CN112241402A - Empty pipe data supply chain system and data management method - Google Patents
Empty pipe data supply chain system and data management method Download PDFInfo
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Abstract
The invention discloses an air traffic control data supply chain system and a data management method, and belongs to the technical field of air traffic control data processing. Aiming at the problems that the prior art has serious intersystem data barrier caused by the fact that the blank management data has no complete data supply chain system, and has no perfect data management system, disordered data management and the like, the invention provides the blank management data supply chain system. When the data is managed, the empty pipe data supply chain system is firstly constructed, and a management platform is established in a data management module of the empty pipe data supply chain to implement data management; and a data interaction publishing platform is built, and the data supply chain management and control and service capability are improved. The invention can realize that a data circulation pipeline is opened by establishing a data supply chain in the air management data management, thereby completing the asset and service of data and improving the data value in the data circulation process.
Description
Technical Field
The invention relates to the technical field of air traffic control data processing, in particular to an air traffic control data supply chain system and a data management method.
Background
With the continuous development of society and the increase of aviation business, the air traffic control bureau generates a large amount of data in the production operation and function management processes. Meanwhile, ecological partners of the air traffic control bureau such as navigation department and airport can also generate a large amount of data in the service operation process and perform data interaction with the air traffic control bureau. Data is increasingly becoming a core asset supporting the operation of various systems. Whether the data of each system is fully integrated or not, whether the data is effectively managed or not, whether the data quality is reliable or not, whether the data standard is consistent or not, whether the data safety is effectively controlled or not, whether the data value is fully mined or not, and how to develop and utilize the data resources is more beneficial to realizing business innovation, risk control, auxiliary decision and the like become more and more important subjects.
The integration, management and use of the empty management data at the present stage have the following problems:
firstly, a complete data supply chain system is not established;
in order to meet the requirement of rapid development of air traffic control business, the construction of a civil aviation air traffic control information system is comprehensively spread. However, from the analysis of information management, the current system construction mostly adopts a chimney-type longitudinal architecture, the information island is more, the data barrier between systems is serious, a pipeline from data acquisition to data storage, data governance, data exchange, data mining, data development and data value utilization is not opened, a perfect data supply chain system is not established, the full life cycle of data is not managed, the data value mining is insufficient, the data calculation analysis and mining capability is insufficient, and the intelligent application is insufficient.
Secondly, a perfect data management system is not established;
at present, most of the air traffic authorities have not established a perfect data management system, and the following reasons mainly exist:
(1) in the aspects of data strategy and control, a global data management strategy and an implementation path are not released, a data control and long-acting treatment mechanism is lacked, and a clear data management system is lacked;
(2) in the aspect of data resource catalogs, clear data resource catalogs and data maps are not established at present, data are dispersed in each system, unified management is lacked, and the data assets owned at present are difficult to determine;
(3) in the aspect of data architecture, the existing information system is huge and dispersed in quantity, the integration level of data is not high, a global unified data architecture is lacked, the data distribution and flow direction are not clear, and a trusted data source cannot be quickly positioned. Making efficient use of data resources impossible.
(4) In terms of data standards, the main data is distributed in different systems, and no uniform format standard exists. The collaborative cooperation cost of each department on data is high;
(5) in the aspect of data quality, systematic data quality evaluation indexes and inspection rules are lacked, and the data quality problem cannot be identified comprehensively and efficiently;
(6) in the aspect of data security, data classification and classification are not carried out, a data security management strategy is difficult to make, and data classification security protection measures are not implemented;
(7) data lifecycle aspects, where data lifecycle management is not performed;
(8) in the aspect of data application, the current data application and service capability is weak.
The difficulty of data management is mainly reflected in different data standards, and due to the fact that main data are maintained at multiple ends, system integration difficulty is high, and data integration difficulty across business units and professionalism is high. For example, the same asset and equipment data needs to be repeatedly entered into a financial system, an equipment maintenance system and a purchase management system, and the data format standards of the systems are not uniform, so that the data are inconsistent. Data standard management work is scattered in each business unit, a global data standard specification is undefined, and the construction process of an information system cannot be strictly controlled. On the other hand, the construction of the information system does not strictly follow the unified data standard specification, so that the data is not specified, and the difficulty of data integration is increased.
Disclosure of Invention
1. Technical problem to be solved
The invention provides an air traffic control data supply chain system and a data management method, aiming at the problems that the air traffic control data has no complete data supply chain system, so that the inter-system data barrier is serious, and simultaneously, no complete data management system exists, the data management is disordered and the like in the prior art.
2. Technical scheme
The purpose of the invention is realized by the following technical scheme.
An air traffic control data supply chain system comprises a data production module, a data acquisition module, a data storage module, a data management module and a data application module, wherein the data production module acquires data comprising a production system, a management system and an external system; the data acquisition module uses different acquisition modes and interfaces according to the data type and the data source system; the data storage module comprises a data platform for storing the air traffic control data and constructing, and integrates and processes the data of different systems; the data management module constructs a data management platform to manage the empty pipe data; the data application module comprises data exchange, mining and development application, and the chain nodes of the data supply chain comprise data production, data acquisition, data storage, data management and data application.
When the data supply chain system is built, data acquisition is carried out according to different data types and data systems, the data types of the system are unified, the data barriers among the systems are broken, and a complete data management system is constructed.
Furthermore, the data collected by the data collection module comprises data of external systems such as various production systems, management systems, airports, navigation departments and the like; the data acquisition module acquires modes including FTP, multicast and message or library synchronization. Generally, systems such as a report system and a field monitoring system adopt a serial port mode to acquire data; collecting data in a UDP multicast mode developed by automation, ADSB and field supervision; the meteorological system, the internal phone system and the like can acquire data in an ftp mode; the intelligence uses the synchronous way of the storehouse to gather the data; the flight planning system, the CDM system and the tower electronic process single system acquire data in a kafka message synchronization mode. The interfaces used by different data acquisition modes are different, and in the data supply chain system described in this embodiment, the interfaces include a CDC tool, a file interface, an ETL tool, an ESB, an FTP, an IP stream, Kafka, a customized interface, and the like.
Furthermore, the data storage module comprises a source pasting layer, a buffer layer, a number bin layer, a market layer and an application layer, the source pasting layer stores data, the source pasting layer data are divided into a professional base and a basic base on the buffer layer, the professional base and the basic base form a theme database through processing and analysis, the theme database processes the data to form a shared publishing base for interaction and external publishing of the system data, the data of the number bin layer is reconstructed by the deep data value and stored in the market layer, and the market layer realizes the reconstruction of the data value and then enters the application layer.
Furthermore, the pasting layer is connected with an information system, an automatic system, a meteorological system, an internal telephone system, an automatic system, an ADSB system, a field supervision system, a rebroadcasting system, a tower electronic process single system, a flight planning system, a CDM system, a management system and an external system data system; the source layer data includes production run system data, job management system data, semi-structured, unstructured, real-time streaming data, and third party data.
Furthermore, the application layer comprises situation awareness, digital general aviation service, air management equipment intelligent management, intelligent scheduling, flight real-time operation analysis application and equipment situation operation application.
The air traffic control data supply chain system starts from the production of air traffic control data, acquires, stores, governs, exchanges, excavates and develops the data, finally supplies the data to a demand-related application party to complete the value utilization of the data, opens up data pipelines of related units such as a data production party, a data acquisition party, a data governance party, a platform party, a data developer, an application party and the like, and completes the assets, services and valuables of the data in the data circulation process. The barrier among different systems is broken through during data acquisition, and data is processed in a data storage module by means of machine learning, artificial intelligence, image recognition, voice recognition, semantic analysis, data modeling, data calculation and the like, so that a data island can be broken through, and the value of data aggregation is exerted.
A data management method comprises the steps of firstly constructing the empty pipe data supply chain system and opening a data circulation pipeline; a management platform is established in a data management module of the air traffic control data supply chain, data management is implemented, and the operation capacity of the air traffic control data supply chain system is improved; and a data interaction publishing platform is built, and the data supply chain management and control and service capability are improved.
Further, data governance includes the following governance strategies: establishing a data management strategy and formulating a data management method; (II) clearing the data assets, and drawing a data asset map; (III) designing an overall data architecture, and identifying data distribution and flow direction; (IV) establishing a data standard system; (V) enhance data quality management; (VI) strengthening data security management; (VII) data lifecycle management.
Furthermore, the data governance core layer functions of the data governance platform comprise data resource catalog management, data model management, data source management, data standard management, data quality management and data security management; the data standard management comprises standard system revision, standard execution, data resource catalogue and label management; the data quality management comprises data quality rules, data quality audit, model management and relationship mapping; data security management includes data access authorization, data desensitization, trusted source authentication, data source distribution, and data flow direction.
Furthermore, when the data assets are cleared in the step (II), the data assets are divided by using a theme domain, wherein the theme domain comprises a business part and a function management part; the business part comprises airspace and flow, communication navigation monitoring, flight information, aeronautical meteorology, control operation assistance and equipment operation management, and the function management part comprises investment projects, asset management and other management.
Further, the data management standard in the step (IV) comprises a basic data standard and an index data standard, wherein the basic data standard comprises a participant data standard, a contract agreement data standard, a product data standard, a position data standard, a project data standard, a resource item data standard, a public data standard and a production operation data standard; the index class data standard comprises a basic index standard and a derivative index standard.
The data management method is based on the air traffic control data supply chain system, different subject domains are designed, different data standards are formulated for data management, the data is characterized by assets, standardization, servitization and value, the operation capacity of the air traffic control data supply chain system is improved, and the method is an important guarantee mechanism of the air traffic control data supply chain system.
3. Advantageous effects
Compared with the prior art, the invention has the advantages that:
the method analyzes the problems of the east China air traffic administration in the aspects of data integration, data management, data use and the like, and completes the asset and service of data and the improvement strategy of improving the data value in the data circulation process by establishing an air traffic data supply chain system and opening a data circulation pipeline. When a data supply chain system is built, data acquisition is carried out according to different data types and data systems, the data types of the system are unified, the data barriers among the systems are broken, and a complete data management system is constructed.
The method combines the characteristics of air management, combines the data storage module and the data management module, designs a specific scheme for establishing the air management data supply chain system and managing the data, ensures the operation of the data supply chain system, and improves the management and control and service capabilities of the data supply chain. And a data management platform and a method are established, a technical system covering the full life cycle of the data is established, and the operation of a data supply chain is guaranteed.
When the data supply chain is established, the method gets through the pipelines from data acquisition, storage, management, exchange, mining, development and finally data service, and completes the assets, services and valuables of the data in the data circulation process. And by means of data management work of the air traffic control data supply chain system, the operation, control and service capabilities of the data supply chain system are effectively improved, so that data integration and deep reconstruction of a business process are better realized, the data value is better exerted, and the operation and service requirements are better met.
Drawings
FIG. 1 is a prior art air traffic control inter-system communication architecture;
FIG. 2 is a block diagram of a data platform data structure in the air traffic control data supply chain system according to the present invention;
FIG. 3 is a schematic diagram of a data governance platform architecture of the present invention;
FIG. 4 is a schematic flow diagram of a data governance platform of the present invention;
FIG. 5 is a diagram of a data supply chain system according to the present invention.
Detailed Description
The invention is described in detail below with reference to the drawings and specific examples.
Example 1
In the prior art, a communication architecture among the hollow pipe systems adopts a mesh structure as shown in fig. 1, information exchange and sharing among the hollow pipe systems are realized, the support of a unified basic communication platform is lacked, information resources are not fully shared and utilized, hidden dangers in the aspects of data consistency, accuracy and timeliness are easily generated among the hollow pipe systems, the exertion of system functions is seriously influenced, and a plurality of information isolated islands are formed. In addition, because the existing air traffic control system lacks a uniform information processing standard, the information interaction formats and modes of the systems are greatly different, and the application and the research and the development of a new system are all started from scratch, so that repeated construction is caused, and the development cost is increased.
Because the development requirements of air traffic control services cannot be met due to various defects of the traditional network, technical means must be improved, the air traffic control information resources are comprehensively shared and exchanged, the air traffic control information resources can be effectively, sufficiently and flexibly used and managed, and the safety and the efficiency of the operation of the civil aviation system are improved by improving the comprehensive application capacity of the air traffic control information resources. And a data supply chain is established, so that the data of each system can be effectively integrated, data barriers are opened, and the data value is exerted.
At present, the air traffic control system in east China comprises an information system, an automatic system, a meteorological system, a telephone system, an automatic system, an ADSB system, a field supervision system, a report system, a tower electronic process list system, a flight planning system, a CDM system, a management system and the like. Data of all production systems, management systems and external systems enter a data platform through data acquisition, and data assets are formed through data management. The data assets are analyzed, utilized and explored by means of technologies such as machine learning and artificial intelligence, on one hand, the data value is fully mined, and data support is provided for various applications, decisions and the like; on the other hand, data can be shared to various systems and business chain partners, data value conversion is driven, and various systems are energized.
The present embodiment is described in detail with respect to an empty pipe data supply chain system. The air traffic control data supply chain system comprises a data production module, a data acquisition module, a data storage module, a data management module and a data application module, wherein the data production module is a production party of air traffic control data, the data acquisition module is acquisition of the air traffic control data, the data storage module is storage of the air traffic control data and a data platform is constructed, the data management module is used for managing the data, the data application module comprises exchange, mining and development application of the data, and chain nodes of the data supply chain comprise data production, data acquisition, data storage, data management and data application.
As shown in fig. 2, the data acquisition module and the data source system, the data acquired by the data source system includes data of external systems such as production systems, management systems, airports, navigation systems, etc., after the data source system issues data, the self-developed interface program receives the data issued by the data source in the manners of FTP, multicast, message, library synchronization, etc., and after the data management module performs operations such as preliminary data processing, log recording, etc., the data is sent to a distributed message bus cluster such as Kafka, etc. Message bus data such as kafka are consumed by a back-end data consumption cluster, and the data consumption cluster stores the data into a data storage module by adopting a streaming data processing framework such as Spark and flight; meanwhile, the requirement of real-time data release with low time delay requirements such as automation is met, and the data are released to the data application module.
The air traffic control data supply chain system of the embodiment starts from the production of air traffic control data, acquires, stores, governs, exchanges, mines and develops the data, finally supplies the data to a demand-related application party to complete the value utilization of the data, opens up data pipelines of related units such as a data production party, a data acquisition party, a data governance party, a platform party, a data development party and a data application party, and completes the assets, services and valuables of the data in the data circulation process.
The construction of the air traffic control data chain system is mainly realized by four steps of acquisition, storage, management and application of air traffic control data.
The method comprises the following steps: collecting data;
in this embodiment, the collection mode of the air traffic control data includes using serial ports, multicast, library synchronization, FTP, message reception, and other modes to realize collection of the air traffic control service data. As shown in fig. 5 and 2, the data sources mainly include a production system, a management system and external data, the external data is an airport or an airline department, wherein the production system includes flight automation processing, air traffic control automation processing, civil aviation meteorological database system, ground-air data link and the like, and the management system includes human resource management, equipment asset management, G51 financial special system and engineering construction project management system and the like.
The data collected by the data collection module comprises service information of each system, network management information, equipment state, management information, service upstream and downstream user data and negative data. The specific acquisition mode is different for different data types and data source systems. The system such as the rebroadcasting, the field supervision and the like adopts a serial port mode to acquire data; collecting data in a UDP multicast mode developed by automation, ADSB and field supervision; the meteorological system, the internal phone system and the like can acquire data in an ftp mode; the intelligence uses the synchronous way of the storehouse to gather the data; the flight planning system, the CDM system and the tower electronic process single system acquire data in a kafka message synchronization mode. The interfaces used by different data acquisition modes are different, and in the data supply chain system described in this embodiment, the interfaces include a CDC tool, a file interface, an ETL tool, an ESB, an FTP, an IP stream, Kafka, a customized interface, and the like.
Step two: storing data;
the data storage module stores data and constructs a data platform, and the data storage module performs convergence storage, data processing, data calculation, data analysis, data service and visualization on the acquired data.
A data platform constructed by the data storage module integrates data of different systems, and processes the data by means of machine learning, artificial intelligence, image recognition, voice recognition, semantic analysis, data modeling, data calculation and the like, so that a data island can be broken, and the value of data aggregation is exerted.
The data in the data platform adopts a layered storage mode, and the data layer in the data platform sequentially comprises a source pasting layer, a buffer layer, a warehouse layer, a market layer and an application layer from bottom to top;
as shown in fig. 2, the data platform architecture diagram, the data platform overlay layer, the data system such as the intelligence system, the automation system, the meteorological system, the intercom system, the automation system, the ADSB system, the field supervision system, the rebroadcasting system, the tower electronic process list system, the flight planning system, the CDM system, the management system, the external system, etc., are connected to mainly include several types, including production operation system data, function management system data, semi-structured, unstructured, real-time stream data and third party data; the data of the source pasting layer enter a buffer layer after primary processing and pretreatment, and the buffer layer comprises a standardized processing area, a mass processing area and a flow processing area; the buffer layer data is processed to form each data subject domain, and the data subject domains enter a warehouse counting layer, wherein the subject domains comprise airspace and flow, communication navigation monitoring, flight information, aeronautical meteorology, control operation assistance, equipment operation management, investment projects, asset management and the like, and the warehouse counting layer comprises a production operation area and a function management area; data of the warehouse layers are reconstructed through deep data values and stored in a market layer to realize enabling of data with better application programs, platforms and ecology, wherein the market layer comprises a large broad-form area, a data application area, a data product area, a data directory area and a data experiment area; in addition, an application layer facing business application and data deep processing is constructed from business dimensions to meet the data-driven business requirements, and the application layer comprises situation awareness, digital general aviation service, intelligent management of air management equipment, intelligent typesetting, flight implementation operation analysis application, equipment situation operation application and other applications.
The data consumption cluster stores the data into the data storage module, as shown in fig. 2, and the original data enters the data pasting layer. The data of the source pasting layer can be stored by Hadoop HDFS, Hbase, Hive and the like. The large file data such as weather can be stored in HDFS, automation and other data and Hbase, and structured data such as intelligence and the like can be stored in Hive. And data of the source pasting layer are subjected to data management, data standards are unified, and layered storage is carried out. And after the data in the source pasting layer are subjected to operations such as cleaning, duplicate removal, processing, analysis, structured warehousing and the like, a professional library is formed. The professional library mainly comprises an automatic system database, a very high frequency system database, an information system database, a report system database, a meteorological system database, a NAIP environment database, a tower power system database, a field supervision system database, a flight plan database, an equipment operation system database, an equipment management system database, a comprehensive management system database and the like. And static data which is manually imported or set is stored in the basic library. Such as a personnel information repository, a performance criteria repository, a security index repository, a system information repository, an equipment information repository, a service index repository, and the like. The data is processed and analyzed by means of data management, machine learning, artificial intelligence, image recognition, voice recognition, semantic analysis, data modeling, data calculation and the like, and a plurality of theme databases are formed by combining application requirements. The subject database can be set according to application requirements, such as an automation database, a navigation database, a VHF database, an intelligence database, a control air area database, a flight dynamic database, a weather service database and the like. Desensitizing the related theme data to form a shared publishing library for interaction and external publishing of the system data. Such as flight dynamics libraries, automation data, navigation data, etc.
Step three: managing data;
the data management module enables the data to better exert the value of the data through the management processes and implementation means such as organization, system, flow, technology and the like. The method comprises the steps of implementing data management, formulating data management specifications and the like, and performing unified management on the data catalog, the data architecture, the data standard, the data quality, the data safety and the data life cycle of the eastern China air administration, so that the data quality of the eastern China air administration is improved in an all-around manner, the data is more consistent, accurate and reliable, and the business process related to the data is deeply reproduced, thereby better meeting the requirements of operation and service.
Step four: application of data;
the data application module drives data value conversion by relying on data of a data platform, the data value is preposed, various services are enabled through data, and an application scene comprises data real-time interaction and big data analysis. The data real-time interaction provides centralized exchange and monitoring of data for internal and external service objects in a unified standard, and the overall integration, safety and stability of the systems are improved. And the big data analysis provides data services for the fields of air traffic control operation business optimization, situation perception, management decision, performance management and the like through data mining, and the data value is realized.
Example 2
The data management is an important guarantee mechanism of an air traffic control data supply chain system, and by means of the data management, the assets, standardization, service and valuation of the data can be achieved. In order to improve the management and control and service capability of the air traffic control data supply chain system, a technical system covering the data life cycle of data acquisition, data storage, data modeling development, data asset management, data sharing service, data security management and the like is established. The technical platform required by data governance is set up and comprises a data platform, a data governance platform and a data sharing interaction platform, so that effective technical tool support is provided for data supply chain management and control and service.
In this embodiment, taking the eastern China air traffic bureau as an example, the following steps are included in data management:
step 1: building a data platform;
in order to realize the management of data assets, two-stage data platforms of Shanghai area and branch office (station) of the eastern China air traffic administration are constructed, and taking fig. 3 as an example, the data platforms at the data management platform integration layer comprise the eastern China area, Anhui, Zhejiang and other areas. The data platform is responsible for production operation, function management, collection, exchange, storage and calculation of aviation ecological data, data analysis, data service, visualization and the like.
Based on the business situation of the eastern China air administration, the data platform of each region in data management is based on the air administration data supply chain system described in embodiment 1, and comprises a data production module, a data acquisition module, a data storage module, a data management module and a data application module. The data acquisition comprises data source management and interface management, the data storage module comprises a pasting layer, a buffer layer, a warehouse layer, a market layer and an application layer, the pasting layer realizes the storage of original data and comprises various types of data and structured and semi-structured data, the buffer layer mainly realizes the basic processing and preprocessing work of the data, the warehouse layer is mainly divided into a production operation area and a function management area, each part consists of various data subject domains, the market layer mainly realizes the deep data value reconstruction and provides better data enabling of a downstream application program, a platform and ecology, and the application layer constructs a set of service application-oriented and boundary-isolated data deep processing architecture units from the service dimension, thereby comprehensively meeting the data-driven service requirements.
Step 2: constructing a data management platform;
a management platform is established in a data management module of an air traffic control data supply chain, as shown in fig. 3, a data management strategy is expressed in a data management core layer, data management core functions such as data resource catalog management, data model management, data source management, data standard management, data quality management, data security management and the like are realized, and data management results are solidified. The data standard management comprises standard system revision, standard execution, data resource catalogue and label management; the data quality management comprises data quality rules, data quality audit, model management and relationship mapping; data security management includes data access authorization, data desensitization, trusted source authentication, data source distribution, and data flow direction.
The data management core layer is connected with the service layer and the basic layer, and the basic layer comprises basic functions of data search, document management, process management, system management, metadata management and the like; the service layer comprises a data governance service center and a data governance sharing center, wherein the data governance service center comprises a data dictionary, a data map, blood relationship analysis and governance evaluation, and the data governance sharing center comprises service registration, data release and data access.
The data governance comprises the following governance strategies:
establishing a data management strategy and formulating a data management method;
the data management is implemented, a data management strategy needs to be formulated, a complete data management system is built, and system specifications related to data management are formulated, including but not limited to a data demand management method, a data model management method, a data standard management method, a data quality management method, a data sharing management method, a data security management method and the like.
(II) clearing the data assets, and drawing a data asset map;
the method comprises the steps of performing systematic data resource inventory, comprehensively carding the current system and data situation of the east China air administration, cleaning up data resources and supply modes thereof, fully investigating technical schemes, development tools, deployment environments and transmission networks related to the systems, arranging data information provided by the systems, grading and classifying the systems and the data according to different attributes, and dividing global data assets in a subject domain mode. A data resource directory is formed. And a data asset map is drawn step by step, and a foundation is laid for data application development and data management.
In this embodiment, in combination with the actual service situation of the eastern China air administration, the design theme domain is as shown in table 1:
TABLE 1
(III) designing an overall data architecture, and identifying data distribution and flow direction;
based on a data resource catalog, a global enterprise level data model is designed, the data model comprises a conceptual data model, a logic data model in a specific field and the like, the distribution and flow direction relation of data assets in all systems is identified, and the credible data source of the data assets is determined.
(IV) establishing a data standard system;
the formulation of the data standard is the premise of improving the quality of the air traffic control data and realizing the data normalization. Therefore, the eastern China air administration needs to establish a data standard management system and uniformly manage and control the global data standard, so that each information system has uniform standard constraint when using data. The data standards are established by reference to international standards (e.g., SWIM) and industry standards. The consistency of data in the service chain range is ensured, and data foundation guarantee is made for realizing data integration sharing, service cooperation and integrated operation. The data standard system comprises metadata management, a main data standard, a data model, a data integration standard, a data application and service standard and the like.
The data management standard in this embodiment is shown in table 2:
TABLE 2
(V) enhance data quality management;
and data quality management is enhanced, data quality evaluation is carried out periodically, and a data quality closed-loop management mechanism of definition-evaluation-analysis-perfection is formed. And formulating a data quality rule based on a data standard/data model, a data analysis result and data characteristics, strengthening the control of data quality from a data source, and realizing the conversion from data to high-quality assets.
(VI) strengthening data security management;
the data security classification and grading are comprehensively developed, a global data desensitization rule base is established, a data access authorization mechanism of a data platform is established, a data security protection scheme is designed based on a data security scene, data security management requirements of all related organizations are defined, and data security is guaranteed.
(VII) data lifecycle management;
the method comprises the steps of formulating a value-based data life cycle filing strategy, wherein the strategy comprises data classification, storage position, retention period, filing, backup and reduction processes, developing data life cycle assessment and demand analysis, and through standardizing life cycle management of data, improving the overall level of data management, optimizing a data storage structure and improving data access efficiency.
As shown in the flow chart of the data management platform in fig. 4, the regional data automation system, the meteorological system, the field monitoring system, the internalization system, the intelligence system, the ADSB system, the CDM system, the rebroadcasting system, the NAIP environment database, the flight plan processing system, the tower electronic progress list system, the equipment management system, the comprehensive management system and the like are acquired by the data source system; the data acquired by the branch station comprises an ATS system, a management system, a session system and an automation system.
When the data passes through the interface, the data processing method further comprises log recording, error processing, data auditing, data loading, data conversion and data grabbing operation. The data management platform of the data management module is used for managing the data of the data storage module, the data storage module comprises a data factory, a subject bank, a shared release bank, a data lake, a professional bank and a basic bank, and the data lake stores unprocessed original data of all systems. And after the data in the source pasting layer are subjected to operations such as cleaning, duplication removal, processing, analysis, structured warehousing and the like, a professional library is formed, and the professional library comprises automation data, CDM system data, equipment operation data, VHF data, tower power system data, ADSB data, flight plan data, meteorological service data, information system data, rebroadcasting system data, NAIP environment database and equipment management data. The basic library stores manually imported or set static data, and comprises a personnel information library, a system information library, a performance standard library, an equipment information library, a safety index library, a service index library and other static information; the data factory includes operations such as machine learning, speech recognition, image recognition, virtual reality, data modeling, data mining, and data desensitization. The method comprises the steps of processing and analyzing data by means of data management, machine learning, artificial intelligence, image recognition, voice recognition, semantic analysis, data modeling, data calculation and the like, and forming a plurality of topic databases by combining application requirements, wherein the topic databases comprise automation data, management airspace data, VHF data, flight dynamic data, information data, meteorological service data, equipment operation data, navigation data and comprehensive management data. And performing desensitization processing on related theme data to form a shared release library for interaction and external release of each system data, wherein the shared release library comprises flight dynamic data, automation data, information data and navigation data.
In the data management process, a data source system acquires data of regions, substations, external systems and the like, the acquired data is sent to a data storage module for data storage through different interfaces according to different types and is also sent to a data management module for data management, the data after management is sent to a data application module, the data is issued to each system through a data sharing exchange platform, and the data application comprises air traffic control equipment intelligent management, flight implementation operation analysis application and equipment situation operation application.
And step 3: building a data interactive publishing platform;
and (4) building a data exchange and release platform by combining the SWIM framework and the standard. Depending on a data interactive publishing platform, the eastern China air administration may interact data with a main office air administration, each regional air administration, and external systems such as airports, navigation departments and the like through a unified interface, such as a production operation system and a function management system of an integration layer shown in fig. 3. The problems of inconsistent data standards, interface resource waste, repeated development and the like of point-to-point interconnection in the existing system interconnection architecture are solved. The data interaction platform supports related systems to unify models and standard interaction data, such as FIXM, AIXM, WXXM and the like; and the functions of link tracking injection, log drainage, service registration discovery, intelligent routing, fusing, current limiting and the like are supported. And realizing real-time data interaction and data value output by depending on a data interaction platform.
The data interaction platform adopts an SOA + ESB architecture or a micro-service plus message component architecture. An soa (service Oriented architecture) is a service-Oriented architecture, which includes a plurality of services that ultimately provide a series of functions through interdependencies. The ESB (enterprise service bus) is called among the services through the network, and is a pipeline used for connecting the service nodes. In order to integrate services of different systems and different protocols, the ESB performs message conversion, interpretation and routing work, so that different services are interconnected and intercommunicated.
If the architecture of the microservice plus the message component is adopted, the deployment can be realized in a multi-cluster mode. The micro-service architecture is similar to the SOA architecture, the micro-service is a derivative made on the SOA, the key point emphasized by the micro-service architecture is that the service needs to be completely modularized and serviced, and an original single service system can be divided into a plurality of small applications which can be independently developed, designed and operated. The small applications are interacted and integrated through the service. Such as a spring cluster framework, to support capabilities such as service discovery registration, configuration centers, message buses, load balancing, circuit breakers, data monitoring, and the like.
The microservice can be integrated in an asynchronous scene, and the publishing and subscribing of the message are realized through a queue and a subscription topic, such as kafka and the like. A microservice may be a publisher of a message that is sent to a queue or under a subscription topic in an asynchronous manner. The microservice as a consumer may co-fetch messages from a queue or topic. Direct calls between services are decoupled through message middleware.
The API gateway is the only entry into the system. All clients and consumers access the microservice through a unified gateway, and all non-business functions are processed in a gateway layer. A unified specification interface may be exposed to the outside. The non-service functions of light message routing, format conversion, unified control safety, monitoring, current limiting and the like are realized.
The invention and its embodiments have been described above schematically, without limitation, and the invention can be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The representation in the drawings is only one of the embodiments of the invention, the actual construction is not limited thereto, and any reference signs in the claims shall not limit the claims concerned. Therefore, if a person skilled in the art receives the teachings of the present invention, without inventive design, a similar structure and an embodiment to the above technical solution should be covered by the protection scope of the present patent. Furthermore, the word "comprising" does not exclude other elements or steps, and the word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. Several of the elements recited in the product claims may also be implemented by one element in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Claims (10)
1. The air traffic control data supply chain system is characterized by comprising a data production module, a data acquisition module, a data storage module, a data management module and a data application module, wherein the data production module acquires data comprising a production system, a management system and an external system; the data acquisition module uses different acquisition modes and interfaces according to the data type and the data source system; the data storage module comprises a data platform for storing the air traffic control data and constructing, and integrates and processes the data of different systems; the data management module constructs a data management platform to manage the empty pipe data; the data application module comprises data exchange, mining and development application, and the chain nodes of the data supply chain comprise data production, data acquisition, data storage, data management and data application.
2. The air traffic control data supply chain system according to claim 1, wherein the data collected by the data collection module comprises external system data of each production system, management system, airport, navigation department and the like; the data acquisition module acquires modes including FTP, multicast and message or library synchronization.
3. The air traffic control data supply chain system according to claim 1, wherein the data storage module comprises a source pasting layer, a buffer layer, a warehouse counting layer, a market layer and an application layer, the source pasting layer stores data, the source pasting layer data is divided into a professional library and a basic library in the buffer layer, the professional library and the basic library form a theme database through processing and analysis, the theme database processes the data to form a shared publishing library for interaction and external publishing of the system data, the data in the warehouse counting layer is reconstructed by a deep data value and stored in the market layer, and the market layer realizes data value reconstruction and then enters the application layer.
4. The air traffic control data supply chain system according to claim 3, wherein the pasting layer is connected with an intelligence system, an automation system, a meteorological system, an internal telephone system, an automation system, an ADSB system, a field supervision system, a rebroadcasting system, a tower electronic process list system, a flight planning system, a CDM system, a management system and an external system data system; the source layer data includes production run system data, job management system data, semi-structured, unstructured, real-time streaming data, and third party data.
5. The air traffic control data supply chain system according to claim 3, wherein the application layer comprises situation awareness, digital general aviation service, air traffic control equipment intelligent management, intelligent scheduling, flight real-time operation analysis application and equipment situation operation application.
6. A data governance method is characterized in that an empty pipe data supply chain system according to any one of claims 1 to 5 is constructed firstly, and a data flow pipeline is opened; a management platform is established in a data management module of the air traffic control data supply chain, data management is implemented, and the operation capacity of the air traffic control data supply chain system is improved; and a data interaction publishing platform is built, and the data supply chain management and control and service capability are improved.
7. A data governance method according to claim 6, wherein the data governance comprises the following governance strategies: establishing a data management strategy and formulating a data management method; (II) clearing the data assets, and drawing a data asset map; (III) designing an overall data architecture, and identifying data distribution and flow direction; (IV) establishing a data standard system; (V) enhance data quality management; (VI) strengthening data security management; (VII) data lifecycle management.
8. The data governance method according to claim 7, wherein the data governance core layer functions of the data governance platform include data resource catalog management, data model management, data source management, data standard management, data quality management, data security management; the data standard management comprises standard system revision, standard execution, data resource catalogue and label management; the data quality management comprises data quality rules, data quality audit, model management and relationship mapping; data security management includes data access authorization, data desensitization, trusted source authentication, data source distribution, and data flow direction.
9. The data governance method according to claim 7, wherein in the clearing of the data assets in step (ii), the data assets are divided into theme domains, and the theme domains comprise a business part and a function management part; the business part comprises airspace and flow, communication navigation monitoring, flight information, aeronautical meteorology, control operation assistance and equipment operation management, and the function management part comprises investment projects, asset management and other management.
10. The data governance method according to claim 7, wherein the data management criteria in step (iv) include basic data criteria and index data criteria, the basic data criteria including participant data criteria, contractual agreement data criteria, product data criteria, location data criteria, project data criteria, resource item data criteria, public data criteria and production operation data criteria; the index class data standard comprises a basic index standard and a derivative index standard.
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