Nuclear power plant DCS system data warehouse multi-source heterogeneous data visualization method and system

Abstract

The invention belongs to the technical field of a DCS (Distributed Control System) of a nuclear power plant, aims to solve the problems of poor data quality of DCS multi-source heterogeneous data, serious metadata missing, difficulty in data sharing, complexity in user data access and the like, and discloses a multi-source heterogeneous data visualization method for a data warehouse of the DCS of the nuclear power plant. According to the method, metadata management, data source configuration, PASS tenant selection, data large-screen display and the like are carried out. According to the invention, the quality of data related to DCS equipment is improved, unified management and visual display of multi-source heterogeneous data are realized, a convenient and efficient data query and analysis platform is provided for business personnel, the state of a data warehouse can be monitored and mastered in real time, and the operation management level of a nuclear power plant is improved.

Description

Technical Field

[0001] The present application belongs to the technical field of DCS systems of nuclear power plants, and specifically relates to a method for visualizing multi-source heterogeneous data in a data warehouse of a DCS system of a nuclear power plant. Background Art

[0002] The DCS system of a nuclear power plant involves multiple data sources, such as sensor data, equipment operation data, and production management data. These data sources have large differences in data structure, data format, data type, etc. The data sources are diverse, such as reactor control systems, production management systems, etc., forming a multi-source heterogeneous data structure. These subsystems operate independently, with different data storage methods and access interfaces, making data integration difficult. When analyzing problems, business personnel need to log in to multiple systems to find relevant data, which not only increases the workload, but may also lead to data omissions and analysis errors. At the same time, due to the lack of a unified data display platform, business personnel cannot grasp the status of the data warehouse in real time, which is not conducive to the safe and stable operation of nuclear power plants. Summary of the invention

[0003] The purpose of this application is to provide a method for visualizing multi-source heterogeneous data in a nuclear power plant DCS system data warehouse, which solves the problems of poor data quality of DCS multi-source heterogeneous data, serious metadata loss, difficulty in data sharing, and complex user access to data.

[0004] In order to achieve the above objectives, this application provides the following technical solutions:

[0005] In a first aspect, the present application provides a method for visualizing multi-source heterogeneous data in a DCS system data warehouse of a nuclear power plant, comprising:

[0006] Metadata management: clarify the data source type, obtain and supplement metadata information, identify metadata that does not conform to business logic and data specifications, write all types of metadata information into the metadata table and perform consistency checks, and write supplemented metadata to the metadata management database after repair;

[0007] Data source configuration: clarify the data source information related to DCS equipment, select the data source driver, enter the data source connection information, test the data source connection successfully, and save the configuration content. It supports connecting to various types of data sources such as MySQL, Hive, DAMO database, IoTDB, OSS, etc.

[0008] PASS tenant selection: The system adopts the PASS technical architecture and a multi-tenant approach to display the data content of the corresponding tenant according to the tenant's permissions;

[0009] Data large screen display: real-time monitoring of multi-source heterogeneous data collection, storage and application, and fusion statistics of multi-source heterogeneous data;

[0010] Structured data: search structured data, view metadata information, preview data, query data without code, and download data;

[0011] Time series data: retrieve time series measurement point data, view single measurement point data, compare and analyze multi-measurement point data, visualize data trends and download data;

[0012] Semi-structured data: file retrieval, file preview, associated component query and data download for configuration data;

[0013] Unstructured data: perform file retrieval, file preview and data download on unstructured data.

[0014] According to one embodiment of the present application, metadata management includes:

[0015] Extract technical metadata information from the metadata database and write it into the structured data metadata table, and supplement the salesperson data and management metadata;

[0016] Collect basic metadata information of nuclear power plant PI system measurement points and supplement metadata information;

[0017] Extract basic metadata information of file data and add file tags;

[0018] Identify metadata entries that do not conform to business logic and data specifications;

[0019] All types of metadata information are written into the metadata table, and the metadata information is checked for consistency each time the metadata is supplemented or updated;

[0020] If the supplementary metadata conflicts with the existing metadata information, the system will issue an alarm and write them into the metadata management database after repair;

[0021] A data synchronization mechanism is adopted to interact with the metadata management database through the RESTful API interface to achieve real-time monitoring and updating of metadata display.

[0022] According to an embodiment of the present application, multi-source heterogeneous data of DCS equipment includes structured data, semi-structured data, unstructured data, and time series data.

[0023] According to one embodiment of the present application, the fusion statistics of multi-source heterogeneous data include:

[0024] Determine the source of multi-source heterogeneous data;

[0025] If the multi-source heterogeneous data comes from the business database of the DCS health management platform, the relationship mapping is performed between the business module and the data table, and the business module data volume statistics are performed. The number of tables and table fields are counted according to the data warehouse hierarchy, and the distribution of the source system of the statistical table is calculated according to the source system of the original data table;

[0026] If multi-source heterogeneous data comes from the big data governance platform, the relationship between the China Nuclear Power 2.0 standard subject domain and the data table is mapped, the subject domain quantity is counted, the data interface service call status is monitored, the number of tables and table fields are counted according to the data warehouse hierarchy, and the distribution of the source system of the statistical table is calculated according to the source system of the original data table;

[0027] If the multi-source heterogeneous data comes from the PI system measurement point data or DCS status monitoring data, monitor the time series data collection status; determine whether the collection status is abnormal, if so, issue a time series data collection alarm information reminder, otherwise enter the time series data governance platform, perform measurement point data grouping statistics, and perform time series data hourly growth statistics;

[0028] If the multi-source heterogeneous data comes from the OSS object repository, determine the data type. If it is configuration data, group and count the number of files according to the configuration data type (IA configuration data, TRICON configuration data, ADACS configuration data). If it is unstructured data, group and count the number of files according to the business classification (manufacturer files, operation and maintenance files, design files).

[0029] The total results of configuration data and unstructured data files, the statistical data of configuration data groups, and the statistical data of unstructured data groups are displayed on the data screen.

[0030] According to an embodiment of the present application, structured data management includes: structured data list display, structured data viewing, data preview and query.

[0031] According to one embodiment of the present application, time series data management includes:

[0032] Time series data group management: group the measurement points according to the unit, process system, and operating status, add new measurement points to the newly added groups, and view the measurement point information from different dimensions through grouping;

[0033] Retrieve and display the historical real data values ​​of the measuring points, and apply interpolation algorithms and sampling techniques to generate and present the data trend analysis results of the measuring points.

[0034] According to one embodiment of the present application, file data management includes:

[0035] Obtain DCS-related file data through OSSSDK and display file metadata information that meets the search criteria;

[0036] Based on the tags and content features of the configuration files, the document relevance retrieval algorithm is executed to view the relevant file data.

[0037] In a second aspect, the present application provides a multi-source heterogeneous data visualization system for a DCS system data warehouse of a nuclear power plant, comprising:

[0038] The application layer is used for user interaction, business logic processing and service interface provision. The system display and control are implemented using Vue, html / CSS, and ElementUI. The interface call uses Axios to send HTTP requests, and the chart is drawn by Echarts.

[0039] The technical support layer is used for service management and performance optimization, and provides communication mechanisms between different system components, including middleware and development and operation environment; the middleware includes application server Tomcat, distributed cache Redis, WEB server Nginx and ZK server ZooKeeper; the whole system is developed in Java language, based on the SpringFramework basic framework, and permission verification is implemented using SpringSecurity+JWT+Redis, providing RestfulAPI style and HTTP interface for front-end calls;

[0040] The data docking layer is used to achieve data integration, data conversion, data mapping and data synchronization. It connects to the big data warehouse through Hive (data warehouse tool), uses HTTP Client to call the time series interface, accesses Alibaba object storage service through OSSSDK to dock and call the underlying storage data, and communicates data with the service support layer through RestFulAPI.

[0041] The data storage layer is used for data management, data backup and recovery, and data access control. System-related business data is stored in the relational database MySQL, time series measurement point data is stored in the IoTDB database, and unstructured data such as files and pictures are stored in the object storage service OSS. Redis is used as the system cache data storage.

[0042] In a third aspect, the present application provides an electronic device including a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the method for visualizing multi-source heterogeneous data in a nuclear power plant DCS system data warehouse is implemented.

[0043] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed, the multi-source heterogeneous data visualization method of the nuclear power plant DCS system data warehouse is implemented.

[0044] Compared with the prior art, the multi-source heterogeneous data visualization method of the nuclear power plant DCS system data warehouse provided by this application has the following beneficial effects:

[0045] This application improves the quality of DCS equipment-related data through technical means such as data integration and conversion, with metadata management as the core, and realizes unified management and visual display of multi-source heterogeneous data. It provides business personnel with a convenient and efficient data query and analysis platform, which helps to monitor and grasp the status of data warehouses in real time and improve the operation management level of nuclear power plants. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solution of the present application, the following is a brief introduction to the drawings required for the technical description.

[0047] Figure 1 A flowchart of a method and system for visualizing multi-source heterogeneous data in a DCS system data warehouse of a nuclear power plant provided in this application;

[0048] Figure 2 The metadata configuration management flow chart provided for this application;

[0049] Figure 3 The multi-source heterogeneous data architecture diagram of the DCS equipment provided for this application;

[0050] Figure 4 The DCS multi-source heterogeneous data indicator fusion calculation flow chart provided for this application;

[0051] Figure 5 A flow chart of structured data management provided for this application;

[0052] Figure 6 The time series data management flow chart provided for this application;

[0053] Figure 7 The document data management flow chart provided for this application;

[0054] Figure 8 The technical architecture diagram of the multi-source heterogeneous data visualization system of the DCS system data warehouse of a nuclear power plant provided for this application;

[0055] Fig. 9 This is the business architecture diagram of the multi-source heterogeneous data visualization system of the nuclear power plant DCS system data warehouse provided in this application. DETAILED DESCRIPTION

[0056] The following is further explained in detail through specific implementation methods.

[0057] like Figure 1As shown, the present application provides a method for visualizing multi-source heterogeneous data in a DCS system data warehouse of a nuclear power plant, including:

[0058] Metadata management: clarify the data source type, obtain and supplement metadata information, identify metadata that does not conform to business logic and data specifications, write all types of metadata information into the metadata table and perform consistency checks, and write supplemented metadata to the metadata management database after repair;

[0059] Data source configuration: clarify the data source information related to DCS equipment, select the data source driver, enter the data source connection information, test the data source connection successfully, and save the configuration content. It supports connecting to various types of data sources such as MySQL, Hive, DAMO database, IoTDB, OSS, etc.

[0060] PASS tenant selection: The system adopts the PASS technical architecture and a multi-tenant approach to display the data content of the corresponding tenant according to the tenant's permissions;

[0061] Data big screen display: The data big screen is divided into three major sections: structured data, time series data, and file data. It monitors the collection, storage, and application of multi-source heterogeneous data in real time, and performs integrated statistics on multi-source heterogeneous data.

[0062] Structured data: Select structured data to enter the structured data management module, which provides functions such as structured data table retrieval, basic information viewing of data tables, data preview, and zero-code data query;

[0063] Time series data: Select time series data to enter the time series data management module, which provides functions such as time series measurement point retrieval, single measurement point viewing, multi-measurement point trend comparison analysis, and data query;

[0064] Semi-structured data: Select semi-structured data to enter the semi-structured management module, which provides file retrieval and the ability to view associated components based on data tags;

[0065] Unstructured data: Select unstructured data to enter the unstructured management module, which provides functions such as file retrieval and online preview of files in multiple formats;

[0066] Data export: Determine whether the user has download permissions. If so, the user can download various types of data as needed.

[0067] This application provides standardized definitions for metadata of various types of data. Among them, metadata information of structured data includes business metadata, management metadata, and technical metadata; metadata information of time series data includes measurement point name, equipment name, and system name, etc.; metadata information of semi-structured data and unstructured data includes file format, file size, upload time, file tag, etc.

[0068] like Figure 2 As shown in the figure, the specific process of metadata management includes:

[0069] Identify the data source type, write data source configuration files according to different data sources, and configure data source connections;

[0070] The automatic metadata extraction program periodically extracts technical metadata information from the metadata database of MySQL and HIVE tables and writes it into the structured data metadata table. The salesperson data and management metadata are supplemented by the business personnel according to the template.

[0071] The basic metadata information of the nuclear power plant PI system measurement points is collected regularly through the measurement point metadata automatic collection program, and the metadata information is automatically supplemented according to the system-equipment-measurement point corresponding configuration rules;

[0072] The file name, file format, file size and other basic metadata information of the file data are extracted regularly through OSSSDK, and the file tags are supplemented by business personnel;

[0073] The rule-based anomaly detection algorithm identifies metadata that does not conform to business logic and data specifications, and the system issues an alarm reminder;

[0074] All types of metadata information are written into the metadata table, and a data comparison and verification algorithm is introduced to perform consistency checks on metadata information every time metadata is supplemented or updated;

[0075] If the supplementary metadata conflicts with the existing metadata information, the system will issue an alarm and write them into the metadata management database after repair;

[0076] Using a data synchronization mechanism, the multi-source heterogeneous data visualization system of the nuclear power plant DCS system data warehouse interacts with the metadata management database through a RESTful API interface to achieve real-time monitoring and updating of metadata display.

[0077] like Figure 3 As shown in the figure, the multi-source heterogeneous data of DCS equipment includes structured data, semi-structured data, unstructured data, and time series data. Semi-structured data and unstructured data are stored in the OSS object repository, structured data is stored in the DCS health management platform MySQL business library and big data governance platform, and time series data is stored in the time series database.

[0078] Structured data includes basic equipment information, experience feedback data, equipment work order data, etc.; semi-structured data includes IA configuration data, TRICON configuration data, etc.; unstructured data includes manufacturer files, operation and maintenance files, and design files, etc.; time series data includes process status data, DCS status data, etc.

[0079] like Figure 4As shown in the figure, the specific process of multi-source heterogeneous data fusion statistics includes:

[0080] Determine the source of multi-source heterogeneous data;

[0081] If the multi-source heterogeneous data comes from the DCS health management platform business database, the relationship between the business module and the data table is mapped; then the business module data volume statistics are performed; the statistical results are stored in the DCS health management platform business module table data volume statistics module of the structured data indicator statistics;

[0082] If the multi-source heterogeneous data comes from the big data governance platform, the relationship between the China Nuclear Power 2.0 standard subject domain and the data table is mapped; then the subject domain quantity is counted, and the statistical results are stored in the business domain table data quantity statistics module of the big data governance platform for structured data indicator statistics;

[0083] According to the data warehouse hierarchy, the number of structured data tables and table fields in the data warehouse are counted, and the levels are divided into ODS, DWD, DWS, DIM, DM, and business tables; the source system distribution of the data tables is calculated according to the source system of the original data tables; the statistical results are stored in the structured data indicator statistics module;

[0084] In the data service open platform, monitor the data interface service call status and complete the top 10 data table call status - word cloud chart; according to the data warehouse hierarchical statistical table number and table field number, the hierarchy is divided into ODS, DWD, DWS, DIM, DM, and business tables; according to the source system of the original data table, the source system distribution of the statistical table is calculated; the statistical results are stored in the structured data indicator statistical module;

[0085] If the multi-source heterogeneous data comes from the PI system measurement point data or DCS status monitoring data, monitor the time series data collection status; determine whether the collection status is abnormal, if so, issue a time series data collection alarm reminder, otherwise enter the time series data governance platform; then perform measurement point data grouping statistics; perform time series data hourly growth statistics; and store the statistical results in the time series data statistical indicator module;

[0086] If the multi-source heterogeneous data comes from the OSS object repository, determine the data type. If it is configuration data, count the number of files by configuration data type (IA, TRICON, ADACS). If it is unstructured data, count the number of files by business classification (manufacturer files, operation and maintenance files, design files). The statistical results are stored in the file data statistical indicator module.

[0087] Visualize the data in the structured data indicator statistics module, time series data statistics indicator module, and file data statistics indicator module, and display them on the data screen.

[0088] Multi-source heterogeneous data fusion statistics include:

[0089] (1) DCS health management platform business module table data volume statistics (directly connected to the DCS business library for data statistics): According to the regular mapping relationship between business modules and data tables, the data volume of the business library table is quantitatively counted, and the data volume statistics of the structured data of all business modules of the DCS health management platform are displayed in the form of a water wave chart;

[0090] (2) Data volume statistics of business domain tables on the big data governance platform (directly connect to the big data governance platform for data statistics, support multiple databases (MySQL, Hive, etc.)): Based on the China Nuclear Power 2.0 standard division and the rule mapping relationship between the big data governance process table, the data volume of the tables on the big data governance platform is quantitatively counted, and the data volume statistics of structured data in all business domains are presented in the form of a water wave graph. The business domains involved include the equipment management domain, training and qualification domain, document domain, experience feedback and human factors domain, maintenance domain, and configuration management domain.

[0091] (3) Distribution of the number of tables at each level of the data warehouse: Extract the metadata information of the data table from the metadata management database of the data warehouse, and count the number of tables and table fields according to the warehouse level identifier of the data table, and present it in the form of a bar chart. The data warehouse layer includes ODS, DWD, DWS, DIM, DM, and business tables.

[0092] (4) Automatic statistics of data source systems: Extract metadata information of data tables from the metadata management database of the data warehouse, count the number of tables based on the data source system fields of the data tables, and present the statistics of the source systems of all tables of the big data governance platform in the form of a bar chart.

[0093] (5) Statistics on data service calls: The total number of data service calls, as well as the number of normal and abnormal times, are recorded on a daily basis and presented in the form of a discount chart.

[0094] (6) Top 10 data table calls: collects data table calls, extracts data table names from the data service interface, and displays the top ten data tables in terms of call frequency in the form of a word cloud chart, helping users quickly identify and focus on data tables with high frequency of calls.

[0095] (7) Time series data overview: Comprehensively calculate the time series measurement point data and DCS monitoring measurement point data in the time series database, and statistically display the total number of time series data measurement points, total data volume, and storage capacity.

[0096] (8) Distribution of measurement points: The distribution of process status measurement points and DCS status measurement points of each unit in the power plant is counted based on the measurement point metadata information and displayed in the form of a ring chart.

[0097] (9) Measurement point collection alarm: monitor the edge measurement point collection status, access the measurement point alarm information for real-time scrolling display, click to view details, and a pop-up window will display the cause of the collection abnormality.

[0098] (10) Time series data growth: The bar chart shows the growth of data volume of all measuring points in the power plant in units of hours. The default display is the data of the most recent 12 hours.

[0099] (11) File data overview: Statistics show the total amount of file data and storage capacity.

[0100] (12) File number statistics: The number of different types of semi-structured data files stored in OSS is counted based on file metadata information. File types include IA configuration files, TRICON configuration files, ADACS configuration files, cabinet drawings, expert knowledge, etc.

[0101] Preferably, if Figure 5 As shown in the figure, the structured data management process specifically includes:

[0102] (1) Display of structured data list

[0103] The structured data directory divides the data warehouse structured data table according to the DCS health management system business module and the big data governance platform. The directory is associated with the water wave diagram on the data large screen. According to the directory configuration, the directory can be updated in real time to meet the data requirements of different power plants. The page refreshes to display the data in the corresponding directory table.

[0104] Structured data search: Provides a combined search method. Users can fuzzily search for structured data in the data warehouse by entering the table name (Chinese / English), field name, business description, selecting the source system, and selecting the update time. Through the query, the page is refreshed to display the search content, and all search input box contents can be reset and cleared.

[0105] (2) View structured data

[0106] Metadata information viewing: Data originates from the metadata management module. Based on the association of table names, metadata information can be updated, modified, and supplemented in real time.

[0107] Structural information viewing: The system interacts with the database through the RESTful API interface to achieve real-time viewing and updating of data display, and display field information, field type and other information of the data table.

[0108] (3) Data preview and query

[0109] Provide condition selection, greater than, less than, equal to, and or not components, and filter conditions by selecting combinations in the system. The system combines the conditions into SQL statements to achieve zero-code data query functions and eliminate SQL injection risks;

[0110] Click Data Export to determine whether the user role has download permissions. User roles with download permissions can export filtered data, and the maximum number of data downloads is limited to 1,000.

[0111] Preferably, if Figure 6 As shown in the figure, the time series data management process specifically includes:

[0112] Time series data list: The time series data list is associated with the metadata management database, providing a combined search method, and fuzzy query of measurement points by entering measurement point name, measurement point code, equipment name, equipment code, system code, system name, measurement point description and other information. Through the query, the page refreshes to display the measurement point information that meets the conditions. The system directly obtains the time series data management platform data through data services to ensure the real-time nature of the data.

[0113] Time series data group management: supports users to group measurement points according to dimensions such as unit, process system, and operating status. Users can add new measurement points in the newly added group and view measurement point information from different dimensions through grouping.

[0114] It provides historical viewing and aggregate viewing methods. The historical data query module retrieves and displays the historical real data values ​​of the measuring points. The data aggregation display module applies interpolation algorithms and sampling techniques to generate and present the data trend analysis results of the measuring points.

[0115] View details of a single measuring point: Select the time range for viewing the measuring point. The default display is from midnight to the current time, and the measuring points can be filtered; all historical measuring point data can be viewed.

[0116] Measuring point details data display: Displays the measuring point metadata information within the selected time range, including measuring point value, time, measuring point unit, power plant code and unit code.

[0117] Comparative viewing of multiple measuring points: Select multiple measuring points on the list page and compare the data of the selected measuring points in the same time period.

[0118] Measurement point trend chart: Displays the trend of the measurement point within the selected time range in the form of a line chart.

[0119] Data export: Roles with download permissions can export filtered data, and the maximum amount of data downloaded is limited to 10,000 items.

[0120] Preferably, if Figure 7 As shown in the figure, the file data management process specifically includes:

[0121] File data list: obtain DCS-related file data through OSSSDK and display the file metadata information that meets the search criteria, including file name, file format, file size, file tag, person in charge, upload time, etc.

[0122] Online preview of file data: The system integrates the online file editing plug-in onlyoffice, which supports online preview of file data in multiple formats (doc, xlsx, html, cad, xps, etc.).

[0123] Configuration file association viewing: Based on the tags and content features of the configuration files, execute the document relevance retrieval algorithm to view related file data.

[0124] Data export: Roles with download permissions can export filtered data, and can choose to download individual files or batches.

[0125] like Figure 8 and Fig. 9 As shown, the present application also provides a multi-source heterogeneous data visualization system for a nuclear power plant DCS system data warehouse, including:

[0126] The application layer is used for user interaction, business logic processing and service interface provision. The system display and control are implemented using Vue, html / CSS, and ElementUI. The interface call uses Axios to send HTTP requests, and the chart is drawn by Echarts.

[0127] The technical support layer is used for service management and performance optimization, and provides communication mechanisms between different system components, including middleware and development and operation environment; the middleware includes application server Tomcat, distributed cache Redis, WEB server Nginx and ZK server ZooKeeper; the whole system is developed in Java language, based on the SpringFramework basic framework, and permission verification is implemented using SpringSecurity+JWT+Redis, providing RestfulAPI style and HTTP interface for front-end calls;

[0128] The data docking layer is used to achieve data integration, data conversion, data mapping and data synchronization. It connects to the big data warehouse through Hive (data warehouse tool), uses HTTP Client to call the time series interface, accesses Alibaba object storage service through OSSSDK to dock and call the underlying storage data, and communicates data with the service support layer through RestFulAPI.

[0129] The data storage layer is used for data management, data backup and recovery, and data access control. System-related business data is stored in the relational database MySQL, time series measurement point data is stored in the IoTDB database, and unstructured data such as files and pictures are stored in the object storage service OSS. Redis is used as the system cache data storage.

[0130] The main function of the multi-source heterogeneous data visualization system of the DCS system data warehouse of a nuclear power plant is to facilitate business personnel to monitor the status of the DCS health management data warehouse. The overall business process includes data large-screen display, metadata management, structured data management, time series data management, semi-structured data management, unstructured data management, data query and export, etc. The overall business process is as follows Fig. 9 shown.

[0131] In addition, the present application also provides an electronic device, including a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the above-mentioned nuclear power plant DCS system data warehouse multi-source heterogeneous data visualization method is implemented.

[0132] In addition, the present application also provides a computer-readable storage medium, on which computer-readable instructions are stored. When the computer-readable instructions are executed, the above-mentioned nuclear power plant DCS system data warehouse multi-source heterogeneous data visualization method is implemented.

[0133] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0134] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed in the present application should be covered within the protection scope of the present application.

Claims

1. A method for visualizing multi-source heterogeneous data in a nuclear power plant DCS system data warehouse, characterized in that: include: Metadata management: clarify the data source type, obtain and supplement metadata information, identify metadata that does not conform to business logic and data specifications, write all types of metadata information into the metadata table and perform consistency checks, and write supplemented metadata to the metadata management database after repair; Data source configuration: clarify the data source information related to DCS equipment, select the data source driver, enter the data source connection information, test the data source connection successfully, save the configuration content, and support connecting to multiple types of data sources; PASS tenant selection: The system adopts the PASS technical architecture and a multi-tenant approach to display the data content of the corresponding tenant according to the tenant's permissions; Data large-screen display: Real-time monitoring of multi-source heterogeneous data collection, storage and application, and fusion statistics of multi-source heterogeneous data.

2. The method for visualizing multi-source heterogeneous data in a DCS system data warehouse of a nuclear power plant according to claim 1, characterized in that: Metadata management includes: Extract technical metadata information from the metadata database and write it into the structured data metadata table, and supplement the salesperson data and management metadata; Collect basic metadata information of nuclear power plant PI system measurement points and supplement metadata information; Extract basic metadata information of file data and add file tags; Identify metadata entries that do not conform to business logic and data specifications; All types of metadata information are written into the metadata table, and the metadata information is checked for consistency each time the metadata is supplemented or updated; If the supplementary metadata conflicts with the existing metadata information, the system will issue an alarm and write them into the metadata management database after repair; A data synchronization mechanism is adopted to interact with the metadata management database through the RESTful API interface to achieve real-time monitoring and updating of metadata display.

3. The method for visualizing multi-source heterogeneous data in a DCS system data warehouse of a nuclear power plant according to claim 1, characterized in that: The multi-source heterogeneous data of DCS equipment includes structured data, semi-structured data, unstructured data, and time series data.

4. The method for visualizing multi-source heterogeneous data in a DCS system data warehouse of a nuclear power plant according to claim 1, characterized in that: Multi-source heterogeneous data fusion statistics include: Determine the source of multi-source heterogeneous data; If the multi-source heterogeneous data comes from the business database of the DCS health management platform, the relationship mapping is performed between the business module and the data table, and the business module data volume statistics are performed. The number of tables and table fields are counted according to the data warehouse hierarchy, and the distribution of the source system of the statistical table is calculated according to the source system of the original data table; If multi-source heterogeneous data comes from the big data governance platform, the relationship between the China Nuclear Power 2.0 standard subject domain and the data table is mapped, the subject domain quantity is counted, the data interface service call status is monitored, the number of tables and table fields are counted according to the data warehouse hierarchy, and the distribution of the source system of the statistical table is calculated according to the source system of the original data table; If the multi-source heterogeneous data comes from the PI system measurement point data or DCS status monitoring data, monitor the time series data collection status; determine whether the collection status is abnormal, if so, issue a time series data collection alarm information reminder, otherwise enter the time series data governance platform, perform measurement point data grouping statistics, and perform time series data hourly growth statistics; If the multi-source heterogeneous data comes from the OSS object repository, determine the data type. If it is configuration data, count the number of files by configuration data type. If it is unstructured data, count the number of files by business classification. The total results of configuration data and unstructured data files, the statistical data of configuration data groups, and the statistical data of unstructured data groups are displayed on the data screen.

5. The method for visualizing multi-source heterogeneous data in a nuclear power plant DCS system data warehouse according to claim 1, characterized in that: Structured data management includes: structured data list display, structured data viewing, data preview and query.

6. The method for visualizing multi-source heterogeneous data in a DCS system data warehouse of a nuclear power plant according to claim 1, characterized in that: Time series data management includes: Time series data group management: group the measurement points according to the unit, process system, and operating status, add new measurement points to the newly added groups, and view the measurement point information from different dimensions through grouping; Retrieve and display the historical real data values ​​of the measuring points, and apply interpolation algorithms and sampling techniques to generate and present the data trend analysis results of the measuring points.

7. The method for visualizing multi-source heterogeneous data in a DCS system data warehouse of a nuclear power plant according to claim 1, characterized in that: File data management includes: Obtain DCS-related file data through OSSSDK and display file metadata information that meets the search criteria; Based on the tags and content features of the configuration files, the document relevance retrieval algorithm is executed to view the relevant file data.

8. A multi-source heterogeneous data visualization system for a nuclear power plant DCS system data warehouse, characterized in that: include: Application layer, used for user interaction, business logic processing and service interface provision; The technical support layer is used for service management and performance optimization, and provides a communication mechanism between different system components; Data docking layer, used to achieve data integration, data conversion, data mapping and data synchronization; The data storage layer is used for data management, data backup and recovery, and data access control.

9. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the method for visualizing multi-source heterogeneous data of a DCS system data warehouse of a nuclear power plant as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, which, when executed, implement the method for visualizing multi-source heterogeneous data of a nuclear power plant DCS system data warehouse according to any one of claims 1 to 7.

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