Supervisory control and data acquisition (SCADA) system label generation method and system based on structured multi-source information fusion

Abstract

The invention provides an SCADA system label generation method and system based on structured multi-source information fusion, and relates to the technical field of industrial automation and control. According to the method and the system, firstly, an initial information modeling framework including a tag name, a data type and an equipment association relationship is generated based on an SCADA system architecture; establishing a multi-level constraint rule base according to engineering domain characteristics, analyzing the information modeling framework through a large language model, performing semantic verification in combination with a domain knowledge graph, and generating a structured tag file conforming to a used SCADA (Supervisory Control and Data Acquisition) system; and finally, through an application platform of an SCADA (Supervisory Control and Data Acquisition) system or by constructing a digital twinborn verification platform containing a simulation test environment, carrying out an injection test on the generated tag, and verifying the field integrity, logic consistency and system compatibility of a tag file. According to the method, the large language model can adapt to a specific industrial control scene when processing SCADA system label generation.

Description

Technical Field

[0001] The present invention relates to the field of industrial automation and control technology, and particularly to a method and system for generating SCADA system tags based on structured multi-source information fusion. Background Art

[0002] As a core component in the field of industrial control, the SCADA system (Supervisory Control and Data Acquisition system) is widely used in the monitoring and control of key infrastructures such as power generation and water treatment. This system has basic control functions for predefined settings, which are implemented by a distributed database. The database contains data elements called "tags" or "points", and each tag (point) represents a single input or output value that is controlled or adjusted in the system. Therefore, tags (points) play a core role in the data flow of the SCADA system. It is not only used to represent the states of various devices and sensors in the system, but also for the transmission of alarm, monitoring, and control signals, and is an important data structure to ensure the stable operation and real-time response of the system. However, the existing SCADA system tag configuration methods have the following deficiencies and drawbacks: 1. Configuration errors are prone to occur in manual input and batch import: The traditional SCADA system tag configuration method relies on manual input or batch import of data. Although this improves efficiency, it is prone to configuration errors, which affect the accuracy of tags. Especially in large-scale complex systems, the risk of human operation errors is higher, which may lead to unstable system operation. Therefore, the existing methods have deficiencies in terms of accuracy and reliability.

[0003] 2. Unable to adapt to complex control logic and large-scale system requirements: With the continuous development of industrial automation technology, the control logic and requirements of the SCADA system are becoming increasingly complex. Traditional tag configuration methods often have difficulty quickly adapting to system changes and expansions. Although they can handle some basic control requirements, for complex industrial scenarios, the flexibility and adaptability of traditional methods are poor, and it is difficult to meet the requirements of large-scale systems.

[0004] 3. Limitations of large language model methods: In recent years, generative artificial intelligence models based on natural language processing, such as large language models, have been widely used in tasks such as code generation. However, these large language models face certain challenges in processing complex industrial control instructions. The basic training data of existing large language models mostly focuses on general scenarios, and there is still a blank in specialized training for industrial control logic. At the same time, research shows that the output results of large language models are poor in specific fields and are prone to produce inaccurate results. In the process of configuring SCADA tags through large language models, it involves complex conditional logic, strict industrial standards, and the interrelationships between devices, which all pose higher requirements for the training and application of large language models.

[0005] Therefore, the existing technologies still have significant deficiencies in the accuracy, adaptability of tag configuration, and in dealing with complex requirements. There is an urgent need for an innovative method to improve the efficiency and flexibility of tag configuration to meet the high standards of tag configuration in modern industrial control systems. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a method and system for generating SCADA system tags based on structured multi-source information fusion in view of the above deficiencies of the existing technologies. By constructing an information modeling framework with engineering semantics, introducing multi-dimensional constraints based on domain knowledge, and combining semantic parsing and simulation verification mechanisms, the automation, structuring, and verification closed-loop of industrial tag configuration are realized, and the overall configuration efficiency and system compatibility are improved.

[0007] To solve the above technical problems, the technical solutions adopted by the present invention are as follows: On the one hand, the present invention provides a method for generating SCADA system tags based on structured multi-source information fusion, including: Analyze the device communication protocol, control logic topology, and data acquisition frequency based on the SCADA system architecture to generate an initial information modeling framework including tag names, data types, and device association relationships; Establish a multi-level constraint rule library according to the characteristics of the engineering field. The rule library includes process safety constraints, device interlock constraints, and multi-threshold alarm priority constraints, where the multi-threshold alarm priority constraints support non-linear piecewise threshold settings; Parse the information modeling framework through a large language model, perform semantic verification in combination with a domain knowledge graph, and generate a structured tag file that conforms to the used SCADA system. The tag file includes an extensible metadata description layer; Perform injection testing on the generated tags through the application platform of the SCADA system or build a digital twin verification platform including a simulation test environment to verify the field integrity, logical consistency, and system compatibility of the tag file.

[0008] Furthermore, the method further includes: optimizing the generated information modeling framework through a feedback mechanism, correcting the defects in the information modeling framework until the generated tag file meets the requirements.

[0009] Furthermore, the generation of the initial information modeling framework includes: Establish a standard tag information modeling framework including a device type ontology library. The information modeling framework integrates a data type automatic matching algorithm and supports dynamic precision configuration of floating-point numbers, boolean values, and timestamps; Introduce a device relationship graph construction module, and automatically generate a device association matrix by parsing a P&ID diagram or an electrical single-line diagram. The matrix supports the predefined device fault propagation path; Set up an alarm rule configuration interface, allowing users to define alarm delay time, confirmation mechanism, and notification levels through a visual interface. Among them, multi-threshold alarms use a sliding window algorithm for dynamic baseline calculation.

[0010] Furthermore, the multi-level constraint rule library established according to the characteristics of the engineering field includes: Process safety constraints: Based on the results of HAZOP analysis, establish parameter boundary conditions and automatically associate logical nodes of the safety instrumented system SIS; Equipment interlock constraints: Generate an interlock rule table by parsing the equipment interlock logic, supporting cascade alarm settings across control loops; Alarm priority constraints: Use a fuzzy logic algorithm to automatically adjust the alarm level according to the process impact degree and occurrence frequency, generating a dynamic priority queue.

[0011] Furthermore, the process of generating the structured tag file includes: Domain knowledge injection: Integrate industry-specific term libraries and engineering standards, and enhance the understanding of professional terms by the large language model through the attention mechanism; Automatic verification: Use the abstract syntax tree AST to perform syntax checks on the generated tags, and combine with the data dictionary for semantic consistency verification; Version control: Perform hash verification on each generated tag file, supporting multi-version comparison and rollback operations.

[0012] Furthermore, the injection test of the generated tags to verify the field integrity, logical consistency, and system compatibility of the tag file includes: Digital simulation verification: Through the simulation test function of the SCADA system application platform or establish a simulation environment that runs synchronously with the physical SCADA system, perform verification and stress tests on the tag file; Compatibility detection: Automatically check the interface matching degree of the tags with configuration software, historical databases, and human-machine interfaces; Intelligent feedback: Dynamically adjust the prompt word weights of the large language model based on the verification results, and optimize the tag generation strategy through reinforcement learning.

[0013] On the other hand, the present invention also provides a SCADA system tag generation system based on structured multi-source information fusion, including: An information modeling unit for generating a structured information modeling framework according to the SCADA system architecture and device characteristics; A constraint rule configuration unit that supports the rule-based expression and storage of multi-domain expert knowledge; An intelligent generation unit that integrates a large language model and a domain knowledge graph for collaborative reasoning to generate SCADA system tags; Verification and optimization unit, constructing a closed-loop test environment to achieve automatic verification and iterative optimization of tags.

[0014] Furthermore, the intelligent generation unit further includes: A context awareness module for parsing device context relationships and automatically completing associated tags; A multi-modal output module that supports generating tag formats that meet the requirements of different SCADA systems; A security enhancement module with built-in data desensitization algorithms to ensure the secure processing of sensitive information.

[0015] In a third aspect, the present invention also provides a computer-readable storage medium storing a computer program, which when executed by a processor implements the SCADA system tag generation method based on structured multi-source information fusion described above.

[0016] In a fourth aspect, the present invention also provides a SCADA system engineering implementation system integrating a SCADA device implementing the SCADA system tag generation method based on structured multi-source information fusion, and configured with: An engineering configuration interface that supports multi-user collaborative tag design; A tag lifecycle management module that records tag version iterations and verification histories; A system compatibility database that stores technical specifications and interface documents of different SCADA systems.

[0017] The beneficial effects of adopting the above technical solutions are as follows: The SCADA system tag generation method and system based on structured multi-source information fusion provided by the present invention generate tag files by combining structured information modeling and large language models, reducing the errors of manual operations; the guidance of the structured information modeling framework makes the tag generation process more standardized and automated, thereby improving the accuracy of the generated tags. Through the structured information modeling framework, complex control logics and device relationships are incorporated into the tag generation process, enabling the tag generation process to be customized and adjusted according to actual needs, so that in the face of complex control logics and large-scale systems, it can better adapt to system changes and expansion requirements.

[0018] By optimizing the information modeling framework, the large language model can adapt to specific industrial control scenarios when processing SCADA system tag generation. The structured information modeling framework can help the large language model better understand and process complex conditional logics, strict industrial standards, and the interrelationships between devices, overcoming the limitations of poor application effects of large language models in specific fields in the prior art, and improving the accuracy and professionalism of tag generation.

[0019] This invention provides new ideas for the practical application of large language models in industrial control systems and lays a solid foundation for the future development of industrial intelligence driven by large language models. By optimizing the prompt strategy, this invention promotes the application of large language models in specific industrial fields, demonstrates its potential in improving the efficiency and accuracy of SCADA tag generation, and opens up a new path for the development of industrial intelligence. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a flow chart of a SCADA system label generation method based on structured multi-source information fusion provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0021] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0022] Embodiment 1:

[0023] In this embodiment, a SCADA system label generation method based on structured multi-source information fusion is as follows: Figure 1 As shown, including:

[0024] Step S1: Analyze the device communication protocol, control logic topology and data acquisition frequency based on the SCADA system architecture, and generate an initial information modeling framework including tag name, data type and device association relationship, specifically including: Establish a standard tag information modeling framework including a device type ontology library, wherein the information modeling framework integrates an automatic data type matching algorithm and supports dynamic precision configuration of floating point numbers, Boolean quantities, and timestamps; The equipment relationship map construction module is introduced to automatically generate the equipment association matrix by parsing the P&ID diagram or electrical single-line diagram. The matrix supports the pre-definition of the equipment fault propagation path; The alarm rule configuration interface allows users to define the alarm delay time, confirmation mechanism and notification level through a visual interface. The multi-threshold alarm uses a sliding window algorithm for dynamic baseline calculation.

[0025] In this embodiment, the generation of the information modeling framework first needs to clarify the basic requirements for SCADA system tag generation. According to the different requirements of the specific SCADA system, the necessary specifications are provided for generating tags. At this time, the information modeling framework will involve the core attributes of the tag, such as tag name, data type, etc. The goal of this requirement analysis is to ensure that the basic content in the information modeling framework can cover the most basic information required for tag generation.

[0026] Step S2: Establish a multi-level constraint rule library according to the engineering field characteristics. The rule library includes process safety constraints, equipment interlock constraints, and multi-threshold alarm priority constraints, where the multi-threshold alarm priority constraints support non-linear piecewise threshold settings; Among them, the process safety constraints: establish parameter boundary conditions based on the HAZOP analysis results, and automatically associate the logic nodes of the safety instrument system SIS; Equipment interlock constraints: generate an interlock rule table by parsing the equipment interlock logic, and support cascade alarm settings across control loops; Alarm priority constraints: use a fuzzy logic algorithm to automatically adjust the alarm level according to the process impact degree and occurrence frequency, and generate a dynamic priority queue.

[0027] The establishment of the multi-level constraint rule library can ensure that the generated tags meet the specific control requirements of the SCADA system. For example, joint control of multiple devices or setting complex alarm logics. By introducing these constraint conditions, it is ensured that the generated tags can reflect the complex control requirements of the SCADA system.

[0028] Step S3: Parse the information modeling framework through a large language model, perform semantic verification in combination with the domain knowledge graph, and generate a structured tag file that conforms to the used SCADA system. The tag file includes an extensible metadata description layer, specifically including: Domain knowledge injection: Integrate industry-specific term libraries and engineering standards, and enhance the large language model's understanding of professional terms through the attention mechanism; Automatic verification: Use the abstract syntax tree AST to perform syntax checks on the generated tags, and perform semantic consistency verification in combination with the data dictionary; Version control: Perform hash verification on each generated tag file, and support multi-version comparison and rollback operations.

[0029] Step S4: Through the application platform of the SCADA system or build a digital twin verification platform containing a simulation test environment, perform injection tests on the generated tags to verify the field integrity, logical consistency, and system compatibility of the tag file, specifically including: Digital simulation verification: Through the simulation test function of the SCADA system application platform or establish a simulation environment that runs synchronously with the physical SCADA system, perform verification and stress tests on the tag file; Compatibility detection: Automatically check the interface matching degree of the tags with the configuration software, historical database, and human-machine interface (HMI); Intelligent feedback: Dynamically adjust the prompt word weights of the large language model based on the verification results, and optimize the tag generation strategy through reinforcement learning.

[0030] Through simulation testing, it is possible to ensure that the generated tags can correctly respond to the control requirements of the system, such as alarm triggering or device linkage operations. If the verification result meets the predetermined goal, the generation of the tags is completed; if the result fails to meet the requirements, a prompt for optimization is given. Step S5: Optimize the generated information modeling framework through a feedback mechanism, correct the defects in the information modeling framework until the generated tag file meets the requirements.

[0031] After tag generation and verification, if problems are found with the generated tags (such as inaccurate tags, logical errors, or inconsistencies with the initial requirements), optimize the generated prompt content through a feedback mechanism. Through feedback, adjust the specific content in the information modeling framework and correct the existing loopholes or errors. This optimization process aims to gradually refine tag generation through iterative optimization to ensure that the subsequent generated tags can fully meet expectations.

[0032] Example 2:

[0033] In this example, a SCADA system tag generation system based on structured multi-source information fusion includes: An information modeling generation unit for generating a structured information modeling framework according to the SCADA system architecture and device characteristics; A constraint rule configuration unit that supports the rule-based expression and storage of multi-domain expert knowledge; An intelligent generation unit that integrates a large language model and a domain knowledge graph for collaborative reasoning to generate SCADA system tags; A verification and optimization unit that constructs a closed-loop test environment to achieve automatic verification and iterative optimization of tags.

[0034] Among them, the intelligent generation unit further includes: A context awareness module for parsing device context relationships and automatically completing associated tags; A multi-modal output module that supports generating tag formats that meet the requirements of different SCADA systems; A security enhancement module with built-in data desensitization algorithms to ensure the secure processing of sensitive information.

[0035] Example 3:

[0036] This example provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, it implements the SCADA system tag generation method based on structured multi-source information fusion described above.

[0037] Example 4:

[0038] This embodiment provides a SCADA system engineering implementation system, integrating the SCADA device generated based on the SCADA system tag of structured multi-source information fusion, and configured with: An engineering configuration interface that supports multi-user collaborative tag design; A tag lifecycle management module that records tag version iteration and verification history; A system compatibility database that stores technical specifications and interface documents of different SCADA systems.

[0039] Example 5:

[0040] Based on the SCADA system tag construction task of a certain power automation plant, this embodiment demonstrates the application process of the SCADA system tag generation method based on structured multi-source information fusion proposed by the present invention in engineering, specifically including: 1. Information modeling framework generation stage; First, extract the OPC communication identification, update frequency, and naming convention of the plant equipment, provide a brief prompt description for the large language model, and lay a foundation for subsequent tag generation to ensure that the generated tag file has a basic structure and function. Then, by importing the P&ID drawings of the plant, call the equipment relationship graph construction module to automatically generate an equipment association matrix including the relationships between boilers, sensors, and valves.

[0041] In this stage, it is necessary to determine the specific requirements of the SCADA system, define the basic information for generating tags, and initially construct an information modeling framework. For example: "Ensure that the file field names use camelCase format and the field values use PascalCase format." 2. Multi-level constraint rule library construction stage; In this stage, based on the actual operation logic of the plant, it is necessary to further provide more complex control logic and constraint conditions for the large language model to ensure that the generated tags can accurately reflect the control requirements of the SCADA system and meet the control logic and alarm requirements of the SCADA system; In this stage, additional constraint conditions are added to the information modeling framework.

[0042] Specifically, based on the information modeling framework constructed in the requirements clarification stage, constraint conditions such as multi-threshold alarms, mutual relationships between devices, and complex control logic are introduced.

[0043] For example, based on alarm frequency analysis, combined with logic rules to set dynamic changes in alarm levels and give priority to responding to variables with high process impact, the alarm constraint of "an alarm is triggered only when the temperature of device A exceeds the set threshold and the temperature of device B exceeds the set threshold" can be added to the information modeling framework.

[0044] By introducing these logics and constraints, it is ensured that the generated tags not only meet the basic requirements but also can adapt to the complex control relationships and alarm requirements involved in the SCADA system.

[0045] 3. Semantic Generation and Tag Construction Phase; In this phase, the information modeling framework is combined with natural language templates and input into the Qwen 2.5 large language model deployed on the local server. After receiving the content, the model generates a preliminary tag structure, and the output format is a JSON structure. Each tag record contains basic elements such as field name, type, unit, alarm condition, and threshold.

[0046] Through the operations in this phase, the tags required by the SCADA system can be automatically generated in a structured prompt and model-assisted manner, providing an available initial file basis for the subsequent verification and analysis phase.

[0047] 4. Verification and Analysis Phase; In this phase, the generated tags are verified through format checking + SCADA simulation testing to check whether the generated tags can accurately respond to control requirements.

[0048] If the verification result meets the predetermined requirements, it is considered that an information modeling framework that meets the requirements is obtained; if there are problems, it enters the prompt optimization phase.

[0049] In this embodiment, the SCADA tag file is generated by using a large language model combined with an information modeling framework, and the generated tag file is checked and semantically verified to determine whether the generated tag fields are accurate, such as whether the data type, alarm condition, etc. meet the requirements. Then the tag file is imported into the SCADA system for simulation testing to verify whether the generated tags can accurately respond to control requirements, such as triggering alarms, performing device linkage, etc. If the verification result meets the predetermined requirements, it is considered that an information modeling framework that meets the requirements is obtained; if there are problems, it enters the feedback optimization phase.

[0050] 5. Feedback Optimization Phase; The feedback optimization phase mainly optimizes the obtained information modeling framework through a feedback mechanism to correct the problems in the tag generation process. The specific steps include: First, analyze the gap between the generated tags and the actual requirements, and find out the missing fields or logical errors in the tags; Secondly, modify the constraints or tag definitions in the information modeling framework to ensure that the generated tags fully meet the requirements of the SCADA system; Finally, perform multiple rounds of iterative optimization. After each optimization, correct the defects in the information modeling framework through feedback until the generated tag file fully meets the requirements.

[0051] The goal of this stage is to gradually improve the information modeling framework through repeated iterations, so as to ensure that the tags generated by the large language model can accurately meet the actual needs of the SCADA system.

[0052] In this embodiment, the information modeling framework is optimized and iterated by using the SCADA system tag generation method based on structured multi-source information fusion. Each round of optimization and adjustment enables the information modeling framework to be continuously improved. The finally obtained information modeling framework can efficiently generate tag files that meet industrial requirements through natural language combined with the large language model.

[0053] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope defined by the claims of the present invention.

Claims

1. A SCADA system label generation method based on structured multi-source information fusion, characterized in that: include: Analyze the equipment communication protocol, control logic topology and data collection frequency based on the SCADA system architecture, and generate an initial information modeling framework including tag names, data types and equipment association relationships; Establish a multi-level constraint rule base according to the characteristics of the engineering field, wherein the rule base includes process safety constraints, equipment linkage constraints and multi-threshold alarm priority constraints, wherein the multi-threshold alarm priority constraints support nonlinear segmented threshold settings; The information modeling framework is parsed through a large language model, and semantic verification is performed in combination with a domain knowledge graph to generate a structured tag file that conforms to the SCADA system used, wherein the tag file includes an extensible metadata description layer; Through the application platform of the SCADA system or by building a digital twin verification platform that includes a simulation test environment, the generated tags are injected and tested to verify the field integrity, logical consistency, and system compatibility of the tag files.

2. The SCADA system label generation method based on structured multi-source information fusion according to claim 1, characterized in that: The method further includes: optimizing the generated information modeling framework through a feedback mechanism, and correcting defects in the information modeling framework until the generated label file meets the requirements.

3. The SCADA system label generation method based on structured multi-source information fusion according to claim 2, characterized in that: The generation of the initial information modeling framework includes: Establish a standard tag information modeling framework including a device type ontology library, wherein the information modeling framework integrates an automatic data type matching algorithm and supports dynamic precision configuration of floating point numbers, Boolean quantities, and timestamps; The equipment relationship map construction module is introduced to automatically generate the equipment association matrix by parsing the P&ID diagram or electrical single-line diagram. The matrix supports the pre-definition of the equipment fault propagation path; The alarm rule configuration interface allows users to define the alarm delay time, confirmation mechanism and notification level through a visual interface. The multi-threshold alarm uses a sliding window algorithm for dynamic baseline calculation.

4. The SCADA system label generation method based on structured multi-source information fusion according to claim 3 is characterized in that: The multi-level constraint rule library established according to the characteristics of the engineering field includes: Process safety constraints: Establish parameter boundary conditions based on HAZOP analysis results and automatically associate logical nodes of the safety instrumented system SIS; Equipment linkage constraints: Generate linkage rule tables by analyzing equipment interlocking logic, supporting cascade alarm settings across control loops; Alarm priority constraint: Fuzzy logic algorithm is used to automatically adjust the alarm level according to process impact and frequency of occurrence to generate a dynamic priority queue.

5. The SCADA system label generation method based on structured multi-source information fusion according to claim 4 is characterized in that: The structured tag file generation process includes: Domain knowledge injection: Integrate industry-specific terminology libraries and engineering standards, and enhance the large language model's understanding of professional terminology through the attention mechanism; Automatic verification: Use the abstract syntax tree AST to perform syntax check on the generated tags, and combine it with the data dictionary to verify semantic consistency; Version control: Perform hash verification on each generated tag file, and support multi-version comparison and rollback operations.

6. The SCADA system label generation method based on structured multi-source information fusion according to claim 5 is characterized in that: The injection test of the generated label to verify the field integrity, logical consistency and system compatibility of the label file includes: Digital simulation verification: Verify and stress test the tag files by using the simulation test function of the SCADA system application platform or by establishing a simulation environment that runs synchronously with the physical SCADA system; Compatibility detection: automatically check the interface matching degree between the tag and the configuration software, historical database and human-machine interface; Intelligent feedback: Dynamically adjust the prompt word weights of the large language model based on verification results, and optimize the label generation strategy through reinforcement learning.

7. A SCADA system label generation system based on structured multi-source information fusion, which is implemented based on the SCADA system label generation method based on structured multi-source information fusion according to claim 1, characterized in that: include: An information modeling unit, used to generate a structured information modeling framework according to the SCADA system architecture and equipment characteristics; Constraint rule configuration unit, supporting the regular expression and storage of multi-domain expert knowledge; Intelligent generation unit, which integrates large language models and domain knowledge graphs for collaborative reasoning and generates SCADA system labels; Verify the optimization unit and build a closed-loop test environment to achieve automatic verification and iterative optimization of tags.

8. The SCADA system label generation system based on structured multi-source information fusion according to claim 7, characterized in that: The intelligent generation unit further comprises: Context-aware module, used to parse device context and automatically complete associated tags; Multi-modal output module supports the generation of label formats that meet the requirements of different SCADA systems; Security enhancement module with built-in data desensitization algorithm to ensure the safe processing of sensitive information.

9. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the method for generating labels for a SCADA system based on structured multi-source information fusion according to claim 1 is implemented.

10. A SCADA system engineering implementation system, integrating the SCADA device of the SCADA system label generation method based on structured multi-source information fusion according to claim 1, and configured with: Engineering configuration interface, supporting multi-user collaborative label design; The label lifecycle management module records the label version iteration and verification history; System compatibility database, storing technical specifications and interface documents of different SCADA systems.

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