Civil aircraft load calculation processing method and system based on cloud native

Abstract

The invention discloses a civil aircraft load calculation processing method and system based on cloud native. The method comprises the steps of S1, data preprocessing and data management and control; s2, a load calculation process is configured in a self-defined mode; s3, parallel load calculation is carried out in combination with a distributed calculation framework; and S4, analyzing and visualizing a result. According to the method, the load calculation efficiency is remarkably improved, manual intervention is reduced, the data accuracy and reliability are ensured, dependence on professionals is reduced, file and parameter management is standardized, whole-process integration and standardization are achieved, the calculation resource utilization rate is increased, and technical collaborative development is promoted.

Description

Technical Field

[0001] The present invention relates to the field of aerospace technology, and in particular, to a civil aircraft payload calculation and processing method and system based on cloud native. Background Art

[0002] In the current implementation of aircraft payload calculation, engineers usually develop a series of small and single-function and scattered programs by themselves to complete the calculation tasks. Although this scattered calculation mode meets specific requirements to a certain extent, there are many limitations. For example, it is difficult to achieve batch operations, resulting in a long payload calculation cycle. In terms of automation, the existing payload calculation process includes a large number of manual preprocessing and manual processing links, which are not only inefficient but also error-prone, increasing the uncertainty of the calculation results.

[0003] Since the analysis scenarios involved in payload calculation are rich and diverse, and the working conditions are complex and changeable, and the specific calculation algorithms are often confidential, the calculation process lacks a unified standard. At present, an integrated unified calculation platform has not been formed, which can provide engineers with an efficient and convenient payload calculation solution. At the same time, the input files and calculation parameters involved in payload calculation are of various types and require version control and traceability. However, the existing technology lacks a unified management solution, resulting in chaotic management of files and parameters, making it difficult to trace and control versions, increasing management costs and error risks. In addition, the specific algorithms of payload calculation are confidential. Due to the lack of unity in the calculation process, it is difficult to share and integrate the tools and algorithms developed by different engineers, restricting the collaborative development and optimization of technologies.

[0004] For the calculation results, relying on the experience of professionals, the analysis process is slow. This dependence limits the rapid verification and optimization of the calculation results, and has high requirements for the experience of professionals, which is not conducive to the popularization and promotion of technologies.

[0005] In view of the above problems, the existing technology urgently needs to be improved. Summary of the Invention

[0006] To solve at least one of the problems existing in the above-mentioned prior art, the first aspect of the present invention provides a civil aircraft payload calculation and processing method based on cloud native, which includes:

[0007] Step S1: Data preprocessing and data control; wherein, the system automatically verifies and standardizes the original data in the original input file, and generates a preprocessing result file for the processed data and stores it in the input file library of the cloud database for unified storage, version control, traceability management, and as a standardized input source for subsequent payload calculation;

[0008] Step S2: Customize the load calculation process; specifically, select the load calculation module, configure the process verification rules and store them in the calculation configuration library of the cloud database, and arrange the algorithm formulas and store them in the algorithm library of the cloud database;

[0009] Step S3: Perform parallel load calculation in combination with the distributed computing framework; specifically, first generate working conditions based on the preprocessing result file, allocate them to multiple computing nodes through load balancing for parallel calculation, and after completing the working condition calculation, perform load post-processing on the results and store them in the calculation result library of the cloud database;

[0010] Step S4: Result analysis and visualization; specifically, perform visual comparison analysis and rationality judgment on the load calculation results across rounds and models, track the time consumption and abnormal status of the load calculation process, and output a standardized report in a unified format.

[0011] In the above-mentioned cloud-native-based civil aircraft load calculation and processing method, optionally, step S1 specifically includes the following steps:

[0012] Step S1.1: Upload the original input file; the original data in the original input file includes at least one of the following data: overall, weight, aerodynamics, landing gear, control law, and thrust data input by the upstream overall specialty.

[0013] Step S1.2: Automatic verification; perform format verification on the original data to ensure compliance with the predefined format requirements, and perform rationality verification on the content of the original data.

[0014] Step S1.3: Standardized preprocessing; unify the data units of the original data and perform formatting processing to ensure that the data is in a consistent format when input into the load calculation module.

[0015] Step S1.4: Generate the preprocessing result file and store it in the cloud database.

[0016] In the above-mentioned cloud-native-based civil aircraft load calculation and processing method, optionally, step S2 specifically includes the following steps:

[0017] Step S2.1: Configure the load calculation module; specifically, select the load calculation module according to business needs and configure the input and output parameters of each load calculation module to ensure smooth data flow between modules;

[0018] Step S2.2: Arrange the algorithm formulas used in the load calculation module to make the operation logic between each load calculation module rigorous and orderly; the algorithm formulas include at least one of the following: ground load simulation calculation, flight load simulation calculation, dynamic load simulation calculation;

[0019] Step S2.3: The configured load calculation module and the arranged algorithm formula are stored in the calculation configuration library and algorithm library of the cloud database respectively for subsequent calling and updating.

[0020] In the cloud-native-based civil aircraft load calculation and processing method as described above, optionally, a message queue is used to improve efficiency during the execution of step S2, and multiple rounds of parameter calculations are configured to obtain multiple versions of results each time.

[0021] In the aforementioned cloud-native-based civil aircraft load calculation and processing method, optionally, step S3 specifically includes the following steps:

[0022] Step S3.1: Read the preprocessing result file as input for generating working conditions;

[0023] Step S3.2: Generate various operating condition files according to the data analysis in the preprocessing result file, wherein the operating condition files include at least one of the following: flight phase, environmental condition, and load condition;

[0024] Step S3.3: Distributing the load condition file to multiple computing nodes to ensure parallel processing of load calculation tasks;

[0025] Step S3.4: each computing node performs load post-processing calculation according to the assigned working condition file, and the load post-processing calculation includes at least one of the following: incremental load processing, total load processing, component overload processing, engine overload processing, oblique gust processing, and load matching;

[0026] Step S3.5: The load calculation results returned by each computing node are summarized, rationality checked, and stored in the calculation result library of the cloud database for subsequent sharing, analysis, and calling; wherein the load calculation results include at least one of the following: load distribution, stress distribution, and deformation.

[0027] In the aforementioned cloud-native-based civil aircraft load calculation and processing method, optionally, step S4 specifically includes the following steps:

[0028] Step S4.1: Obtain the load calculation results of different rounds and different models from the calculation result library of the cloud database;

[0029] Step S4.2: Generate a comparison chart to visually display the load changes under different conditions;

[0030] Step S4.3: Perform a rationality analysis on the load calculation results to evaluate whether the load calculation results meet the design requirements and safety standards and identify potential problems;

[0031] Step S4.4: Track the time consumption of each computing node in the load calculation process, analyze the calculation efficiency, and detect abnormal states during the calculation process; wherein, the abnormal states include at least one of the following: node failure, data loss;

[0032] Step S4.5: Display the analysis results in an interactive manner in a visual form; wherein, the visual form includes at least one of the following: stress nephogram, deformation diagram, comparison chart;

[0033] Step S4.6: Output a report according to the content selected by the user interaction.

[0034] In the civil aircraft load calculation and processing method based on cloud native as described above, optionally, in the step S4.4, optimize the solution process of the load calculation according to the load calculation time consumption of each computing node and the abnormal state during the calculation process.

[0035] To achieve the above object, the second aspect of the present invention provides a civil aircraft load calculation and processing system based on cloud native, wherein the civil aircraft load calculation and processing method described in any one of the foregoing first aspect embodiments is used, including:

[0036] Data preprocessing and data control module, including a cloud storage module and an algorithm module, for automatically verifying and standardizing the original data in the original input file, and generating a preprocessing result file for the processed data and storing it in the input file library of the cloud database for unified storage, version control, traceability management, and serving as a standardized input source for subsequent load calculations;

[0037] Among them, the cloud storage module includes: an input file database, a calculation configuration library, an algorithm library, a calculation result library; the algorithm module includes: a load calculation algorithm, a preprocessing and verification algorithm, a result analysis and comparison algorithm, and performs parallel load calculations in combination with a distributed computing framework; the system first generates working conditions according to the preprocessing result file, distributes them to multiple computing nodes through load balancing for parallel calculations, and after completing the working condition calculations, performs post-processing on the results and stores them in the calculation result library of the cloud storage module;

[0038] Process control module, used for customizing and configuring the load calculation process, one-key startup of batch tasks, and real-time monitoring of the process status; wherein, the user customizes and selects the load calculation module, configures the process verification rules and stores them in the calculation configuration library of the cloud storage module, and arranges the algorithm formulas and stores them in the algorithm library of the cloud storage module;

[0039] Result analysis and visualization module, used for visual comparison analysis and rationality judgment of load calculation results across rounds and models, tracking the time consumption and abnormal states of the load calculation process, and outputting a standardized report in a unified format.

[0040] To achieve the above object, a third aspect of the present invention provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. Wherein, when the processor runs the program, it implements the cloud-native-based civil aircraft payload calculation and processing method as described in any one of the foregoing first aspects of the embodiments.

[0041] To achieve the above object, a fourth aspect of the present invention provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores computer-executable instructions or a computer program, and when the computer-executable instructions or the computer program are processed and executed, it implements the cloud-native-based civil aircraft payload calculation and processing method as described in any one of the foregoing first aspects of the embodiments.

[0042] The cloud-native-based civil aircraft payload calculation and processing method and system provided by the present invention significantly shorten the payload calculation cycle, improve the calculation efficiency, can quickly complete large-scale and complex payload calculation tasks, reduce manual intervention, reduce the complexity and error rate of manual operations, improve the stability and reliability of the calculation process, and at the same time free up the time and energy of engineering personnel, enabling them to focus on more critical tasks. In addition, the result visualization comparison and analysis of the present invention support the comparison of payload results across rounds and models, reduce the dependence on the experience of professionals, and improve the efficiency and accuracy of result analysis. The civil aircraft payload calculation and processing system based on cloud-native technology constructed by the present invention integrates functional modules such as data preprocessing, process control, parallel calculation, and result analysis to form a unified calculation platform, realizing the integration and standardization of the entire payload calculation process. Engineering personnel can complete all calculation tasks on one platform, reducing the complexity of tool switching, and improving work efficiency and collaboration ability. At the same time, the present invention uses a cloud storage module to uniformly manage the files related to the calculation, standardizes the management of input files and calculation parameters, ensures the integrity and traceability of the data, facilitates version control and problem troubleshooting, improves management efficiency and data security, arranges and stores algorithm formulas through the formation of a unified payload calculation algorithm library, realizes the standardization and unified management of the algorithms, facilitates the sharing, update, and optimization of the algorithms, improves the confidentiality and security of the algorithms, and promotes the collaborative development of technologies.

[0043] In summary, through innovative designs such as cloud-native technology, automated processes, distributed computing, and unified data management, the present invention comprehensively addresses the drawbacks existing in the existing civil aircraft load calculation technology, including long calculation cycles, low automation levels, lack of verification tools, reliance on professionals, chaotic management, lack of a unified platform, low calculation efficiency, insufficient algorithm confidentiality and unity, etc. The beneficial effects it brings include: significantly improving calculation efficiency, reducing manual intervention, ensuring data accuracy and reliability, reducing reliance on professionals, standardizing document and parameter management, achieving full-process integration and standardization, enhancing the utilization rate of computing resources, and promoting the collaborative development of technologies. These improvements not only enhance the overall performance of civil aircraft load calculation but also provide strong support for the digital transformation and intelligent development in the field of aerospace engineering.

[0044] The following will further illustrate the concept, specific structure, and technical effects of the present invention in conjunction with the accompanying drawings to fully understand the purpose, features, and effects of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for use in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0046] Figure 1 is a schematic flowchart of an embodiment of a cloud-native-based civil aircraft load calculation and processing method of the present invention;

[0047] Figure 2 is Figure 1 a detailed flowchart of the cloud-native-based civil aircraft load calculation and processing method in

[0048] Figure 3 is a schematic structural diagram of an embodiment of a cloud-native-based civil aircraft load calculation and processing system of the present invention;

[0049] Figure 4 is Figure 3 a schematic structural diagram of each part of the cloud-native-based civil aircraft load calculation and processing system in implementing the civil aircraft load calculation process. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0050] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0051] Terms such as "comprising" and "including" indicate that in addition to the components directly and explicitly stated in the description and claims, the technical solutions of the present invention do not exclude the situation of having other components that are not directly or explicitly stated.

[0052] First of all, it should be noted that cloud-native technology is an application design, development, and deployment method optimized for cloud computing environments, aiming to make full use of the elasticity, flexibility, and scalability of cloud platforms.

[0053] As Figure 1 and Figure 2 shown, a civil aircraft payload calculation and processing method based on cloud-native of the present invention may specifically include the following steps:

[0054] Step S1: Data preprocessing and data control.

[0055] In step S1, the system will perform automated verification and standardization processing on the original data in the original input file, and generate a preprocessing result file for the processed data and store it in the input file library of the cloud database for unified storage, version control, traceability management, and as a standardized input source for subsequent payload calculations.

[0056] In this embodiment, the system adopts a front-end and back-end separation design. The front end uses Vue.js to build an interactive interface, and the back end uses Spring Boot to provide RESTful API interfaces to ensure the scalability of the system. And multi-language collaboration is introduced. Java is responsible for permission management and process scheduling, and Python focuses on algorithm development (such as Pandas data preprocessing and SciPy numerical calculations).

[0057] In an alternative embodiment, step S1 may include the following steps:

[0058] Step S1.1: Upload the original input file; wherein, the original data in the original input file may include at least one of the following data: overall, weight, aerodynamics, landing gear, control law, and thrust data originally input by the upstream overall specialty.

[0059] Specifically, engineers can develop a data upload interface (RESTful API) to support batch uploading of original input files. This interface can efficiently handle large-scale data input and enable the uploading of original files from different data sources to the same platform, ensuring the stability, reliability, and sharing of data transmission. Optionally, the formats of the original input files include, but are not limited to, formats such as.xlsx,.txt,.COUNT, etc. For example, weight data is in the.xlsx format, configuration files can be in the.txt or.COUNT format, and calculation parameters and models can be in the.txt format.

[0060] Step S1.2: Automated verification; among which, format verification is performed on the original data to ensure compliance with predefined format requirements, and rationality verification is performed on the content of the original data. Specifically, the Pandas library of Python can be used to perform automated verification on the uploaded original data. Format verification can check the field integrity and whether the data types conform to the predefined format requirements to ensure the correctness of the data structure. Content rationality verification can perform numerical range and logical relevance checks on the data content to identify and exclude unreasonable or incorrect data, ensuring data quality.

[0061] Step S1.3: Standardized preprocessing; among which, the units of the original data are unified and formatted to ensure that the format of the data input into the load calculation module is consistent. For example, the pneumatic data units from different sources are unified into international standard units, and at the same time, the data is formatted so that it can be smoothly input into the load calculation module.

[0062] Specifically, engineers can use a unit conversion tool such as the Pint library to unify the units of the data to ensure the consistency of data from different sources. At the same time, a template engine such as Jinja2 is used to generate a standardized preprocessing file, providing a standardized input source for subsequent load calculations and solving the problem of scattered input files in the prior art.

[0063] Step S1.4: Generate a preprocessing result file and store it in the cloud database. Specifically, the preprocessed and verified data is stored in the cloud database, and its powerful data storage and management capabilities are used to achieve efficient data storage and fast retrieval. At the same time, combined with a version control tool, multi-version management of the stored files is performed to ensure data traceability and the queryability of historical versions, providing reliable data support for the entire process of load calculation. In this embodiment, the cloud database is MongoDB, and the version control tool is Git LFS.

[0064] Step S2: Customize and configure the load calculation process.

[0065] In step S2, select the load calculation module, configure the process verification rules and store them in the calculation configuration library of the cloud database, and arrange the algorithm formulas and store them in the algorithm library of the cloud database.

[0066] In an optional embodiment, step S2 may specifically include the following steps:

[0067] Step S2.1: Configure the load calculation module; wherein, select the load calculation module according to business needs and configure the input and output parameters of each load calculation module to ensure smooth data flow between modules. Specifically, the user can select and combine multiple calculation modules according to specific requirements, such as the ground load module, the flight load module, etc., to implement personalized calculation process design.

[0068] Step S2.2: Arrange the algorithm formulas used in the load calculation module so that the operation logic between each load calculation module is rigorous and orderly; wherein, the algorithm formulas can at least include one of the following: ground load simulation calculation, flight load simulation calculation, dynamic load simulation calculation. Specifically, engineers can use a workflow engine to arrange the algorithm formulas to ensure the logical rigor of the calculation process and the accuracy of the execution order. At the same time, configure verification rules to verify the result range of input and output parameters to ensure the rationality of input and output data and the reliability of the calculation process.

[0069] Step S2.3: Store the configured load calculation module and the arranged algorithm formulas in the calculation configuration library and algorithm library of the cloud database respectively for subsequent calling and updating. Specifically, store the configured calculation module and its related parameters in the calculation configuration library of the cloud database to achieve efficient management and quick retrieval of configuration information. At the same time, store the algorithm formulas in the algorithm library, and use its powerful graph structure management ability to support the management of dependencies between algorithms, ensuring the efficient calling and update maintenance of algorithms.

[0070] In this embodiment, the calculation configuration library is MySQL, and the algorithm library is the Neo4j graph database. The structure of the Neo4j graph database is flexible, and new algorithms can be easily added and the relationships between algorithms can be modified without the need for complex table structure adjustments like traditional relational databases. For scenarios that require frequent querying of algorithm dependencies, the query efficiency of Neo4j is much higher than that of traditional relational databases, and it can quickly retrieve the call paths and dependencies of algorithms. Taking the ground load calculation algorithm and the flight load calculation algorithm as examples, there are dependencies between these algorithms. The flight load calculation algorithm may need to first call the results of the ground load calculation algorithm. In this case, Neo4j can be used to store these algorithms and their dependencies, as follows:

[0071] Nodes: Algorithm1 (Ground Load Calculation Algorithm), Algorithm2 (Flight Load Calculation Algorithm).

[0072] Edges: Algorithm1 -> Algorithm2 (indicating that Algorithm2 depends on the results of Algorithm1).

[0073] Attributes: The attributes of Algorithm1 can include algorithm name, algorithm description, input parameters, output parameters, etc.

[0074] In this way, Neo4j can not only store the information of the algorithms themselves, but also efficiently manage the complex relationships between algorithms, thus realizing an efficient and flexible algorithm library.

[0075] In an alternative embodiment, during the execution of step S2, the message queue Kafka can be adopted to improve efficiency, configure multi-round parameter calculations, and obtain multiple versions of results each time, significantly reducing manual intervention. For example, the user needs to perform multiple rounds of calculations on the loads of an aircraft under different takeoff weights. The message queue queues these requests in sequence and processes them one by one. Each time, the ground load distribution results under different takeoff weights are obtained. Through the message queue, the system can efficiently process multi-round calculation requests and improve the overall calculation efficiency.

[0076] Step S3: Perform parallel load calculation in combination with a distributed computing framework. Specifically, the distributed computing framework Apache Spark can be deployed based on a Kubernetes cluster to achieve efficient management and elastic expansion of computing resources. The working condition files are reasonably allocated to each computing node through a hash algorithm to ensure balanced task load and improve calculation efficiency.

[0077] In step S3, first generate working conditions according to the preprocessing result file, allocate them to multiple computing nodes through load balancing for parallel calculation. After completing the working condition calculation, post-process the results and store them in the calculation result library of the cloud database. The load balancing and parallel calculation in this step can save 70% of the load calculation time.

[0078] In an alternative embodiment, step S3 can specifically include the following steps:

[0079] Step S3.1: Read the preprocessing result file as the input for working condition generation.

[0080] Step S3.2: Generate various working condition files based on the data analysis in the preprocessing result file. The working condition files can at least include one of the following: flight phase, environmental conditions, and load conditions. It should be noted here that this step converts the operating conditions and load information defined in the working condition generation phase into working condition files. The working condition files in this embodiment are in.bdf format and are used to store data such as the geometric information, material properties, boundary conditions, and load definitions of the finite element model.

[0081] Step S3.3: Allocate the working condition files to multiple computing nodes to ensure parallel processing of the load calculation tasks.

[0082] Specifically, first perform a working condition calculation on the working condition files, and then perform a load calculation (post-processing) on the results of the working condition calculation. The working condition calculation refers to calculating the performance of the system under different working conditions based on the generated.bdf file and outputting a result file in.f06 format. This step includes solving physical quantities such as the stress, strain, and deformation of the structure. For example, by analyzing the working parameters of the system under different operating conditions, such as temperature, pressure, flow rate, speed, etc., and / or determining the operating mode of the system under different working conditions, such as start-up, acceleration, stable operation, deceleration, stop, etc., and / or evaluating the adaptability of the system under different environmental conditions, such as the performance changes under high temperature, low temperature, high humidity, high altitude, etc., to evaluate the performance of the system under various operating conditions. It calculates the response of the system under different working conditions through numerical simulation methods, providing data support for subsequent design optimization and safety assessment.

[0083] During this process, each computing node runs a Docker containerized load calculation task, using container technology to achieve isolation and consistency of the computing environment, ensuring the stable operation of the calculation tasks. The calculation results are asynchronously transmitted back to the calculation module for post-processing through the message queue Kafka, improving the data transmission efficiency and reducing the system latency.

[0084] Step S3.4: Each computing node performs load post-processing calculations based on the allocated working condition files. The load post-processing calculations can at least include one of the following: incremental load processing, total load processing, component overload processing, engine overload processing, gust processing, and load matching.

[0085] Based on the results of the above working condition calculations, this step determines the type and size of loads that the system bears under different working conditions, such as static loads, dynamic loads, cyclic loads, etc., calculates the stress distribution, deformation and fatigue life of the structure under different loads, evaluates the safety and reliability of the structure under different loads, and ensures that the structure can withstand various loads without damage within the design life. Specifically, the post-processing module can use Python scripts to further process the calculation results, including but not limited to incremental load processing, component overload processing, etc., to meet the analysis requirements in different scenarios.

[0086] Step S3.5: Summarize and verify the rationality of the load calculation results returned by each computing node and store them in the calculation result library of the cloud database for subsequent sharing, analysis, and call; the load calculation results may include at least one of the following: load distribution, stress distribution, and deformation. Specifically, the processed results are stored in the cloud database Cassandra, which uses its distributed storage characteristics to achieve high availability and fast access to data, providing support for subsequent analysis and application.

[0087] Step S4: Result analysis and visualization.

[0088] In step S4, visual comparison and analysis of load calculation results across rounds and models are performed, and rationality judgment is made to reduce dependence on human experts, track the time consumption and abnormal status of the load calculation process, and output a standardized report in a unified format.

[0089] In an optional embodiment, step S4 may specifically include the following steps:

[0090] Step S4.1: Obtain the load calculation results of different rounds and different models from the calculation result library of the cloud database.

[0091] Step S4.2: Generate a comparison chart to visually display the load changes under different conditions, for example, comparing the ground load distribution under different takeoff weights and flight stages.

[0092] Step S4.3: Perform a rational analysis on the load calculation results to evaluate whether the load calculation results meet the design requirements and safety standards and identify potential problems. For example, if the stress distribution of a certain model of aircraft in a specific flight phase is close to the safety limit, it indicates that the design needs to be further optimized. Engineers can trace the data source to adjust the aircraft design.

[0093] Step S4.4: Track the time consumption of each computing node in the load calculation process, analyze the calculation efficiency, and detect abnormal states during the calculation process; among them, the abnormal states include at least one of the following: node failure, data loss. For example, if it is found that the time consumption of a certain computing node is relatively long, the reason may be a large amount of data. At the same time, detect abnormal states during the calculation process. For example, it is found that a certain node has experienced a short-term failure, but it has not affected the overall calculation result.

[0094] In step S4.4, the solution process of load calculation can be further optimized according to the load calculation time consumption of each computing node and the abnormal states during the calculation process. For example, if it is found that a certain computing node takes a long time to process complex load conditions, by adjusting the calculation task allocation strategy, some tasks are reallocated to other nodes to improve the overall calculation efficiency. At the same time, for abnormal states that occur during the calculation process, such as data loss, the system adopts a data backup and recovery mechanism to ensure the continuity and accuracy of the calculation.

[0095] Step S4.5: Display the analysis results in an interactive manner in a visual form; among them, the visual forms include at least one of the following: stress nephogram, deformation diagram, comparison chart.

[0096] Step S4.6: Output a report according to the content selected by the user interaction. For example, if the user selects to output a report containing the load distribution and stress nephogram of a specific flight phase, the system generates and provides it to the user as required.

[0097] To achieve the above object, the present invention also provides a civil aircraft load calculation and processing system based on cloud native, which uses the cloud-native-based civil aircraft load calculation and processing method described in any one of the above embodiments, and specifically may include: a data preprocessing and data control module, a process control module, and a result analysis and visualization module.

[0098] Specifically, as Figure 3 and Figure 4 shown, the data preprocessing and data control module may include a cloud storage module and an algorithm module, which are used to perform automatic verification and standardization processing on the original data in the original input file, and generate a preprocessing result file for the processed data and store it in the input file library of the cloud database for unified storage, version control, traceability management, and serve as a standardized input source for subsequent load calculations. Among them, the cloud storage module may respectively include: an input file database, a calculation configuration library, an algorithm library, and a calculation result library. The algorithm module may respectively include: a load calculation algorithm, a preprocessing and verification algorithm, and a result analysis and comparison algorithm, and perform parallel load calculations in combination with a distributed computing framework.

[0099] The system first generates working conditions based on the preprocessing result file, distributes them to multiple computing nodes through load balancing for parallel computing, and after completing the working condition calculation, performs post-processing on the results and stores them in the calculation result library of the cloud storage module.

[0100] The process control module is used to customize the configuration of the load calculation process, start batch tasks with one key, and monitor the real-time status of the process. Among them, the user can customize the selection of the load calculation module, configure the process verification rules and store them in the calculation configuration library of the cloud storage module, and arrange the algorithm formulas and store them in the algorithm library of the cloud storage module.

[0101] The result analysis and visualization module is used to perform visual comparison analysis and rationality judgment on the load calculation results across rounds and models, track the time consumption and abnormal status of the load calculation process, and output a standardized report in a unified format. The specific implementation method has been described in detail above and will not be elaborated here.

[0102] To achieve the above object, the present invention also provides a computer device, including a memory, a processor, and a program stored on the memory and executable on the processor. When the processor runs the program, it can implement the steps of a method for calculating and processing civil aircraft loads based on cloud native as described in any one of the foregoing embodiments.

[0103] The processor and the memory can be set separately or integrated together. For example, they can be integrated on a system on chip (SOC) of a terminal device. It should be understood that the processor in the embodiments of the present application may be an integrated circuit chip with signal processing capabilities. During implementation, the steps of the above method embodiments can be completed by the integrated logic circuit in the hardware of the processor or instructions in software form. The above-mentioned processor can be a general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as being executed and completed by a hardware decoding processor, or executed and completed by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory, and the processor reads the information in the memory and combines its hardware to complete the steps of the above method.

[0104] To achieve the above object, the present invention also provides a computer-readable storage medium. The computer-readable storage medium stores executable instructions or programs. When the executable instructions or programs are processed and executed, the method for calculating and processing civil aircraft payloads based on cloud native as described in any previous embodiment is implemented.

[0105] The readable storage medium is, for example, a memory. The memory can be a volatile memory or a non-volatile memory, or the memory can include both a volatile memory and a non-volatile memory at the same time. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable ROM (PROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0106] If the integrated unit in the above embodiments is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in the above-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The software product is stored in a storage medium and includes several instructions for causing one or more devices (which can be a personal terminal, a client, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0107] The preferred specific embodiments of the present invention have been described in detail above. Only several implementation manners of the present invention are expressed, but it should not be construed as a limitation on the scope of the patent. The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification. It should be understood that those of ordinary skill in the art can make many modifications and variations according to the concept of the present invention without creative labor. Therefore, without departing from the concept of the present invention, all technical solutions that can be obtained by those skilled in the art in the technical field according to the concept of the present invention through logical analysis, reasoning or limited experiments on the basis of the prior art should be within the protection scope determined by the claims.

Claims

1. A method for calculating and processing civil aircraft payloads based on cloud native, characterized in that, Including: Step S1: Data preprocessing and data control; wherein, the system automatically validates and standardizes the raw data in the original input file, and generates a preprocessing result file for the processed data and stores it in the input file library of the cloud database for unified storage, version control, traceability management, and as a standardized input source for subsequent load calculation; Step S2: Customize the load calculation process; wherein, select the load calculation module, configure the process verification rules and store them in the calculation configuration library of the cloud database, and arrange the algorithm formulas and store them in the algorithm library of the cloud database; Step S3: Perform parallel load calculation in combination with the distributed computing framework; wherein, first generate working conditions according to the preprocessing result file, allocate them to multiple computing nodes through load balancing for parallel calculation, and perform post-processing on the results after completing the working condition calculation and store them in the calculation result library of the cloud database; Step S4: Result analysis and visualization; wherein, perform visual comparison analysis and rationality judgment on the load calculation results across rounds and models, track the time consumption and abnormal status of the load calculation process, and output a standardized report in a unified format.

2. The method for calculating and processing civil aircraft payloads based on cloud native according to claim 1, wherein, The specific steps of the said Step S1 include the following steps: Step S1.1: Upload the original input file; wherein, the raw data in the original input file includes at least one of the following data: overall, weight, aerodynamics, landing gear, control law, thrust data originally input by the upstream overall specialty; Step S1.2: Automatic verification; wherein, perform format verification on the raw data to ensure compliance with the predefined format requirements, and perform rationality verification on the content of the raw data; Step S1.3: Standardized preprocessing; wherein, unify the data units of the raw data and perform formatting processing to ensure that the data input into the load calculation module has a consistent format; Step S1.4: Generate the preprocessing result file and store it in the cloud database.

3. The method for calculating and processing civil aircraft payloads based on cloud native according to claim 1, wherein The specific steps of the said Step S2 include the following steps: Step S2.1: Configure the load calculation module; wherein, select the load calculation module according to business needs and configure the input and output parameters of each load calculation module to ensure smooth data flow between modules; Step S2.2: Arrange the algorithm formulas used in the load calculation module to make the operation logic between each load calculation module rigorous and orderly; wherein, the algorithm formulas include at least one of the following: ground load simulation calculation, flight load simulation calculation, dynamic load simulation calculation; Step S2.3: Store the configured load calculation module and the arranged algorithm formulas in the calculation configuration library and algorithm library of the cloud database respectively for subsequent call and update.

4. The method for calculating and processing civil aircraft loads based on cloud native according to claim 3, wherein, During the execution of the said Step S2, a message queue is adopted to improve efficiency, multi-round parameter calculation is configured, and multiple versions of results are obtained each time.

5. The method for calculating and processing civil aircraft payloads based on cloud native according to claim 4, wherein, The specific steps of the said Step S3 include the following steps: Step S3.1: Read the preprocessing result file as the input for generating working conditions; Step S3.2: Generate various working condition files according to the data analysis in the preprocessing result file, where the working condition file includes at least one of the following: flight phase, environmental conditions, and load conditions; Step S3.3: Allocate the working condition files to multiple computing nodes to ensure parallel processing of the load calculation tasks; Step S3.4: Each computing node performs post-processing load calculations according to the allocated working condition file, where the post-processing load calculations include at least one of the following: incremental load processing, total load processing, component overload processing, engine overload processing, gust processing, and load matching; Step S3.5: Summarize, perform rationality verification on the load calculation results returned by each computing node, and store them in the calculation result library of the cloud database for subsequent sharing, analysis, and invocation; where the load calculation results include at least one of the following: load distribution, stress distribution, and deformation conditions.

6. The method for calculating and processing civil aircraft payloads based on cloud native according to claim 5, wherein, The specific steps of Step S4 are as follows: Step S4.1: Obtain the load calculation results of different rounds and different models from the calculation result library of the cloud database; Step S4.2: Generate comparison charts to visually display the load changes under different conditions; Step S4.3: Perform rationality analysis on the load calculation results, evaluate whether the load calculation results meet the design requirements and safety standards, and identify potential problems; Step S4.4: Track the time-consuming of each computing node in the load calculation process, analyze the calculation efficiency, and detect abnormal states during the calculation process; where the abnormal states include at least one of the following: node failure and data loss; Step S4.5: Display the analysis results in an interactive manner in a visual form; where the visual form includes at least one of the following: stress nephogram, deformation diagram, and comparison chart; Step S4.6: Output a report according to the content selected by the user interaction.

7. The method for calculating and processing civil aircraft payloads based on cloud native according to claim 6, wherein, In Step S4.4, optimize the solution process of the load calculation according to the load calculation time-consuming of each computing node and the abnormal states during the calculation process.

8. A civil aircraft payload calculation and processing system based on cloud native, characterized in that, Use the civil aircraft load calculation and processing method based on cloud native as described in any one of claims 1-7, including: A data preprocessing and data control module, including a cloud storage module and an algorithm module, for automatically verifying and standardizing the original data in the original input file, and generating a preprocessing result file from the processed data and storing it in the input file library of the cloud database for unified storage, version control, traceability management, and serving as a standardized input source for subsequent load calculations; Among them, the cloud storage module includes: an input file database, a calculation configuration library, an algorithm library, and a calculation result library; the algorithm module includes: a load calculation algorithm, a preprocessing and verification algorithm, and a result analysis and comparison algorithm, and performs parallel load calculations in combination with a distributed computing framework; the system first generates working conditions according to the preprocessing result file, distributes them to multiple computing nodes through load balancing for parallel calculations, and after completing the working condition calculations, performs post-processing on the results and stores them in the calculation result library of the cloud storage module; A process control module, which is used to customize the configuration of the load calculation process, start batch tasks with one key, and monitor the real-time status of the process; wherein, the user can customarily select a load calculation module, configure process verification rules and store them in the calculation configuration library of the cloud storage module, and arrange algorithm formulas and store them in the algorithm library of the cloud storage module; A result analysis and visualization module, which is used to conduct visual comparison analysis and rationality judgment on the load calculation results across rounds and models, track the time consumption and abnormal status of the load calculation process, and output a standardized report in a unified format.

9. A computer device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor runs the program, it implements the cloud-native civil aircraft load calculation processing method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions or a computer program, and when the computer-executable instructions or the computer program are processed and executed, the cloud-native civil aircraft load calculation processing method according to any one of claims 1 to 7 is implemented.

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