National iron work power supply equipment data fusion treatment method and system

Abstract

The invention provides a national railway power supply equipment data fusion treatment method and system, and the method comprises the steps: inputting source business basic data, and forming at least part of a basic table in a main database; the basic table comprises a professional field and a universal field, and setting a mapping field to establish a mapping relationship between the basic table and the professional data and the multi-source data; establishing a data channel between each professional system and the data warehouse system, preprocessing the professional data input by each professional system, and accessing the preprocessed professional data to the data warehouse system; and / or accessing the multi-source data in the plurality of distributed multi-source databases into the data warehouse system by using a data management tool; and performing secondary processing on all data in the data warehouse system, extracting information in professional data or multi-source data through mapping fields, and filling the information into corresponding positions in the basic table to form a main database with a unified data format. According to the method, data differences are reduced, and collaboration and interaction among multi-specialty and multi-source data are improved.

Description

Technical Field

[0001] This invention belongs to the field of data processing technology, and specifically relates to a data fusion and management method and system for power supply equipment of China Railway. Background Art

[0002] In order to improve the efficiency of infrastructure maintenance, several railway bureaus have set up pilot reforms for integrated high-speed rail maintenance, which integrates the three specialties of track maintenance, signaling, and power supply to form a high-speed rail infrastructure section, achieving a balance between professional integration and quality enhancement.

[0003] Railway engineering construction projects typically involve multiple software vendors. Inconsistent application data standards and formats from different vendors significantly reduce collaboration efficiency and increase the risk of information inconsistencies. Furthermore, railway infrastructure data is characterized by numerous specializations, complex interfaces, and large volumes. Different engineering specialties have significantly different definitions and descriptions of basic data and components. Simultaneously, when business systems need to exchange data, communication can only be achieved through interfaces, which are costly in terms of manpower and time. Summary of the Invention

[0004] The purpose of this invention is to provide a data fusion and governance method and system for national railway power supply equipment to solve the above problems, so as to reduce the differences between data, ensure data quality, and improve the collaboration and interaction between multi-professional and multi-source data.

[0005] To achieve the above objectives, this invention provides a data fusion and governance method for power supply equipment of China's national railways, comprising: inputting source business basic data to form at least a portion of basic tables in a master database; the basic tables include professional fields and general fields, and a mapping field is set to establish a mapping relationship between the basic tables and professional data and multi-source data; establishing data channels between various professional systems and a data warehouse system, and based on the search service of the data channels, preprocessing the professional data input by each professional system before connecting it to the data warehouse system; and / or using data governance tools to connect the multi-source data from multiple distributed multi-source databases to the data warehouse system; performing secondary processing on all data in the data warehouse system, extracting information from the professional data or multi-source data through the mapping field and filling it into the corresponding position in the basic tables to form a master database with a unified data format.

[0006] Optionally, the source business basic data includes route data, station data, personnel data, organization data, role data, equipment type data, and equipment name data; each data includes professional fields and / or general fields, used to form the basic table.

[0007] Optionally, the search service of the data channel is based on Boolean rules.

[0008] Optionally, based on the search service of the data channel, the professional data input from various professional systems is preprocessed and then connected to the data warehouse system, including the following methods:

[0009] Method 1: Based on the pre-judgment conditions, determine the user's actual needs by semantic recognition and / or search content;

[0010] Method 2: Through data processing and analysis technologies, the search results are reordered based on the business data input by the user or the characteristics reflected in the user's interaction with the data warehouse system.

[0011] Method 3: For different professional systems, classify the input professional data according to the professional knowledge of that system.

[0012] Optionally, the prediction conditions include: determining whether the search content entered by the user is duplicated with professional data; determining whether the professional data corresponding to the search content meets the constraints; if the search content is duplicated with professional data and the professional data corresponding to the search content meets the constraints, then the professional data is determined to be what the user actually needs, and the professional data is connected to the data warehouse system; wherein, the constraints include uniqueness constraints and non-empty constraints.

[0013] Optionally, the characteristics exhibited in the interaction between the user and the data warehouse system include timeliness, friendliness, and directness of the interaction.

[0014] Optionally, the distributed multi-source database includes a MySQL source database, an Oracle source database, and a DM source database.

[0015] Optionally, the step of using a data governance tool to connect the multi-source data from multiple distributed multi-source databases to the data warehouse system includes the following steps: connecting the distributed multi-source databases and the data warehouse system through the resource library connection information of the data governance tool; the data governance tool creating and executing a corresponding data transformation task, and connecting the multi-source data from the connected distributed multi-source databases to the data warehouse system through the data transformation task.

[0016] Optionally, the resource repository connection information of the data governance tool includes at least: the type of the distributed multi-source database, IP address, port number, account, and password.

[0017] Optionally, the data transformation task includes: creating a new transformation task; defining input steps: extracting data from a distributed multi-source database through table input; defining transformation steps: configuring specific parameters for multiple transformation steps according to the multi-source data input; defining output steps: outputting the transformed data through table output; and executing the above steps to run the data transformation task.

[0018] Optionally, the secondary processing of all data within the data warehouse system is based on a data cleaning architecture using a rule engine. The data cleaning logic is described by business rules, and the rule engine is used to execute the data cleaning logic, thereby repairing or discarding data within the data warehouse system that does not conform to the data cleaning logic.

[0019] Optionally, the rule engine executes the data cleaning logic by testing and comparing the data objects provided by the data warehouse system with the business rules loaded in the rule engine, activating the business rules that match the current data state, and removing data objects that do not meet the conditions according to the data cleaning logic described by the activated business rules.

[0020] Optionally, the unified data format in the main database includes: date data in YYYY-MM-DD format, where YYYY represents the year, MM represents the month, and DD represents the date of the month; file stream data is stored in Blob type; string data is in Varchar2 type; and numbers and decimal places are in number type.

[0021] This invention also provides a data fusion and governance system for national railway power supply equipment, comprising: a source business basic data processing module for inputting source business basic data and generating at least some basic tables in a main database; a data warehouse system module for storing source business basic data, professional data, and multi-source data, and performing secondary processing on all stored data; a data channel module for preprocessing the professional data and connecting the preprocessed professional data to the data warehouse system module; a data governance tool module for connecting the multi-source data from multiple distributed multi-source databases to the data warehouse system; and a main database module for storing data with a unified data format after secondary processing by the data warehouse system module.

[0022] Optionally, the data channel module includes: a collection unit for collecting professional data from various professional systems; a data transmission buffer unit for receiving the professional data collected by the collection unit; and a forwarding unit for forwarding the professional data received by the data transmission buffer unit to the search engine unit. The search engine unit is used to provide basic search services based on Boolean rules to achieve preprocessing of the professional data.

[0023] Optionally, the main database processing module is connected to the application service module to provide data support for the application service module; the application service module includes an equipment asset center, an equipment health center, a production management center, and an analysis and decision-making center.

[0024] Compared with existing technologies, the data fusion and governance method and system for power supply equipment of China Railway provided by this invention provides an extended foundation for third-party data by generating a basic table from the input source business basic data; secondly, by preprocessing professional data and transforming multi-source data, the differences between data are reduced and data quality is guaranteed; at the same time, by performing secondary processing on all data in the data warehouse system, the data format is unified, thereby improving the collaboration and interaction between multi-professional and multi-source data. Attached Figure Description

[0025] Figure 1 This is a flowchart of the data fusion and management method for power supply equipment of national railways according to the present invention;

[0026] Figure 2 This is a block diagram of the data fusion and governance system for the power supply equipment of the national railway of the present invention;

[0027] Figure 3 This is a block diagram of the data channel module in the data fusion and governance system for national railway power supply equipment of the present invention. Detailed Implementation

[0028] The following will be combined with the appendix in the embodiments of the present invention. Figure 1 ~Attached Figure 3 The technical solutions, structural features, objectives and effects achieved in the embodiments of the present invention will be described in detail.

[0029] It should be noted that the accompanying drawings are in a very simplified form and use non-precise proportions. They are only used to facilitate and clarify the purpose of illustrating the embodiments of the present invention, and are not intended to limit the implementation conditions of the present invention. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportional relationship, or adjustments to the size should still fall within the scope of the technical content disclosed in the present invention, provided that they do not affect the effects and objectives that the present invention can produce.

[0030] It should be noted that, in this invention, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only the expressly listed elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0031] To improve the collaborative interaction of multi-disciplinary and multi-source data among national railway engineering, signaling, and power supply equipment, such as Figure 1 As shown, an embodiment of the present invention provides a data fusion and governance method for power supply equipment of national railways, which includes:

[0032] S1. Input source business basic data to form at least some basic tables in the main database; the basic tables include professional fields and general fields, and mapping fields are set to establish the mapping relationship between the basic tables and professional data and multi-source data;

[0033] The source business basic data includes route data, station data, personnel data, organization data, role data, equipment type data, and equipment name data. Each data item includes specialized fields and / or general fields, used to form the basic table. It should be noted that the aforementioned source business basic data are distinguished by the specialized fields, while the general fields are universal and can be used to establish mappings with specialized data and multi-source data. Furthermore, external data can be used to expand the basic table through input from the source business data to provide a basic table with a data format conforming to the main database.

[0034] In one specific embodiment, as shown in Tables 1 and 2, Table 1 represents third-party data, and Table 2 is the basic table for personnel data provided in this embodiment. The column names of the first column in the basic table are all general fields, such as ORG_ID, which represents the organization ID. In this embodiment, the mapping field corresponding to ORG_ID is AgencyId. Through the mapping field AgencyId, a mapping relationship between the basic table and the third-party data can be established, and the information of the organization ID corresponding to AgencyId in the third-party data can be extracted.

[0035]

[0036] Table 1. Third-party data

[0037]

[0038] Table 2. Basic Table of Personnel Data

[0039] Among them, such as Figure 1 and Figure 2 As shown, the professional data includes electrical engineering data, civil engineering data, and power supply data. For the professional data, step S21 is executed to connect the professional data to the data warehouse system. Step S21 includes: establishing a data channel between each professional system and the data warehouse system, and based on the search service of the data channel, preprocessing the professional data input by each professional system before connecting it to the data warehouse system.

[0040] The search service for the data channel is based on Boolean rules to reduce discrepancies between professional data and ensure data quality. Specifically, the search service preprocesses the professional data input from various professional systems in the following ways:

[0041] Method 1: Based on pre-judgment conditions, the search content input by the user is judged through semantic recognition and / or search content to determine the user's true needs. The pre-judgment conditions include: determining whether the user's search content is duplicated with professional data; determining whether the professional data corresponding to the search content meets the constraints; if the search content is duplicated with professional data and the professional data corresponding to the search content meets the constraints, then the professional data is determined to be the user's true need, and the professional data is connected to the data warehouse system. The constraints include uniqueness constraints and non-empty constraints. For uniqueness constraints, the data warehouse system is queried to see if a duplicate value already exists to determine if the constraint is violated; if a duplicate value exists, the constraint is not met. For non-empty constraints, the search content used for input is directly checked for null values; if a null value is found, the constraint is not met.

[0042] Method 2: Through data processing and analysis technologies, the search results are reordered based on the business data input by the user or the characteristics revealed in the user's interaction with the data warehouse system. The data processing and analysis technologies include data cleaning, large-scale model platforms, and intelligent sensing technologies. The characteristics revealed in the user's interaction with the data warehouse system include the timeliness, user-friendliness, and directness of the interaction. Specifically, for timeliness, weights are assigned to business data with different timeliness requirements based on the type of input business data, assigning higher weights to business data with high timeliness requirements, and reordering the data results based on the weights. For user-friendliness, user preference information is collected through historical search behavior and browsing history, and the data results are reordered based on the degree of matching between preference information and the input business data. For directness, the data results are reordered by calculating the matching degree between the keywords of the input business data and the data content.

[0043] Method 3: For different professional systems, the input professional data is categorized according to the professional knowledge of that system. These professional systems include electrical engineering systems, civil engineering systems, and power supply systems. For the input professional data, first verify the professional category, then check the equipment type under each professional category. For example, rails belong to civil engineering, switch machines belong to electrical engineering, and overhead contact lines belong to power supply. Based on the professional category, the input professional data is categorized to reduce the differences between different professional data.

[0044] It should be noted that when preprocessing the professional data input from various professional systems, any one or more of the above methods can be used in combination to reduce differences between data and ensure data quality.

[0045] Among them, such as Figure 1 and Figure 2 As shown, the distributed multi-source database includes a MySQL source database, an Oracle source database, and a DM source database. For the multi-source data, step S22 is executed to connect the multi-source data to the data warehouse system. Step S22 includes: using data governance tools to connect the multi-source data from multiple distributed multi-source databases to the data warehouse system, thereby completing the multi-source data connection and minimizing information loss during data transfer between different applications.

[0046] Because different data sources have different storage methods and are not unique, the key to data extraction from these multi-source data is the ability to access both local and remote data sources. To achieve this, the process of using data governance tools to integrate the multi-source data from multiple distributed multi-source databases into the data warehouse system includes the following steps:

[0047] S221. Connect the distributed multi-source database and the data warehouse system through the resource library connection information of the data governance tool; wherein, the resource library connection information of the data governance tool includes at least: the type, IP address, port number, account and password of the distributed multi-source database.

[0048] S222, The data governance tool creates and executes a corresponding data transformation task, and uses the data transformation task to connect multi-source data from the connected distributed multi-source database to the data warehouse system.

[0049] Specifically, the data transformation task includes: creating a new transformation task;

[0050] Define the input steps: Extract data from the distributed multi-source database through table input (in other embodiments, it can also be input through text files);

[0051] Define transformation steps: Configure specific parameters for multiple transformation steps based on the multi-source data input as needed (for example, in the "Field Selection" step, select the fields to keep or exclude; in the "Value Mapping" step, define "Mapping Fields" to establish the mapping relationship between source values ​​and target values);

[0052] Define the output steps: Output the transformed data through table output (in other embodiments, it can also be output through text file to write the data to a text file); for the "text file output" step, configure parameters such as configuration file path, file name, and file format (such as CSV, XML, etc.); for the "table output" step, configure parameters such as database connection and target table name, field mapping relationship (to ensure that the fields of the input data match the fields of the target table).

[0053] By performing the above steps and running the data transformation task, the process of integrating multi-source data into the data warehouse system is completed. In this embodiment, Kettle software is used as the data governance tool, which can effectively integrate and consolidate multiple data sources, cleanse and improve data quality, transform and process data, and improve data analysis efficiency.

[0054] Furthermore, after professional data and multi-source data are connected to the data warehouse system, step S3 is executed, as follows: Figure 1 As shown, step S3 includes: performing secondary processing on all data in the data warehouse system, extracting information from the professional data or multi-source data through the mapping field and filling it into the corresponding position in the basic table to form a main database with a unified data format.

[0055] The secondary processing of all data within the data warehouse system is based on a data cleaning architecture using a rule engine. Business rules describe the data cleaning logic, and the rule engine executes the data cleaning logic to repair or discard data within the data warehouse system that does not conform to the data cleaning logic.

[0056] Furthermore, the rule engine executes the data cleaning logic by testing and comparing the data objects provided by the data warehouse system with the business rules loaded in the rule engine, activating business rules that match the current data state, and removing data objects that do not meet the conditions according to the data cleaning logic described by the activated business rules. It should be noted that the business rules can be set according to requirements, and this invention does not limit them.

[0057] The data state refers to the form of data at a certain moment, reflecting the attributes and characteristics of the data during the process of change, mainly manifested in the atomicity, consistency, isolation, and persistence of the data. In this embodiment, the data state includes static state and dynamic state. For example, real-time data of train power supply system, dynamic response data of railway vehicles, and real-time display information of station yard are dynamic state data; data such as equipment basic information, line parameter data, and equipment maintenance standards and specifications are static state data.

[0058] In one specific embodiment, taking the signal lights in the electrical engineering system data as an example, the types of signal lights are mainly divided into entry signal lights, exit signal lights, route signal lights, double signal lights, shunting signal lights, etc. Due to user needs, it is necessary to reclassify the types of signal lights (divided into section signal lights and station signal lights). At this time, the business rule activated in the rule engine is: equipment names consisting entirely of numbers are classified as section signal lights, and equipment names containing letters and numbers are classified as station signal lights. Based on the above business rule, the reclassification of signal light types can be achieved.

[0059] The unified data format in the main database includes: date data in YYYY-MM-DD format, where YYYY represents the year, MM represents the month, and DD represents the day of the month; file stream data is stored in Blob type; string data is in Varchar2 type; and numbers and digits after the decimal point are in number type.

[0060] Another embodiment of the present invention provides a data fusion and governance system for power supply equipment of national railways, such as... Figure 2 As shown, it includes: a source business basic data processing module 11, which is connected to a data warehouse system module 12, for inputting source business basic data and generating at least some basic tables in the main database; a data warehouse system module 12, for storing source business basic data, professional data, and multi-source data, and for performing secondary processing on all stored data; a data channel module 13, whose input end is connected to multiple professional systems and whose output end is connected to the data warehouse system module 12, for preprocessing the professional data and connecting the preprocessed professional data to the data warehouse system module 12; a data governance tool module 14, whose input end is connected to multiple multi-source databases and whose output end is connected to the data warehouse system module 12, for connecting the multi-source data from multiple distributed multi-source databases to the data warehouse system; and a main database module 15, for storing data with a unified data format after secondary processing by the data warehouse system module.

[0061] Furthermore, such as Figure 3As shown, the data channel module 13 includes a collection unit 131 for collecting professional data from various professional systems; a data transmission buffer unit 132 for receiving the professional data collected by the collection unit 131; and a forwarding unit 133 for forwarding the professional data received by the data transmission buffer unit 132 to the search engine unit 134. The search engine unit 134 provides basic search services based on Boolean rules to achieve preprocessing of professional data. In a specific embodiment, the data transmission buffer unit 132 is Kafka, the forwarding unit 133 is Logstash, and the search engine unit 134 is Elasticsearch. The collection unit 131 uniformly sends the collected professional data to the data transmission buffer Kafka, and then transmits it to the Elasticsearch search engine through Logstash. Basic search services are performed using the Boolean rules provided by Elasticsearch to reduce data discrepancies and ensure data quality.

[0062] The professional systems include electrical engineering, civil engineering, and power supply systems; the multiple data source databases include MySQL, Oracle, and DM databases.

[0063] Furthermore, such as Figure 2 As shown, the main database processing module 15 is connected to the application service module 16 and is used to provide data support for the application service module 16; the application service module 16 includes an equipment asset center, an equipment health center, a production management center, and an analysis and decision-making center.

[0064] The main functions of the Equipment Asset Center module are to combine monitoring equipment data and maintenance base material data to generate an equipment asset ledger and perform statistical analysis on the equipment data from various dimensions; the main functions of the Equipment Health Center module are to comprehensively analyze data such as online monitoring alarms, equipment problem records, equipment fault records, operating environment, service life, and equipment importance, and use corresponding health evaluation algorithms to assess the health status of the equipment; the main functions of the Production Management Center module are to comprehensively analyze monitoring systems, TDCS / CTC systems, transportation scheduling management systems, safety production management information systems, and multimedia on-site operation control systems to achieve production process quality assessment; and the main functions of the Analysis and Decision Center module are to comprehensively analyze equipment assets, health, and other information to generate scientific maintenance and investment recommendations.

[0065] In summary, the data fusion and governance method and system for power supply equipment of China Railway provided by this invention provides an extended foundation for third-party data by generating a basic table from the input source business basic data; secondly, by preprocessing professional data and transforming multi-source data, the differences between data are reduced and data quality is guaranteed; at the same time, by performing secondary processing on all data in the data warehouse system, the data format is unified, thereby improving the collaboration and interaction between multi-professional and multi-source data.

[0066] Although the present invention has been described in detail through the preferred embodiments above, it should be understood that the above description should not be considered as a limitation of the present invention. Various modifications and substitutions to the present invention will be apparent to those skilled in the art after reading the above description. Therefore, the scope of protection of the present invention should be defined by the appended claims.

Claims

1. A method for data fusion management of national railway power supply equipment, characterized in that: include: Input source business basic data to form at least part of the basic tables in the master database; The basic table includes professional fields and general fields, and mapping fields are set to establish a mapping relationship between the basic table and professional data and multi-source data; Establishing a data channel between each professional system and the data warehouse system, and pre-processing the professional data input by each professional system based on the search service of the data channel before connecting it to the data warehouse system; and / or using a data governance tool to connect the multi-source data in a plurality of distributed multi-source databases to the data warehouse system; All data in the data warehouse system are processed again, and the information in the professional data or multi-source data is extracted through the mapping fields and filled into the corresponding positions in the basic table to form a main database with a unified data format.

2. The method for data fusion management of national railway power supply equipment according to claim 1, characterized in that: The source business basic data includes line data, station data, personnel data, organization data, role data, equipment type data and equipment name data; each data includes professional fields and / or general fields for forming the basic table.

3. The method for data fusion management of national railway power supply equipment according to claim 1, characterized in that: The search service of the data channel is based on Boolean rules.

4. The method for data fusion management of national railway power supply equipment as claimed in claim 3 is characterized in that: Based on the search service of the data channel, the professional data input by each professional system is pre-processed and then connected to the data warehouse system, including the following methods: Method 1: Based on the pre-judgment conditions, the search content input by the user is judged through semantic recognition and / or search content to determine what the user really needs; Method 2: Re-rank the searched data results based on the business data input by the user or the characteristics reflected in the interaction between the user and the data warehouse system through data processing and analysis technology; Method three: for different professional systems, the input professional data are classified according to the professional knowledge of the professional system.

5. The method for data fusion management of national railway power supply equipment as claimed in claim 4, characterized in that: The pre-judgment conditions include: determining whether the search content input by the user is repeated with the professional data; determining whether the professional data corresponding to the search content meets the constraint conditions; if the search content is repeated with the professional data and the professional data corresponding to the search content meets the constraint conditions, then determining that the professional data is truly required by the user, and connecting the professional data to the data warehouse system; wherein the constraint conditions include uniqueness constraints and non-empty constraints.

6. The method for data fusion management of national railway power supply equipment as claimed in claim 4, characterized in that: The characteristics reflected in the interaction between the user and the data warehouse system include timeliness, friendliness and directness of the interaction.

7. The method for data fusion management of national railway power supply equipment according to claim 1, characterized in that: The distributed multi-source database includes a MySql source database, an Oracle source database and a DAMO source database.

8. The method for data fusion management of national railway power supply equipment as claimed in claim 7, characterized in that: The step of using a data governance tool to connect the multi-source data in multiple distributed multi-source databases to the data warehouse system comprises the following steps: Connecting the distributed multi-source database and the data warehouse system through the resource library connection information of the data governance tool; The data governance tool creates and executes corresponding data conversion tasks, and connects multi-source data in the connected distributed multi-source database to the data warehouse system through the data conversion tasks.

9. The method for data fusion management of national railway power supply equipment as claimed in claim 8, characterized in that: The resource library connection information of the data governance tool includes at least: the type, IP address, port number, account number and password of the distributed multi-source database.

10. The method for data fusion management of national railway power supply equipment according to claim 8, characterized in that: The data conversion task includes: creating a new conversion task; defining an input step: extracting data from a distributed multi-source database through table input; defining a conversion step: configuring specific parameters of multiple conversion steps according to the multi-source data that needs to be input; defining an output step: outputting the converted data through table output; executing the above steps to run the data conversion task.

11. The method for data fusion management of national railway power supply equipment according to claim 1, characterized in that: The secondary processing of all data in the data warehouse system is based on the data cleaning architecture of the rule engine. The data cleaning logic is described by business rules and executed by the rule engine to repair or discard the data in the data warehouse system that does not conform to the data cleaning logic.

12. The method for data fusion management of national railway power supply equipment according to claim 11, characterized in that: The rule engine executes the data cleaning logic by testing and comparing the data objects provided by the data warehouse system with the business rules loaded in the rule engine, activating the business rules that conform to the current data status and eliminating data objects that do not meet the conditions according to the data cleaning logic described by the activated business rules.

13. The method for data fusion management of national railway power supply equipment according to claim 1, characterized in that: The unified data format in the master database includes: the format of date data is YYYY-MM-DD, where YYYY represents the year, MM represents the month, and DD represents the date in a month; the format of file stream data is stored in Blob type; the format of string data is Varchar2 type; the format of numbers and numbers after the decimal point is number type.

14. A data fusion management system for national railway power supply equipment, characterized in that: include: A source business basic data processing module, used for inputting source business basic data and generating at least part of the basic tables in the master database; Data warehouse system module, used to store source business basic data, professional data and multi-source data, and perform secondary processing on all stored data; A data channel module, used for preprocessing the professional data and connecting the preprocessed professional data to the data warehouse system module; A data governance tool module, used to connect the multi-source data in multiple distributed multi-source databases to the data warehouse system; The main database module is used to store the data in a unified data format after secondary processing by the data warehouse system module.

15. The national railway power supply equipment data fusion management system as claimed in claim 14, characterized in that: The data channel module comprises: A collection unit, used to collect professional data of various professional systems; A data transmission buffer unit, used for receiving the professional data collected by the collection unit; a forwarding unit, used for forwarding the professional data received by the data transmission buffer unit to the search engine unit; The search engine unit is used to provide a basic search service based on Boolean rules to achieve preprocessing of professional data.

16. The national railway power supply equipment data fusion management system as claimed in claim 14, characterized in that: The main database processing module is connected to the application service module to provide data support for the application service module; the application service module includes an equipment asset center, an equipment health center, a production management center, and an analysis and decision-making center.

Images