CN116756140A - Data management method and system for online repair of railway freight cars - Google Patents

Abstract

The invention provides a data management method and system for online maintenance of railway trucks. The system includes a metadata collection module, a metadata classification module and a database management module; the metadata collection module is used to determine the data source type and adopt the data source type. The data adapter corresponding to the type collects metadata of railway trucks during the online maintenance process, uses the data cache layer to decouple the data collection and data processing of metadata, and uses the data pipeline to transmit metadata; the metadata classification module is used According to the business needs of online maintenance of railway trucks, the collected metadata is divided into categories, and the collected metadata is stored in the corresponding database according to the data category; the database management module is used to manage the database. The data adapter can effectively access metadata from multiple sources and formats, which can significantly improve the data management efficiency of online maintenance of railway trucks.

Description

Data management method and system for online repair of railway freight cars

Technical field

Embodiments of the present invention relate to the field of data processing technology, and specifically relate to a data management method and system for online maintenance of railway trucks.

Background technique

With the continuous development of science and technology, the amount of data generated by enterprises during the production process has increased significantly, and mankind has entered the era of big data. In the process of digitization, business objects exist in the form of data in the information system. Data is the true reflection of business activities in the information system. The online maintenance of railway freight cars adopts a data-driven operation mode, which has the characteristics of large amount of basic data, accurate fault location and real-time data interaction. At the same time, during the construction of the condition monitoring and maintenance (HCCBM) system, non-condition repair of railway freight cars has also accumulated a large amount of data. T alarm and other maintenance use data. Throughout the enterprise, different people have different levels of data knowledge, but it is difficult for anyone to master all the data. This data must therefore be recorded, otherwise valuable knowledge about oneself may be lost.

At present, the train inspection offices of each maintenance branch mainly use broadcasting or intercom equipment to carry out online repair operations for railway freight cars and issue and feedback multi-T faults. The operators record the operation information through paper cards and submit it to the train inspection duty after the operation is completed. The staff enters the system and uploads it regularly every day. This data management model of centralized entry and timed one-way reporting is inefficient and can no longer satisfy the online maintenance operations of railway freight cars after status repair is implemented. Therefore, there is an urgent need to provide an efficient data management method for online maintenance of railway freight cars.

Contents of the invention

Embodiments of the present invention provide a data management method and system for online maintenance of railway trucks to solve the problem of low efficiency of existing methods.

In a first aspect, embodiments of the present invention provide a data management system for online maintenance of railway trucks, including: a metadata collection module, a metadata classification module and a database management module;

The metadata collection module is used to determine the data source type and use the data adapter corresponding to the data source type to collect metadata of railway trucks during the online maintenance process. The data cache layer is used to decouple the data collection and data processing of metadata. Transmit metadata through pipelines;

The metadata classification module is used to classify the collected metadata according to the business needs of online maintenance of railway trucks, and store the collected metadata in the corresponding database according to the data category;

The database management module is used to manage the database.

In one embodiment, it also includes a metadata model module;

The metadata model module is used to build data models according to data categories, monitor the structure of the data model, and update the data model when the structure changes;

The metadata model module is also used to identify the relationship between the metadata collected during the online repair process of railway freight cars through the data model according to the specified algorithm type and business data.

In one embodiment, it also includes a metadata visual display module;

The metadata visual display module is used to graphically or drag-and-drop the collected metadata of railway freight cars during the online maintenance process.

In one embodiment, it also includes a metadata quality detection module;

The metadata quality inspection module is used to perform pre-quality inspection and periodic quality inspection on the collected metadata based on one or more of completeness, uniqueness, accuracy, consistency, timeliness, authenticity and relevance. Detect and automatically clean unqualified metadata.

In a second aspect, embodiments of the present invention provide a data management method for online maintenance of railway trucks, including:

Determine the data source type and use the data adapter corresponding to the data source type to collect metadata during the online maintenance process of railway freight cars;

Use the data cache layer to decouple the data collection and data processing of metadata;

Use data pipelines to transmit metadata;

Classify the collected metadata according to the business needs of online maintenance of railway trucks;

Store the collected metadata in the corresponding database according to the data category.

In one embodiment, the method further includes:

Build a data model according to data categories, monitor the structure of the data model, and update the data model when the structure changes;

According to the specified algorithm type and business data, the relationship between the metadata collected during the online repair process of railway trucks is identified through the data model.

In one embodiment, the method further includes:

The collected metadata of railway freight cars during the online maintenance process is displayed graphically or in a drag-and-drop manner.

In one embodiment, the method further includes:

Conduct pre-quality testing and periodic quality testing on the collected metadata based on one or more of completeness, uniqueness, accuracy, consistency, timeliness, authenticity and relevance;

Automatically clean unqualified metadata.

In a third aspect, an embodiment of the present invention provides an electronic device, including:

at least one processor and memory;

Memory stores instructions for execution by the computer;

At least one processor executes computer execution instructions stored in the memory, so that at least one processor executes the data management method for online maintenance of railway freight cars as described in any one of the first aspects.

In a fourth aspect, embodiments of the present invention provide a computer-readable storage medium. The computer-readable storage medium stores computer-executable instructions. When executed by a processor, the computer-executable instructions are used to implement any one of the first aspects. The above-mentioned data management method for online maintenance of railway freight cars.

The embodiment of the present invention provides a data management method and system for online maintenance of railway trucks. The system includes: a metadata collection module, a metadata classification module and a database management module; the metadata collection module is used to determine the data source type and adopt the The data adapter corresponding to the data source type collects metadata of railway trucks during the online maintenance process, uses the data cache layer to decouple the data collection and data processing of metadata, and uses the data pipeline to transmit metadata; the metadata classification module It is used to classify the collected metadata according to the business needs of online maintenance of railway trucks, and store the collected metadata in the corresponding database according to the data category; the database management module is used to manage the database. Not only can it significantly improve the data management efficiency of online maintenance of railway trucks, but it can also effectively access multi-source and multi-format metadata through data adapters, and improve data collection performance through the data cache layer and data transmission methods.

Description of the drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description serve to explain the principles of the invention.

Figure 1 is a schematic structural diagram of a data management system for online maintenance of railway trucks provided by an embodiment of the present invention;

Figure 2 is a flow chart of a data management method for online maintenance of railway trucks provided by an embodiment of the present invention;

FIG. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.

Specific embodiments of the present invention have been shown in the above-mentioned drawings and will be described in more detail below. These drawings and written description are not intended to limit the scope of the inventive concept in any way, but rather to illustrate the concept of the present invention to those skilled in the art with reference to specific embodiments.

Detailed ways

The present invention will be further described in detail below through specific embodiments in conjunction with the accompanying drawings. Similar elements in different embodiments use associated similar element numbers. In the following embodiments, many details are described in order to make the present application better understood. However, those skilled in the art can readily recognize that some of the features may be omitted in different situations, or may be replaced by other elements, materials, and methods. In some cases, some operations related to the present application are not shown or described in the specification. This is to avoid the core part of the present application being overwhelmed by excessive descriptions. For those skilled in the art, it is difficult to describe these in detail. The relevant operations are not necessary, and they can fully understand the relevant operations based on the descriptions in the instructions and general technical knowledge in the field.

Additionally, the features, operations, or characteristics described in the specification may be combined in any suitable manner to form various embodiments. At the same time, each step or action in the method description can also be sequentially exchanged or adjusted in a manner that is obvious to those skilled in the art. Therefore, the various sequences in the description and drawings are only for clearly describing a certain embodiment, and do not imply a necessary sequence, unless otherwise stated that a certain sequence must be followed.

The serial numbers assigned to components in this article, such as "first", "second", etc., are only used to distinguish the described objects and do not have any sequential or technical meaning. The terms "connection" and "connection" mentioned in this application include direct and indirect connections (connections) unless otherwise specified.

The existing model of centralized data entry and timed one-way reporting of data used in online maintenance of railway freight cars can no longer meet the needs of online maintenance operations after the implementation of status maintenance, and will face the following problems:

1. Currently, equipment truck train inspection operators are unable to obtain fault forecast information such as multi-T faults, three-vehicle allusions, and key indicator component wear warnings pushed by the HCCBM system in time before operation. The large amount of business data accumulated during the truck inspection and application process cannot be effectively Guide the implementation of online repair work.

2. Online maintenance uses broadcasting and intercom equipment to distribute and provide feedback on tasks. It is impossible to achieve one-time, point-to-point efficient job distribution, especially when information is repeatedly transmitted or information is inaccurately transmitted. Train inspection attendants only rely on intercoms. Communicating with on-site workers, unable to grasp the production progress, will lead to hidden dangers of leakage of fault feedback. As the vehicle data content pushed by HCCBM continues to increase, brake shoe thickness prediction information, wheel set tread scratch information, and all-round camera equipment push fault information, etc., The traditional manual work method will not be able to support the point-to-point delivery and feedback of large data volume tasks during technical inspection operations.

3. New faults discovered by operators during vehicle inspection and forecast fault handling information received during maintenance can only be recorded on paper cards. After the operation is completed, they will be submitted to the train inspection attendant for input into the system and uploaded. This mode is a post-information supplementary recording, which cannot realize real-time closed-loop processing of faults. As a result, the trains running online have actually processed the relevant faults, but there is no fault cancellation number in the data, resulting in inaccurate real-time status of the trains.

4. The operation site of the train inspection station has actual conditions such as frequent incoming and outgoing traffic and short operation technical inspection time. It lacks effective safety monitoring measures. It is easy to ignore whether key operation equipment such as red flags and red lights are accurately installed during on-site operations, and the operation cannot be automatically obtained. There is no auxiliary management method for online maintenance operations at the safety level.

5. Parts replacement is involved in the online repair and maintenance process, but it is impossible to track parts loading and unloading trucks and costs, leaving hidden dangers in the tracking and cost management of the entire life cycle of condition repair life management parts.

In view of the above problems and difficulties, this application proposes a metadata-based data management method for intelligent online maintenance of heavy-haul railway freight cars. Metadata is data that describes data. It refers to data extracted from information resources to describe its characteristics and content. In a general sense, metadata refers to the type, name, and value of data; in relational databases , often refers to the attributes, value ranges, data sources, and relationships between data in the data table. Metadata is essential for both data management and data usage. Metadata-related management activities need to be business-based, and standardize the unified definition and application of business objects in various information systems in a standard form to improve the enterprise's business collaboration, regulatory compliance, data sharing and openness, data analysis applications, etc. Ability in all aspects. Metadata management provides the primary method for capturing and managing an organization's data. However, metadata management is not only a challenge for knowledge management but also a necessity for risk management. Metadata can ensure that enterprises identify private or sensitive data and can manage the data lifecycle to their own benefit, meet compliance requirements, and reduce risk exposure. Without reliable metadata, a business doesn't know what data it has, what it represents, where it comes from, how it moves through the system, who has access to it, or what it means to keep the data high quality. Without metadata, enterprises cannot manage their data as an asset. In fact, without metadata, enterprises may not be able to manage their data at all. When using data, metadata needs to be presented in XML or other formats, and other types of metadata require data exchange based on attributes such as retention of ownership and security requirements. Like other data, metadata needs to be managed. As enterprises improve their ability to collect and store data, the role of metadata in data management becomes increasingly important. To become data-driven, organizations must first become metadata-driven.

Figure 1 is a schematic structural diagram of a data management system for online maintenance of railway trucks provided by an embodiment of the present invention. As shown in Figure 1, the data management system 10 for online maintenance of railway trucks provided by this embodiment may include: a metadata collection module 101, a metadata classification module 102 and a database management module 103; the metadata collection module 101 is used to determine The data source type is used and the data adapter corresponding to the data source type is used to collect metadata of railway freight cars during the online maintenance process. The data cache layer is used to decouple the data collection and data processing of metadata, and the metadata is processed using a data pipeline. Transmission; the metadata classification module 102 is used to classify the collected metadata according to the business needs of online maintenance of railway trucks, and store the collected metadata in the corresponding database according to the data category; the database management module 103 uses For database management.

The metadata in the online maintenance process of railway freight cars includes business metadata and technical metadata. Among them, business metadata mainly focuses on the content and conditions of data, and also includes detailed information related to data governance. Business metadata includes non-technical names and definitions of subject areas, concepts, entities, attributes, data types of attributes and other characteristics, such as range descriptions, calculation formulas, algorithms and business rules, valid domain values and their definitions. Business metadata can specifically include:

1) Definition and description of data sets, tables and fields;

2) Business rules, conversion rules, calculation formulas and derivation formulas;

3) Data model;

4) Data quality rules and inspection results;

5) Data update plan;

6) Data traceability and data lineage;

7) Data standards;

8) Specific data element recording system;

9) Valid value constraints;

10) Data security/privacy level;

11) Known data issues;

12) Data usage instructions.

Technical metadata provides information about the technical details of the data, the systems in which the data is stored, and the processes by which data flows within and between systems. Technical metadata can specifically include:

1) Physical database table name and field name;

2) Field attributes;

3) Attributes of database objects;

4)Access rights;

5) Data CRUD rules, namely data create, read, update and delete rules;

6) Physical data model, including data table names, keys and indexes;

7) Record the relationship between the data model and physical assets;

8) Extract-Transform-Load (ETL) job details;

9) File format mode definition.

Existing methods are difficult to meet the multi-source, multi-format, and high-concurrency metadata collection requirements, and are difficult to effectively and quickly operate and query metadata in multi-user, multi-tenant, multi-device, and multi-collection point scenarios. In order to solve the multi-source, multi-format, and high-concurrency metadata collection requirements, in this embodiment, the data adapter, data cache layer, and data transmission method in the metadata collection module are used to solve the problem.

Specifically, the metadata collection module is the source of metadata. In order to solve the multi-source, multi-format, and high-concurrency metadata collection requirements, the adapter mode is adopted in the design of the metadata collection module, which can be adapted to different systems and collection methods. , add corresponding collection rules. The data source types of metadata in the online repair process of railway trucks can specifically include structured data, unstructured data, interface data, hardware data, historical data, offline data, etc. The relevant data can be determined through the input parameters of the data source type. Corresponding data adapter. For example, you can determine the data adapter through the input parameter of the data source type according to the getAdapterByType(String type) method. Among them, unstructured data, also known as file metadata, supports the collection and synchronization of file metadata, and provides attribute management applications such as system attribute management, attribute customization, classification attribute assignment, etc.; it supports authorized users to uniformly manage file attributes in the platform. And file attributes can be assigned to different file categories. Users can manually create custom file attributes. Field information includes attribute name, English name, data type, length, reference data dictionary, whether it is non-empty, sort number, etc. At the same time, the platform provides default built-in file attributes such as file name, file size, extension, category, storage location, confidentiality level, uploader, upload time, etc., and supports enabling and disabling file attributes.

The data cache layer solves the decoupling between collection and data processing, realizes dynamic horizontal expansion of collection module resources, and improves collection performance.

In terms of data transmission, the data pipeline method is used to achieve unified transmission of data by formulating a unified data format standard for the data pipeline. At the same time, custom data filters and data processing mechanisms are provided in the data pipeline to realize a copy of the data. Continuous and multiple processing enables one data to be used for multiple purposes. In terms of data collection performance, dynamic network management technology is used to realize automatic distribution of data processing. For example, the automatic distribution method auto Distribution (Text Data) can be called to automatically distribute data to the data layer for persistent storage. At the same time, the historical data of the business system is integrated, and data integration tools are used for incremental data of the business system using incremental timestamps of the business system to carry out incremental data integration.

The metadata classification module can classify the metadata collected by the system according to the business needs of online maintenance of railway trucks without relying on the physical storage location of the data, which can meet different data classification management and application needs. The system supports the management of adding, deleting, moving, editing, viewing, querying, and starting/deactivating data classifications, and provides a quick entry for data classification authorization, which can quickly perform data classification authorization.

The database management module is a module used to manage the database. The database is the source of data storage and extraction, and they are all "physically" created databases. One type is the data storage database of the platform itself, such as a database used to store metadata collected during the online repair process of railway trucks; the other type is an external database, which usually refers to the business database of a third-party business platform, used to extract data. Models and data, such as Oracle, SQLServer, MySQL and other types of databases. Database registration can connect the platform to the database, support unified management of multiple data sources, and lay the foundation for data storage and extraction. Supports quick access to multi-source data, supports automatic data extraction, and synchronizes source data into the database with one click; supports unified management of multiple data sources. Data source access can be quickly completed through a database read-only account and password. With the help of the perception ability of metadata management, the automatic data extraction function is realized, and the source data is synchronized into the database with one click.

The data management system for online maintenance of railway trucks provided by this embodiment includes a metadata collection module, a metadata classification module and a database management module; the metadata collection module is used to determine the data source type and use data corresponding to the data source type. The adapter collects metadata during the online repair process of railway trucks, uses the data cache layer to decouple the data collection and data processing of metadata, and uses data pipelines to transmit metadata; the metadata classification module is used to perform online maintenance of railway trucks based on According to the business needs, the collected metadata is divided into categories, and the collected metadata is stored in the corresponding database according to the data category; the database management module is used to manage the database. Not only can it significantly improve the data management efficiency of online maintenance of railway trucks, but it can also effectively access multi-source and multi-format metadata through data adapters, and improve data collection performance through the data cache layer and data transmission methods.

In order to solve the problems that the existing method does not support automatic extraction of multiple data sources, does not support automatic creation of database models, does not support dynamic perception of business system table structures, does not support multi-version data management, and does not support data model relationship discovery, in the above embodiment On the basis of The data model is updated when changes occur; the metadata model module is also used to identify the relationship between the metadata collected during the line repair process of railway freight cars through the data model according to the specified algorithm type and business data. For example, the metadata model module can automatically discover the algorithm autoFindAlgorithm(Stringtype, List<String>datasource) through the data model relationship, and automatically organize the relationships between metadata according to the incoming algorithm type and data source list. In addition, you can also intelligently sense table structure changes through the smart sensing method smartChangeTable(String dataSource).

Metadata model, also known as data model, is the basis of data management. Users can build data models under data classification through various methods to achieve unified collection and management of metadata. The data model can be dynamically expanded according to changes in enterprise business development. Users can manage the created models and their attributes through the platform, including revisions, version management, model validation, model activation and deactivation, etc. Data modeling methods include interface addition, model copying, template import, model extraction, data fusion addition, etc. Supports structural monitoring of extracted models, intelligently senses table structure changes, and provides relevant prompts and log record information to remind users to update, ensuring the consistency and accuracy of data model information and physical database table structure information. Supports automatic discovery of data model relationships. A large amount of historical data and data relationships in the business system have become untraceable over time and version iterations. Manual sorting is time-consuming and labor-intensive, and the accuracy cannot be verified. The metadata management system provides a way to use the business The data itself verifies the algorithm of the data model relationship, which can automatically discover the relationship of the data model using business data. Multi-table models are newly created and customized. One or more single-table models associated through configuration are multi-table models. Supports simultaneous management of different sub-model information through multi-table models, and supports simultaneous viewing of data from multiple sub-models. Supports automatic creation of database models. After docking with the source business database, the metadata management system automatically creates a database model to uniformly manage and present the database table structure, relationships, and database change logs.

In order to facilitate the intuitive display of metadata, on the basis of the above embodiments, the data management system for online repair of railway trucks provided in this embodiment may also include a metadata visual display module; the metadata visual display module is used to The collected metadata of railway freight cars during the online maintenance process is displayed graphically or in a drag-and-drop manner. This enables comprehensive and in-depth display and construction of metadata.

In order to solve the problem that the existing technology does not support metadata quality detection and does not support the intelligent construction of cleaning strategies based on discovered metadata quality problems, on the basis of the above embodiments, this embodiment provides data management for online repair of railway freight cars. The system may also include a metadata quality detection module; the metadata quality detection module is used to evaluate the collected metadata based on one or more of completeness, uniqueness, accuracy, consistency, timeliness, authenticity and relevance. The data undergoes pre-quality inspection and periodic quality inspection, and unqualified metadata is automatically cleaned. Specifically, the metadata quality detection module can be driven by business metadata to conduct quality inspection and management of data tables, wide tables and interfaces. It supports custom configuration of quality inspection objects according to different business scenarios, flexible configuration of inspection rules, customized structure of output inspection results and custom configuration of corresponding inspection reports, and displays them in the form of a visual dashboard. Supports setting the weight values of different rules and calculating model quality assessment scores based on the weight values; supports the verification order of rules for multiple model objects in demining tasks. Supports intelligent construction of cleaning strategies based on discovered data quality issues. Different data management methods have different data cleaning strategies: controlled data provides data cleaning functions to achieve data rectification.

The metadata quality detection module can customize data quality rules according to business needs, perform pre-quality verification and periodic quality inspections on data, automatically discover data quality problems, generate quality analysis reports, and carry out targeted cleaning and rectification, thus achieving high Quality data assets support the realization of data value. For example, you can create metadata quality assessment rules through the rule method createRule(Map<String,Object>rules), and pass in the weight of the corresponding rules through the weight distribution method weightDistribution(Map<String,Rule>rules), and finally pass the quality assessment qualityAssessment (List rules, bool createReport) Automatically evaluates metadata quality and can generate evaluation reports. For some metadata determined to be dirty data, the metadata can be cleaned through the automatic cleaning program autoCleanData (Textdata).

Figure 2 is a flow chart of a data management method for online maintenance of railway freight cars provided by an embodiment of the present invention. As shown in Figure 2, the data management method for online maintenance of railway trucks provided by this embodiment may include:

S201. Determine the data source type and use a data adapter corresponding to the data source type to collect metadata of the railway freight car during the online maintenance process;

S202. Use the data cache layer to decouple the data collection and data processing of metadata;

S203. Transmit metadata using a data pipeline;

S204. Classify the collected metadata according to the business needs of online maintenance of railway trucks;

S205. Store the collected metadata in the corresponding database according to the data category.

The data adapter can solve the problem of multi-source and multi-format data sources. According to the adapter method getAdapterByType(String type), the data adapter is determined through the input parameter of the data source type. The data cache layer solves the decoupling between collection and data processing, enables dynamic horizontal expansion of collection module resources, and improves collection performance. In terms of data transmission, the data pipeline transmission method is used to achieve unified transmission of data by formulating a unified data format standard for the data pipeline. At the same time, custom data filters and data processing mechanisms are provided in the data pipeline to realize a Continuous and multiple processing of data allows one data to be used for multiple purposes. In terms of data collection performance, dynamic network management technology is used to realize automatic distribution of data processing. Calling the automatic distribution method auto Distribution (Text Data) can automatically distribute data to the data layer for persistent storage.

The data management method for online maintenance of railway trucks provided by this embodiment can effectively access multi-source and multi-format metadata through the data adapter, and improves the collection performance by using the data cache layer and data pipeline transmission method, which significantly improves the collection performance. Improved data management efficiency for online maintenance of railway freight cars.

In order to solve the problems that the existing method does not support automatic extraction of multiple data sources, does not support automatic creation of database models, does not support dynamic perception of business system table structures, does not support multi-version data management, and does not support data model relationship discovery, in the above embodiment On the basis of The specified algorithm type and business data identify the relationship between the metadata collected during the online repair process of railway freight cars through the data model. For example, the algorithm autoFindAlgorithm(String type, List<String>datasource) can be automatically discovered through the data model relationship, passing in the algorithm type and data source list, and automatically sorting out the relationship between metadata. In addition, you can also intelligently sense table structure changes through the smart sensing method smartChangeTable(String dataSource).

In order to facilitate the intuitive display of metadata, based on the above embodiments, the data management method for online repair of railway trucks provided in this embodiment may also include: graphically or drag-and-drop the collected data. The metadata of railway freight cars during online maintenance is displayed.

In order to solve the problem that the existing method does not support metadata quality detection and does not support the intelligent construction of cleaning strategies based on discovered metadata quality problems, on the basis of the above embodiments, this embodiment provides data management for online repair of railway freight cars. The method may also include: performing pre-quality testing and periodic quality testing on the collected metadata based on one or more of completeness, uniqueness, accuracy, consistency, timeliness, authenticity and relevance; Automatically clean unqualified metadata. Specifically, data quality rules can be customized according to business needs, pre-quality verification and periodic quality inspections can be performed on the data, data quality problems can be automatically discovered, quality analysis reports can be generated, and targeted cleaning and rectification can be carried out to precipitate high-quality data. Data assets support the realization of data value. Metadata quality assessment rules can be established through the create rule method createRule(Map<String,Object>rules), and the weight of the corresponding rules can be passed in through the weight distribution method weightDistribution(Map<String,Rule>rules), and finally the quality assessment can be passed qualityAssessment( List rules, bool createReport) automatically evaluates metadata quality and can generate evaluation reports. For some metadata determined to be dirty data, the metadata can be cleaned through the automatic cleaning program autoCleanData (Textdata).

In summary, the data management method and system for online maintenance of railway trucks provided by this application solves the problem of existing metadata management that is difficult to achieve from multiple sources and through the data adapter, data cache layer and data transmission method in the metadata collection module. Multi-format, high-concurrency metadata collection requirements make it difficult to operate and query metadata effectively and quickly in scenarios with multi-users, multi-tenants, multi-devices, and multi-collection points; through the data in the metadata model module Automatic discovery of model relationships, automatic extraction of multiple data sources, and intelligent perception of table structures realize automatic extraction of multiple data sources, automatic creation of database models, dynamic perception of business system table structures, multi-version data management, and automatic discovery of data model relationships; After the data model and scene rules are established, the quality of the metadata is evaluated based on factors such as completeness, uniqueness, accuracy, consistency, timeliness, authenticity, and relevance, and through quality rules and weight distribution, based on the quality assessment algorithm To conduct metadata quality assessment, and use automatic cleaning data to manage unqualified metadata, solving the problems existing in the existing technology that do not support metadata quality detection, do not support the intelligent construction of cleaning strategies based on discovered metadata quality issues, etc. question.

An embodiment of the present invention also provides an electronic device, as shown in Figure 3. The embodiment of the present invention is only described using Figure 3 as an example, which does not mean that the present invention is limited thereto. FIG. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. As shown in FIG. 3 , the electronic device 30 provided in this embodiment may include: a memory 301 , a processor 302 and a bus 303 . Among them, the bus 303 is used to realize the connection between various components.

The computer program is stored in the memory 301. When the computer program is executed by the processor 302, the technical solutions of any of the above method embodiments can be implemented.

The memory 301 and the processor 302 are electrically connected directly or indirectly to realize data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses or signal lines, such as through the bus 303 . The memory 301 stores a computer program that implements a data management method for online maintenance of railway freight cars, including at least one software function module that can be stored in the memory 301 in the form of software or firmware. The processor 302 runs the program stored in the memory 301. Software programs and modules to perform various functional applications and data processing.

The memory 301 may be, but is not limited to, random access memory (Random Access Memory, RAM for short), Read Only Memory (ROM for short), Programmable Read-Only Memory (PROM for short) ), Erasable Programmable Read-Only Memory (EPROM for short), Electric Erasable Programmable Read-Only Memory (EEPROM for short), etc. The memory 301 is used to store the program, and the processor 302 executes the program after receiving the execution instruction. Furthermore, the software programs and modules in the memory 301 may also include an operating system, which may include various software components and/or drivers for managing system tasks (such as memory management, storage device control, power management, etc.), and Can communicate with various hardware or software components to provide a running environment for other software components.

The processor 302 may be an integrated circuit chip with signal processing capabilities. The above-mentioned processor 302 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU for short), a network processor (Network Processor, NP for short), etc. Each method, step and logical block diagram disclosed in the embodiment of the present invention can be implemented or executed. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc. It can be understood that the structure of FIG. 3 is only illustrative, and may also include more or fewer components than shown in FIG. 3 , or have a different configuration than that shown in FIG. 3 . Each component shown in Figure 3 may be implemented in hardware and/or software.

Embodiments of the present invention also provide a computer-readable storage medium on which a computer program is stored, and the computer program is executed by a processor to implement the technical solutions of any of the above method embodiments.

Each embodiment in the present disclosure is described in a progressive manner, and the same and similar parts between the various embodiments can be referred to each other. Each embodiment focuses on its differences from other embodiments.

The protection scope of the present disclosure is not limited to the above-mentioned embodiments. Obviously, those skilled in the art can make various changes and transformations to the present disclosure without departing from the scope and spirit of the present disclosure. If these changes and modifications fall within the scope of the claims of the present disclosure and its equivalent technology, the present disclosure is also intended to include these changes and modifications.

Claims (10)

1. A data management system for on-line repair of a railway wagon, comprising: the system comprises a metadata acquisition module, a metadata classification module and a database management module;

the metadata acquisition module is used for determining a data source type, acquiring metadata of the railway wagon in the online repair process by adopting a data adapter corresponding to the data source type, acquiring and processing the metadata by adopting a data caching layer decoupling, and transmitting the metadata by adopting a data pipeline mode;

the metadata classification module is used for classifying the acquired metadata according to the service requirement of the on-line repair of the railway wagon and storing the acquired metadata into a corresponding database according to the data type;

the database management module is used for managing the database.

2. The system of claim 1, further comprising a metadata model module;

the metadata model module is used for constructing a data model according to data types, carrying out structure monitoring on the data model, and updating the data model when the structure changes;

the metadata model module is also used for identifying the relation between metadata collected in the railway wagon online repair process through the data model according to the designated algorithm type and service data.

3. The system of claim 1, further comprising a metadata visualization presentation module;

the metadata visual display module is used for displaying the metadata of the collected railway wagon in the online repair process in a graphical or dragging mode.

4. A system according to any one of claims 1-3, further comprising a metadata quality detection module;

the metadata quality detection module is used for performing front quality detection and periodic quality detection on the collected metadata according to one or more of integrity, uniqueness, accuracy, consistency, timeliness, authenticity and relativity, and automatically cleaning unqualified metadata.

5. A data management method for on-line repair of a railway wagon, comprising:

determining a data source type and acquiring metadata of the railway wagon in the online repair process by adopting a data adapter corresponding to the data source type;

adopting a data cache layer decoupling to collect and process the data of the metadata;

transmitting the metadata in a data pipeline mode;

classifying the collected metadata according to the service requirement of the on-line repair of the railway freight car;

and storing the acquired metadata into a corresponding database according to the data category.

6. The method of claim 5, wherein the method further comprises:

constructing a data model according to data types, carrying out structure monitoring on the data model, and updating the data model when the structure changes;

and identifying the relation between metadata acquired in the online repair process of the railway freight car according to the designated algorithm type and the service data through the data model.

7. The method of claim 5, wherein the method further comprises:

and displaying the metadata collected in the online repair process of the railway wagon in a graphical or dragging mode.

8. The method according to any one of claims 5-7, further comprising:

performing front quality detection and periodic quality detection on the acquired metadata according to one or more of integrity, uniqueness, accuracy, consistency, timeliness, authenticity and correlation;

and automatically cleaning unqualified metadata.

9. An electronic device, comprising: at least one processor and memory;

the memory stores computer-executable instructions;

the at least one processor executing computer-executable instructions stored in the memory causes the at least one processor to perform the data management method for rail wagon online repair of any of claims 5-8.

10. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor are adapted to implement a data management method for on-line repair of rail wagons as claimed in any one of claims 5 to 8.