CN117591515A - Centralized processing method and device for airplane flight test data - Google Patents
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Abstract
The application belongs to the technical field of data processing, and particularly relates to a centralized processing method and device for airplane test flight data. The method comprises the following steps: s1, acquiring aircraft test flight description data and aircraft test flight data; s2, structuring test flight description data and associating the test flight description data with the aircraft test flight data through file names; s3, writing the aircraft test flight data associated with the test flight description into a Hadoop distributed file system; s4, performing CSV format conversion on the aircraft flight test data written into the Hadoop distributed file system, and storing the aircraft flight test data in a column database; and S5, acquiring a query template filled in by each specialty and having one or more data attributes, and searching and forming secondary library flight parameter data in the column database for downloading. According to the method and the device, the mass data is effectively managed, and the test flight data management efficiency is improved.
Description
Technical Field
The application belongs to the technical field of data processing, and particularly relates to a centralized processing method and device for airplane test flight data.
Background
The test flight of the aircraft is also called as a flight test, and is an important link in the development process of the aircraft, and whether the function and performance index of the aircraft meet the design requirements is verified through the test flight. A large amount of flight test data, including flight parameter data, bus data, video data and the like, are generated in the flight test process, and are important bases for analyzing flight test problems and checking the design quality of the aircraft.
Because of lacking a unified storage management system and a unified flow method, the volume of the aircraft test flight data is large, the flow is relatively complex, and the traditional data management technology cannot meet the requirement of centralized management of the test flight data, the current processing method of the aircraft test flight data mainly relies on a unit personnel to copy out the test flight data optical disk directly from an evaluation center and bring the test flight data optical disk back by a special person, the test flight data optical disk is copied into an intranet for relevant professional analysis of the unit, a small amount of data is returned to the intranet through an external field point network, after the data reaches an intranet environment, each professional analyzes and processes the original file, but analysis results are not stored in a centralized way, and the analysis results cannot be shared, so that a large amount of repetitive work exists.
The whole process relies on manual coordination, the efficiency is low, and the scattered management of test flight data and data description files and the scattered management of analysis software often appear that data and description files are not corresponding, the version of the data and analysis software are not corresponding, and the like, so that the problems that data reading is not understood, analysis is not carried out, and the like are caused, and great difficulty is caused to the management and the use of the test flight data.
Disclosure of Invention
In order to solve the problems, the application provides a centralized processing method and device for aircraft pilot flight data, which utilize Hadoop big data management technology to improve data storage capacity and solve the problems of complex pilot flight data management process, long interaction time and low multiplexing rate.
In a first aspect of the present application, a method for centralized processing of aircraft test flight data is provided, which mainly includes:
s1, acquiring aircraft test flight description data and aircraft test flight data;
s2, structuring test flight description data and associating the test flight description data with the aircraft test flight data through file names;
s3, writing the aircraft test flight data associated with the test flight description into a Hadoop distributed file system;
s4, performing CSV format conversion on the aircraft flight test data written into the Hadoop distributed file system, and storing the aircraft flight test data in a column database;
and S5, acquiring a query template filled in by each specialty and having one or more data attributes, and searching and forming secondary library flight parameter data in the column database for downloading.
Preferably, in step S1, the acquiring aircraft test flight description data includes:
manually-entered aircraft test flight description data is obtained by providing format verification, dictionary selection or filling prompt; or alternatively
Acquiring aircraft flight description data by analyzing excel with flight description data; or alternatively
And searching and acquiring the aircraft test flight description data in a preset file path or test flight description database.
Preferably, step S2 further includes identifying date and time information of the aircraft flight test based on the aircraft flight test data, and associating with the flight test description data.
Preferably, step S3 further comprises:
step S31, a local client based on a Hadoop distributed file system initiates a file writing request to a central node thereof;
step S32, dividing the aircraft flight test data to be written into a plurality of blocks based on the storage node information fed back by the central node;
and step S33, writing the blocks into the storage node in sequence, and sending the writing result to the central node.
The second aspect of the present application provides an aircraft test flight data centralized processing device, mainly including:
the data acquisition module is used for acquiring the aircraft test flight description data and the aircraft test flight data;
the data association module is used for structuring the test flight description data and associating the test flight description data with the aircraft test flight data through file names;
the data storage module is used for writing the aircraft test flight data associated with the test flight description into the Hadoop distributed file system;
the data analysis module is used for converting the aircraft flight test data written into the Hadoop distributed file system into a CSV format and storing the CSV format into a column database;
the data application module is used for acquiring a query template filled in by each specialty and having one or more data attributes, and retrieving and forming secondary library flight parameter data in the column database for downloading.
Preferably, the data acquisition module includes:
the manual entry unit is used for acquiring manually entered aircraft test flight description data in a mode of providing format verification, dictionary selection or filling prompt; or alternatively
The manual importing unit is used for acquiring the aircraft flight description data by analyzing the excel with the flight description data; or alternatively
And the automatic importing unit is used for retrieving and acquiring the aircraft flight description data from a preset file path or flight description database.
Preferably, the data association module comprises a test flight date and time identification unit, which is used for identifying the date and time information of the test flight of the aircraft based on the test flight data of the aircraft and associating the test flight description data.
Preferably, the data storage module includes:
the writing request unit is used for initiating a file writing request to a central node of the Hadoop distributed file system based on a local client of the Hadoop distributed file system;
the data dividing unit is used for dividing the aircraft flight test data to be written into a plurality of blocks based on the storage node information fed back by the central node;
and the data writing unit is used for writing the plurality of blocks into the storage node in sequence and sending the writing result to the central node.
The method establishes a complete process of uploading association, centralized storage and data analysis to data query and utilization of the test flight data, realizes effective management of mass data, improves test flight data management efficiency, supports analysis, mining and utilization of the data, and is beneficial to accurate discovery of design problems and improvement of aided design capability.
Drawings
FIG. 1 is a flow chart of a preferred embodiment of a method for centralized processing of flight test data of an aircraft according to the present application.
Fig. 2 is a flow chart of data association in the present application.
FIG. 3 is a flow chart of data storage according to the present application.
Fig. 4 is a flow chart of the data application of the present application.
Detailed Description
For the purposes, technical solutions and advantages of the present application, the following describes the technical solutions in the embodiments of the present application in more detail with reference to the drawings in the embodiments of the present application. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are some, but not all, of the embodiments of the present application. The embodiments described below by referring to the drawings are exemplary and intended for the purpose of explaining the present application and are not to be construed as limiting the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure. Embodiments of the present application will be described in detail below with reference to the accompanying drawings.
The application provides a centralized processing method and device for aircraft test flight data, which utilize Hadoop big data technology to store and manage massive test flight data. The Hadoop distributed file system is a distributed system infrastructure, can perform distributed processing on a large amount of data in a reliable, efficient and scalable manner, and is widely used in big data processing applications. The column storage technology of the architecture is adopted, so that the storage cost is reduced, and the query efficiency is improved.
The first aspect of the present application provides a method for centralized processing of aircraft test flight data, as shown in fig. 1, mainly including:
and S1, acquiring aircraft test flight description data and aircraft test flight data.
In some optional embodiments, in step S1, acquiring aircraft test flight description data includes:
manually-entered aircraft test flight description data is obtained by providing format verification, dictionary selection or filling prompt; or obtaining the flight description data of the aircraft by analyzing excel with the flight description data; or searching and obtaining the aircraft test flight description data in a preset file path or test flight description database.
In this embodiment, three modes are supported by the data warehouse entry of the airplane test flight description: manual filling, manual introduction and automatic introduction. The manual filling means that the system supports modes of staff filling forms and the like, airplane test flight description data information is input into the system, and the input form pages are friendly and easy to use, and provide auxiliary functions of format verification, dictionary selection, filling prompt and the like; the manual importing refers to that a system provides a friendly importing interface, and the system supports analysis according to the existing Excel module to realize automatic structured warehousing of the description information; the automatic import means that the system supports to the appointed file catalogue, automatically searches, realizes automatic uploading according to file names, or connects to the database intermediate table, and realizes data synchronization. After the test flight description data enter the system, the test flight description data are stored as structured data, so that the test flight description data are convenient to check and inquire.
The aircraft test flight data also support various uploading modes, and after the aircraft test flight description data is uploaded, the corresponding test flight original data can be further imported, for example, by manual uploading or automatic importing. For manual uploading, it should be noted that, in the present stage, when the data return system has not been built or has a fault after the future construction is completed, the manual uploading of the test flight original data is supported. And the manual uploading is to click a browsing button at a corresponding position of the corresponding description data record by a user, browse and select the test flight original data file, and click the uploading to realize data uploading. For automatic uploading, it should be noted that the system supports to a designated file directory, automatically searches, realizes automatic uploading according to file names, directly accesses through a large file data reading interface, reserves an inter-system data synchronous uploading interface, and supports the direct data storage of a data remote return system for future planning and construction.
And step S2, structuring the test flight description data and associating the test flight description data with the aircraft test flight data through file names.
In this step, after the test flight unit stores the test flight original data and the test flight description data in the designated "middle area" through the remote network, the database platform starts the automatic monitoring program to monitor that there is a new added file, triggers the uploading and association program, first performs automatic uploading and storage of the data, then performs data mining according to the file names of the test flight original file and the description file by using mainstream data mining techniques such as cluster analysis, classification analysis, association rules and regression analysis, and checks the accuracy of matching according to naming rules, and establishes association of the two, and in some alternative embodiments, step S2 further includes identifying the date and the frame information of the test flight of the aircraft based on the aircraft test flight data and associating with the test flight description data.
The uploading analysis process is shown in fig. 2, after the file is imported, the data administrator can expand and perfect the data, and the legibility of the data is enhanced by manually adding other descriptive attribute information of the file by the data administrator.
The data extraction mechanism is mainly defined as two types: the data with smaller data volume can be extracted in full volume, and the data with larger data volume needs to be extracted in increment as much as possible. The data obtained through the data interface is first stored in the data exchange area of the big data management general-purpose base environment (as a temporary storage area before the data enters the big data platform). And finishing data verification, cleaning, loading, conversion, calculation, summarization and classification of the data through the ETL tool.
After the data is cleaned and converted, unified scheduling and management are carried out through an ETL tool. On the basis of comprehensively considering service analysis requirements and cost of system loading and maintaining the integrity of service data at the same time, different loading periods are adopted for the data of different service systems, but daily loading periods are the main. The data loading strategy is used for respectively carrying out direct loading, full coverage and update loading according to the data extraction strategy, the definition of the business rule and the loading efficiency. Irregular manual loading is also supported.
And step S3, writing the aircraft test flight data associated with the test flight description into a Hadoop distributed file system.
For tens of G of single files, the general data management method cannot meet the requirement of hundred T test flight data, so that the Hadoop big data technology is utilized to store and manage massive test flight data, a Hadoop system mainly comprises NameNode, dateNode, client and other important components, nameNode is a basic unit for overall scheduling and configuration of the distributed system, dateNode is a basic unit for file storage and is used for storing data, clients are local systems, interaction is performed among the components, data are segmented and compressed, the data are mounted on each physical node of the Hadoop, independent processes and memory space are provided for HBase and Spark by an RDBMS part of BDP, and massive data are effectively stored.
In some alternative embodiments, step S3 further comprises:
step S31, a local client based on a Hadoop distributed file system initiates a file writing request to a central node thereof;
step S32, dividing the aircraft flight test data to be written into a plurality of blocks based on the storage node information fed back by the central node;
and step S33, writing the blocks into the storage node in sequence, and sending the writing result to the central node.
As shown in fig. 3, in this embodiment, a Client (local Client) initiates a file writing request to a NameNode (central node), performs file pre-verification, checks authority and directory conditions, and the NameNode returns data node (storage node) information managed by the Client to the Client according to the file size and the file Block configuration conditions, the Client divides the file into a plurality of blocks, writes each data node in sequence according to address information of the data node, and reports a writing result to the NameNode after the writing is completed. On each physical node of Hadoop, separate processes and memory space are provided for HBase and Spark by the RDBMS portion of BDP. The parser, planner, optimizer and executor are placed in the HBase Region Server process and the OLTP is distributed across regions (i.e., slices) using the HBase coprocessor to complete the computational tasks.
And S4, performing CSV format conversion on the aircraft flight test data written into the Hadoop distributed file system, and storing the aircraft flight test data in a column database.
In this step, the data type cannot be directly applied for PHY format data, and for the uploaded data, an automatic parsing program in the platform is triggered to convert PHY format flight parameter data into CSV format file, and version creation and version entity management are performed. After the conversion of the CSV format is completed, the large data platform starts an automatic monitoring program to automatically store CSV files in a column database of the large data platform, firstly, data compression is carried out on fly parameter data in a dictionary table mode, the order of magnitude is improved, the disk IO is lowered, the acceleration query is convenient, then a plurality of attributes of one data item are spliced, the Hadoop database is stored, and the column storage after the analysis of the fly parameter data is completed.
According to the embodiment, the data is compressed, the module information is connected in series, the in-disk and IO reading times are saved, the query efficiency of mass data is greatly improved, and the method is used for subsequent application.
And S5, acquiring a query template filled in by each specialty and having one or more data attributes, and searching and forming secondary library flight parameter data in the column database for downloading.
Because thousands of test flight data parameters exist, simple single-attribute or multi-attribute retrieval is difficult to meet the data analysis requirement of designers, data retrieval based on templates is provided, each specialty can set a professional export template according to requirements, and the templates support to select parameters needing to be exported from all analyzed parameters, so that the parameter export according to the templates is realized. The templates support flexible adjustments for each specialty. And supporting various export formats, such as txt, CSV and the like, packing concerned attribute information by each professional to form a query template, querying own professional information by each investigation of the query template, integrating data processing analysis software of each professional, and automatically calling related data of the system by the software for automatic analysis.
Specifically, the application process is shown in fig. 4, based on the query template, the user can download the data in the authority range, so that the user can conveniently process and analyze the data based on the local data processing tool. The method can inquire and download original files (including the original data files of the flight parameters, the bus and the video), wherein the flight parameters can be retrieved and the analyzed structured data can be exported; based on the customized data template, the integration of the platform and data analysis tools such as MATLAB can be established, data can be directly output to the integrated analysis tools according to parameters selected by the template, and a data analysis processing program in the data analysis tools is automatically started, so that manual processing links of manual export and reintroduction are omitted, and the automation degree of data analysis processing is improved. Meanwhile, the data before the test flight data are processed and the processed conclusions can be stored in the professional secondary libraries, the tags can be further added to the professional secondary libraries by combining the characteristics of the profession, the readability of the data is improved, and the subsequent repeated use of the user is facilitated.
Compared with the prior art, the method combines the big data technology application with the airplane test flight data processing service, realizes the effective management of mass data and the analysis, mining and utilization of supporting data, is favorable for accurately finding design problems and improving auxiliary design capacity, establishes a unified collaborative work platform for professionals such as test flight, performance, flight quality, flight parameter data management and the like, and opens up a complete flow from test flight data uploading association, centralized storage and data analysis to data query and utilization. The model tree management mode, the flight time query mode, the total number of take-off and landing query mode and the like are established, the query efficiency is improved to the second level, the average time of the initial data entry is 0.5 hour, and the pain point problems of aircraft test flight data management and the like for a long time are solved.
The second aspect of the present application provides an aircraft test flight data centralized processing device corresponding to the above method, mainly including:
the data acquisition module is used for acquiring the aircraft test flight description data and the aircraft test flight data;
the data association module is used for structuring the test flight description data and associating the test flight description data with the aircraft test flight data through file names;
the data storage module is used for writing the aircraft test flight data associated with the test flight description into the Hadoop distributed file system;
the data analysis module is used for converting the aircraft flight test data written into the Hadoop distributed file system into a CSV format and storing the CSV format into a column database;
the data application module is used for acquiring a query template filled in by each specialty and having one or more data attributes, and retrieving and forming secondary library flight parameter data in the column database for downloading.
In some alternative embodiments, the data acquisition module includes:
the manual entry unit is used for acquiring manually entered aircraft test flight description data in a mode of providing format verification, dictionary selection or filling prompt; or alternatively
The manual importing unit is used for acquiring the aircraft flight description data by analyzing the excel with the flight description data; or alternatively
And the automatic importing unit is used for retrieving and acquiring the aircraft flight description data from a preset file path or flight description database.
In some optional embodiments, the data association module includes a flight date and duration identification unit for identifying the date and duration information of the flight of the aircraft based on the flight data, and associating with the flight description data.
In some alternative embodiments, the data storage module includes:
the writing request unit is used for initiating a file writing request to a central node of the Hadoop distributed file system based on a local client of the Hadoop distributed file system;
the data dividing unit is used for dividing the aircraft flight test data to be written into a plurality of blocks based on the storage node information fed back by the central node;
and the data writing unit is used for writing the plurality of blocks into the storage node in sequence and sending the writing result to the central node.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions easily conceivable by those skilled in the art within the technical scope of the present application should be covered in the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (8)
1. The method for intensively processing the aircraft test flight data is characterized by comprising the following steps of:
s1, acquiring aircraft test flight description data and aircraft test flight data;
s2, structuring test flight description data and associating the test flight description data with the aircraft test flight data through file names;
s3, writing the aircraft test flight data associated with the test flight description into a Hadoop distributed file system;
s4, performing CSV format conversion on the aircraft flight test data written into the Hadoop distributed file system, and storing the aircraft flight test data in a column database;
and S5, acquiring a query template filled in by each specialty and having one or more data attributes, and searching and forming secondary library flight parameter data in the column database for downloading.
2. The method for centralized processing of aircraft test flight data according to claim 1, wherein in step S1, acquiring the aircraft test flight description data comprises:
manually-entered aircraft test flight description data is obtained by providing format verification, dictionary selection or filling prompt; or alternatively
Acquiring aircraft flight description data by analyzing excel with flight description data; or alternatively
And searching and acquiring the aircraft test flight description data in a preset file path or test flight description database.
3. The method of centralized processing of aircraft test flight data of claim 1, wherein step S2 further comprises identifying date and time information of aircraft test flights based on the aircraft test flight data and associating with the test flight description data.
4. The method for centralized processing of aircraft test flight data according to claim 1, wherein step S3 further comprises:
step S31, a local client based on a Hadoop distributed file system initiates a file writing request to a central node thereof;
step S32, dividing the aircraft flight test data to be written into a plurality of blocks based on the storage node information fed back by the central node;
and step S33, writing the blocks into the storage node in sequence, and sending the writing result to the central node.
5. An aircraft test flight data centralized processing device, which is characterized by comprising:
the data acquisition module is used for acquiring the aircraft test flight description data and the aircraft test flight data;
the data association module is used for structuring the test flight description data and associating the test flight description data with the aircraft test flight data through file names;
the data storage module is used for writing the aircraft test flight data associated with the test flight description into the Hadoop distributed file system;
the data analysis module is used for converting the aircraft flight test data written into the Hadoop distributed file system into a CSV format and storing the CSV format into a column database;
the data application module is used for acquiring a query template filled in by each specialty and having one or more data attributes, and retrieving and forming secondary library flight parameter data in the column database for downloading.
6. The aircraft test flight data centralized processing device of claim 5, wherein the data acquisition module comprises:
the manual entry unit is used for acquiring manually entered aircraft test flight description data in a mode of providing format verification, dictionary selection or filling prompt; or alternatively
The manual importing unit is used for acquiring the aircraft flight description data by analyzing the excel with the flight description data; or alternatively
And the automatic importing unit is used for retrieving and acquiring the aircraft flight description data from a preset file path or flight description database.
7. The apparatus according to claim 5, wherein the data association module includes a pilot date and pilot time identification unit for identifying date and pilot time information of pilot flight of the aircraft based on the pilot flight data, and associating with the pilot flight description data.
8. The aircraft test flight data centralized processing device of claim 5, wherein the data storage module comprises:
the writing request unit is used for initiating a file writing request to a central node of the Hadoop distributed file system based on a local client of the Hadoop distributed file system;
the data dividing unit is used for dividing the aircraft flight test data to be written into a plurality of blocks based on the storage node information fed back by the central node;
and the data writing unit is used for writing the plurality of blocks into the storage node in sequence and sending the writing result to the central node.
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