynamic load, flow field parameter and aerodynamic pressure distribution to the processing data layer; and carrying out aircraft aerodynamic model construction on the data in the processing data layer, transmitting and storing the data to a high-level model data layer, and carrying out R-tree multi-dimensional index connection to obtain an aircraft aerodynamic model multi-level storage database. According to the invention, an efficient and intelligent aerodynamic model database can be established.

Description

Technical Field

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

[0002] In aerospace engineering, 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 verification and optimization, which often requires a large amount of experimental data and computing resources. By establishing a comprehensive aerodynamic model database, the aerodynamic data of different aircraft under various flight conditions can be systematically collected, stored and managed. The establishment of such a database involves several key steps. First, by defining and collecting aerodynamic data under different flight conditions, including speed, angle, airflow characteristics, etc., and analyzing these data by using data mining and machine learning techniques, an aerodynamic model with wide applicability is established. Then, the relevant data of the aerodynamic model is stored and updated through the database management system to ensure the integrity and real-time nature of the aerodynamic data. However, the traditional method of establishing an aerodynamic model database is usually based on a single model or a specific flight state. Such a model cannot cover all flight conditions and configurations in practical applications, thus limiting the specific process of updating and maintaining its 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 comprises the following steps:

[0005] Step S1: Obtaining aircraft aerodynamic data storage requirements and aircraft aerodynamic data query requirements, and designing a multi-level storage architecture according to the aircraft aerodynamic data storage requirements and aircraft aerodynamic data query requirements to obtain an aircraft aerodynamic multi-level database storage architecture, wherein 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 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 of the aircraft aerodynamic multi-level database storage architecture;

[0006] Step S2: performing aerodynamic load, flow field parameter and aerodynamic pressure distribution analysis on the original aerodynamic data set of the aircraft in the basic data layer to obtain aircraft aerodynamic load data, aircraft aerodynamic flow field parameter data and aircraft aerodynamic pressure distribution data, and transmitting and storing them in the processing data layer in the aircraft aerodynamic multi-level database storage architecture;

[0007] Step S3: constructing 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 transmitting and storing it in a high-level model data layer in the aircraft aerodynamic multi-level database storage architecture;

[0008] Step S4: Perform R-tree multidimensional index connection on the data index relationships in the basic data layer, the processing data layer and the high-level model data layer to obtain a multi-level storage database of the aircraft aerodynamic model.

[0009] Further, step S1 includes the following steps:

[0010] Step S11: Obtaining aircraft pneumatic data storage requirements and aircraft pneumatic data query requirements;

[0011] Step S12: designing a multi-level storage architecture according to the aircraft pneumatic data storage requirements and the aircraft pneumatic data query requirements to obtain an aircraft pneumatic multi-level database storage architecture, wherein the aircraft pneumatic multi-level database storage architecture includes a basic data layer, a processing data layer, and a high-level model data layer;

[0012] Step S13: collecting original data sets of aircraft wind tunnel experiments, original data sets of aircraft flight tests, and original data sets of aircraft CFD simulations from wind tunnel experiments, flight tests, and CFD numerical simulations;

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

[0014] Further, step S12 includes the following steps:

[0015] Step S121: performing a multi-level storage target analysis according to the aircraft pneumatic data storage requirements and the aircraft pneumatic data query requirements, and obtaining an aircraft pneumatic data real-time storage target, an aircraft pneumatic intermediate data processing storage target, and an aircraft pneumatic model construction storage target;

[0016] Step S122: designing a basic data layer according to the real-time storage target of aircraft aerodynamic data to obtain a basic data layer;

[0017] Step S123: Designing a processing data layer according to the aircraft aerodynamic intermediate data processing and storage target to obtain a processing data layer;

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

[0019] 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 an aircraft aerodynamic multi-level database storage architecture.

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

[0021] Step S21: preprocessing and standardizing the original aerodynamic data set of the aircraft in the basic data layer to obtain a standardized aerodynamic data set of the aircraft;

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

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

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

[0025] Step S25: storing the aircraft aerodynamic load data, aircraft aerodynamic flow field parameter data and aircraft aerodynamic pressure distribution data in the processing data layer of the aircraft aerodynamic multi-level database storage architecture.

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

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

[0028] Step S232: performing 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: performing aerodynamic parameterized 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 parameterized simulation operation field;

[0030] Step S234: sampling the parameter changes of the aircraft aerodynamic parameterized simulation running field frame by frame to obtain the aerodynamic lift change value and the aerodynamic drag change value at each time frame in the aircraft aerodynamic simulation field; performing frame by frame transient aerodynamic load quantitative calculation on the aircraft aerodynamic parameterized simulation running 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: performing aerodynamic load trend analysis on the aircraft frame-by-frame transient aerodynamic load data to obtain aircraft aerodynamic load change trend data; performing 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 aircraft dynamic aerodynamic load fluctuation change data;

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

[0033] Further, the frame-by-frame transient aerodynamic load quantification calculation in step S234 is calculated by using the aircraft frame-by-frame transient aerodynamic load calculation formula, wherein the aircraft frame-by-frame transient aerodynamic load calculation formula is specifically:

[0034]

[0035] Where 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, and C L (α(t), V(t)) is the change value of aerodynamic lift in the aircraft aerodynamic simulation field at time frame t, V(t) is the instantaneous speed 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 lift coefficient of the aircraft, α0 is the zero lift angle of the aircraft, max is the maximum aerodynamic lift angle of the aircraft, C D (β(t), V(t)) is the change value of aerodynamic drag in the aircraft aerodynamic simulation field at time frame t, β(t) is the pitch angle of aerodynamic drag of the aircraft at time frame t, is the maximum aerodynamic drag coefficient of the aircraft, β0 is the aerodynamic zero drag pitch angle of the aircraft, β max is the pitch angle of maximum aerodynamic drag of the aircraft, S is the reference area of ​​the aircraft carrier, and θ is the correction coefficient of the transient aerodynamic load.

[0036] Further, step S24 includes the following steps:

[0037] Step S241: performing aerodynamic flow field boundary condition analysis on the aircraft aerodynamic storage formatted data set to obtain the aircraft aerodynamic flow field boundary condition;

[0038] 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;

[0039] 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;

[0040] Step S244: extracting aerodynamic pressure field data from the aircraft aerodynamic storage formatted data set to obtain aircraft aerodynamic pressure field data; performing pressure field distribution pattern recognition analysis on the aircraft aerodynamic pressure field data to obtain aircraft aerodynamic pressure field distribution pattern data;

[0041] Step S245: performing dynamic pressure field distribution correction analysis on the aircraft aerodynamic pressure field data based on the aircraft aerodynamic pressure field distribution pattern data to obtain the aircraft aerodynamic pressure distribution data.

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

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

[0044] Step S32: performing aerodynamic flight behavior fitting analysis on the aircraft aerodynamic characteristics comprehensive data set to generate an aircraft aerodynamic flight behavior fitting model; performing aerodynamic load response modeling on the aircraft aerodynamic load data based on the aircraft aerodynamic flight behavior fitting model to generate an aircraft aerodynamic behavior fitting load response field sub-model;

[0045] Step S33: performing aerodynamic parameter characteristic 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 characteristic point cloud set and an aircraft aerodynamic pressure distribution field characteristic point cloud set;

[0046] Step S34: performing aerodynamic flow field modeling according to the characteristic point cloud set of the aircraft aerodynamic flow field parameters to generate an aircraft aerodynamic flow field sub-model; performing aerodynamic pressure distribution field modeling according to the characteristic point cloud set of the aircraft aerodynamic pressure distribution field to generate an aircraft aerodynamic pressure distribution field sub-model;

[0047] 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 within the aircraft aerodynamic multi-level database storage architecture.

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

[0049] Step S41: calibrate the pneumatic multi-dimensional data points of the basic data layer to generate a calibration data point set of the basic data layer;

[0050] Step S42: mapping the data index relationship of the processing data layer based on the calibration data point set of the basic data layer to obtain the pneumatic data index relationship structure between the basic layer data points and the processing layer;

[0051] Step S43: performing dynamic hierarchical index optimization on the corresponding aircraft aerodynamic model in the high-level model data layer according to the aerodynamic data index relationship structure between the base layer data points and the processing layer, so as to generate a base-processing-high-level model dynamic hierarchical data index relationship table;

[0052] Step S44: Based on the basic-processing-high-level model dynamic hierarchical data index relationship table, the data index relationships within the basic data layer, the processing data layer and the high-level model data layer are connected by R-tree multidimensional indexing to obtain a multi-level storage database of the aircraft aerodynamic model.

[0053] 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 comprises:

[0054] The storage architecture is established and the basic data layer storage module is used to obtain the aircraft aerodynamic data storage requirements and the aircraft aerodynamic data query requirements, and a multi-level storage architecture is designed 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, wherein 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;

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

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

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

[0058] Beneficial effects of the present invention:

[0059] 1. Compared with the prior art, the method for establishing an aircraft aerodynamic model database proposed in the present invention has the beneficial effect that by obtaining the corresponding aircraft aerodynamic data storage requirements and aircraft aerodynamic data query requirements, it can ensure that the storage and query database design of aircraft aerodynamic data can fully meet the actual needs. 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, data type, storage frequency and storage duration of the data volume, so as to select appropriate storage technology and strategy, for example, whether real-time storage, archiving of historical data, data backup, etc. are required, and the understanding of 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 rights, etc., so as to ensure that the database design can optimize storage efficiency, improve data access speed, and can handle data requests and analysis 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 efficient management and flexible processing of data by designing a multi-level database storage architecture. The multi-level storage architecture includes a basic data layer, a processing 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 the original data. These data come from different experiments and simulations, so efficient storage and retrieval capabilities are required. The processing 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. These data are usually used for further decision analysis and model prediction. Through this hierarchical design, efficient storage and management of data can be achieved, data redundancy and processing bottlenecks can be avoided, and the flexibility and scalability of the database establishment process can be improved. The hierarchical design can also optimize the data access speed and processing efficiency, and the corresponding aerodynamic model can be updated quickly and conveniently according to the real-time changes of the underlying aerodynamic data.At the same time, by collecting data sets 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 its sources help to improve the reliability and accuracy of the analysis results, and can reduce potential errors through cross-validation of different data sources. Such a data collection process helps to build a more complete aerodynamic database, so that subsequent data integration and analysis work can be carried out with sufficient data support, thereby being able to obtain data covering all flight conditions and configurations of the aircraft, and can improve the overall performance of the aerodynamic model database establishment process. By storing the integrated data in the basic data layer, the persistence and security of the data can be ensured, and the foundation for further processing and analysis of the data can be laid. The design of the basic data layer can support efficient data storage and retrieval, and ensure the fluidity and accessibility of the original data in the entire database. Secondly, by performing aerodynamic load analysis on the original aerodynamic data set of the aircraft in the basic data layer, we can understand the key processes of the aerodynamic forces acting on the aircraft under different flight conditions. By performing aerodynamic load analysis on the corresponding data set, we can accurately calculate and predict the aerodynamic loads of the aircraft, thereby evaluating the pressure and stress it is subjected to in actual flight. This analysis helps to identify potential weaknesses and safety hazards in the aircraft design, and optimize the structural design of the aircraft to improve its performance and safety. The aerodynamic flow field parameters and aerodynamic pressure distribution are also analyzed on the original aerodynamic data set of the aircraft. This step can obtain detailed flow field characteristics and pressure distribution information by performing these two analyses on the stored formatted data set, which helps to identify the key factors of aerodynamic performance. The aerodynamic flow field parameter analysis can reveal the details 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. The aerodynamic pressure distribution analysis can display the pressure changes in various areas of the aircraft surface and provide important data on the distribution of aerodynamic loads. These data help to 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 aircraft's thermal management system and ensure its structural strength in high-pressure areas. Overall, these analyses can deeply reveal the aerodynamic behavior of the aircraft in actual flight, thereby providing valuable design improvement suggestions. Furthermore, by transmitting and storing aircraft aerodynamic load data, aircraft aerodynamic flow field parameter data, and aircraft aerodynamic pressure distribution data in the processing data layer within 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 data access faster and processing more powerful. This architecture can organize different types of data in layers, improve data management efficiency and query performance, and thus meet the high-performance computing needs of aircraft aerodynamic data.Finally, by performing R-tree multidimensional index connection on the data index relationships within the basic data layer, processing data layer and high-level model data layer, the index relationships of the basic, processing and high-level model data layers can be effectively integrated, thereby creating a multi-level storage database, in which the R-tree index structure can handle the complexity of multidimensional data and optimize the data access efficiency. Through this multidimensional 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 dynamic maintenance and updating of corresponding aerodynamic models to adapt to the ever-changing aerodynamic data requirements and model complexity, enabling it to process complex aerodynamic model data, thereby providing stable and reliable data support, enhancing the processing capabilities of large-scale aerodynamic data, and providing comprehensive data guarantee for aircraft design and performance analysis.

[0060] 2. The aircraft aerodynamic model database establishment system proposed in the present invention is generally composed of a storage architecture establishment and a basic data layer storage module, a processing data layer storage module, a high-level model data layer storage module and an aerodynamic model database index connection establishment module. It can implement any aircraft aerodynamic model database establishment method described in the present invention, and is used to combine the operations between computer programs running on each module to implement the aircraft aerodynamic model database establishment method. The internal structures of the system cooperate with each other, which can greatly reduce duplication of work and manpower investment, and can quickly and effectively provide a more accurate and efficient aircraft aerodynamic model database establishment process, thereby simplifying the operation flow of the aircraft aerodynamic model database establishment system. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

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

[0063] Figure 2 for Figure 1 Detailed step flow diagram of step S1;

[0064] Figure 3 for Figure 2 Detailed step flow chart of step S12 in FIG. DETAILED DESCRIPTION

[0065] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

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

[0067] Step S1: Obtaining aircraft aerodynamic data storage requirements and aircraft aerodynamic data query requirements, and designing a multi-level storage architecture according to the aircraft aerodynamic data storage requirements and aircraft aerodynamic data query requirements to obtain an aircraft aerodynamic multi-level database storage architecture, wherein 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 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 of the aircraft aerodynamic multi-level database storage architecture;

[0068] Step S2: performing aerodynamic load, flow field parameter and aerodynamic pressure distribution analysis on the original aerodynamic data set of the aircraft in the basic data layer to obtain aircraft aerodynamic load data, aircraft aerodynamic flow field parameter data and aircraft aerodynamic pressure distribution data, and transmitting and storing them in the processing data layer in the aircraft aerodynamic multi-level database storage architecture;

[0069] Step S3: constructing 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 transmitting and storing it in a high-level model data layer in the aircraft aerodynamic multi-level database storage architecture;

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

[0071] In the embodiment of the present invention, please refer to Figure 1 FIG. 1 is a schematic diagram of the steps of the method for establishing an aircraft aerodynamic model database according to the present invention. In this example, the method for establishing an aircraft aerodynamic model database comprises the following steps:

[0072] Step S1: Obtaining aircraft aerodynamic data storage requirements and aircraft aerodynamic data query requirements, and designing a multi-level storage architecture according to the aircraft aerodynamic data storage requirements and aircraft aerodynamic data query requirements to obtain an aircraft aerodynamic multi-level database storage architecture, wherein 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 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 of the aircraft aerodynamic multi-level database storage architecture;

[0073] In an embodiment of the present invention, detailed requirements for aircraft aerodynamic data are collected from the aircraft design process and aerodynamic analysis process, including recording the type of aerodynamic data, data generation frequency, data volume, data accuracy, and data update frequency, and the aerodynamic data is divided 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, the data storage requirements such as the storage capacity, storage format (such as relational database, non-relational database, file system, etc.) and backup and recovery strategy required for each type of data are determined, and the performance requirements for data query are determined, such as response time, number of concurrent queries, complexity of data retrieval, and other data query requirements, thereby obtaining the aircraft aerodynamic data storage requirements and aircraft aerodynamic data query requirements. In addition, a multi-level data storage architecture is designed by combining the aircraft aerodynamic data storage requirements and aircraft aerodynamic data query requirements obtained through previous analysis to design and generate corresponding basic data layers, processing data layers, and high-level model data layers. Each layer has specific storage and processing functions, wherein the basic data layer is designed to store raw data, including wind tunnel test 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 large-scale data storage requirements, and the processing data layer is used to store the results of raw aerodynamic data processing, such as data 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) to perform complex data analysis and model query, thereby designing an aircraft aerodynamic multi-level database storage architecture.Secondly, by extracting the corresponding aerodynamic data from the wind tunnel experimental equipment, including measurement data such as pressure, speed, 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 speed, attitude, aerodynamic lift, aerodynamic drag, etc., the data format includes 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 dedicated CFD software formats, such as CFD data files (such as OpenFOAM result files), and then, by using data integration tools (such as Apache Kafka or Apache Spark unifies the format of data from different sources and merges them into one data set. It cleans the original data, removes noise and inconsistencies, and performs data conversion operations, such as standardizing the data format and processing missing values, to obtain the original aerodynamic data set of the aircraft. It also transfers and stores the processed original aerodynamic data set of the aircraft to the basic data layer in the aircraft aerodynamic multi-level database storage architecture, and uses the interface provided by the data storage system (such as the INSERT statement of the database) for data storage.

[0074] Step S2: performing aerodynamic load, flow field parameter and aerodynamic pressure distribution analysis on the original aerodynamic data set of the aircraft in the basic data layer to obtain aircraft aerodynamic load data, aircraft aerodynamic flow field parameter data and aircraft aerodynamic pressure distribution data, and transmitting and storing them in the processing data layer in the aircraft aerodynamic multi-level database storage architecture;

[0075] In an embodiment of the present invention, the original aerodynamic data set of the aircraft in the basic data layer is preprocessed and standardized. The preprocessing process includes data cleaning and denoising. Data cleaning removes duplicate records and repairs missing data by writing a data cleaning algorithm. Denoising reduces measurement errors by applying a smoothing filter, such as a Gaussian filter or a median filter. Data standardization uses the mean and standard deviation method to standardize each original aerodynamic data to ensure that the scale of all data features is consistent, so that the data can be compared and analyzed under the same standard. The original aerodynamic data set of the aircraft obtained after the previous processing is converted into a data format suitable for storage in a relational database to ensure accurate insertion of data and establishment of relationships between tables. For example, using INSERT The INTO statement inserts the data into the corresponding table one by one and creates necessary indexes to improve query efficiency. At the same time, the aerodynamic load is quantitatively analyzed by the original aerodynamic data set of the aircraft obtained after 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 data stored in the formatted data set is imported, and by setting analysis parameters, such as the motion state of the aircraft, airflow conditions, etc., the analysis calculation is run to use fluid dynamics theory (such as Navier-Stokes equations) or empirical formulas to quantitatively calculate the corresponding transient aerodynamic loads, and through statistical and mathematical models (such as regression analysis) to statistically analyze the trend characteristics of the aerodynamic loads over time to analyze the dynamic fluctuation components in the trend data, so as to obtain detailed dynamic aerodynamic load fluctuation data, and through data merging algorithms, such as weighted average method or interpolation method, the transient aerodynamic load and fluctuation change data are combined. The merged data should fully cover the aerodynamic loads of the aircraft in the simulation environment, including static and dynamic parts, so as to obtain the aircraft aerodynamic load data. Then, the aerodynamic flow field parameters and pressure field distribution of the original aerodynamic data set of the aircraft obtained after data formatting are statistically analyzed, and the aerodynamic flow field data in the formatted data set are imported using computational fluid dynamics (CFD) software, such as ANSYS Fluent or CFD++, and the flow field area and boundary conditions are defined to perform flow field simulation calculations, thereby obtaining the aerodynamic flow field parameter data of the aircraft. The aerodynamic pressure distribution data is also obtained by using the pressure distribution analysis method, and the characteristics and pressure distribution of the aerodynamic flow field are displayed by drawing visualization results such as contour maps, pressure distribution maps, and streamline maps, thereby analyzing and obtaining the aerodynamic pressure distribution data of the aircraft.Finally, by using data transmission 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, and 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 of the processing data layer to confirm the integrity and consistency of the data. Finally, the above data are securely stored in the processing data layer of the aircraft aerodynamic multi-level database storage architecture.

[0076] Step S3: constructing 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 transmitting and storing it in a high-level model data layer in the aircraft aerodynamic multi-level database storage architecture;

[0077] In an embodiment of the present invention, the aircraft aerodynamic load data, aircraft aerodynamic flow field parameter data and aircraft aerodynamic pressure distribution data in the processing data layer are imported into a corresponding data analysis processing library, such as a data analysis library of MATLAB or Python, by using data processing software, and a fusion analysis of aerodynamic characteristic parameters is performed on different aircraft aerodynamic load data, aircraft aerodynamic flow field parameter data and aircraft aerodynamic pressure distribution data, and a curve fitting technology (such as the least squares method or a nonlinear regression model) is used to perform a fitting analysis of the flight behavior path pattern on the aerodynamic characteristic comprehensive data set obtained after the previous fusion analysis, so that the aerodynamic characteristic data is classified according to different flight states and flight stages for statistical analysis, and a flight behavior fitting pattern is generated by fitting using a fitting algorithm. At the same time, the corresponding aircraft aerodynamic load data is response modeled by combining the flight behavior fitting pattern generated by the previous fitting, so that aerodynamic load response sub-model is constructed using a modeling tool (such as Simulink of MATLAB or Scikit-learn of Python) for real-time prediction of the aerodynamic load response of the aircraft. Secondly, the aerodynamic flow field parameter data and the aerodynamic pressure distribution data of the aircraft in the previously analyzed processed data layer are analyzed by using a characteristic point cloud analysis tool (such as CloudCompare or MATLAB's point cloud toolbox) to perform aerodynamic characteristic parameter feature point cloud analysis, so as to convert the aerodynamic flow field parameter data and the aerodynamic pressure distribution data into point cloud format respectively, and the aerodynamic flow field modeling is performed on the previously extracted aircraft aerodynamic flow field parameter characteristic point cloud set by using a three-dimensional modeling software (such as ANSYS Fluent or OpenFOAM) to convert the characteristic point cloud data into a grid model, perform grid optimization and refinement, and calculate the velocity, pressure and other parameters in the aerodynamic flow field by numerical simulation methods, so as to establish a sub-model of the aircraft aerodynamic flow field. Similarly, the aerodynamic pressure distribution field is modeled based on the characteristic point cloud set of the aerodynamic pressure distribution field, and the same modeling tools and methods are used to establish a sub-model of the aerodynamic pressure distribution field. Finally, the aerodynamic model data of the previously established aerodynamic load response sub-model, aircraft aerodynamic flow field sub-model and aircraft aerodynamic pressure distribution field sub-model are integrated by using a data integration tool to merge the data of the above sub-models, thereby integrating and generating the 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, thereby ensuring that all aerodynamic model data are properly managed in the database for subsequent query and analysis.

[0078] Step S4: Perform R-tree multidimensional index connection on the data index relationships in the basic data layer, the processing data layer and the 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, the aerodynamic data previously stored in the basic data layer are calibrated, and the data points include but are not limited to aerodynamic data such as aerodynamic forces, aerodynamic torques, and airflow characteristics at different angles of attack, speeds, and altitudes. The aerodynamic modeling software is used to calibrate these data points through an interpolation algorithm, such as using a spline interpolation or a Kriging interpolation method, to ensure that each data point in the basic data layer has an accurate description of the aerodynamic characteristics, thereby calibrating and generating a calibrated data point set for the basic data layer, and using a data mapping algorithm to calibrate each calibrated data point in the calibrated data point set of the basic data layer generated by combining the previous calibration. The method (such as multidimensional space mapping or principal component analysis) is used to map and match the data index relationship of the corresponding aerodynamic processing parameter data in the processing data layer, so as to realize the mapping between the calibration data points of the basic data layer and the processing data layer. The specific operation includes comparing the aerodynamic characteristic value corresponding to each calibration data point in the basic data layer with the data in the processing data layer, thereby generating an aerodynamic data index relationship between the basic data layer data point and the processing data layer. At the same time, the aerodynamic data index relationship structure between the basic layer data point and the processing layer established by combining the previous mapping is used to map the corresponding aerodynamic model in the high-level model data layer. The model performs dynamic hierarchical index optimization 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, to ensure that the data points of each high-level model accurately match the data points of the basic data layer and the processing data layer, and then, by combining the data point index relationship from the basic layer to the high-level model obtained by the previous matching, the corresponding data index relationship in the basic data layer, the processing data layer and the high-level model data layer is multi-indexed. The multi-dimensional index is connected to establish a multi-level storage database of the aircraft aerodynamic model, and the aerodynamic data of the basic data layer, the processing data layer and the high-level model data layer are indexed by using the R-tree (or other multi-dimensional spatial index structures, such as the KD-tree). The specific steps include constructing an R-tree index structure to store the spatial position 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 node, and by constructing a multi-dimensional index, the relevant data points can be quickly located during the query, thereby connecting to establish a multi-level storage database, and finally obtaining a multi-level storage database of the aircraft aerodynamic model.

[0080] Further, as an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S1 in FIG. 1 , in this embodiment, step S1 includes the following steps:

[0081] Step S11: Obtaining aircraft pneumatic data storage requirements and aircraft pneumatic data query requirements;

[0082] In an embodiment of the present invention, detailed requirements for aircraft aerodynamic data are collected from the aircraft design process and aerodynamic analysis process, including recording the type of aerodynamic data, data generation frequency, data volume, data accuracy, and data update frequency, and the aerodynamic data is divided 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, the data storage requirements such as the storage capacity, storage format (such as relational database, non-relational database, file system, etc.) and backup and recovery strategy required for each type of data are determined, and the performance requirements for data query are determined, such as response time, number of concurrent queries, complexity of data retrieval, and other data query requirements, and finally the aircraft aerodynamic data storage requirements and aircraft aerodynamic data query requirements are obtained.

[0083] Step S12: designing a multi-level storage architecture according to the aircraft pneumatic data storage requirements and the aircraft pneumatic data query requirements to obtain an aircraft pneumatic multi-level database storage architecture, wherein the aircraft pneumatic multi-level database storage architecture includes a basic data layer, a processing data layer, and a high-level model data layer;

[0084] In an embodiment of the present invention, a multi-level data storage architecture is designed by combining the aircraft aerodynamic data storage requirements and the aircraft aerodynamic data query requirements obtained through previous analysis to design and generate corresponding basic data layers, processing data layers, and high-level model data layers, each of which has specific storage and processing functions. The basic data layer is designed to be a layer for storing raw data, including wind tunnel test 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 large-scale data storage requirements. The processing data layer is used to store the results of raw aerodynamic data processing, such as data after data 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 graph databases (such as Neo4j) are used to perform complex data analysis and model query. At the same time, different paths for aerodynamic data to flow between different levels are designed, including data collection, transmission, storage and query processes. ETL (extraction, transformation, loading) tools (such as Apache NiFi or Talend) are used to achieve data flow and conversion, thereby ensuring that efficient aerodynamic data storage and query requirements can be met. Finally, a multi-level database storage architecture for aircraft aerodynamics is designed.

[0085] Step S13: collecting original data sets of aircraft wind tunnel experiments, original data sets of aircraft flight tests, and original data sets of aircraft CFD simulations from wind tunnel experiments, flight tests, and CFD numerical simulations;

[0086] In an embodiment of the present invention, by extracting corresponding aerodynamic data from a wind tunnel test device, including measurement data such as pressure, speed, and temperature, these data are usually in the form of experimental records, such as CSV or Excel files, so as to obtain an original data set of an aircraft wind tunnel experiment. At the same time, by collecting relevant aerodynamic data from actual flight tests, such as flight parameters recorded by sensors, including speed, attitude, aerodynamic lift, aerodynamic drag, etc., the data format includes binary files, text files, or database records, so as to obtain an original data set of an aircraft flight test. Then, by extracting relevant aerodynamic data from a computational fluid dynamics (CFD) simulation, such as flow field distribution, pressure field, and temperature field, these data are usually stored in a dedicated CFD software format, such as a CFD data file (such as an OpenFOAM result file), the original data set of an aircraft CFD simulation is finally obtained.

[0087] Step S14: Perform data integration analysis on the aircraft wind tunnel experiment original data set, the aircraft flight test original data set and the aircraft CFD simulation original data set to obtain the aircraft original aerodynamic data set, and transmit and store it in the basic data layer in the aircraft aerodynamic multi-level database storage architecture.

[0088] In an embodiment of the present invention, the aircraft wind tunnel experiment original data set, the aircraft flight test original data set and the aircraft CFD simulation original data set collected previously are integrated into a unified aircraft original aerodynamic data set, and the data from different sources are unified in format and merged into one data set by using a data integration tool (such as Apache Kafka or Apache Spark). At the same time, the original data is cleaned to remove noise and inconsistency, and data conversion operations are performed, such as standardizing the data format and processing missing values. Then, the processed aircraft original aerodynamic data set is transmitted and stored in the basic data layer in the aircraft aerodynamic multi-level database storage architecture, and the data is stored using the interface provided by the data storage system (such as the INSERT statement of the database).

[0089] Further, as an embodiment of the present invention, refer to Figure 3 As shown, Figure 2 Detailed step flow diagram of step S12 in the embodiment, step S12 includes the following steps:

[0090] Step S121: performing a multi-level storage target analysis according to the aircraft pneumatic data storage requirements and the aircraft pneumatic data query requirements, and obtaining an aircraft pneumatic data real-time storage target, an aircraft pneumatic intermediate data processing storage target, and an aircraft pneumatic model construction storage target;

[0091] In the embodiment of the present invention, a multi-level analysis of the aircraft pneumatic data storage target is performed on the previously acquired aircraft pneumatic data storage requirements and aircraft pneumatic data query requirements (i.e., the initial pneumatic data, the pneumatic intermediate data after analysis and processing, and the pneumatic model data after modeling, so that the corresponding pneumatic model can be updated at any time after the pneumatic data changes). The aircraft's real-time storage requirements for pneumatic data in real-time simulated flight are evaluated and analyzed, and which pneumatic data must be stored in real time to support flight control and real-time monitoring are determined, thereby obtaining the aircraft pneumatic data real-time storage target. At the same time, by analyzing the storage targets corresponding to the aircraft pneumatic data intermediate processing process, that is, the temporary data storage required for the pneumatic data during the processing process, including data cleaning, conversion, and storage of intermediate calculation results, the aircraft pneumatic intermediate data processing storage target is obtained. Then, by analyzing the storage goals of the aircraft aerodynamic model data, which involves long-term storage and processing of the necessary data of the aircraft aerodynamic model to support the optimization and verification of the model, the storage goals of the aircraft aerodynamic model construction are obtained. This analysis can be done by creating a detailed data requirement document and using data flow diagrams and requirement analysis tools for hierarchical description to clearly identify data requirements and storage goals at different levels.

[0092] Step S122: designing a basic data layer according to the real-time storage target of aircraft aerodynamic data to obtain a basic data layer;

[0093] In an embodiment of the present invention, the basic data layer is designed according to the real-time storage target of the aircraft aerodynamic data obtained by the previous analysis to clarify the basic data types that need to be supported, 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 basic data layer design should include the field definition of the data table, the data type selection, the data update frequency, etc. For example, for real-time aerodynamic parameters, a table containing the sensor ID, timestamp, data type and data value can be created, and an index can be set to speed up data retrieval. The design of the database in the basic data layer should ensure support for high-speed data write operations and be able to handle high-concurrency data access requests. In addition, in order to ensure the integrity and consistency of the data, a data verification mechanism and a backup strategy are designed to deal with data loss or damage, and finally the basic data layer is designed.

[0094] Step S123: Designing a processing data layer according to the aircraft aerodynamic intermediate data processing and storage target to obtain a processing data layer;

[0095] In an embodiment of the present invention, the processing data layer is designed according to the aircraft aerodynamic intermediate data processing and storage target obtained from the previous analysis to determine the type 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, and intermediate results of aerodynamic pressure calculations. 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 containing fields such as simulation parameters, calculation steps, and simulation results can be designed. In addition, appropriate indexes and data partitioning strategies need to be set to ultimately design a processing data layer.

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

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

[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 an aircraft aerodynamic multi-level database storage architecture.

[0099] In an embodiment of the present invention, the basic data layer, processing data layer and high-level model data layer generated by the previous design are connected and integrated in a multi-level architecture to integrate and create a unified database architecture, including a three-layer data storage structure, corresponding to basic data storage, processing data storage and model data storage, respectively. In order to achieve effective communication between 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 should be designed to ensure the stability and reliability of the multi-level database, and finally an aircraft aerodynamic multi-level database storage architecture is obtained.

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

[0101] Step S21: preprocessing and standardizing the original aerodynamic data set of the aircraft in the basic data layer to obtain a standardized aerodynamic data set of the aircraft;

[0102] In an embodiment of the present invention, the original aerodynamic data set of the aircraft in the basic data layer is preprocessed and standardized. The original data usually contains measurement values ​​in various formats, such as aerodynamic loads, flow field parameters and pressure distribution. The preprocessing process includes data cleaning and denoising. Data cleaning removes duplicate records and repairs missing data by writing a data cleaning algorithm. Denoising reduces measurement errors by applying a smoothing filter, such as a Gaussian filter or a median filter. Data standardization uses the mean and standard deviation method to standardize each original aerodynamic data to ensure that the scales of all data features are consistent, so that the data can be compared and analyzed under the same standard, and finally the aircraft aerodynamic standardized data set is obtained.

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

[0104] In an embodiment of the present invention, the aircraft aerodynamic standardized data set obtained after previous processing is converted into a data format suitable for storage in a relational database. First, by designing the table structure of the relational database, including data tables, fields and data types, for example, creating an "aerodynamic load table", "flow field parameter table" and "pressure distribution table", defining fields such as "timestamp", "position coordinates" and "load value", and importing the standardized data sets into these tables by using a data import tool in a database management system (DBMS), and by writing an SQL script to ensure accurate insertion of data and establishment of relationships between tables, for example, using an INSERT INTO statement to insert data into the corresponding table one by one, and creating necessary indexes to improve query efficiency, the aircraft aerodynamic storage formatted data set is finally converted to effectively support subsequent database query and management operations.

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

[0106] In an embodiment of the present invention, aerodynamic loads are quantitatively analyzed on a previously formatted aerodynamic storage formatted data set of an aircraft, wherein the analysis tool includes professional aerodynamic analysis software, such as ANSYS Fluent or OpenFOAM. First, an aerodynamic load analysis module is selected to import aerodynamic load data in the storage formatted data set, and analysis and calculation are performed by setting analysis parameters, such as the motion state of the aircraft, airflow conditions, etc., so as to quantitatively calculate the corresponding transient aerodynamic loads using fluid dynamics theory (such as the Navier-Stokes equations) or empirical formulas, and calculate the transient aerodynamic load data at each time point frame by frame. At the same time, a trend analysis tool (such as a moving average method or a fitting algorithm) is used to perform a statistical analysis of the change trend of the aerodynamic loads on the previously quantitatively calculated transient aerodynamic load data, so as to obtain the aerodynamic loads through statistical and mathematical models (such as regression models). (Analysis) Statistically analyze the trend characteristics of the aerodynamic load changes over time, and perform statistical analysis of the dynamic fluctuations of the aerodynamic load on the corresponding transient aerodynamic load data by combining the aerodynamic load change trend obtained in the previous analysis, so as to analyze the dynamic fluctuation components in the trend data, thereby obtaining detailed dynamic aerodynamic load fluctuation data, thereby describing the aerodynamic load changes of the aircraft in actual flight, and then, merge the transient aerodynamic load data obtained in the previous analysis and the dynamic aerodynamic load fluctuation change data under dynamic transient conditions to ensure that the time axes of the transient aerodynamic load data and the dynamic fluctuation data frame by frame are aligned, and combine the transient aerodynamic load and the fluctuation change data through a data merging algorithm, such as a weighted average method or an interpolation method. The merged data should comprehensively cover the aerodynamic loads of the aircraft in the simulation environment, including static and dynamic parts, and finally obtain the aerodynamic load data of the aircraft.

[0107] Step S24: performing aerodynamic flow field parameter and aerodynamic pressure distribution analysis on the aircraft aerodynamic storage formatted data set to obtain aircraft aerodynamic flow field parameter data and aircraft aerodynamic pressure distribution data;

[0108] In an embodiment of the present invention, the aerodynamic flow field parameters and pressure field distribution of the aircraft aerodynamic storage formatted data set obtained after data formatting are statistically analyzed, and the aerodynamic flow field data in the formatted data set are imported using computational fluid dynamics (CFD) software, such as ANSYS Fluent or CFD++, and the flow field area and boundary conditions are defined to perform flow field simulation calculations, thereby obtaining the aircraft aerodynamic flow field parameter data. Then, the aerodynamic pressure distribution data is obtained by using the pressure distribution analysis method, and the characteristics and pressure distribution of the aerodynamic flow field are displayed by drawing visualization results such as contour maps, pressure distribution maps, and streamline maps, and finally the aircraft aerodynamic pressure distribution data is obtained by analysis.

[0109] Step S25: storing the aircraft aerodynamic load data, aircraft aerodynamic flow field parameter data and aircraft aerodynamic pressure distribution data in the processing data layer of the aircraft aerodynamic multi-level database storage architecture.

[0110] In an embodiment of the present invention, by using data transmission 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, and 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 of the processing data layer to confirm the integrity and consistency of the data, for example, checking the data checksum to ensure that the data is not lost or damaged during the transmission process. Finally, the above data is safely 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: extracting aerodynamic lift and drag parameters from 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, parameters related to aerodynamic lift and drag are extracted from a previously formatted aircraft aerodynamic storage formatted data set, which usually contains original measured aerodynamic data obtained through sensors, simulators or wind tunnel tests. The extraction process involves the following specific operations: the formatted data set is read by using data processing software (such as the NumPy library in MATLAB or Python), and the raw data in the data set is processed by an algorithm to separate parameters related to aerodynamic lift (such as lift coefficient Cl, airflow velocity, angle of attack, etc.) and parameters related to aerodynamic drag (such as drag coefficient Cd, airflow velocity, angle of attack, etc.), and data screening and filtering techniques are used to ensure that the extracted lift and drag parameters are accurate and meet the requirements of the experimental design. At the same time, the screened parameter data are cleaned by using data processing tools to remove noise and outliers, thereby ensuring the quality and reliability of the extracted aerodynamic lift and drag parameters, and finally obtaining aircraft aerodynamic lift parameter data and aircraft aerodynamic drag parameter data.

[0114] Step S232: performing 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;

[0115] In an embodiment of the present invention, a time series synchronization processing method is used to perform time series synchronization processing on the previously extracted aircraft aerodynamic lift parameter data and aircraft aerodynamic drag parameter data to ensure that they are aligned in the same time series. First, the timestamps or time tags in the aerodynamic lift and drag parameter data are determined, and a time series data processing tool (such as the Pandas library in Python) is used to time align the two data sets to ensure that the lift data and drag data at each time point correspond to each other at the same time. Interpolation technology (such as linear interpolation or spline interpolation) is used to fill in the missing data points in the time series to ensure the integrity of the time series, and finally the aircraft aerodynamic lift time series data and the aircraft aerodynamic drag time series data in the same time series dimension are obtained.

[0116] Step S233: performing aerodynamic parameterized 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 parameterized simulation operation field;

[0117] In an embodiment of the present invention, the aircraft aerodynamic lift time series data and the aircraft aerodynamic drag time series data that have been time-synchronized are used for aerodynamic parametric simulation design. In the specific implementation process, aerodynamic simulation software (such as ANSYS Fluent or OpenFOAM) is first selected, and an aerodynamic model of the aircraft is created in the software. The synchronized lift and drag data are imported, and the model is parameterized by using aerodynamic parameterization technology (such as a statistical method or a machine learning model). Finally, the aircraft aerodynamic parameterized simulation operation field is simulated to generate, which includes defining the simulation range, setting boundary conditions, and determining the accuracy and resolution of the simulation grid. The aerodynamic parameterized simulation operation field will be based on the time series data to simulate the aerodynamic behavior of the aircraft under different flight conditions.

[0118] Step S234: sampling the parameter changes of the aircraft aerodynamic parameterized simulation running field frame by frame to obtain the aerodynamic lift change value and the aerodynamic drag change value at each time frame in the aircraft aerodynamic simulation field; performing frame by frame transient aerodynamic load quantitative calculation on the aircraft aerodynamic parameterized simulation running 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;

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

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

[0121] In an embodiment of the present invention, a trend analysis tool (such as a moving average method or a fitting algorithm) is used to perform a statistical analysis on the frame-by-frame transient aerodynamic load data of the aircraft obtained by previous quantitative calculations to statistically analyze the trend characteristics of the aerodynamic load changes over 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 change law of the aerodynamic load of the aircraft under different flight conditions, thereby obtaining the aircraft aerodynamic load change trend data. At the same time, by combining the aircraft aerodynamic load change trend data obtained in the previous analysis, the corresponding aircraft frame-by-frame transient aerodynamic load data is subjected to statistical analysis of the dynamic fluctuations of the aerodynamic load, 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. Combined with time domain data, the influencing factors of load fluctuations, such as airflow instability and aircraft attitude changes, are evaluated, thereby obtaining detailed dynamic aerodynamic load fluctuation data, thereby describing the aerodynamic load changes of the aircraft in actual flight, and finally obtaining the dynamic aerodynamic load fluctuation change data of the aircraft.

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

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

[0124] Further, the frame-by-frame transient aerodynamic load quantification calculation in step S234 is calculated by using the aircraft frame-by-frame transient aerodynamic load calculation formula, wherein the aircraft frame-by-frame transient aerodynamic load calculation formula is specifically:

[0125]

[0126] Where 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, and C L(α(t), V(t)) is the change value of aerodynamic lift in the aircraft aerodynamic simulation field at time frame t, V(t) is the instantaneous speed 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 lift coefficient of the aircraft, α0 is the zero lift angle of the aircraft, max is the maximum aerodynamic lift angle of the aircraft, C D (β(t), V(t)) is the change value of aerodynamic drag in the aircraft aerodynamic simulation field at time frame t, β(t) is the pitch angle of aerodynamic drag of the aircraft at time frame t, is the maximum aerodynamic drag coefficient of the aircraft, β0 is the aerodynamic zero drag pitch angle of the aircraft, β max is the pitch angle of maximum aerodynamic drag of the aircraft, S is the reference area of ​​the aircraft carrier, and θ is the correction coefficient of the transient aerodynamic load.

[0127] The present invention obtains a frame-by-frame transient aerodynamic load calculation formula for an aircraft by using a specific mathematical model and after verification, which is used to perform frame-by-frame transient aerodynamic load quantitative calculation on an aircraft aerodynamic parameterized simulation operation field. The frame-by-frame transient aerodynamic load calculation formula for an aircraft can provide transient aerodynamic load data of the aircraft in each time frame by integrating the changes in aerodynamic lift and aerodynamic drag. This accurate transient load evaluation 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 under various flight conditions can be captured, which helps to identify the rapid changes in aerodynamic loads and potential aerodynamic instability. Secondly, the aerodynamic lift and drag coefficients in the formula involve nonlinear changes in the angle of attack and pitch of the aircraft, as well as the speed adjustment coefficient, and take into account the complex aerodynamic effects in actual flight. This comprehensive consideration makes the calculation result closer to the actual flight situation. In addition, 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 improving 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 aerodynamic pressure and loads in different flight phases, which helps to make more accurate performance predictions and verifications in the design phase. In addition, the introduction of correction coefficients allows the load calculation to be adjusted according to actual conditions, further improving the accuracy and practicality of the calculation results, thereby ensuring that the analysis results can reflect actual flight conditions. In short, this formula provides the ability to refine the calculation and in-depth analysis of the aerodynamic loads of the aircraft, which helps to improve aircraft design, optimize performance, and improve flight safety. In summary, this formula fully considers the transient aerodynamic load ε(t) of the aircraft at time frame t, the time frame variable parameter t, the gas density ρ, and the aerodynamic lift change value C in the aircraft aerodynamic simulation field at time frame t.L (α(t), V(t)), the instantaneous velocity of the aircraft at time frame t V(t), the aircraft velocity adjustment coefficient γ, the aircraft aerodynamic lift angle of attack α(t) at time frame t, the aircraft aerodynamic maximum lift coefficient The aircraft aerodynamic zero lift angle of attack is α0, the aircraft aerodynamic maximum lift angle of attack is α max , the change value of aerodynamic drag C in the aircraft aerodynamic simulation field at time frame t D (β(t), V(t)), the aircraft aerodynamic drag pitch angle β(t) at time frame t, the aircraft aerodynamic maximum drag coefficient The aircraft aerodynamic zero drag pitch angle β0, the aircraft aerodynamic maximum drag pitch angle β max , aircraft carrier reference area S, transient aerodynamic load correction coefficient θ, where, through the time frame variable parameter t, the aircraft instantaneous speed V(t) at time frame t, the aircraft speed adjustment coefficient γ, the aircraft aerodynamic lift angle of attack α(t) at time frame t, the aircraft aerodynamic maximum lift coefficient The aircraft aerodynamic zero lift angle of attack α0 and the aircraft aerodynamic maximum lift angle of attack α max It constitutes a change value C of aerodynamic lift in the time frame t of an aircraft aerodynamic simulation field. L Functional relationship of (α(t),V(t)) At the same time, by combining the time frame variable parameter t, the aircraft instantaneous speed V(t) at time frame t, the aircraft speed adjustment coefficient γ, β(t) is the aircraft aerodynamic drag pitch angle at time frame t, is the maximum aerodynamic drag coefficient of the aircraft, β0 is the aerodynamic zero drag pitch angle of the aircraft, and β max The maximum aerodynamic drag pitch angle of the aircraft constitutes a change value of aerodynamic drag C in the time frame t of the aircraft aerodynamic simulation field. D Functional relationship of (β(t),V(t)) According to the correlation between the transient aerodynamic load ε(t) of the aircraft at time frame t and the above parameters, a functional relationship is formed: The formula can realize the quantitative calculation process of the frame-by-frame transient aerodynamic load of the aircraft aerodynamic parameterized simulation operation field. At the same time, by introducing the correction coefficient θ of the transient aerodynamic load, it can be adjusted according to the error occurring in the calculation process, thereby improving the accuracy and applicability of the aircraft frame-by-frame transient aerodynamic load calculation formula.

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

[0129] Step S241: performing aerodynamic flow field boundary condition analysis on the aircraft aerodynamic storage formatted data set to obtain the aircraft aerodynamic flow field boundary condition;

[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 MATLAB's aerodynamic data processing toolbox), key aerodynamic flow field parameters, including velocity distribution, pressure distribution, and turbulence intensity, are extracted from the aircraft aerodynamic flow field topological structure distribution diagram previously obtained through topological analysis, and parameterization 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 aircraft aerodynamic flow field parameter data are obtained.

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

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

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

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

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

[0140] Step S31: integrating the aircraft aerodynamic load data, the aircraft aerodynamic flow field parameter data and the aircraft aerodynamic pressure distribution data in the processed data layer to obtain a comprehensive data set of aircraft aerodynamic characteristics;

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

[0142] Step S32: performing aerodynamic flight behavior fitting analysis on the aircraft aerodynamic characteristics comprehensive data set to generate an aircraft aerodynamic flight behavior fitting model; performing aerodynamic load response modeling on the aircraft aerodynamic load data based on the aircraft aerodynamic flight behavior fitting model to generate an aircraft aerodynamic behavior fitting load response field sub-model;

[0143] In an embodiment of the present invention, a curve fitting technique (such as the least square method or a nonlinear regression model) is used to perform a fitting analysis of the flight behavior path pattern on the aircraft aerodynamic characteristics comprehensive data set obtained after the previous fusion analysis, so as to classify the aerodynamic characteristics data according to different flight states and flight stages for statistical analysis, and at the same time, a relationship model between aerodynamic behavior and flight state is established by using a fitting algorithm, and the parameters of the model are optimized by a cross-validation method, so as to fit and generate an aircraft aerodynamic flight behavior fitting pattern. At the same time, by combining the previously fitted aircraft aerodynamic flight behavior fitting pattern, the corresponding aircraft aerodynamic load data is modeled in response, so as to use a modeling tool (such as Simulink of MATLAB or Scikit-learn of Python) to build an aerodynamic load response model. First, key features are extracted from the aircraft aerodynamic flight behavior fitting pattern, and the input parameters of the response model are constructed. The aircraft aerodynamic load is trained using these feature data to generate an aerodynamic load response model. In the modeling process, a model validation technique (such as K-fold cross validation) is used to evaluate the accuracy and stability of the model, and finally a simulated aircraft aerodynamic behavior fitting load response field sub-model is generated for real-time prediction of the aircraft aerodynamic load response.

[0144] Step S33: performing aerodynamic parameter characteristic 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 characteristic point cloud set and an aircraft aerodynamic pressure distribution field characteristic point cloud set;

[0145] In an embodiment of the present invention, a point cloud analysis of aerodynamic characteristic parameter characteristics is performed on the aircraft aerodynamic flow field parameter data and the aircraft aerodynamic pressure distribution data in the processed data layer obtained previously by using a feature point cloud analysis tool (such as CloudCompare or MATLAB's point cloud toolbox), so that the aerodynamic flow field parameter data and the aerodynamic pressure distribution data are respectively converted into point cloud formats and denoised, and a point cloud clustering algorithm (such as DBSCAN or K-means) is applied to extract feature points, and finally a feature point cloud set of aircraft aerodynamic flow field parameters and a feature point cloud set of aircraft aerodynamic pressure distribution field are obtained.

[0146] Step S34: performing aerodynamic flow field modeling according to the characteristic point cloud set of the aircraft aerodynamic flow field parameters to generate an aircraft aerodynamic flow field sub-model; performing aerodynamic pressure distribution field modeling according to the characteristic point cloud set of the aircraft aerodynamic pressure distribution field to generate an aircraft aerodynamic pressure distribution field sub-model;

[0147] In an embodiment of the present invention, aerodynamic flow field modeling is performed on the previously extracted feature point cloud set of aircraft aerodynamic flow field parameters by using 3D modeling software (such as ANSYS Fluent or OpenFOAM) to convert feature point cloud data into a grid model, perform grid optimization and refinement, and calculate the velocity, pressure and other parameters in the aerodynamic flow field by numerical simulation methods, thereby establishing a sub-model of the aircraft aerodynamic flow field. Similarly, aerodynamic pressure distribution field modeling is performed based on the feature point cloud set of the aerodynamic pressure distribution field, and the same modeling tools and methods are used to establish a sub-model of the aerodynamic pressure distribution field, and the accuracy and reliability of the sub-model are ensured by simulation and verification, and finally the aircraft aerodynamic pressure distribution field sub-model is generated.

[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 within the aircraft aerodynamic multi-level database storage architecture.

[0149] In an embodiment of the present invention, a data integration tool is used to integrate the data of the aerodynamic model with the previously established 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, 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, thereby ensuring 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 pneumatic multi-dimensional data points of the basic data layer to generate a calibration data point set of the basic data layer;

[0152] In an embodiment of the present invention, the data points of the aerodynamic data previously stored in the basic data layer are calibrated, and these data include but are not limited to aerodynamic data such as aerodynamic force, aerodynamic torque and airflow characteristics at different angles of attack, speeds and altitudes. These experimental data are recorded by using a data acquisition system, and are preliminarily cleaned and normalized 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 spline interpolation or Kriging interpolation methods, to ensure that each data point in the basic data layer has an accurate description of the aerodynamic characteristics. Finally, the basic data layer calibration data point set is generated, and this point set will contain the aerodynamic characteristic values ​​corresponding to each type of aerodynamic data.

[0153] Step S42: mapping the data index relationship of the processing data layer based on the calibration data point set of the basic data layer to obtain the pneumatic data index relationship structure between the basic layer data points and the processing layer;

[0154] In an embodiment of the present invention, a data mapping algorithm (such as multidimensional space mapping or principal component analysis) is used to map and match the data index relationship of the corresponding aerodynamic processing parameter data in the processing data layer by combining each calibration data point in the calibration data point set of the basic data layer generated by previous calibration, so as to achieve mapping between the calibration data points of the basic data layer and the processing data layer. The specific operation includes comparing the aerodynamic 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 least squares method, and generating an index relationship structure using the calculation results to record the aerodynamic data index relationship between the basic data layer data points and the processing data layer. The establishment of this relationship structure ensures that the data points corresponding to the basic data layer can be quickly retrieved in the processing data layer, thereby achieving effective connection between data layers and data retrieval optimization, and finally obtaining the aerodynamic data index relationship structure between the basic layer data points and the processing layer.

[0155] Step S43: performing dynamic hierarchical index optimization on the corresponding aircraft aerodynamic model in the high-level model data layer according to the aerodynamic data index relationship structure between the base layer data points and the processing layer, so as to generate a base-processing-high-level model dynamic hierarchical data index relationship table;

[0156] In an embodiment of the present invention, the aerodynamic data index relationship structure between the base layer data points and the processing layer established by combining the previous mapping is used to perform dynamic hierarchical index optimization on the corresponding aerodynamic model in the high-level model data layer, so as to analyze the relationship between the base 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, so as to realize the step-by-step indexing and linking of the basic data points to the high-level model data layer through the mapping relationship of the processing data layer. Specifically, 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 the data points of each high-level model are accurately matched with the data points of the base data layer and the processing data layer, and finally the connection optimization is generated to generate a base-processing-high-level model dynamic hierarchical data index relationship table, which records the data point relationship from the base layer to the high-level model to improve the efficiency of aerodynamic data processing and query.

[0157] Step S44: Based on the basic-processing-high-level model dynamic hierarchical data index relationship table, the data index relationships within the basic data layer, the processing data layer and the high-level model data layer are connected by R-tree multidimensional indexing to obtain a multi-level storage database of the aircraft aerodynamic model.

[0158] In an embodiment of the present invention, the corresponding data index relationships in the basic data layer, the processing data layer and the high-level model data layer are connected by R-tree multidimensional indexing by combining the basic-processing-high-level model dynamic hierarchical data index relationship table obtained by previous dynamic connection optimization, so as to establish a multi-level storage database of the aircraft aerodynamic model, and implement the indexing of the aerodynamic data of the basic data layer, the processing data layer and the high-level model data layer by using R-tree (or other multi-dimensional spatial index structures, such as KD tree). The specific steps include constructing an R-tree index structure to store the spatial position 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 node, and by constructing a multi-dimensional index, the relevant data points can be quickly located during query, and then, the index structure is applied to the data points in the basic-processing-high-level model dynamic hierarchical data index relationship table to implement multi-level aerodynamic data association, thereby connecting to establish a multi-level storage database, so as to store the index structure and the actual aerodynamic model data in the database, implement efficient data management and retrieval, and finally obtain the aircraft aerodynamic model multi-level storage database.

[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 comprises:

[0160] The storage architecture is established and the basic data layer storage module is used to obtain the aircraft aerodynamic data storage requirements and the aircraft aerodynamic data query requirements, and a multi-level storage architecture is designed 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, wherein 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] The processing data layer storage module is used to analyze the aerodynamic load, flow field parameters and aerodynamic pressure distribution of the original aerodynamic data set of the aircraft in the basic data layer to obtain the aerodynamic load data of the aircraft, the aerodynamic flow field parameter data of the aircraft and the aerodynamic pressure distribution data of the aircraft, and transmit and store them in the processing data layer in the aircraft aerodynamic multi-level database storage architecture;

[0162] A high-level model data layer storage module is used to construct an aircraft aerodynamic model by processing aircraft aerodynamic load data, aircraft aerodynamic flow field parameter data, and aircraft aerodynamic pressure distribution data in the data layer to generate an aircraft aerodynamic model data set, and transmit and store it in the high-level model data layer in the aircraft aerodynamic multi-level database storage architecture;

[0163] The aerodynamic model database index connection establishment module is used to perform R-tree multidimensional index connection on the data index relationships in the basic data layer, processing data layer and high-level model data layer to obtain a multi-level storage database of the aircraft aerodynamic model.

[0164] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may 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 the embodiments shown herein, but should 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: The following steps are involved: Step S1: Obtaining aircraft aerodynamic data storage requirements and aircraft aerodynamic data query requirements, and designing a multi-level storage architecture according to the aircraft aerodynamic data storage requirements and aircraft aerodynamic data query requirements to obtain an aircraft aerodynamic multi-level database storage architecture, wherein 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 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 in the aircraft aerodynamic multi-level database storage architecture; Step S2: performing aerodynamic load, flow field parameter and aerodynamic pressure distribution analysis on the original aerodynamic data set of the aircraft in the basic data layer to obtain aircraft aerodynamic load data, aircraft aerodynamic flow field parameter data and aircraft aerodynamic pressure distribution data, and transmitting and storing them in the processing data layer in the aircraft aerodynamic multi-level database storage architecture; Step S3: constructing 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 transmitting and storing it in a high-level model data layer in the aircraft aerodynamic multi-level database storage architecture; Step S4: Perform R-tree multidimensional index connection on the data index relationships in the basic data layer, the processing data layer and the high-level model data layer to obtain a multi-level storage database of the aircraft aerodynamic model.

2. The method for establishing an aircraft aerodynamic model database according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Obtaining aircraft pneumatic data storage requirements and aircraft pneumatic data query requirements; Step S12: designing a multi-level storage architecture according to the aircraft pneumatic data storage requirements and the aircraft pneumatic data query requirements to obtain an aircraft pneumatic multi-level database storage architecture, wherein the aircraft pneumatic multi-level database storage architecture includes a basic data layer, a processing data layer, and a high-level model data layer; Step S13: collecting original data sets of aircraft wind tunnel experiments, original data sets of aircraft flight tests, and original data sets of aircraft CFD simulations from wind tunnel experiments, flight tests, and CFD numerical simulations; Step S14: Perform data integration analysis on the aircraft wind tunnel experiment original data set, the aircraft flight test original data set and the aircraft CFD simulation original data set to obtain the aircraft original aerodynamic data set, and transmit and store it in the basic data layer in 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: performing a multi-level storage target analysis according to the aircraft pneumatic data storage requirements and the aircraft pneumatic data query requirements, and obtaining an aircraft pneumatic data real-time storage target, an aircraft pneumatic intermediate data processing storage target, and an aircraft pneumatic model construction storage target; Step S122: designing a basic data layer according to the real-time storage target of aircraft aerodynamic data to obtain a basic data layer; Step S123: Designing a processing data layer according to the aircraft aerodynamic intermediate data processing and storage target to obtain a processing data layer; Step S124: Designing a high-level model data layer according to the storage target of the aircraft aerodynamic model to obtain a high-level model data layer; 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 an aircraft aerodynamic multi-level database storage architecture.

4. The method for establishing an aircraft aerodynamic model database according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: preprocessing and standardizing the original aerodynamic data set of the aircraft in the basic data layer to obtain a standardized aerodynamic data set of the aircraft; Step S22: performing relational database storage format conversion on the aircraft aerodynamic standardized data set to obtain an aircraft aerodynamic storage formatted data set; Step S23: performing aerodynamic load analysis on the aircraft aerodynamic storage formatted data set to obtain aircraft aerodynamic load data; Step S24: performing aerodynamic flow field parameter and aerodynamic pressure distribution analysis on the aircraft aerodynamic storage formatted data set to obtain aircraft aerodynamic flow field parameter data and aircraft aerodynamic pressure distribution data; Step S25: storing the aircraft aerodynamic load data, aircraft aerodynamic flow field parameter data and aircraft aerodynamic pressure distribution data in the processing data layer of the aircraft aerodynamic multi-level database storage architecture.

5. The method for establishing an aircraft aerodynamic model database according to claim 4, characterized in that: Step S23 includes the following steps: Step S231: extracting aerodynamic lift and drag parameters from the aircraft aerodynamic storage formatted data set to obtain aircraft aerodynamic lift parameter data and aircraft aerodynamic drag parameter data; Step S232: performing 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: performing aerodynamic parameterized 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 parameterized simulation operation field; Step S234: sampling the parameter changes of the aircraft aerodynamic parameterized simulation running field frame by frame to obtain the aerodynamic lift change value and the aerodynamic drag change value at each time frame in the aircraft aerodynamic simulation field; performing frame by frame transient aerodynamic load quantitative calculation on the aircraft aerodynamic parameterized simulation running 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; Step S235: performing aerodynamic load trend analysis on the aircraft frame-by-frame transient aerodynamic load data to obtain aircraft aerodynamic load change trend data; performing 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 aircraft dynamic aerodynamic load fluctuation change data; Step S236: merging the aircraft frame-by-frame transient aerodynamic load data and the aircraft dynamic aerodynamic load fluctuation change data into dynamic transient load data to obtain aircraft aerodynamic load data.

6. The method for establishing an aircraft aerodynamic model database according to claim 5, characterized in that: The frame-by-frame transient aerodynamic load quantification calculation in step S234 is calculated by the aircraft frame-by-frame transient aerodynamic load calculation formula, wherein the aircraft frame-by-frame transient aerodynamic load calculation formula is specifically: Where 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, and C L (α(t), V(t)) is the change value of aerodynamic lift in the aircraft aerodynamic simulation field at time frame t, V(t) is the instantaneous speed 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 lift coefficient of the aircraft, α0 is the zero lift angle of the aircraft, max is the maximum aerodynamic lift angle of the aircraft, C D (β(t), V(t)) is the change value of aerodynamic drag in the aircraft aerodynamic simulation field at time frame t, β(t) is the pitch angle of aerodynamic drag of the aircraft at time frame t, is the maximum aerodynamic drag coefficient of the aircraft, β0 is the aerodynamic zero drag pitch angle of the aircraft, β max is the pitch angle of maximum aerodynamic drag of the aircraft, 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: performing aerodynamic flow field boundary condition analysis on the aircraft aerodynamic storage formatted data set to obtain the aircraft aerodynamic flow field boundary condition; 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; 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; Step S244: extracting aerodynamic pressure field data from the aircraft aerodynamic storage formatted data set to obtain aircraft aerodynamic pressure field data; performing pressure field distribution pattern recognition analysis on the aircraft aerodynamic pressure field data to obtain aircraft aerodynamic pressure field distribution pattern data; Step S245: performing dynamic pressure field distribution correction analysis on the aircraft aerodynamic pressure field data based on the aircraft aerodynamic pressure field distribution pattern data to obtain the aircraft aerodynamic pressure distribution data.

8. The method for establishing an aircraft aerodynamic model database according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: integrating the aircraft aerodynamic load data, the aircraft aerodynamic flow field parameter data and the aircraft aerodynamic pressure distribution data in the processed data layer to obtain a comprehensive data set of aircraft aerodynamic characteristics; Step S32: performing aerodynamic flight behavior fitting analysis on the aircraft aerodynamic characteristics comprehensive data set to generate an aircraft aerodynamic flight behavior fitting model; performing aerodynamic load response modeling on the aircraft aerodynamic load data based on the aircraft aerodynamic flight behavior fitting model to generate an aircraft aerodynamic behavior fitting load response field sub-model; Step S33: performing aerodynamic parameter characteristic 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 characteristic point cloud set and an aircraft aerodynamic pressure distribution field characteristic point cloud set; Step S34: performing aerodynamic flow field modeling according to the characteristic point cloud set of the aircraft aerodynamic flow field parameters to generate an aircraft aerodynamic flow field sub-model; performing aerodynamic pressure distribution field modeling according to the characteristic point cloud set of the aircraft aerodynamic pressure distribution field 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 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 within the aircraft aerodynamic multi-level database storage architecture.

9. The method for establishing an aircraft aerodynamic model database according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: calibrate the pneumatic multi-dimensional data points of the basic data layer to generate a calibration data point set of the basic data layer; Step S42: mapping the data index relationship of the processing data layer based on the calibration data point set of the basic data layer to obtain the pneumatic data index relationship structure between the basic layer data points and the processing layer; Step S43: performing dynamic hierarchical index optimization on the corresponding aircraft aerodynamic model in the high-level model data layer according to the aerodynamic data index relationship structure between the base layer data points and the processing layer, so as to generate a base-processing-high-level model dynamic hierarchical data index relationship table; Step S44: Based on the basic-processing-high-level model dynamic hierarchical data index relationship table, the data index relationships within the basic data layer, the processing data layer and the high-level model data layer are connected by R-tree multidimensional indexing to obtain a multi-level storage database of the aircraft aerodynamic model.

10. A system for establishing an aircraft aerodynamic model database, characterized in that: The method for establishing a database based on an aircraft aerodynamic model according to claim 1, wherein the system for establishing a database based on an aircraft aerodynamic model comprises: The storage architecture is established and the basic data layer storage module is used to obtain the aircraft aerodynamic data storage requirements and the aircraft aerodynamic data query requirements, and a multi-level storage architecture is designed 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, wherein 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; The processing data layer storage module is used to analyze the aerodynamic load, flow field parameters and aerodynamic pressure distribution of the original aerodynamic data set of the aircraft in the basic data layer to obtain the aerodynamic load data of the aircraft, the aerodynamic flow field parameter data of the aircraft and the aerodynamic pressure distribution data of the aircraft, and transmit and store them in the processing data layer in the aircraft aerodynamic multi-level database storage architecture; A high-level model data layer storage module is used to construct an aircraft aerodynamic model by processing aircraft aerodynamic load data, aircraft aerodynamic flow field parameter data, and aircraft aerodynamic pressure distribution data in the data layer to generate an aircraft aerodynamic model data set, and transmit and store it in the high-level model data layer in the aircraft aerodynamic multi-level database storage architecture; The aerodynamic model database index connection establishment module is used to perform R-tree multidimensional index connection on the data index relationships in the basic data layer, processing data layer and high-level model data layer to obtain a multi-level storage database of the aircraft aerodynamic model.

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