Label-based digital equipment model dynamic creation method and system

Abstract

The invention discloses a label-based digital equipment model dynamic creation method and system, and relates to the field of complex data integration and equipment management technology application in the power industry, and the method comprises the following steps: predefining a general data label model, dynamically configuring a query model, and establishing a database; and constructing a digital equipment data entity and designing a technical process of parallel computing and asynchronous processing. Data standardization is realized, and the query efficiency is improved; through a dynamic configuration mechanism, the system can automatically generate the query model, and the flexibility is improved; through the data entity construction method, different business data can be associated through a standardized model, and the data consistency is improved; according to the method, parallel computing and cache optimization technologies are utilized, and data query and processing are accelerated; according to the method, an incremental updating mechanism is adopted, so that the real-time performance and consistency of data are ensured; according to the method, the creation efficiency, query performance and system flexibility of the equipment model can be remarkably improved, and better technical support is provided for digital transformation of the power industry.

Description

Technical Field

[0001] The present invention relates to the technical field of complex data integration and equipment management in the power industry. Specifically, it relates to a method and system for dynamically creating a digital device model based on tags. Background Art

[0002] Existing digital device model creation solutions usually rely on manually writing query statements or scripts to extract data from multiple data sources and integrate it into a new data model after processing. Its typical process is as Figure 1 shown.

[0003] Detailed description of the prior art solution:

[0004] 1. Data extraction: Device-related data is scattered and stored in multiple business databases (such as SCADA systems, ERP systems, AMS asset management systems, etc.). SQL queries or ETL tools are used to manually extract the operating data, maintenance records, asset information, etc. of the devices.

[0005] 2. Data conversion: Since the data formats and field names of different systems may be different, developers need to write data conversion scripts to unify the data structure. It may involve data cleaning and conversion work such as time format conversion, unit conversion, field mapping, etc.

[0006] 3. Data association: By manually writing SQL statements, data from different data sources is associated based on fields such as device ID and timestamp. Due to cross-database and cross-table queries, the query performance is limited and the data integration efficiency is low.

[0007] 4. Device data storage: The integrated data is stored in an intermediate table or a dedicated device model database for subsequent business systems to call. This storage process is usually static and cannot dynamically adapt to new business requirements.

[0008] 5. Device model creation: Based on the stored data, developers manually define the logical structure of the device model, such as parameters of a motor like power and voltage. The business system obtains data by querying the device model database and performs visual display or analysis and calculation.

[0009] Objective drawbacks of the prior art: In the current digital device model creation method, there are the following technical defects, which can be improved or solved by the technical solution proposed in the present invention. The data integration method depends on a fixed query logic and lacks flexibility

[0010] The prior art relies on manually writing SQL statements or ETL processes to extract data from different data sources. This method is difficult to adapt to the ever-changing business requirements. For example, when new data fields or data sources are added, the SQL query logic needs to be modified, or even the data processing flow needs to be reconstructed, resulting in increased development and maintenance costs. In addition, traditional query logic is often static and unable to dynamically adjust the query strategy, resulting in poor system adaptability.

[0011] In the existing solutions, since device data is scattered and stored in multiple business databases, cross-database and cross-table queries are essential operations. However, the traditional query methods have the following problems in large-scale data scenarios: SQL queries require multiple JOINs or nested queries, resulting in a decline in query performance. Especially in the processing of massive data, the database is prone to bottlenecks. Due to the lack of a unified data index and optimization mechanism, the query execution time is long, the system response speed is slow, and the real-time requirements are affected.

[0012] The existing methods for creating device models mainly rely on developers to manually define field mapping relationships, resulting in the following problems: The coding styles and logics of different developers may be inconsistent, resulting in a lack of unified standards for the model structure. The field naming rules and formats of different data sources are different, and the data mapping process is prone to errors, affecting data quality. The maintenance cost of the device model is high, and the update or adjustment of data fields requires manual intervention, affecting system stability.

[0013] In the existing methods, every time a new device type or data field is added, developers need to manually write query code and adjust the data processing flow, increasing the following costs: Development cost: Developers need to deeply understand the database structure and business logic and manually write complex SQL, increasing R & D investment. Maintenance cost: When business requirements change, the existing code may need to be greatly adjusted, and the system maintenance cost is high. High error rate: Due to relying on manually written SQL, query logic errors are prone to occur, resulting in inaccurate data.

[0014] The existing methods for creating device models are usually customized for specific business scenarios. When new data sources need to be extended or new calculation logics are added, the code often needs to be greatly modified. Due to the different data structures between business systems, the data integration process faces the difficulties of data format conversion and mapping, resulting in limited system expansion capabilities. Summary of the Invention

[0015] To solve the above problems, the object of the present invention is to provide a tag-based dynamic creation technology for digital device models, which is applicable to complex data integration and device management in the power industry, aiming to significantly improve the creation efficiency, query performance and system flexibility of device models, and provide better technical support for the digital transformation of the power industry.

[0016] To achieve the above technical objectives, this application provides a method for dynamically creating a digital device model based on tags, which is applied to complex data integration and device management in the power industry, including:

[0017] Pre-define a general data tag model: Define that each tag corresponds to a business data type, and describe the information contained in the tag through multiple attributes;

[0018] Dynamically configure a query model: Based on the pre-defined general data tag model, dynamically define query requirements according to business needs and query conditions input by users;

[0019] Build a digital device data entity: Map the data model generated through business needs into the digital device entity, where the digital device entity is a logical abstraction of business data and is used to represent an associated data set in a specific business scenario;

[0020] Parallel computing and asynchronous processing: Process multiple query tasks in parallel, and asynchronously process the data query and update process, and directly extract the results from the cache during query.

[0021] Preferably, when pre-defining the general data tag model, control each tag to map with database fields so that the tag model contains data types suitable for multiple business scenarios.

[0022] Preferably, when pre-defining the general data tag model, generate a general data tag model by pre-defining geographical location tags, device information tags, device manufacturer tags, device model tags, device classification tags, device topology tags, location type tags, channel tags, measurement point tags, and communication terminal tags.

[0023] Preferably, when generating the general data tag model, manage the geographical location through geographical location tags, and perform positioning and query according to geographical information;

[0024] Build a complete device file through device information tags for device management and monitoring;

[0025] Distinguish the characteristics, usage requirements, and maintenance cycles of different model devices through device manufacturer tags;

[0026] Realize the grouping, management, and maintenance of devices through device classification tags;

[0027] Identify the mutual relationships and workflows between devices through device topology tags, and support fault diagnosis and network optimization;

[0028] Realize the monitoring and management of devices in different geographical locations through location type tags;

[0029] Configure and connect the measurement point data acquisition device according to the specific communication method of the device through the channel label;

[0030] Associate the real-time operation data of the device with the monitoring system through the measurement point label;

[0031] Identify and manage the terminal device through the communication terminal label.

[0032] Preferably, when dynamically configuring the query model, based on the dynamically defined query requirements, automatically identify the query conditions, and generate a data model corresponding to the business requirements according to the predefined label model.

[0033] Preferably, when performing parallel computing and asynchronous processing, by retrieving and caching the required data sets in advance, quickly return the results according to the user's query requirements.

[0034] Preferably, when constructing the general data label model, through the incremental update technology, set the synchronization mechanism between the label model and the database to ensure that the data in the digital device entity can reflect the changes of the business and the system in a timely manner.

[0035] The present invention also discloses a dynamic creation system of a digital device model based on labels, which is used to implement the above-mentioned dynamic creation method of a digital device model based on labels, including:

[0036] A general data label model predefined module, which is used to define that each label corresponds to a business data type, and describe the information contained in the label through multiple attributes;

[0037] A query model dynamic configuration module, which is used to dynamically define query requirements based on the predefined general data label model according to business requirements and query conditions input by the user;

[0038] A digital device data entity construction module, which is used to map the data model generated through business requirements into the digital device entity, where the digital device entity is a logical abstraction of business data and is used to represent an associated data set in a specific business scenario;

[0039] A parallel computing and asynchronous processing module, which is used to process multiple query tasks in parallel, and perform asynchronous processing on the data query and update process, and directly extract the results from the cache during the query;

[0040] A data update and synchronization module, which is used to set the synchronization mechanism between the label model and the database through the incremental update technology to ensure that the data in the digital device entity can reflect the changes of the business and the system in a timely manner.

[0041] The present invention discloses the following technical effects:

[0042] The present invention adopts a tag-based dynamic data model. By predefined tag systems, the querying and integration of data can be dynamically adjusted through configuration without modifying the underlying query logic. This can reduce the manual coding work of developers and improve the flexibility of data integration.

[0043] The present invention provides an incremental data update and caching mechanism. Through data tag indexing and data preprocessing technologies, it avoids directly relying on SQL for complex queries, but quickly obtains associated data through tag matching. This method can greatly improve the data query efficiency, reduce the database load, and improve the response speed of the system.

[0044] The present invention proposes a general data tag model. Through a standardized tag system and a dynamic data mapping mechanism, it ensures the consistency and scalability of the data model. No matter how the data source changes, only the tag configuration needs to be adjusted without modifying the underlying code, thereby improving the degree of data standardization and reducing the maintenance cost.

[0045] The present invention adopts an automated device model creation method, combines dynamic configuration with data mapping, and avoids manually writing SQL and adjusting the data structure. Through the tag-driven data model management method, it can quickly adapt to changes in business requirements, reduce development and maintenance costs, and improve the stability and reliability of the system.

[0046] Based on the tagged data model, the present invention supports the dynamic expansion of data fields and calculation logics. No matter what new device types or business requirements are added, adjustments can be completed through configuration without modifying the code, enhancing the scalability and adaptability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0048] Figure 1 is the existing digital device model creation solution described in the present invention;

[0049] Figure 2 is the flowchart of the digital device model creation described in the present invention;

[0050] Figure 3 is the relationship schematic diagram of the data tag model described in the present invention;

[0051] Figure 4 is the schematic diagram of customizing the data model according to business requirements described in the present invention;

[0052] Figure 5 It is a schematic diagram of the digital device entity construction described in the present invention. Specific implementation manners

[0053] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part rather than all of the embodiments of the present application. Usually, the components of the embodiments of the present application described and illustrated herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application claimed, but merely represents selected embodiments of the present application. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative efforts fall within the scope of protection of the present application.

[0054] As Figure 2-5 shown, the present invention provides a technology for dynamically creating a digital device model based on tags, which is applicable to complex data integration and device management in the power industry, and specifically includes the following processes:

[0055] 1. Pre-define a general data tag model. The present invention defines multiple general data tag models ( Figure 3 ) in the database. Each tag corresponds to a type of business data, and the information contained in the tag is described by multiple attributes. Each tag ensures data consistency and structured management through mapping with database fields. The tag model contains data types suitable for multiple business scenarios and provides a standardized basis for subsequent data integration and query.

[0056] Specific tag examples are as follows:

[0057] Geographical location tag: This tag contains location information related to a device or a group of devices, such as location type (such as plant area, region, workshop, etc.), location name, geographical coordinates, etc. These attributes enable data to be managed by geographical location, facilitating quick positioning and query based on geographical information.

[0058] Device information tag: This tag covers key information related to power devices, such as device name, device status, device loop information, device model, installation time, maintenance cycle, etc. These attributes help the system build a complete device file and support device management and monitoring.

[0059] Device manufacturer tag: This tag records the manufacturer information of the device, including manufacturer name, manufacturer number, manufacturer contact information, etc. Through the device manufacturer tag, the source of the device can be quickly identified, helping to manage the quality, supply chain, and subsequent maintenance and service support of the device.

[0060] Device Model Label: This label stores model information related to the device, including the device model name, model code, nameplate information, etc. By uniformly managing the device model information, the system can effectively distinguish the characteristics, usage requirements, and maintenance cycles of different model devices.

[0061] Device Classification Label: This label is used for the classification management of devices and includes device type, classification code, classification description, etc. For example, different categories of power equipment (such as transformers, motors, distribution cabinets, etc.) can be distinguished through the device classification label, which helps to achieve efficient grouping, management, and maintenance of devices.

[0062] Device Topology Label: This label describes the connection relationship and topology structure between devices and includes information such as the dependency relationship, connection points, and transmission paths between devices. Through the device topology label, the system can effectively identify the mutual relationship and workflow between devices, supporting fault diagnosis and network optimization.

[0063] Location Type Label: This label represents the geographical location type where the device is located and includes location type (such as plant area, region, workshop, machine room, substation, etc.), location name, and other relevant geographical information. This label helps the system to classify and manage devices by location, facilitating rapid retrieval, scheduling, and maintenance based on the device location, and at the same time supporting the monitoring and management of devices at different geographical locations.

[0064] Channel Label: This label records communication channel information related to the acquisition of measurement point data, including channel name, protocol type, data transmission rate, port number, IP address, etc. The channel label enables the monitoring system to quickly configure and connect measurement point data acquisition devices according to the specific communication method of the device.

[0065] Measurement Point Label: This label records key information related to the measurement points of the device, such as measurement point type (remote measurement, remote signal, setting value, etc.), measurement point name, measurement point number, measurement point unit, the range and accuracy of measurement point data, etc. The measurement point label can accurately associate the real-time operation data of the device with the monitoring system, providing the necessary basic data for data analysis, fault diagnosis, and operation optimization.

[0066] Communication Terminal Label: This label is used to store information about the data acquisition terminal of the device, including terminal name, terminal number, terminal type, IP address, communication protocol, etc. Through the communication terminal label, the system can quickly identify and manage terminal devices, ensuring the stability and reliability of device data acquisition and transmission.

[0067] 2. Dynamic Configuration Query Model. According to specific business requirements, the user can input query conditions ( Figure 4) to dynamically define query requirements. The system will automatically identify these query conditions and generate corresponding data models based on predefined tag models. This process is fully automated, avoiding the cumbersome operation of frequently writing query statements in traditional methods. The generation of the query model not only supports complex cross-table and cross-database queries but also flexible data filtering. For example, when querying transformer equipment at a specific location, the system automatically generates query logic based on "geographical location tags" and "equipment information tags" and returns equipment data that meets the conditions. This process greatly improves the efficiency and accuracy of data access.

[0068] 3. Construct digital device data entities. The data models generated by the system based on business requirements will be mapped into a unified digital device entity ( Figure 5 ). The digital device entity is a logical abstraction of business data and can represent an associated data set in a specific business scenario.

[0069] For example:

[0070] "Transformer equipment at the gasoline adsorption location": This entity combines the geographical location of the "gasoline adsorption" area with transformer equipment information (such as name, model, operating status) to form an equipment entity containing all key information.

[0071] "Phase A current measurement point in the 6kV substation location": This entity associates the geographical location of the 6kV substation with specific Phase A current measurement point data, integrating current data with location, equipment model, and other information to form a complete equipment data view.

[0072] These digital device entities are not only convenient for querying and analysis but also provide clear basic data support for equipment monitoring and fault diagnosis.

[0073] 4. Parallel computing and asynchronous processing. To improve query performance, the present invention adopts distributed parallel computing technology. By retrieving and caching the required data sets in advance, the system can quickly return results when a user queries, reducing query latency. Parallel computing can process multiple query tasks in parallel, significantly improving the overall response speed of the system. The asynchronous processing mechanism further accelerates the data query and update process, ensuring that results are directly retrieved from the cache during a query and avoiding performance bottlenecks during real-time computing. This solution is particularly suitable for scenarios with large amounts of data and complex queries, improving the overall processing capacity and query efficiency of the system.

[0074] 5. Data Update and Synchronization Mechanism. The present invention designs a synchronization mechanism between the tag model and the database to ensure that the data in the digital device entity can timely reflect the changes in the business and the system. The incremental update technology enables data to be processed only when new data is added or changes occur, rather than performing a full update on the entire dataset, thereby reducing the consumption of system resources and improving the efficiency of data update. This mechanism not only ensures data consistency but also effectively reduces the system burden and improves the stability and data processing capacity of the system.

[0075] 6. Modular Design and Scalability. The present invention adopts a modular design, and functional modules such as data tags, query models, and digital device entities can operate independently and can be flexibly extended according to actual business requirements. The system architecture supports distributed processing and can be extended as the data volume and business requirements grow, ensuring the stability and efficiency of the system in large-scale data processing. Through this design, the present invention can adapt to different business scenarios, support the integration of multi-source heterogeneous data, and provide an efficient solution for cross-database, cross-table queries, and the creation of complex device models.

[0076] The present invention focuses on the definition and application of a general data tag model: by predefining a general data tag model, different types of data are unified and standardized, and a query model is flexibly generated through a dynamic configuration mechanism to meet the requirements in complex business scenarios.

[0077] The present invention focuses on the dynamic configuration of data models: according to user requirements, a custom query model is dynamically generated, avoiding manual coding, improving the flexibility and adaptability of the system, and enhancing data query efficiency and system scalability.

[0078] The present invention focuses on the construction of digital device entities and data mapping: by constructing a unified digital device entity, data from different data sources are associated through the tag model to form a clear data view, supporting efficient data query and analysis.

[0079] The present invention is used to flexibly generate and manage digital device models. Through the general data tag model and its definition method, it is applicable to complex data integration and device management in the power industry.

[0080] The mapping mechanism for the construction of digital device entities designed by the present invention maps multi-source data into a unified device entity through the tag model.

[0081] The data query optimization method designed by the present invention includes parallel computing and asynchronous processing technologies for improving data query efficiency.

[0082] The data synchronization and incremental update mechanism designed by the present invention ensures that the data of the digital device entity is updated in real time and is consistent with the underlying database.

[0083] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to generate a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices generate means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in multiple blocks.

[0084] In the description of the present invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, "a plurality of" means two or more unless otherwise specifically defined.

[0085] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these modifications and variations.

Claims

1. A method for dynamically creating a digital device model based on tags, characterized in that Applied to complex data integration and equipment management in the power industry, including: Pre-defined general data tag model: Define that each tag corresponds to a type of business data, and describe the information contained in the tag through multiple attributes; Dynamic configuration query model: Based on the pre-defined general data tag model, dynamically define query requirements according to business needs and query conditions input by users; Construct digital device data entities: Map the data model generated by business needs into digital device entities, where the digital device entities are logical abstractions of business data and are used to represent an associated data set in a specific business scenario; Parallel computing and asynchronous processing: Process multiple query tasks in parallel, and asynchronously process the data query and update processes, and directly extract the results from the cache during query.

2. The method for dynamically creating a digital device model based on tags according to claim 1, characterized in that: When pre-defining the general data tag model, control each tag to map with database fields, so that the tag model contains data types suitable for multiple business scenarios.

3. The method for dynamically creating a digital device model based on tags according to claim 2, characterized in that: When pre-defining the general data tag model, generate a general data tag model by pre-defining geographical location tags, device information tags, device manufacturer tags, device model tags, device classification tags, device topology tags, location type tags, channel tags, measurement point tags, and communication terminal tags.

4. The method for dynamically creating a digital device model based on tags according to claim 3, characterized in that: When generating the general data tag model, manage the geographical location through the geographical location tag, and perform positioning and query according to geographical information; Construct a complete device file through the device information tag for device management and monitoring; Distinguish the characteristics, usage requirements, and maintenance cycles of different model devices through the device manufacturer tag; Realize the grouping, management, and maintenance of devices through the device classification tag; Identify the mutual relationships and workflows between devices through the device topology tag, and support fault diagnosis and network optimization; Realize the monitoring and management of devices in different geographical locations through the location type tag; Configure and connect measurement point data acquisition devices according to the specific communication method of the device through the channel tag; Associate the real-time operation data of the device with the monitoring system through the measurement point tag; Identify and manage terminal devices through the communication terminal tag.

5. The method for dynamically creating a digital device model based on tags according to claim 4, characterized in that: When dynamically configuring the query model, automatically identify the query conditions based on the dynamically defined query requirements, and generate a data model corresponding to the business requirements according to the pre-defined tag model.

6. The method for dynamically creating a digital device model based on tags according to claim 5, characterized in that: When performing parallel computing and asynchronous processing, quickly return the results according to the user's query requirements by retrieving and caching the required data sets in advance.

7. The method for dynamically creating a digital device model based on tags according to claim 1, characterized in that: When constructing the general data tag model, through the incremental update technology, a synchronization mechanism between the tag model and the database is set to ensure that the data in the digital device entity can timely reflect the changes in the business and the system.

8. A tag-based digital device model dynamic creation system for implementing a tag-based digital device model dynamic creation method as described in any one of claims 1-7, characterized in that, It includes: A general data tag model predefined module, used to define that each tag corresponds to a business data type, and describe the information contained in the tag through multiple attributes; A query model dynamic configuration module, used to dynamically define query requirements based on the predefined general data tag model according to business requirements and query conditions input by users; A digital device data entity construction module, used to map the data model generated by business requirements into the digital device entity, wherein the digital device entity is a logical abstraction of business data and is used to represent an associated data set in a specific business scenario; A parallel computing and asynchronous processing module, used to process multiple query tasks in parallel and perform asynchronous processing on the data query and update process, and directly extract the results from the cache during the query; A data update and synchronization module, used to set a synchronization mechanism between the tag model and the database through the incremental update technology to ensure that the data in the digital device entity can timely reflect the changes in the business and the system.

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