Industrial digital twin multi-modal data connection method and system

Abstract

The invention provides an industrial digital twin multi-modal data connection method and system, and the method comprises the steps: configuring data connection information for a data source, and generating a data connection pool; acquiring data source types according to the data connection information, and compiling configuration item information for different data source types; based on the configuration item information, requesting data through a data interface with a uniform format, and obtaining return data; analyzing the return data and returning return information in a unified format; and accessing a browser flow analysis page, and obtaining detailed information for executing the data connection. Through a standardized and configurable data source configuration mode, connection and management of different types of multiple data sources are supported, and data from different systems and platforms are integrated, so that integration between programs is more convenient, the data source configuration efficiency of the industrial digital twin application is effectively improved, the operation and maintenance cost is reduced, and the data source configuration efficiency of the industrial digital twin application is improved. The expansibility and maintainability are remarkably improved, and all-around data support is provided for construction of the digital twinborn body.

Description

Technical Field

[0001] The present invention belongs to the field of data connection, and specifically, relates to an industrial digital twin multimodal data connection method and system. Background Art

[0002] Digital twin applications cannot be separated from the support of massive data. In digital twin application scenarios, it is not only necessary to obtain data from common relational databases, but also to process real-time data, streaming data, etc. Traditional database queries focus more on the consistency and persistence of transactions, and have lower requirements for real-time performance. With the continuous development of data services, various types of data use different databases and middleware products to manage business data based on their own business characteristics, which means that digital twin application systems must be able to respond quickly to data changes and provide low-latency data access.

[0003] In business scenarios, real-time data may come from different types of heterogeneous data sources such as real-time databases, message middleware, REST APIs, etc. Ordinary database query methods cannot meet the needs of obtaining these data sources. Traditional digital twin application data acquisition lacks unified configuration and management, and usually achieves data connection through separate programming within the system, resulting in high management costs, high programming difficulty, and low system availability.

[0004] At the same time, for multiple heterogeneous data sources such as relational databases, real-time databases, middleware, message queues, REST APIs, etc., how to perform unified query operations on these structured and semi-structured data is also a very urgent issue at present. In the digital twin application scenario, the real-time performance of data is crucial. In order to meet this requirement, the system needs to process data from multiple cross-platform and cross-system sources, including real-time databases, middleware, message queues, and REST APIs.

[0005] The query and integration of these data sources requires the system to have strong real-time data processing capabilities to ensure that the status of physical devices and systems can be accurately reflected at any time. Through efficient query and data fusion mechanisms, the system can timely obtain and process information from different data sources to support real-time monitoring, analysis and decision-making. Therefore, a data source management application that is suitable for cross-platform and unified configuration is needed.

[0006] In the prior art, the patent document "A digital twin platform data management method and system based on three-dimensional space-time system" (CN118349559A) discloses a query method for different database types. Although it can meet the query of commonly used databases such as MySQL, it cannot meet the requirements of digital twin application scenarios.

[0007] The patent document "Method and system for constructing a digital twin model data management platform for equipment operation" (CN115099776A) discloses a physical model data display scenario of a digital twin, but does not have a prediction model and a decision-making model for observing the digital twin to predict the physical entity and control the physical entity.

[0008] The method disclosed in the patent document "Unified SQL Query Method for Heterogeneous Data Sources" (CN202311065187) is only applicable to relational and non-relational databases, and cannot integrate multi-source heterogeneous data from real-time databases, message middleware, REST APIs, etc. in instant response scenarios. These data cannot be converted into SQL and then distributed for execution, which limits its application scope and flexibility in complex industrial environments. Summary of the invention

[0009] In view of the defects in the prior art, the object of the present invention is to provide an industrial digital twin multimodal data connection method and system.

[0010] According to the present invention, an industrial digital twin multimodal data connection method is provided, comprising:

[0011] Step S1: configure data connection information for the data source and generate a data connection pool;

[0012] Step S2: Obtain the data source type according to the data connection information, and write configuration item information for different data source types;

[0013] Step S3: Based on the configuration item information, request data through a data interface in a unified format to obtain return data;

[0014] Step S4: Parse the returned data and return the returned information in a unified format;

[0015] Step S5: Access the browser traffic analysis page to obtain detailed information on the execution data connection.

[0016] Preferably, the step S1 supports the connection and management of different types of multi-source heterogeneous data sources including relational databases, real-time databases, middleware, message queues, REST APIs, configures the corresponding data connection information in the screen in turn, reloads the data source information, and generates a data connection pool.

[0017] In step S2, a standardized REST API data acquisition interface for multi-source heterogeneous data sources is used to integrate and unify data from different data sources, and if there are corresponding variables, standard placeholders are used for configuration.

[0018] In the step S3, the unified standardized data request interface provided for the multi-source heterogeneous data sources, if no data configuration is configured, uses the default data acquisition interface of different data source types to acquire data.

[0019] In step S4, the data returned after the request in step S3 is parsed and return information in a unified format.

[0020] In step S5, the traffic analysis page is logged in to view the result information of the execution data connection, and the call statistics and execution results obtained from the call history statistics.

[0021] Preferably, the step S2 includes:

[0022] Relational database steps: If the data source type is a relational database, fill in the SQL statement used in the business scenario as the configuration item information in the data configuration.

[0023] JSON configuration steps: If the data source type is middleware, message queue, or REST API interface type, fill in the corresponding request JSON as the configuration item information in the data configuration.

[0024] Real-time database steps: If the data source type is real-time database, fill in the tag point name and time interval in the corresponding format as the configuration item information in the data configuration.

[0025] Preferably, the step S3 includes: determining whether there is matching configuration item information, and if there is matching configuration item information, obtaining a connection using a data connection pool according to the configuration item information to obtain data;

[0026] If there is no matching configuration item information, the data source type is obtained from the request data. According to different data source types, a connection is obtained from the configuration item information data connection pool corresponding to the data source type in the request data to obtain data.

[0027] Preferably, in step S1, the digital twin obtains the production plan and production performance from the relational database Mysql, Oracle, DB2 or PostgreSQL, obtains the real-time steelmaking data from the real-time database iHyperDB, obtains the message push information from the middleware, configures the corresponding data connection information, reloads the data source information, and generates a data connection pool according to the data source type.

[0028] The data connection information includes a database IP port of a database type, a database type, a database name, and a middleware IP port of a middleware.

[0029] In step S3, if the request does not carry configuration item information, the data source type is DB2 database DBMS1, and the request data carries corresponding SQL1, SQL2 and SQL3 in the format, then a database connection is obtained from the data connection pool of the data source DBMS1, and these SQLs are executed in sequence through the database connection;

[0030] The data source type is middleware, and the corresponding virtual-host queue information is used to obtain the data source;

[0031] The data source type is real-time database and REST API, and the data source is obtained using the JSON data in the configuration or request.

[0032] The result information in step S5 is the SQL execution count, total SQL execution time, slowest SQL execution time, number of rows read and maximum concurrency contained in the SQL statement executed when calling the relational database, and the call execution time and call count / frequency statistics obtained when calling the json and real-time database.

[0033] An industrial digital twin multimodal data connection system provided by the present invention includes:

[0034] Module M1: configure data connection information for the data source and generate a data connection pool;

[0035] Module M2: Obtain the data source type according to the data connection information, and write configuration item information for different data source types;

[0036] Module M3: Based on the configuration item information, request data through a data interface in a unified format and obtain return data;

[0037] Module M4: parse the returned data and return the returned information in a unified format;

[0038] Module M5: Access the browser traffic analysis page to obtain detailed information about the execution data connection.

[0039] Preferably, the module M1 supports the connection and management of different types of multi-source heterogeneous data sources including relational databases, real-time databases, middleware, message queues, REST APIs, configures the corresponding data connection information in the screen in turn, reloads the data source information, and generates a data connection pool.

[0040] The module M2 provides a standardized REST API data acquisition interface for multi-source heterogeneous data sources, integrates and unifies data from different data sources, and uses standard placeholders to configure corresponding variables if there are any.

[0041] The module M3 provides a unified standardized data request interface for multi-source heterogeneous data sources. If no data configuration is configured, the default data acquisition interface of different data source types is used to acquire data.

[0042] The module M4 parses the data returned after the request in the module M3 and returns the return information in a unified format.

[0043] In the module M5, the traffic analysis page is logged in to view the result information of the execution data connection, and the call statistics and execution results obtained from the call history statistics.

[0044] Preferably, the module M2 includes:

[0045] Relational database module: If the data source type is a relational database, fill in the SQL statement used in the business scenario as the configuration item information in the data configuration.

[0046] JSON configuration module: For data source types such as middleware, message queue, or REST API interface, fill in the corresponding request JSON as the configuration item information in the data configuration.

[0047] Real-time database module: If the data source type is real-time database, fill in the tag point name and time interval in the corresponding format as the configuration item information in the data configuration.

[0048] Preferably, the module M3 comprises: determining whether there is matching configuration item information, and if there is matching configuration item information, obtaining a connection using a data connection pool according to the configuration item information to obtain data;

[0049] If there is no matching configuration item information, the data source type is obtained from the request data. According to different data source types, a connection is obtained from the configuration item information data connection pool corresponding to the data source type in the request data to obtain data.

[0050] Preferably, the digital twin in the module M1 obtains the production plan and production performance from the relational database Mysql, Oracle, DB2 or PostgreSQL, obtains the real-time steelmaking data from the real-time database iHyperDB, obtains the message push information from the middleware, configures the corresponding data connection information, reloads the data source information, and generates a data connection pool according to the data source type.

[0051] The data connection information includes a database IP port of a database type, a database type, a database name, and a middleware IP port of a middleware.

[0052] In the module M3, if the request does not carry configuration item information, the data source type is DB2 database DBMS1, and the request data carries the corresponding SQL1, SQL2 and SQL3 in the format, then a database connection is obtained from the data connection pool of the data source DBMS1, and these SQLs are executed in sequence through the database connection;

[0053] The data source type is middleware, and the corresponding virtual-host queue information is used to obtain the data source;

[0054] The data source type is real-time database and REST API, and the data source is obtained using the JSON data in the configuration or request.

[0055] The result information in the module M5 is the SQL execution count, total SQL execution time, slowest SQL execution time, number of rows read and maximum concurrency contained in the SQL statements executed when calling the relational database, and the call execution time and call count / frequency statistics obtained when calling the json and real-time databases.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] 1. The present invention not only supports traditional databases, but also supports the connection and management of multiple data sources of different types, expands to multiple data sources such as real-time databases, message middleware, REST API, etc., integrates data from different systems and platforms, provides more comprehensive data access capabilities, and provides all-round data support for the construction of digital twins.

[0058] 2. The present invention adopts a standardized and configurable data source configuration method, making the configuration item information richer and more flexible, effectively improving the data source configuration efficiency of industrial digital twin applications and reducing operation and maintenance costs.

[0059] 3. The present invention can adapt to the characteristics and requirements of different types of data sources by providing a unified standardized data request interface for multi-source heterogeneous data sources, making integration between programs more convenient, and significantly improving scalability, adaptability and maintainability. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0061] Figure 1 Configure a diagram for the data source.

[0062] Figure 2 Configure a schematic for a data item.

[0063] Figure 3Schematic diagram of the data acquisition process. DETAILED DESCRIPTION

[0064] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0065] An industrial digital twin multimodal data connection method provided by the present invention is used to overcome the problems of increased complexity, high management cost, high programming difficulty, and low system availability caused by the diversity of data source formats in industrial digital twin applications, including:

[0066] Step S1: configure data connection information for the data source and generate a data connection pool;

[0067] Real-time data may come from different types of heterogeneous data sources such as real-time databases, message middleware, REST API, etc. Ordinary database query methods cannot meet the needs of obtaining these data sources. It supports connecting and managing different types of multi-source heterogeneous data sources, that is, different data source types, such as relational databases, real-time databases, middleware, message queues, REST API, etc., integrating data from different systems and platforms, and realizing rapid configuration of data sources through standardized configuration interfaces, providing all-round data support for the construction of digital twins.

[0068] The supported configuration relational databases include Mysql, Oracle, DB2, PostgreSQL, etc., support non-relational field data collection real-time databases, including real-time databases, etc., support data acquisition of message middleware, including kafka, RabbitMQ, etc., and reload the data source information to Figure 1 As an example, generate a data connection pool.

[0069] In more preferred examples, various data sources, that is, data source types, exist independently. The digital twin needs to obtain production plans and production performance from the relational database DB2, and obtain steelmaking real-time data from the real-time database iHyperDB (iHyperDB is Baosight's distributed high-performance real-time database software, including massive storage, efficient retrieval, statistical analysis, data processing and screen display, etc.). It is also necessary to obtain message push information from the middleware. The developer configures the corresponding data connection information in the screen in turn, including the database IP port, database type, database name and other information of the database type data source, the middleware IP port of the middleware data source, etc. After configuration, the data source is reloaded to enable the configured data source. At the same time, a connection pool is generated according to the data source type to facilitate subsequent data acquisition and use.

[0070] Through standardized and configurable data source configuration methods, the data source configuration efficiency of industrial digital twin applications can be effectively improved and operation and maintenance costs can be reduced.

[0071] Step S2: Based on step S1, obtain the data source type configured by the user, and then write the configuration item information in the data configuration for different data source types. Figure 2 For example, if there are corresponding variables, use standard placeholders to configure them.

[0072] In digital twin applications, the required data may come from various data sources. For example, plans, historical data and other information are stored in common relational and non-relational databases. Some high-frequency data and real-time data are provided by multiple data sources such as real-time databases, message middleware, and REST API due to timeliness considerations. In order to integrate these different data sources, unify management and provide standard interface calls to the outside world, different contents are selected as configuration item information for different data sources.

[0073] In more preferred examples, a standardized REST API data acquisition interface for multi-source heterogeneous data sources integrates and unifies data from different data sources, realizes data fusion, simplifies the data acquisition operation process, improves data processing efficiency, and realizes efficient management and control of data connections for digital twin applications in the industrial field, including:

[0074] Relational database steps: For the relational database data source type, fill in the SQL statement used in the business scenario as the configuration item information in the data configuration.

[0075] JSON configuration steps: For data source types of middleware and REST API interface types, fill in the corresponding request JSON as the configuration item information in the data configuration.

[0076] Real-time database steps: For the real-time database, fill in the input information of the corresponding format in the data configuration, and configure the corresponding tag point name and time interval as the configuration item information.

[0077] Step S3: Figure 3 For example, based on the configured data configuration items, the requester requests data through a data interface in a unified format. If no data configuration is performed, the default data acquisition interface of different data source types is used to acquire data.

[0078] By providing a unified and standardized data request interface for multi-source heterogeneous data sources, the integration between programs is more convenient, and the scalability and maintainability are significantly improved, including:

[0079] Step S3.1: Obtain configuration item information from the request data and determine whether there is matching configuration item information.

[0080] If there is matching configuration item information, according to the configured SQL or request JSON, use the data connection pool generated in step S1 to obtain a connection for data acquisition.

[0081] If the corresponding configuration item information is not filled in, the data source type is obtained from the request data. According to different data source types, the configuration item information corresponding to the respective data source types in the request data is used to obtain a connection from the data connection pool generated in step S1 to obtain data.

[0082] For database type data sources, use the SQL in the request data to obtain data; for middleware type data sources, use the corresponding virtual-host queue information to obtain the data source; for real-time database and REST API type data sources, use the configuration or JSON data in the request to obtain the data source.

[0083] In more preferred examples, the request does not carry configuration item information, the data source type uses the DB2 database DBMS1 configured in step S1, and the request data carries the corresponding SQL1, SQL2, and SQL3 in format, then the database connection is obtained from the data connection pool of the configured data source DBMS1, and these SQLs are executed in sequence through the database connection.

[0084] Step S4: Based on the request executed in step S3, the data returned after the request is parsed and the return information in a unified format is returned.

[0085] Step S5: Based on the execution of step S3, access the browser traffic analysis page to obtain detailed information on the execution data connection.

[0086] The detailed information is the result information of executing the data connection, namely the call statistics and execution results mentioned below, which are obtained from the call history statistics.

[0087] In more preferred examples, when calling a relational database, log in to the traffic analysis page to view the SQL instruction execution status contained in the SQL statement executed in step S3, such as the number of SQL executions, the total SQL execution time, the slowest SQL execution time, the number of rows read, the maximum number of concurrent connections, etc. When calling json and real-time databases, statistics such as the call execution time, the number of calls / frequency, etc. are obtained.

[0088] The present invention also provides a flow statistics function, which can monitor and analyze the data flow conditions of various parts of the system in real time, helping users to identify bottlenecks and efficiently utilize resources.

[0089] The present invention also provides an industrial digital twin multimodal data connection system, which can be implemented by executing the process steps of the industrial digital twin multimodal data connection method, that is, those skilled in the art can understand the industrial digital twin multimodal data connection method as a preferred implementation of the industrial digital twin multimodal data connection system.

[0090] Those skilled in the art know that, in addition to realizing the system and its various devices, modules, and units provided by the present invention in a purely computer-readable program code, it is entirely possible to realize the same functions in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, the system and its various devices, modules, and units provided by the present invention can be considered as a hardware component, and the devices, modules, and units included therein for realizing various functions can also be regarded as structures within the hardware component; the devices, modules, and units for realizing various functions can also be regarded as both software modules for realizing the method and structures within the hardware component.

[0091] An industrial digital twin multimodal data connection system provided by the present invention includes:

[0092] Module M1: configure data connection information for the data source and generate a data connection pool;

[0093] Module M2: Obtain the data source type according to the data connection information, and write configuration item information for different data source types;

[0094] Module M3: Based on the configuration item information, request data through a data interface in a unified format and obtain return data;

[0095] Module M4: parse the returned data and return the returned information in a unified format;

[0096] Module M5: Access the browser traffic analysis page to obtain detailed information about the execution data connection.

[0097] In addition to the data display of the physical model, the industrial digital twin scenario also includes a prediction model and a decision model, that is, it is necessary to predict and control physical entities by observing the digital twin. The training data and label data required by the prediction model and the decision model are provided by multiple complex business systems (such as equipment data from the automation system, and label data from the equipment management system, which reflects equipment anomalies through maintenance records), and each system has its own data acquisition method, such as relational and non-relational data, real-time databases, message middleware, REST API, etc. Usually, these data have been collected and stored in various systems, and related digital systems have also been built.

[0098] By integrating various system data sources, a comprehensive data processing component is built to support data acquisition from multiple data sources such as relational databases, non-relational databases, real-time databases, message middleware, REST API, etc., to meet various needs such as display, prediction, and decision-making in digital twin application scenarios.

[0099] In more preferred examples, the module M1 supports the connection and management of different types of multi-source heterogeneous data sources including relational databases, real-time databases, middleware, message queues, REST APIs, and configures the corresponding data connection information in the screen in turn, reloads the data source information, and generates a data connection pool.

[0100] The module M2 provides a standardized REST API data acquisition interface for multi-source heterogeneous data sources, integrates and unifies data from different data sources, and uses standard placeholders to configure corresponding variables if there are any.

[0101] The module M3 provides a unified standardized data request interface for multi-source heterogeneous data sources. If no data configuration is configured, the default data acquisition interface of different data source types is used to acquire data.

[0102] The module M4 parses the data returned after the request in the module M3 and returns the return information in a unified format.

[0103] In the module M5, the traffic analysis page is logged in to view the result information of the execution data connection, and the call statistics and execution results obtained from the call history statistics.

[0104] In more preferred embodiments, the module M2 includes:

[0105] Relational database module: If the data source type is a relational database, fill in the SQL statement used in the business scenario as the configuration item information in the data configuration.

[0106] JSON configuration module: For data source types such as middleware, message queue, or REST API interface, fill in the corresponding request JSON as the configuration item information in the data configuration.

[0107] Real-time database module: If the data source type is real-time database, fill in the tag point name and time interval in the corresponding format as the configuration item information in the data configuration.

[0108] In more preferred examples, the module M3 includes: determining whether there is matching configuration item information, and if there is matching configuration item information, obtaining a connection using a data connection pool according to the configuration item information to obtain data;

[0109] If there is no matching configuration item information, the data source type is obtained from the request data. According to different data source types, a connection is obtained from the configuration item information data connection pool corresponding to the data source type in the request data to obtain data.

[0110] In more preferred examples, the digital twin in the module M1 obtains the production plan and production performance from the relational database Mysql, Oracle, DB2 or PostgreSQL, obtains the real-time steelmaking data from the real-time database iHyperDB, obtains the message push information from the middleware, configures the corresponding data connection information, reloads the data source information, and generates a data connection pool according to the data source type.

[0111] The data connection information includes a database IP port of a database type, a database type, a database name, and a middleware IP port of a middleware.

[0112] In the module M3, if the request does not carry configuration item information, the data source type is DB2 database DBMS1, and the request data carries the corresponding SQL1, SQL2 and SQL3 in the format, then a database connection is obtained from the data connection pool of the data source DBMS1, and these SQLs are executed in sequence through the database connection;

[0113] The data source type is middleware, and the corresponding virtual-host queue information is used to obtain the data source;

[0114] The data source type is real-time database and REST API, and the data source is obtained using the JSON data in the configuration or request.

[0115] The result information in the module M5 is the SQL execution count, total SQL execution time, slowest SQL execution time, number of rows read and maximum concurrency contained in the SQL statements executed when calling the relational database, and the call execution time and call count / frequency statistics obtained when calling the json and real-time databases.

[0116] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A method for connecting multimodal data of industrial digital twins, characterized in that: include: Step S1: Configure data connection information for the data source and generate a data connection pool; Step S2: Obtain the data source type according to the data connection information, and write configuration item information for different data source types; Step S3: Based on the configuration item information, request data through a unified format data interface and obtain return data; Step S4: Parse the returned data and return the returned information in a unified format; Step S5: Access the browser traffic analysis page to obtain detailed information on the execution data connection.

2. The industrial digital twin multimodal data connection method according to claim 1, characterized in that: The step S1 supports connecting and managing different types of multi-source heterogeneous data sources, including relational databases, real-time databases, middleware, message queues, and REST APIs. The corresponding data connection information is configured in the screen in turn, and the data source information is reloaded to generate a data connection pool. In step S2, a standardized REST API data acquisition interface is used for multiple heterogeneous data sources to integrate and unify data from different data sources. If there are corresponding variables, standard placeholders are used to configure them. In step S3, if no data configuration is provided for the unified standardized data request interface provided by the multi-source heterogeneous data source, the default data acquisition interface of different data source types is used to acquire data; In step S4, the data returned after the request in step S3 is parsed and return information in a unified format; In step S5, the traffic analysis page is logged in to view the result information of the execution data connection, and the call statistics and execution results obtained from the call history statistics.

3. The industrial digital twin multimodal data connection method according to claim 1, characterized in that: The step S2 includes: Relational database step: If the data source type is a relational database, fill in the SQL statement used in the business scenario as the configuration item information in the data configuration; JSON configuration steps: If the data source type is middleware, message queue, or REST API interface, fill in the corresponding request JSON as the configuration item information in the data configuration; Real-time database step: If the data source type is real-time database, fill in the tag point name and time interval in the corresponding format as configuration item information in the data configuration.

4. The industrial digital twin multimodal data connection method according to claim 1, characterized in that: The step S3 includes: determining whether there is matching configuration item information; if there is matching configuration item information, using the data connection pool to obtain a connection according to the configuration item information to obtain data; If there is no matching configuration item information, the data source type is obtained from the request data. Depending on the data source type, a connection is obtained from the configuration item information data connection pool corresponding to the data source type in the request data to obtain data.

5. The industrial digital twin multimodal data connection method according to claim 2, characterized in that: In step S1, the digital twin obtains production plans and production performance from the relational database Mysql, Oracle, DB2 or PostgreSQL, obtains real-time steelmaking data from the real-time database iHyperDB, obtains message push information from the middleware, configures corresponding data connection information, reloads data source information, and generates a data connection pool according to the data source type; The data connection information includes the database IP port of the database type, the database type, the database name and the middleware IP port of the middleware; In step S3, if the request does not carry configuration item information, the data source type is DB2 database DBMS1, and the request data carries the corresponding SQL1, SQL2, and SQL3 in the format, then a database connection is obtained from the data connection pool of the data source DBMS1, and these SQLs are executed sequentially through the database connection; The data source type is middleware, and the corresponding virtual-host queue information is used to obtain the data source; The data source type is real-time database and REST API, and the data source is obtained using JSON data in the configuration or request; The result information in step S5 is the SQL execution count, total SQL execution time, slowest SQL execution time, number of rows read, and maximum concurrency of the SQL statements executed when calling the relational database, and the call execution time and call count / frequency statistics obtained when calling the json and real-time databases.

6. An industrial digital twin multimodal data connection system, characterized in that: include: Module M1: Configure data connection information for the data source and generate a data connection pool; Module M2: Obtain the data source type based on the data connection information and write configuration item information for different data source types; Module M3: Based on the configuration item information, request data through a unified format data interface and obtain return data; Module M4: parses the returned data and returns the returned information in a unified format; Module M5: Access the browser traffic analysis page to obtain detailed information about the execution data connection.

7. The industrial digital twin multimodal data connection system according to claim 6, characterized in that: The module M1 supports connecting and managing different types of multi-source heterogeneous data sources, including relational databases, real-time databases, middleware, message queues, and REST APIs. The corresponding data connection information is configured in the screen in turn, and the data source information is reloaded to generate a data connection pool. The module M2 provides a standardized REST API data acquisition interface for multi-source heterogeneous data sources, integrating and unifying data from different data sources. If there are corresponding variables, they are configured using standard placeholders; The module M3 provides a unified and standardized data request interface for multi-source heterogeneous data sources. If no data configuration is configured, the default data acquisition interface of different data source types is used to acquire data; The module M4 parses the data returned after the request in the module M3 and returns the return information in a unified format; In the module M5, the traffic analysis page is logged in to view the result information of the execution data connection, and the call statistics and execution results obtained from the call history statistics.

8. The industrial digital twin multimodal data connection system according to claim 6, characterized in that: The module M2 includes: Relational database module: If the data source type is a relational database, fill in the SQL statements used in the business scenario as configuration item information in the data configuration; JSON configuration module: For data source types such as middleware, message queue, and REST API interface, fill in the corresponding request JSON as configuration item information in the data configuration; Real-time database module: If the data source type is real-time database, fill in the tag point name and time interval in the corresponding format as configuration item information in the data configuration.

9. The industrial digital twin multimodal data connection system according to claim 6, characterized in that: The module M3 includes: determining whether there is matching configuration item information; if there is matching configuration item information, using the data connection pool to obtain a connection based on the configuration item information to obtain data; If there is no matching configuration item information, the data source type is obtained from the request data. Depending on the data source type, a connection is obtained from the configuration item information data connection pool corresponding to the data source type in the request data to obtain data.

10. The industrial digital twin multimodal data connection system according to claim 7, characterized in that: The digital twin in module M1 obtains production plans and production performance from the relational database Mysql, Oracle, DB2 or PostgreSQL, obtains real-time steelmaking data from the real-time database iHyperDB, obtains message push information from the middleware, configures corresponding data connection information, reloads data source information, and generates a data connection pool according to the data source type; The data connection information includes the database IP port of the database type, the database type, the database name and the middleware IP port of the middleware; In the module M3, if the request does not carry configuration item information, the data source type is DB2 database DBMS1, and the request data carries the corresponding SQL1, SQL2, and SQL3 in the format, then a database connection is obtained from the data connection pool of the data source DBMS1, and these SQLs are executed sequentially through the database connection; The data source type is middleware, and the corresponding virtual-host queue information is used to obtain the data source; The data source type is real-time database and REST API, and the data source is obtained using JSON data in the configuration or request; The result information in module M5 is the SQL execution count, total SQL execution time, slowest SQL execution time, number of rows read, and maximum concurrency of the SQL statements executed when calling the relational database, and the call execution time and call count / frequency statistics obtained when calling the json and real-time databases.

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