Aircraft comprehensive design platform based on component method

Abstract

The invention relates to the technical field of component method aircraft comprehensive design, and discloses a component method-based aircraft comprehensive design platform, which comprises a front-end user interface module for receiving a design request submitted by a user this time, the design request comprising an aircraft model and design parameters; the back-end data management module is used for obtaining all pause events at this time, wherein k is the total pause frequency; executing an interval dynamic adjustment strategy, and adjusting a saving interval of the automatic saving drawing; when the simulation tool is used for designing the drawing of the airplane, automatically storing the drawing every other storage interval, and recording the stored drawing as the drawing of the airplane; acquiring a drawing set automatically stored in the process of designing the aircraft, wherein n is the total number of storage times; executing an image differentiation storage strategy, and deleting the drawing in the drawing set; the simulation calculation engine module is used for executing a design scheme recommendation strategy and recommending a design scheme according to a historical case and a design request; and the intelligence and efficiency in the design process are improved.

Description

Technical Field

[0001] This invention relates to the field of component-based aircraft integrated design technology, specifically to an aircraft integrated design platform based on the component method. Background Technology

[0002] With the rapid development of the aviation industry, the design of modern aircraft is becoming increasingly complex, involving multiple disciplines such as aerodynamics, structural mechanics, and propulsion systems. This interdisciplinary nature makes the design process highly challenging, especially in optimization design, where multiple objectives and constraints need to be balanced. To address these challenges, more efficient computing platforms, optimization algorithms, and interdisciplinary collaborative tools are needed. Through the integration of innovative technologies and continuous optimization, modern aircraft design will be able to better meet efficiency, safety, and environmental requirements.

[0003] Traditional aircraft design methods often rely on manual calculations and experimental verification by experienced engineers. This approach is not only time-consuming and labor-intensive, but also struggles to meet the demands of rapid iteration. In recent years, the application of technologies such as computer-aided design (CAD) and finite element analysis (FEA) has greatly improved design efficiency, but problems such as low integration and poor data sharing still exist.

[0004] This invention proposes an aircraft integrated design platform based on the component method, which overcomes the shortcomings of existing technologies. Summary of the Invention

[0005] This invention provides an aircraft integrated design platform based on the component method, which helps to solve the problems mentioned in the background art.

[0006] Firstly, this application provides an aircraft integrated design platform based on the component method, employing the following technical solution: An aircraft integrated design platform based on the component method, comprising: Front-end user interface module: Used for users to log in to the aircraft integrated design platform; Receive the design request submitted by the user, which includes the aircraft model and design parameters; Showcase the design results; With a simple and intuitive interface, users can easily log in and quickly submit design requests, including selecting aircraft models and setting design parameters. This user-friendly interface allows users to get started quickly, reducing the learning curve. Furthermore, the interface features real-time display of design results, allowing users to view simulation results immediately after submitting parameters. This enables users to quickly identify and correct potential problems, reducing time wasted on trial and error and improving work efficiency. The platform uses graphical representations of design results, allowing users to intuitively compare the advantages and disadvantages of different design schemes, helping them make the optimal choice among multiple options. This combination of timely feedback and visualization not only optimizes the design process but also enhances design transparency and operational flexibility, enabling users to optimize solutions and make decisions more efficiently, ultimately improving the accuracy and quality of the design.

[0007] Backend data management module: When the simulation tool generates aircraft drawings based on design requests: Record the event where the user pauses drawing as a pause event. ; Get all pause events in this session k is the total number of pauses; Implement a dynamic interval adjustment strategy to adjust the saving interval for automatically saving drawings; When designing aircraft drawings using simulation tools, the drawings are automatically saved at regular intervals, and the saved drawings are recorded as follows: ; Automatically saved drawing sets during the aircraft design process n is the total number of saves; Implement an image differentiation preservation strategy to reduce the number of drawings in the drawing set; Database module: Stores historical cases of aircraft design; Simulation calculation engine module: Based on historical cases and design requests, implement a design solution recommendation strategy and recommend design solutions. The system retrieves the user-selected design scheme, calls the simulation tool to execute the design scheme, and obtains the design results.

[0008] Preferably, the dynamic adjustment strategy for the execution interval, which adjusts the saving interval for automatically saving drawings, includes: Regarding the process of designing this aircraft: Obtain the total time T for designing the aircraft; Calculate the pause frequency of the designed aircraft ; Get the save interval for automatically saving plots ; Calculate the rate of change of user pause time intervals ; Calculate the save interval for the user's next aircraft design. ,in, , It is a weighting of frequency sensitivity and stability sensitivity, which conforms to , For frequency sensitivity, For stability sensitivity; Get the maximum value of the save interval ; Get the minimum value of the save interval ; limited .

[0009] By adjusting the save interval of automatic drawing saving in real time, storage efficiency and the smoothness of the design process are greatly improved. First, the system can obtain the total time T during the design process and calculate pause events based on the user's pause frequency. This function can dynamically adjust the save interval by intelligently identifying active and paused states during the design process. Specifically, when the design process is frequent, the system shortens the save interval to ensure that each important update is saved in a timely manner; while when the design progress is slow or there are many pauses, the system lengthens the save interval to avoid storage pressure caused by frequent saves. Through this dynamic adjustment, the system can ensure timely data saving and effective management without wasting computing resources and storage space. Furthermore, the setting of maximum and minimum save interval values ​​further limits the range of save intervals, avoiding inappropriately long or short save intervals. Ultimately, this strategy not only optimizes the use of storage space and computing resources but also ensures the real-time nature and integrity of design data, enhancing the platform's intelligence and flexibility.

[0010] Preferably, the dynamic adjustment strategy for the execution interval, which adjusts the saving interval for automatically saving drawings, includes: Dynamically adjust frequency sensitivity and stability sensitivity: ,in, For this frequency sensitivity, Frequency fluctuation influencing factors This represents the average frequency of pauses. ,in, For this stability sensitivity, Stability fluctuation influencing factors The mean of the rate of change over the time interval.

[0011] By adjusting frequency sensitivity and stability sensitivity, the system can automatically adjust the save interval based on the user's design operation patterns, enabling the save strategy to accurately respond to user behavior. For example, when the user's design progresses rapidly and pauses infrequently, the system will extend the save interval by reducing frequency sensitivity, minimizing unnecessary storage waste; conversely, when the design process is frequently paused, the system will increase stability sensitivity to ensure that each design update is saved promptly. This intelligent adjustment based on real-time feedback not only improves the adaptability of the design process but also optimizes the use of platform resources. By combining the influence factors of frequency fluctuations and the average of the time interval change rate, the system can flexibly respond to changes in user behavior, ensuring the optimal save strategy is achieved under various working conditions. This dynamic adjustment strategy allows users to focus on design optimization without worrying too much about the save frequency, thereby improving the overall efficiency of the platform and the user experience.

[0012] Preferably, the implementation of the image differentiation preservation strategy, which reduces the number of drawings in the drawing set, includes: Get the drawing set ; For any two drawings in the drawing set and Calculate the weighted mixture difference function ; The weighting coefficients of the difference function conform to... , The value represents the structural similarity difference and ranges from [0,1]. , , Drawing respectively and The gradient magnitude at the s-th scale, where S is the total number of scales; , , Drawing respectively and The value of the color histogram in the k-th interval, where m is the total number of histogram partitions.

[0013] Preferably, the implementation of the image differentiation preservation strategy, which reduces the number of drawings in the drawing set, includes: The retained drawings are grouped into a retention set, and the first drawing is added to the retention set; Let denot the last drawing added to the retain set as , and then iterate through each drawing in sequence according to its index, denoting the drawing currently being iterated as . ,calculate ; Set difference threshold ; like Then the drawing will be drawn. Add to the reserved set; like Then delete the drawing. .

[0014] By eliminating unnecessary saved files based on image differences within the drawing set, the platform's storage efficiency is significantly improved. This strategy calculates a weighted mixture difference function between any two images, accurately measuring visual differences and ensuring that only images with significant differences are saved. The weighted mixture difference function combines multiple difference measures, such as structural similarity, gradient magnitude, and color histograms, to comprehensively reflect image differences, rather than relying solely on a single image feature. This allows the system to more intelligently determine which drawings are important and which are redundant during the saving process, reducing wasted storage space. Furthermore, setting difference thresholds for image filtering ensures that only significantly different drawings are retained, further improving storage accuracy and efficient data management. Through this intelligent filtering mechanism, users don't need to worry about duplicate or irrelevant drawings occupying storage space, allowing them to focus more on optimizing important design content and ensuring efficient use of platform resources.

[0015] Preferably, the implementation of the image differentiation preservation strategy, which reduces the number of drawings in the drawing set, includes: Obtain the final retain set and sort the plots in the retain set in ascending order of their serial numbers; Divide the plots in the retained set into several groups, and randomly select the last plot from any two groups, denoted as _____. and ,in Storage time is less than ; calculate ; like Then delete from the reserved set. and The drawing between them.

[0016] By sorting the drawing sets by sequence number and dividing them into several groups, the platform can further optimize the drawing storage strategy. This strategy calculates the differences between drawings within the storage set, especially when storage time is short, to determine whether redundant images need to be deleted. Through optimization of the retention set, the platform can effectively reduce the storage of redundant data while preserving key changes in the design process. This method reduces storage space while ensuring that the retained image set accurately reflects important changes in the design process, avoiding duplicate saving and the waste of irrelevant data. For example, when a user deletes structures from an aircraft design drawing, the content in the latest drawing will be reduced, while the deleted structure is still saved in previous drawings. The previously saved drawings containing the deleted structure need to be removed from the retention set. Through this differentiated retention strategy, the design platform can manage data more intelligently, not only reducing the storage burden but also improving the efficiency of storage space utilization. Ultimately, users can maximize the conservation of platform resources and improve design accuracy and efficiency without sacrificing design information.

[0017] Preferably, the step of executing a design scheme recommendation strategy based on historical cases and design requests, and recommending design schemes, includes: Each historical case is represented as ,in, For the design parameter set, Let a be the performance set of the design results, 1 ≦ i ≦ z, where z is the number of historical cases, a is the number of elements in the performance set, and b is the number of design parameters; The historical cases were normalized: ,1≦i≦b; ,1≦j≦a; in, , This is the normalized value.

[0018] Preferably, the step of executing a design scheme recommendation strategy based on historical cases and design requests, and recommending design schemes, includes: Get the set of design parameters input by the user this time ; calculate and design parameters for each historical case Similarity: ; Set a similarity threshold; Historical cases with similarity scores greater than the similarity threshold are used as candidate cases. Pareto is used to optimize the output of the best recommendation solution and then output the best recommendation solution to the user.

[0019] Based on a design solution recommendation strategy that considers historical case studies and design requests, the platform provides users with more accurate design solutions. By normalizing historical case studies, the platform eliminates dimensional differences between design parameters and performance metrics, allowing for comparison and integration of data from different historical cases. Based on similarity calculations of design parameters and performance, the system recommends historical cases that best match the current design request, thus providing users with optimized solutions. This recommendation method not only provides design guidance based on past successes but also avoids unnecessary exploration from scratch, saving users time and resources. Combining similarity thresholds and the Pareto optimization method, the system further filters out the optimal design solution, helping users make more targeted design decisions. Ultimately, the combination of historical case studies and design requests provides strong data support for the design process, improving accuracy and reliability while ensuring design innovation. This intelligent design solution recommendation greatly enhances the efficiency and quality of the design process.

[0020] The present invention has the following beneficial effects: 1. This component-based aircraft integrated design platform intelligently optimizes the save interval for automatically saving drawings by implementing a dynamic interval adjustment strategy, thereby improving storage management efficiency and saving computing resources. This strategy dynamically adjusts the save interval based on the user's operating patterns during the design process, particularly the frequency and duration of pauses. Specifically, when the user pauses frequently during the design process, the system automatically extends the save interval to avoid wasting storage space due to frequent saves; conversely, when the design progresses rapidly, the system shortens the save interval to ensure that each design change is saved promptly. This adaptive save strategy can optimize in real time according to the design progress, thereby balancing storage consumption and data real-time performance, and avoiding resource waste caused by excessive saving.

[0021] 2. This component-based aircraft integrated design platform enhances the flexibility and adaptability of its data retention strategy through intelligent adjustments to dynamically adjust frequency and stability sensitivity. The platform automatically adjusts the sensitivity of the data retention interval based on the rate of change of pause time intervals and the fluctuation of pause frequency during the design process. When the pause frequency is high and the interval changes significantly, the system increases stability sensitivity to ensure timely data retention during slower or less frequent design phases. Conversely, when design changes frequently, the system increases frequency sensitivity and shortens the data retention interval to ensure retention for each update. By adjusting stability sensitivity, the platform ensures data retention during unstable design phases and extends the retention interval to reduce storage overhead during stable design phases. Ultimately, this flexible data retention interval adjustment mechanism not only improves storage efficiency but also enables the platform to make precise decisions based on actual needs, enhancing the intelligence and efficiency of the design process.

[0022] 3. This component-based aircraft integrated design platform implements an image differentiation preservation strategy. By calculating visual differences between images, it accurately filters out valuable images and deletes redundant data, optimizing storage management. This strategy combines multi-dimensional image difference calculation methods such as structural similarity, gradient magnitude, and color histograms to ensure that the platform only saves images with significant differences. By calculating a weighted mixed difference function, the system can comprehensively evaluate the differences between images, avoiding storage redundancy caused by minor changes or duplicate data. The weighted mixed difference function considers the structure, texture, and color information of the images according to different weight coefficients, making the differentiation preservation more accurate. The platform prioritizes saving images with significant structural differences or color changes, while deleting images with minor changes. This differentiation preservation not only saves storage space but also ensures that the retained images can truly reflect important changes in the design process, improving the validity and reliability of the data. By setting difference thresholds, the system can flexibly control which images need to be saved and which can be discarded, thereby further optimizing the use of storage space. Ultimately, the image differentiation preservation strategy not only improves storage efficiency but also ensures the accuracy of design data and the preservation of valuable data.

[0023] 4. This component-based aircraft integrated design platform optimizes the management of drawings in the retention set by intelligently filtering and organizing drawings, further improving storage efficiency and the effectiveness of design data. The platform sorts the retained drawings by sequence number and divides them into several groups. Then, by calculating storage time and difference metrics, it determines whether redundant drawings need to be deleted. For example, when a user removes structures from the design aircraft drawings, the content in the latest drawings will decrease, while the deleted structures are still saved in previous drawings. Drawings containing the deleted structures need to be removed from the retention set. This optimization strategy ensures that the images in the retention set not only reflect key changes in the design process but also avoids wasting storage space. For example, the platform can intelligently determine which images need to be retained and which are duplicates that can be deleted based on the time interval and differences between drawings. Through this intelligent difference filtering, the platform can efficiently manage image data, ensuring that each saved drawing has significant differences, and that the retained image information can maximally represent important changes in the design process even with limited storage space.

[0024] 5. This component-based aircraft integrated design platform provides users with scientific and accurate design optimization suggestions through design scheme recommendations based on historical cases and design requests. This strategy eliminates data scale differences between different cases by normalizing design parameters and performance results in historical cases, enabling fair comparison and optimization of different design schemes. This historical data-driven recommendation method helps users quickly filter out the most suitable scheme for the current design request from a large number of historical cases, reducing repetitive work in the design process and providing users with proven successful design paths. The system accurately identifies historical cases similar to the current design requirements based on the similarity calculation of design parameters and further selects the best scheme from candidate cases using the Pareto optimization algorithm. This saves users time exploring solutions from scratch and provides operable and highly successful design schemes, making the design process more efficient and scientific. Attached Figure Description

[0025] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0026] Figure 2 This is a schematic diagram of the module of the present invention. Detailed Implementation

[0027] Example 1, refer to Figure 1 An aircraft integrated design platform based on the component method includes: a front-end user interface module; Used for users to log in to the aircraft integrated design platform; Receive the design request submitted by the user, which includes the aircraft model and design parameters; Showcase the design results; Backend data management module: When the simulation tool generates aircraft drawings based on design requests: Record the event where the user pauses drawing as a pause event. ; Get all pause events in this session k is the total number of pauses; Implement a dynamic interval adjustment strategy to adjust the saving interval for automatically saving drawings; When designing aircraft drawings using simulation tools, the drawings are automatically saved at regular intervals, and the saved drawings are recorded as follows: ; Automatically saved drawing sets during the aircraft design process n is the total number of saves; Implement an image differentiation preservation strategy to reduce the number of drawings in the drawing set; Database module: Stores historical cases of aircraft design; Simulation calculation engine module: Based on historical cases and design requests, implement a design solution recommendation strategy and recommend design solutions. The system retrieves the user-selected design scheme, calls the simulation tool to execute the design scheme, and obtains the design results.

[0028] In this embodiment, refer to Figure 2 Platform architecture: The platform consists of a front-end user interface module, a back-end data management module, a simulation calculation engine module, and a database module.

[0029] The front-end user interface module is responsible for receiving user input data and instructions, and displaying design results and feedback information. The backend data management module is responsible for storing and managing various types of design data, ensuring data consistency and security; The simulation calculation engine module integrates various physical simulation tools, such as CFD, FEA, and MDO, to perform specific calculation tasks; The database module stores a large amount of basic data and historical cases for users to reference and access.

[0030] Data Flow: Users submit design requests through the front-end interface. The data is transmitted to the back-end data management module for preprocessing, and then sent to the simulation calculation engine module to execute calculation tasks. After the calculation is completed, the results are returned to the data management module for organization and archiving, and finally displayed to the user through the front-end interface.

[0031] Component selection: Front-end interface module: adopts HTML5+CSS3+JavaScript technology stack, is compatible with mainstream browsers, and ensures user experience.

[0032] Backend data management module: MySQL is used as the relational database management system, Redis is used as the caching service, and the Spring Boot framework is used to build a microservice architecture.

[0033] Simulation calculation engine module: integrates commercial software such as ANSYS CFX, ABAQUS, and MODEFRONTIER, while also developing some lightweight calculation tools in-house.

[0034] Working process and main principles: After logging into the platform, users can select the desired aircraft model and design objectives, and upload initial design documents or manually enter design parameters.

[0035] The platform automatically calls the corresponding simulation tools to generate performance reports on aerodynamics, structure, heat conduction, and other aspects.

[0036] Based on the report results, users can choose to further optimize the design or save the current version for the next stage of review.

[0037] Throughout the process, the platform records the historical trajectory of every change in real time, facilitating traceability and auditing.

[0038] Implement a dynamic interval adjustment strategy to adjust the saving interval for automatically saving plots, including: Regarding the process of designing this aircraft: Obtain the total time T for designing the aircraft; Calculate the pause frequency of the designed aircraft ; Get the save interval for automatically saving plots ; Calculate the rate of change of user pause time intervals ; Calculate the save interval for the user's next aircraft design. ,in, , It is a weighting of frequency sensitivity and stability sensitivity, which conforms to , For frequency sensitivity, For stability sensitivity; Get the maximum value of the save interval ; Get the minimum value of the save interval; limited .

[0039] By implementing a dynamic interval adjustment strategy, the platform can intelligently adjust the save interval for automatically saving drawings, thereby optimizing the use of storage resources and ensuring that critical design data is saved in a timely manner. This strategy determines the activity level of the design process by acquiring the total design time and calculating the user's pause frequency. When design progress is slow and pauses are frequent, the system automatically extends the save interval to avoid unnecessary storage consumption caused by frequent saves; conversely, when the design changes frequently, the system shortens the save interval to ensure that each design update is saved in a timely manner. This dynamic adjustment mechanism can optimize the save strategy in real time according to the actual needs of the design, avoiding storage pressure caused by excessive saving, while ensuring that data is not lost at critical moments. The introduction of frequency sensitivity and stability sensitivity further enhances the flexibility of the adjustment strategy. By adjusting the sensitivity coefficient, the platform can precisely adjust the save interval according to different design rhythms and needs. Furthermore, by setting the maximum and minimum values ​​for the save interval, the platform can prevent the save interval from being too long or too short, ensuring data security and efficient use of storage space during the design process. Through this intelligent save strategy, designers can focus more on design optimization without worrying about storage issues, thereby improving design efficiency and storage resource utilization, and enhancing the flexibility and intelligence of the overall design process.

[0040] Implement a dynamic interval adjustment strategy to adjust the saving interval for automatically saving plots, including: Dynamically adjust frequency sensitivity and stability sensitivity: ,in, For this frequency sensitivity, Frequency fluctuation influencing factors This represents the average frequency of pauses. ,in, For this stability sensitivity, Stability fluctuation influencing factors The mean of the rate of change over the time interval.

[0041] By dynamically adjusting frequency and stability sensitivity, the system can more accurately optimize the save interval, thereby achieving intelligent responses to user operations. Adjusting frequency and stability sensitivity allows the save interval to adapt to the user's operating mode, ensuring that design data is saved most appropriately at different stages. Specifically, when the design process is fast and changes frequently, the system increases frequency sensitivity and shortens the save interval to ensure that each design update is saved promptly; while when the design progress is relatively stable, the system automatically extends the save interval to avoid resource waste caused by frequent saves. By introducing the impact of pause frequency and the rate of change of pause interval, the system can fine-tune according to the stability of the design, thereby reducing unnecessary storage consumption while ensuring the timely saving of important data. The introduction of stability sensitivity further optimizes the save strategy, appropriately extending the save interval for design processes with frequent pauses, reducing unnecessary saves, and improving storage efficiency. Through this dynamic adjustment mechanism, the system can flexibly adjust the save interval according to real-time changes in the design process, ensuring the safe preservation of data and the rational use of storage space. This intelligent save strategy not only improves the smoothness of the design process but also enables the platform to maintain efficient storage management at different design rhythms.

[0042] Implement an image differentiation preservation strategy to reduce the number of drawings in the drawing set, including: Get the drawing set ; For any two drawings in the drawing set and Calculate the weighted mixture difference function ; The weighting coefficients of the difference function conform to... , The value represents the structural similarity difference and ranges from [0,1]. , , Drawing respectively and The gradient magnitude at the s-th scale, where S is the total number of scales; , , Drawing respectively and The value of the color histogram in the k-th interval, where m is the total number of histogram partitions.

[0043] Implement an image differentiation preservation strategy to reduce the number of drawings in the drawing set, including: The retained drawings are grouped into a retention set, and the first drawing is added to the retention set; Let denot the last drawing added to the retain set as , and then iterate through each drawing in sequence according to its index, denoting the drawing currently being iterated as . ,calculate ; Set difference threshold ; like Then the drawing will be drawn. Add to the reserved set; like Then delete the drawing. .

[0044] By implementing an image differentiation retention strategy, the platform can intelligently filter out important drawings and delete redundant data, thereby effectively optimizing storage space usage. This strategy calculates the differences between images, ensuring that the platform only retains those images that have undergone significant changes during the design process, while deleting those that are repetitive or have minor variations. Through a weighted mixture difference function, the platform comprehensively considers image differences across multiple dimensions, including structural similarity, gradient magnitude, and color histograms, to fully evaluate the differences between drawings. In this way, the system can retain drawings that significantly contribute to the design results based on actual needs, avoiding the accumulation of redundant data. This not only saves storage space but also ensures that the retained images represent key changes in the design process, avoiding interference from irrelevant data. By setting difference thresholds, the system can flexibly control which images need to be retained and which can be deleted, thereby improving storage accuracy and efficient resource utilization. Furthermore, the image differentiation retention strategy reduces the storage overhead of irrelevant drawings during the design process, helping designers focus more on optimizing the actual design without worrying about storage space and redundant data management. Ultimately, this strategy makes the design platform's storage management more efficient and the data more accurate, providing designers with a more efficient and intelligent storage solution.

[0045] Implement an image differentiation preservation strategy to reduce the number of drawings in the drawing set, including: Obtain the final retain set and sort the plots in the retain set in ascending order of their serial numbers; Divide the plots in the retained set into several groups, and randomly select the last plot from any two groups, denoted as _____. and ,in Storage time is less than ; calculate ; like Then delete from the reserved set. and The drawing between them.

[0046] By optimizing the management of drawings in the retained set, the platform can intelligently identify and manage drawing data during the design process, thereby further improving storage efficiency. The platform sorts the retained drawings by sequence number and divides them into several groups. It then filters out redundant drawings by calculating storage time and difference metrics between drawings. Through this optimization strategy, the platform can manage storage space more efficiently, avoiding the storage of too many similar or irrelevant drawings and ensuring that each retained drawing has practical value. The system calculates storage time and differences between drawings to determine which drawings undergo critical changes during the design process, while other redundant images are deleted. The advantage of this strategy is that it not only reduces the storage of irrelevant data but also retains images reflecting important design changes, improving the accuracy and effectiveness of design data. Through intelligent management of the retained set, the platform can maximize the use of storage space, ensuring that every drawing generated during the design process provides practical support for design optimization. Furthermore, the optimized retained set helps designers more clearly review and compare designs at different stages, ensuring the efficiency and operability of the design. Ultimately, this management mechanism makes the storage and management of drawings more efficient, improving the intelligence level of the design platform and the utilization rate of storage resources.

[0047] Based on historical cases and design requests, implement a design solution recommendation strategy, recommending design solutions, including: Each historical case is represented as ,in, For the design parameter set, Let a be the performance set of the design results, 1 ≦ i ≦ z, where z is the number of historical cases, a is the number of elements in the performance set, and b is the number of design parameters; The historical cases were normalized: ,1≦i≦b; ,1≦j≦a; in, , This is the normalized value.

[0048] Based on historical cases and design requests, implement a design solution recommendation strategy, recommending design solutions, including: Get the set of design parameters input by the user this time ; calculate and design parameters for each historical case Similarity: ; Set a similarity threshold; Historical cases with similarity scores greater than the similarity threshold are used as candidate cases; Pareto is used to optimize the output of the best recommendation solution and then output the best recommendation solution to the user.

[0049] By recommending design solutions based on historical case studies and design requests, the platform provides designers with precise design options. This strategy analyzes historical cases, normalizes design parameters and performance data, and eliminates scale differences between different design solutions, allowing for comparison and optimization of various historical cases under the same standard. The platform intelligently filters historical cases that best meet current design needs by calculating the similarity between the design parameter set and historical cases, providing designers with references and optimization suggestions. Through similarity calculations and Pareto optimization methods, the platform selects the optimal solution from multiple alternatives, helping designers quickly find the best design path. This design solution recommendation based on historical data not only saves designers time and effort but also avoids unnecessary exploration and trial and error, reducing the development cycle. Intelligent recommendations based on historical cases allow designers to innovate and optimize more efficiently based on past successes, thereby improving design quality and reliability. This strategy not only improves design efficiency but also ensures design feasibility and success rates, enabling the platform to provide scientific and accurate design solutions, thus accelerating the entire design process.

[0050] Throughout the process, the platform records the historical trajectory of every change in real time, facilitating traceability and auditing. After logging into the platform, users select the desired aircraft model and design objectives, and upload initial design documents or manually enter design parameters. The platform automatically calls the corresponding simulation tools to generate performance reports on aerodynamics, structure, heat transfer, and other aspects.

[0051] Based on the report results, users can choose to further optimize the design or save the current version for the next stage of review. Throughout the process, the platform records the history of every change in real time, facilitating traceability and auditing. The front-end interface module uses an HTML5+CSS3+JavaScript technology stack, compatible with mainstream browsers to ensure a good user experience. The back-end data management module uses MySQL as the relational database management system, Redis as the caching service, and a microservice architecture built using the Spring Boot framework. The simulation calculation engine module integrates commercial software such as ANSYS CFX, ABAQUS, and MODEFRONTIER, while also developing some lightweight calculation tools in-house.

[0052] Users register an account and log in to the platform; select the project type and design goals; upload the necessary design files or manually enter the design parameters; click the "Start Design" button, and the platform starts background calculations; View the calculation progress and intermediate results; you can pause or terminate the process if necessary. Calculations complete. Download the final report and design documents. Optimize the design according to the report's recommendations, and repeat the above steps until you are satisfied. Save the design results and export the documents for future reference.

[0053] The front-end interface module can be replaced with other front-end frameworks such as React or Vue according to actual needs; The database for the backend data management module can be switched from MySQL to PostgreSQL or other relational databases; The simulation computing engine module can flexibly select different commercial software or open source tools according to the characteristics and budget of a specific project.

[0054] Integrate more third-party plugins and services, such as cloud computing platforms and big data analytics tools, to improve the overall functionality and usability of the platform; By introducing machine learning algorithms and learning from a large amount of historical data, the system can intelligently recommend the best design solutions, reducing the time and cost of manual intervention. A mobile application has been developed, allowing users to access the platform anytime, anywhere on their phones and tablets, improving portability and flexibility.

[0055] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0056] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. An aircraft integrated design platform based on the component method, characterized in that, include: Front-end user interface module: Used for users to log in to the aircraft integrated design platform; Receive the design request submitted by the user, which includes the aircraft model and design parameters; Showcase the design results; Backend data management module: When the simulation tool generates aircraft drawings based on design requests: Record the event where the user pauses drawing as a pause event. ; Get all pause events in this session k is the total number of pauses; Implement a dynamic interval adjustment strategy to adjust the saving interval for automatically saving drawings; When designing aircraft drawings using simulation tools, the drawings are automatically saved at regular intervals, and the saved drawings are recorded as follows: ; Automatically saved drawing sets during the aircraft design process n is the total number of saves; Implement an image differentiation preservation strategy to reduce the number of drawings in the drawing set; Database module: Stores historical cases of aircraft design; Simulation calculation engine module: Based on historical cases and design requests, implement a design solution recommendation strategy and recommend design solutions. The system retrieves the user-selected design scheme, calls the simulation tool to execute the design scheme, and obtains the design results.

2. The aircraft integrated design platform based on the component method according to claim 1, characterized in that, The dynamic adjustment strategy for the execution interval adjusts the saving interval for automatically saving drawings, including: Regarding the process of designing this aircraft: Obtain the total time T for designing the aircraft; Calculate the pause frequency of the designed aircraft ; Get the save interval for automatically saving plots ; Calculate the rate of change of user pause time intervals ; Calculate the save interval for the user's next aircraft design. ,in, , It is a weighting of frequency sensitivity and stability sensitivity, which conforms to , For frequency sensitivity, For stability sensitivity; Get the maximum value of the save interval ; Get the minimum value of the save interval ; limited .

3. The aircraft integrated design platform based on the component method according to claim 2, characterized in that, The dynamic adjustment strategy for the execution interval adjusts the saving interval for automatically saving drawings, including: Dynamically adjust frequency sensitivity and stability sensitivity: ,in, For this frequency sensitivity, Frequency fluctuation influencing factors This represents the average frequency of pauses. ,in, For this stability sensitivity, Stability fluctuation influencing factors The mean of the rate of change over the time interval.

4. The aircraft integrated design platform based on the component method according to claim 1, characterized in that, The implementation of the image differentiation preservation strategy, which reduces the number of drawings in the drawing set, includes: Get the drawing set ; For any two drawings in the drawing set and Calculate the weighted mixture difference function ; The weighting coefficients of the difference function conform to... , The value represents the structural similarity difference and ranges from [0,1]. , , Drawing respectively and The gradient magnitude at the s-th scale, where S is the total number of scales; , , Drawing respectively and The value of the color histogram in the k-th interval, where m is the total number of histogram partitions.

5. The component-based aircraft integrated design platform according to claim 4, characterized in that, The implementation of the image differentiation preservation strategy, which reduces the number of drawings in the drawing set, includes: The retained drawings are grouped into a retention set, and the first drawing is added to the retention set; The last drawing added to the retain set is denoted as... Iterate through each drawing in sequence according to its number, and record the drawing being iterated as . ,calculate ; Set difference threshold ; like Then the drawing will be drawn. Add to the reserved set; like Then delete the drawing. .

6. The component-based aircraft integrated design platform according to claim 5, characterized in that, The implementation of the image differentiation preservation strategy, which reduces the number of drawings in the drawing set, includes: Obtain the final retain set and sort the plots in the retain set in ascending order of their serial numbers; Divide the plots in the retained set into several groups, and randomly select the last plot from any two groups, denoted as _____. and ,in Storage time is less than ; calculate ; like Then delete from the reserved set. and The drawing between them.

7. The aircraft integrated design platform based on the component method according to claim 1, characterized in that, The step of implementing a design scheme recommendation strategy based on historical cases and design requests, and recommending design schemes, includes: Each historical case is represented as ,in, For the design parameter set, Let a be the performance set of the design results, 1 ≦ i ≦ z, where z is the number of historical cases, a is the number of elements in the performance set, and b is the number of design parameters; The historical cases were normalized: ,1≦i≦b; ,1≦j≦a; in, , This is the normalized value.

8. The aircraft integrated design platform based on the component method according to claim 7, characterized in that, The step of implementing a design scheme recommendation strategy based on historical cases and design requests, and recommending design schemes, includes: Get the set of design parameters input by the user this time ; calculate and design parameters for each historical case Similarity: ； Set a similarity threshold; Historical cases with similarity scores greater than the similarity threshold are used as candidate cases. Pareto is used to optimize the output of the best recommendation solution and then output the best recommendation solution to the user.

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