Management and control method of power grid digital architecture

Abstract

The invention relates to the technical field of digital architectures, in particular to a management and control method for a power grid digital architecture. The method comprises the following steps: acquiring application architecture current situation data and data architecture current situation data; performing application group planning design based on the current situation data of the application architecture to obtain blueprint data of the application architecture; performing data class, data sub-domain and conceptual entity structured design based on the data architecture current situation data to obtain data architecture blueprint data; acquiring technical architecture current situation data and security architecture current situation data; and constructing a technical component mapping relation based on the technical architecture current situation data, the application architecture blueprint data and the data architecture blueprint data to obtain technical architecture blueprint data. Through matching of architecture design and business requirements and optimization of technology and safety, accurate and efficient management can be realized in digital architecture construction, flexible response of business reform is ensured, and intelligent management and control and sustainable innovation are promoted through continuous optimization.

Description

Technical Field

[0001] This invention relates to the field of digital architecture technology, and in particular to a management and control method for a digital architecture of a power grid. Background Technology

[0002] Currently, power grid companies are gradually advancing their enterprise architecture construction, forming a preliminary blueprint system covering application, data, technology, and security, supporting group-based operations and integrated management. With the rise of emerging demands such as digital grids and the energy internet, power grid companies are gradually introducing standardized, systematic, digitalized, and intelligent management and control methods in their digital architecture construction, emphasizing dynamic updates, asset management, and application value transformation. The power grid digital architecture management and control method is based on comparing the current architecture with the blueprint, forming a closed loop of architecture assets and knowledge through compliance review, dynamic monitoring, application scenario design, and effect optimization. It possesses technical characteristics such as full lifecycle management, visual monitoring, problem early warning, and continuous optimization, and can systematically support the innovation of power grid company management systems and business models.

[0003] However, existing technologies still have shortcomings: First, there is a disconnect between the current state of the architecture and the blueprint evolution, and there is a lack of dynamic consistency maintenance; second, compliance review and monitoring rely heavily on manual intervention, and the level of intelligence is not high; third, the linkage mechanism between business changes and architecture optimization is weak, making it difficult to form a closed-loop management system with rapid response. Summary of the Invention

[0004] Therefore, the present invention needs to provide a management and control method for a digital power grid architecture to solve at least one of the above-mentioned technical problems.

[0005] To achieve the above objectives, a management and control method for a digital power grid architecture includes the following steps: Step S1: Obtain application architecture status data and data architecture status data; based on the application architecture status data, perform application group planning and design to obtain application architecture blueprint data; based on the data architecture status data, perform data class, data subdomain and conceptual entity structured design to obtain data architecture blueprint data. Step S2: Obtain current technical architecture data and current security architecture data; construct the mapping relationship of technical components based on the current technical architecture data, application architecture blueprint data, and data architecture blueprint data to obtain technical architecture blueprint data; design security domains and security services based on the current security architecture data and technical architecture blueprint data to obtain security architecture blueprint data. Step S3: Based on the current status data of application architecture, data architecture, technology architecture, and security architecture, collect and manage architecture assets to obtain architecture asset library data; based on application architecture blueprint data, data architecture blueprint data, technology architecture blueprint data, and security architecture blueprint data, integrate architecture blueprints to obtain architecture blueprint library data. Step S4: Conduct an architecture compliance review based on the architecture asset library data and the architecture blueprint library data, and monitor the architecture compliance review results to obtain architecture management data; Step S5: Design typical business transformation scenarios for the architecture asset library data and architecture management data, and verify them to obtain architecture application scenario data; evaluate the architecture implementation effect for the architecture blueprint library data and architecture management data, and optimize the effect based on the evaluation results to obtain architecture optimization data. Step S6: Construct architecture knowledge outcome data using architecture application scenario data and architecture optimization data.

[0006] This invention, through the implementation of the above steps, enables more precise and efficient management in digital architecture construction. First, acquiring and organizing current application and data architecture data helps to comprehensively understand the current system architecture, providing a scientific basis for subsequent planning and design. The construction of application and data architecture blueprints ensures that the architecture design meets the enterprise's needs and supports the design of subsequent technical and security architectures. Furthermore, the construction of technical and security architecture blueprints not only optimizes the relationships between technical components but also enhances the integration and responsiveness of the security protection system. Second, the collection and management of architecture assets and the integration of architecture blueprints establish a clear architecture asset library and architecture blueprint library, providing systematic support for full lifecycle management. Based on architecture compliance review and monitoring, dynamic tracking and optimization can be achieved, ensuring that the architecture always aligns with business needs and technological development, improving the intelligence level of architecture management. Finally, the combination of typical business transformation scenario design and architecture optimization data enables enterprises to flexibly adjust and optimize their architecture when facing rapidly changing business needs, improving the architecture's responsiveness to business model innovation. The evaluation and continuous optimization of the architecture implementation effect further ensures the efficiency and adaptability of the architecture. Ultimately, by combining the architecture application scenario data and optimization data, knowledge products of the architecture can be formed, which can help enterprises accumulate experience and improve the level of intelligent management and control in the process of digital transformation, thereby achieving sustainable innovation and efficient management.

[0007] Preferably, step S1 includes the following steps: Step S11: Obtain business architecture requirement data; Step S12: Collect and classify existing application systems based on business architecture requirements data to obtain application system hierarchical data; Step S13: Identify the interaction relationships and analyze the problems of the application system hierarchical data to obtain the current status data of the application architecture; Step S14: Based on the current application architecture data, optimize the design of enterprise-level application groups and professional domain application groups to obtain application architecture blueprint data; Step S15: Collect and classify data assets based on business architecture requirements data to obtain data asset hierarchy data; Step S16: Extract conceptual entities and analyze data flow from the data asset-level data to obtain the current data of the data architecture; Step S17: Based on the current data architecture data, perform structured design of data classes, data subdomains and conceptual entities to obtain data architecture blueprint data.

[0008] This invention, through the implementation of the above steps, can more effectively combine business needs with architectural design, promoting the smooth progress of digital transformation. First, acquiring and organizing business architecture requirement data helps clarify the specific needs of the enterprise at the business level, providing a clear direction for subsequent application system and data asset design. Collecting and classifying existing application systems, as well as identifying and analyzing the interaction relationships between application systems, helps to comprehensively understand the dependencies and problems between systems, providing data support for optimized design and ensuring that the application architecture is efficient and scalable while meeting requirements. Second, optimized design based on the current application architecture data enables reasonable planning of enterprise-level and professional domain application groups, improving the flexibility and adaptability of the overall architecture, allowing it to better support the achievement of various business goals. The collection and classification of data assets, as well as the analysis of data flow, ensure that the data architecture, while meeting business needs, can comprehensively manage enterprise data in a structured manner, improving data utilization efficiency and security. Through conceptual entity extraction and structured design of the current data architecture data, the data architecture design can be ensured to have a high degree of clarity and standardization, while optimizing the organization and flow of data, thereby providing stronger data support capabilities for the enterprise and further improving the overall effectiveness of the architecture. These steps together promote the deep integration of business architecture and technical architecture, enabling power grid companies to respond more accurately to business needs, improve business efficiency, and ultimately achieve a more intelligent and digital enterprise management system.

[0009] Preferably, step S2 includes the following steps: Step S21: By surveying technical components and platforms and summarizing the data, obtain the current status data of the technical architecture; by evaluating security measures and security components and integrating the data, obtain the current status data of the security architecture. Step S22: Perform technology domain, primary technology classification, and secondary technology classification planning on the current technology architecture data, application architecture blueprint data, and data architecture blueprint data to obtain technology domain classification planning data; Step S23: Based on the technology domain classification planning data and application architecture blueprint data, design the mapping relationship between application modules and technology components to obtain application module technology mapping data; Step S24: Based on the technology domain classification planning data and data architecture blueprint data, design the mapping between conceptual entities and technical components to obtain conceptual entity technology mapping data; Step S25: Based on the application module technology mapping data and the concept entity technology mapping data, standard technology component relationships are collected to obtain technology component mapping relationship data; Step S26: Standardize the mapping relationship data of technical components and model the interaction relationship of components to obtain technical architecture blueprint data; Step S27: Perform security domain and security classification planning and design on the current security architecture data and technical architecture blueprint data to obtain security domain classification planning data; Step S28: Based on the security domain classification planning data and technical component mapping relationship data, perform the association design of security service and technical component protection requirements to obtain security service design data; Step S29: Perform security component deployment relationship planning and modeling on the security domain classification planning data and security service design data to obtain security architecture blueprint data.

[0010] Through the implementation of the above steps, this invention enables more efficient and intelligent management and optimization of both technical and security architectures. First, by surveying technical components and platforms and evaluating security measures and components, the enterprise gains a comprehensive and clear understanding of the current technical and security status within the existing architecture, laying the foundation for subsequent architecture design and optimization. Integrating current technical and security architecture data helps identify potential technical bottlenecks and security vulnerabilities, thus providing guidance for future planning. Second, based on the classification and planning of the technical architecture, a more systematic and structured arrangement of technical domains, primary technical categories, and secondary technical categories can be achieved, supporting the hierarchical design of the architecture. Designing the mapping relationships between application modules and technical components, as well as the mapping relationships between conceptual entities and technical components, not only optimizes the collaboration between modules in the technical architecture but also ensures the deep integration of the data architecture and the technical architecture, enabling technical support to better serve business needs. Through the standardized design of technical component mapping relationships and the modeling of component interaction relationships, the technical architecture can be standardized and operated efficiently, improving the overall operability and maintainability of the system. Furthermore, the planning of security domains and security classifications within the security architecture, combined with the design of security services within the technical architecture, ensures comprehensive protection and response capabilities in both technology and security. Finally, the planning of security component deployment relationships based on data from the security service design effectively enhances the architecture's security and protection level, ensuring the security of each technical component during architecture implementation. Overall, this series of steps enables power grid companies to maintain the flexibility, security, and efficiency of their architecture in a rapidly changing business and technological environment, driving the intelligent and sustainable optimization of the enterprise architecture.

[0011] Preferably, step S3 includes the following steps: Step S31: Based on the current status data of application architecture, data architecture, technology architecture, and security architecture, classify the current status artifacts of each architecture to obtain aggregated architecture status data; Step S32: Extract each architectural element from the current architecture data and model the structure extracted from each architectural element to obtain the architecture asset library data. Step S33: Standardize and integrate the application architecture blueprint data, data architecture blueprint data, technical architecture blueprint data, and security architecture blueprint data to obtain integrated blueprint data; Step S34: Perform a blueprint content consistency, relevance, and completeness check on the integrated blueprint data, and optimize it based on the check results to obtain optimized blueprint data; Step S35: Based on the blueprint optimization data, perform unified archiving and version management of each architecture blueprint to obtain the architecture blueprint library data.

[0012] This invention, through the implementation of the above steps, enables more precise and efficient architecture management and optimization. First, based on the classification and aggregation of current status data for application architecture, data architecture, technical architecture, and security architecture, key data on the current status of each architecture can be comprehensively collected and organized, laying the foundation for further organization and management of architecture assets. Through the extraction and structural modeling of architecture elements, a detailed architecture asset library can be formed, providing systematic support and resource basis for enterprises in the process of architecture planning and optimization. Second, the standardized integration of various architecture blueprints ensures close connections between different architecture domains, enabling blueprints from different domains to achieve a high degree of coordination in terms of consistency, relevance, and completeness, avoiding possible duplication, omissions, or inconsistencies in architecture design. Through blueprint content verification and optimization, the quality of architecture blueprints can be improved, ensuring that they better support the actual needs and future development of enterprises, further enhancing the stability and flexibility of the architecture. Finally, unified archiving and version management based on blueprint optimization data provides a systematic management method for the long-term maintenance and updating of architecture blueprints, ensuring that architecture documents are always up-to-date and that historical versions can be traced at any time. The implementation of these steps has enabled power grid companies to have more comprehensive resource management capabilities, quality assurance mechanisms, and dynamic optimization capabilities in architecture management, thereby effectively supporting digital transformation and business innovation.

[0013] Preferably, step S4 includes the following steps: Step S41: Based on the data in the architecture asset library and the architecture blueprint library, compare the consistency between the current architecture status and the blueprint to obtain architecture compliance data; Step S42: Identify architectural element deviation issues in the architectural compliance analysis data to obtain architectural deviation identification data; Step S43: Evaluate the scope and severity of the impact of the architectural deviation identification data to obtain architectural deviation evaluation data; Step S44: Formulate an architecture domain monitoring plan based on the architecture deviation assessment data to obtain architecture monitoring strategy data; Step S45: Dynamically update the architecture compliance status of the architecture monitoring strategy data to obtain architecture management data.

[0014] By implementing the above steps, this invention enables more efficient monitoring and optimization in architecture management, ensuring continuous compliance and efficient operation of the architecture. Firstly, by analyzing the current state of the architecture and... Figure 1Consistency comparison clearly identifies the gaps between the actual implementation of the architecture and the design blueprint, thus generating architecture compliance data and providing a foundation for subsequent optimization and adjustments. In-depth analysis of this data effectively identifies deviations in architectural elements, helping enterprises promptly discover potential design flaws or implementation deviations. Furthermore, by assessing the scope and severity of these deviations, enterprises can accurately determine which deviations significantly impact the overall architecture's operation and business objectives, ensuring optimization efforts focus on critical issues. Based on the deviation assessment results, a monitoring plan for the architecture domain can be developed, establishing a targeted and operational monitoring strategy to ensure the real-time and efficient operation of the architecture. Finally, through dynamic updates to the architecture compliance status, enterprises can monitor changes in compliance in real time and take timely adjustments and optimizations, ensuring the architecture always aligns with the enterprise's business needs, technological advancements, and security requirements. Overall, this series of steps not only enhances the intelligence level of architecture management but also strengthens the architecture's dynamic responsiveness, enabling enterprises to flexibly respond to rapidly changing technological environments and business needs, maintaining the stability and optimizability of the architecture.

[0015] Preferably, step S41 includes the following steps: Step S411: Extract each architectural element from the architectural asset library data and identify the relationships between each architectural element to obtain the current status of the architecture extracted data. Step S412: Extract each architectural element from the blueprint data in the architecture blueprint library and identify the structural relationships to obtain the architecture blueprint extraction data; Step S413: Based on the current architecture data and the architecture blueprint data, compare similar architecture elements to obtain the architecture element comparison result data. Step S414: Based on the comparison results of architectural elements, identify the differences to obtain architectural consistency difference data; Step S415: Perform a difference factor impact analysis based on the architecture consistency difference data to obtain architecture consistency analysis data; Step S416: Determine the overall architecture compliance status based on the architecture consistency analysis data to obtain architecture compliance data.

[0016] This invention, by implementing the above steps, effectively enhances the ability to perform architectural consistency analysis and optimization, ensuring efficient integration between architectural design and actual implementation. First, by extracting and analyzing data from the architectural asset library and architectural blueprint library, enterprises can systematically identify architectural elements and their relationships, gaining a comprehensive understanding of the current architecture's composition and structure, providing comprehensive foundational data for subsequent comparison and analysis. The extraction of architectural elements and identification of structural relationships help ensure that various parts of the architectural design can form effective connections and provide a basis for architectural optimization and adjustment. Second, by comparing similar architectural elements, the differences between the current architecture and the blueprint can be deeply explored, clearly revealing deviations and inconsistencies between the actual architecture and the design blueprint. Further identification of discrepancies helps clarify which parts have problems, providing a clear direction for subsequent optimization. Analysis of the impact of discrepancy factors based on consistency discrepancy data can accurately assess the potential impact of these discrepancies on the overall operation of the architecture and business objectives, helping enterprises assess the priority of various discrepancies and ensuring that resources are focused on the most critical issues. Finally, the overall architectural compliance status determination based on consistency analysis data enables enterprises to clearly understand whether the current architecture meets expected requirements and provides necessary data support for subsequent optimization and adjustments. The implementation of this series of steps enables power grid companies to achieve precise, data-driven, and dynamic architecture management, ensuring continuous optimization and efficient operation of the architecture, thereby promoting the company's steady progress in technological development and business model innovation.

[0017] Preferably, step S44 includes the following steps: Step S441: Identify high-risk deviation architectures based on architecture deviation assessment data to obtain high-risk architecture domain data; Step S442: Extract monitoring requirements for each domain based on data from high-risk areas of the architecture, and formulate monitoring indicators based on the extraction results to obtain architecture monitoring requirement data; Step S443: Based on the architecture monitoring requirement data, plan and design the monitoring system to obtain the architecture monitoring solution data; Step S444: Standardize the monitoring elements based on the architecture monitoring solution data to obtain architecture monitoring strategy data.

[0018] This invention, through the implementation of the above steps, effectively identifies and manages high-risk areas in the architecture and establishes a sound monitoring system to ensure the stability and security of the architecture. First, by analyzing architecture deviation assessment data, high-risk areas in the architecture can be accurately identified, helping enterprises focus on key problem areas and avoid potential risks negatively impacting the overall operation of the architecture and the enterprise's business objectives. By extracting monitoring needs from high-risk areas and formulating corresponding monitoring indicators, enterprises can ensure that they can set up effective monitoring measures for these key areas, achieving precise monitoring of the architecture. Second, based on the analysis of architecture monitoring need data, a comprehensive monitoring system can be planned and designed, ensuring that the monitoring scheme covers all key areas of the architecture and has flexibility and scalability. Through standardized processing of monitoring scheme data, monitoring strategies can be unified, making them more consistent and operable during implementation, thereby improving the monitoring efficiency and responsiveness of the architecture. This ensures that enterprises can proactively identify and address potential risks in the architecture, improving the intelligence level of architecture management and risk response capabilities. Through a systematic monitoring scheme, power grid enterprises can achieve continuous monitoring and optimization of the architecture, ensuring that the architecture remains highly efficient and stable and can flexibly respond to rapidly changing technical and business needs.

[0019] Preferably, step S5, which involves designing and validating typical business transformation scenarios based on architecture asset library data and architecture management data, includes: Based on the architecture asset library data and architecture management data, the relationship between existing business processes and technical support is identified to obtain business change requirement data. Based on business transformation demand data, feasible business optimization solutions are designed to obtain business transformation candidate solution data. Conduct feasibility assessments on candidate business transformation solutions to obtain typical business transformation scenario data. Pilot application verification of data from typical business transformation scenarios to obtain business transformation verification data; By integrating and analyzing the verification results of business transformation verification data, we can obtain architecture application scenario data.

[0020] This invention, through the implementation of the above steps, enables more scientific and efficient optimization and verification during business transformation. First, based on the architecture asset library and architecture management data, identifying the relationship between existing business processes and technical support helps clarify the needs of business transformation, ensuring that the transformation addresses actual business pain points and technical bottlenecks, thus improving its relevance and effectiveness. Designing business transformation needs provides enterprises with multiple feasible business optimization solutions, offering diverse options for subsequent decision-making and implementation. Second, the feasibility assessment of candidate business transformation solutions helps enterprises select the most suitable solution from multiple options, ensuring the practical operability and sustainability of the transformation measures. Pilot application verification using typical scenario data allows for testing the effectiveness of business transformation in actual operation, providing reliable data support and practical experience for full-scale rollout. This verification process ensures the actual effectiveness of the transformation plan, allowing for timely identification and adjustment of potential shortcomings, guaranteeing the smooth implementation of the transformation. Finally, through the integrated analysis of verification data, power grid enterprises can obtain accurate architecture application scenario data, providing strong support for future architecture optimization and business model innovation. Overall, these steps enable companies to adopt more scientific, flexible, and effective strategies when facing business changes, ensuring that the implementation of changes can smoothly improve business efficiency, reduce risks, and drive continuous innovation and optimization in the process of digital transformation.

[0021] Preferably, step S5, which involves evaluating the effectiveness of architecture implementation based on architecture blueprint library data and architecture management data, and optimizing the effectiveness based on the evaluation results, includes: Based on the architecture blueprint library data and architecture management data, quantitative analysis of architecture implementation effectiveness indicators is conducted to obtain architecture effectiveness evaluation data. Based on the architecture performance evaluation data, the architecture deviation items and optimization requirements are classified and analyzed to obtain architecture optimization requirement data; Improvement measures are designed based on the architecture optimization requirements data to obtain architecture optimization scheme data; Simulation and verification of the architecture optimization scheme data are performed to obtain architecture optimization verification data; The architecture optimization verification data is archived and organized to obtain architecture optimization data.

[0022] This invention, through the implementation of the above steps, can systematically evaluate and optimize the implementation effect of the architecture, ensuring its continuous optimization and adaptability. First, quantitative analysis based on the architecture blueprint library and architecture management data accurately assesses the implementation effect, providing objective data support for enterprises and helping to identify the advantages and disadvantages of the architecture in actual operation. Analysis of the architecture effect evaluation data effectively categorizes architecture deviations and optimization needs, providing a clear direction for further adjustments and improvements to the architecture. Furthermore, by designing improvement measures based on the architecture optimization need data, enterprises can formulate specific and executable optimization plans, ensuring that the architecture can better adapt to changes in business needs and the challenges of technological development. Simulation and verification of the architecture optimization plan, by simulating the implementation effect in a real environment, can identify potential problems and shortcomings in advance, providing sufficient assurance for the practical application of the optimization plan. Finally, by archiving and organizing the optimization verification data, power grid enterprises can establish comprehensive architecture optimization records, ensuring the transparency and traceability of the optimization process, providing important reference and support for future optimizations. Overall, this series of steps has helped power grid companies achieve continuous monitoring and dynamic optimization of their architecture, ensuring that the architecture can flexibly respond to ever-changing market demands and technological developments, thereby supporting continuous innovation and efficient operation during the digital transformation process.

[0023] Preferably, step S6 includes the following steps: Step S61: Extract typical application practice experience from the architecture application scenario data to obtain architecture application practice summary data; Step S62: Based on the architecture optimization data, perform optimization strategies, effectiveness analysis, and summarize improvement paths to obtain a summary of architecture optimization practices; Step S63: Extract best practice combination data based on the summary data of architecture application practices and the summary data of architecture optimization practices to obtain architecture knowledge element data; Step S64: Construct a knowledge system framework based on the architectural knowledge element data to obtain architectural knowledge system data; Step S65: Based on the architecture knowledge system data, perform various knowledge outcome document processing to obtain architecture knowledge outcome document data; Step S66: Based on the architecture knowledge outcome document data, perform version control and archiving management of the outcome content to obtain architecture knowledge outcome data.

[0024] This invention, through the implementation of the above steps, can efficiently summarize and integrate practical experience in architecture optimization and application, establishing a systematic knowledge management system to provide continuous support for the long-term development of enterprises. First, by extracting typical application experience from architecture application scenario data, enterprises can summarize successful experiences and lessons learned in actual applications, thus providing valuable references for future architecture applications. Based on the optimization strategies and effectiveness analysis of architecture optimization data, the specific effects of architecture optimization can be clarified, and improvement paths can be revealed, providing guidance for further optimization. Next, by combining the summaries of architecture application practices and architecture optimization practices, best practices can be extracted, helping enterprises form a set of validated architecture optimization and application solutions, improving the maturity and practicality of architecture management. Based on these best practices, enterprises can build a systematic architecture knowledge framework, ensuring that architecture-related knowledge is effectively organized and stored, providing continuous support for future architecture design and optimization. Furthermore, through the documentation of architecture knowledge system data, enterprises can standardize knowledge outcomes and form standardized documents, facilitating knowledge dissemination and sharing. Finally, through version control and archiving management of the outcome content, the sustainable updating and inheritance of architecture knowledge within the enterprise can be ensured, preventing knowledge loss or obsolescence. Overall, this series of steps helps enterprises establish an efficient and sustainable architectural knowledge management system, improve the professionalism of architectural design and optimization, and promote the long-term success of enterprises in digital transformation. Attached Figure Description

[0025] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a flowchart illustrating the steps of a control method for a digital power grid architecture according to the present invention. Figure 2 for Figure 1 A detailed flowchart of step S1; Figure 3 for Figure 1 A detailed flowchart of step S2. Detailed Implementation

[0026] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0027] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0028] To achieve the above objectives, please refer to Figures 1 to 3 This invention provides a management and control method for a digital power grid architecture, the method comprising the following steps: Step S1: Obtain application architecture status data and data architecture status data; based on the application architecture status data, perform application group planning and design to obtain application architecture blueprint data; based on the data architecture status data, perform data class, data subdomain and conceptual entity structured design to obtain data architecture blueprint data. Step S2: Obtain current technical architecture data and current security architecture data; construct the mapping relationship of technical components based on the current technical architecture data, application architecture blueprint data, and data architecture blueprint data to obtain technical architecture blueprint data; design security domains and security services based on the current security architecture data and technical architecture blueprint data to obtain security architecture blueprint data. Step S3: Based on the current status data of application architecture, data architecture, technology architecture, and security architecture, collect and manage architecture assets to obtain architecture asset library data; based on application architecture blueprint data, data architecture blueprint data, technology architecture blueprint data, and security architecture blueprint data, integrate architecture blueprints to obtain architecture blueprint library data. Step S4: Conduct an architecture compliance review based on the architecture asset library data and the architecture blueprint library data, and monitor the architecture compliance review results to obtain architecture management data; Step S5: Design typical business transformation scenarios for the architecture asset library data and architecture management data, and verify them to obtain architecture application scenario data; evaluate the architecture implementation effect for the architecture blueprint library data and architecture management data, and optimize the effect based on the evaluation results to obtain architecture optimization data. Step S6: Construct architecture knowledge outcome data using architecture application scenario data and architecture optimization data.

[0029] In this embodiment of the invention, firstly, the enterprise architecture management system connects to the enterprise master data platform, calls the system's built-in interface, and extracts the application system list, application function items, data table list, and data field attribute information in batches using JSON format. This obtains the current application architecture data and the current data architecture data. The extraction frequency is set to be synchronized once daily, and the interface call timeout does not exceed 10 seconds. Secondly, based on the current application architecture data, the standard functions of the enterprise architecture modeling tool ArchiMate are used to automatically group the application systems according to their business domain, application function clustering, and data flow interaction relationships. The number of application groups is set to no more than 50, and the number of systems within each group is controlled between 5 and 15, thus completing the application grouping. The process begins with designing and generating application architecture blueprint data. Next, based on the current data architecture, the data governance platform's data modeling module manually categorizes data tables into data classes and subdomains according to business subject areas. Each subdomain must contain at least 10 data tables. Based on primary and foreign key relationships, structured relationships between conceptual entities and between them (one-to-many and many-to-many) are derived, generating the data architecture blueprint. Subsequently, the system's hardware and software environment information is exported through the technology asset management platform. Server architecture, operating system, middleware version, and deployment topology are extracted according to infrastructure platform classification to obtain the current technical architecture data. Finally, the security management system interface is called to extract network security domain divisions and access... The system compiles a list of control policies and data encryption mechanisms to obtain current security architecture data. Based on the current technical architecture data, application architecture blueprint data, and data architecture blueprint data, a rule engine is used to automatically establish mapping relationships between application functions and technical components according to the deployment mapping from application systems to servers, application call chains, and data transmission protocols. Each application function must map to at least two technical components, generating technical architecture blueprint data. Simultaneously, based on the current security architecture data and technical architecture blueprint data, and in accordance with the Southern Power Grid security standards and specifications, application systems within different security domains are categorized. Each security domain must contain at least three application systems. Access control policies and security services are designed according to the application system grade protection requirements. The data is combined to generate security architecture blueprint data. Then, based on the current status data of application architecture, data architecture, technical architecture, and security architecture, a unified architecture asset management platform is used to archive various architecture elements, attributes, and relationships, establish a unique identification coding system, and form architecture asset library data. Relational database is used for archiving, and the architecture element table and relationship table follow a one-to-many structure. On this basis, based on the application architecture blueprint data, data architecture blueprint data, technical architecture blueprint data, and security architecture blueprint data, according to the TOGAF standard architecture domain classification, the blueprint views are uniformly coded and version controlled to complete the integration of blueprint elements and the removal of redundancy, resulting in architecture blueprint library data.Next, based on the architecture asset library and architecture blueprint library data, a compliance comparison engine is used to review the data according to three rules: element attribute value consistency verification, relational integrity verification, and missing item detection, resulting in architecture compliance data. Based on the compliance analysis results, architecture elements with deviations are collected, the causes and impact ranges of the differences are marked, and architecture deviation identification data and architecture deviation assessment data are generated. Then, based on the architecture deviation assessment data, monitoring priorities are set according to deviation levels L1 (low), L2 (medium), and L3 (high), and monitoring plans are formulated for different architecture areas, such as setting a daily monitoring frequency for high-risk areas and a weekly monitoring frequency for medium-risk areas, generating architecture monitoring strategy data. Based on the monitoring strategy data, an automatic inspection script is used to periodically capture asset changes and dynamically update the architecture compliance status, forming architecture control data. Subsequently... Based on architecture asset library data and architecture control data, business process modeling tools are used to identify business processes associated with deviations. Combined with typical digital transformation scenarios such as process reengineering and system integration, typical business transformation scenarios are designed and deployed and verified in a test environment, generating architecture application scenario data. Simultaneously, based on architecture blueprint library data and architecture control data, the implementation effect of existing blueprints is evaluated using an indicator scoring method. Individual scoring is set from 0 to 5 points, and those with a comprehensive score below 3 points are classified as requiring optimization. Optimization measures are formulated and implemented for verification, generating architecture optimization data. Finally, based on architecture application scenario data and architecture optimization data, practical elements are extracted according to knowledge categories (application, data, technology, and security), a knowledge topic dictionary is established, and the knowledge results are stored in the enterprise knowledge management system in document form, forming structured architecture knowledge outcome data.

[0030] This invention, through the implementation of the above steps, enables more precise and efficient management in digital architecture construction. First, acquiring and organizing current application and data architecture data helps to comprehensively understand the current system architecture, providing a scientific basis for subsequent planning and design. The construction of application and data architecture blueprints ensures that the architecture design meets the enterprise's needs and supports the design of subsequent technical and security architectures. Furthermore, the construction of technical and security architecture blueprints not only optimizes the relationships between technical components but also enhances the integration and responsiveness of the security protection system. Second, the collection and management of architecture assets and the integration of architecture blueprints establish a clear architecture asset library and architecture blueprint library, providing systematic support for full lifecycle management. Based on architecture compliance review and monitoring, dynamic tracking and optimization can be achieved, ensuring that the architecture always aligns with business needs and technological development, improving the intelligence level of architecture management. Finally, the combination of typical business transformation scenario design and architecture optimization data enables enterprises to flexibly adjust and optimize their architecture when facing rapidly changing business needs, improving the architecture's responsiveness to business model innovation. The evaluation and continuous optimization of the architecture implementation effect further ensures the efficiency and adaptability of the architecture. Ultimately, by combining the architecture application scenario data and optimization data, knowledge products of the architecture can be formed, which can help enterprises accumulate experience and improve the level of intelligent management and control in the process of digital transformation, thereby achieving sustainable innovation and efficient management.

[0031] Preferably, step S1 includes the following steps: Step S11: Obtain business architecture requirement data; Step S12: Collect and classify existing application systems based on business architecture requirements data to obtain application system hierarchical data; Step S13: Identify the interaction relationships and analyze the problems of the application system hierarchical data to obtain the current status data of the application architecture; Step S14: Based on the current application architecture data, optimize the design of enterprise-level application groups and professional domain application groups to obtain application architecture blueprint data; Step S15: Collect and classify data assets based on business architecture requirements data to obtain data asset hierarchy data; Step S16: Extract conceptual entities and analyze data flow from the data asset-level data to obtain the current data of the data architecture; Step S17: Based on the current data architecture data, perform structured design of data classes, data subdomains and conceptual entities to obtain data architecture blueprint data.

[0032] In this embodiment of the invention, firstly, the project initiation documents, process optimization lists, and information technology transformation plans are retrieved through the business requirements management system. Business architecture requirement data is obtained using a batch API retrieval method, with the API call frequency set to twice daily and the API response latency controlled within 3 seconds. The retrieved data format is structured JSON. Subsequently, based on the business architecture requirement data, the existing application systems are batch-searched using the application system management platform. They are categorized and organized according to three dimensions: business affiliation, system function, and data dependency. Each category of application systems is limited to at least 10 systems, ultimately yielding application system-level data. Next, a system interaction log analysis tool is used to analyze the data from the application... System interaction relationships were extracted from I-call records, database access records, and message queue transmission logs. Interaction links were selected based on a high-frequency interaction criterion of "call count > 1000 times / month." Through classification and comparison analysis of interface standard consistency and data sharing consistency between systems, redundancy, isolation, and duplication issues were identified, forming application architecture status data. Based on this data, the component diagram design specifications in the Unified Modeling Language (UML) were used to divide enterprise-level application groups on the modeling platform according to business domain. The number of systems under a single application group was limited to 5 to 15. Furthermore, systems within professional domain application groups were further subdivided according to functional granularity. The application architecture blueprint data is generated by grouping applications into groups, with the number of groups in each professional domain limited to no more than 20. In parallel, based on business architecture requirements data, the metadata collection interface of the data asset management platform extracts all databases, table structures, field definitions, and data item standards related to the systems. Data tables are then categorized and organized according to business subject domains, with each data subject domain containing at least five data tables, resulting in hierarchical data for data assets. Next, data flow analysis tools are used to extract conceptual entities based on primary and foreign key relationships between fields. Deduplication and merging are performed according to entity name standardization rules. Simultaneously, by analyzing data interface protocols between systems, data flow diagrams are drawn, and data is filtered. Data exchange paths with a data flow frequency greater than 500 times / day are identified, cross-system data flow relationships are determined, and current data architecture data is formed. Finally, based on the current data architecture data, and in accordance with the data class and subdomain division standards of the Southern Power Grid enterprise architecture methodology, each data class is set to contain at least two data subdomains, and each data subdomain is set to collect at least 10 conceptual entities. Within each entity, a structured relationship based on primary keys, foreign keys, and unique indexes is established. The structured design of data classes, data subdomains, and conceptual entities is completed, and data architecture blueprint data is generated. The entity relationship modeling adopts the third normal form (3NF) design method to ensure that the data redundancy rate is less than 5% and the data consistency reaches 100%.

[0033] This invention, through the implementation of the above steps, can more effectively combine business needs with architectural design, promoting the smooth progress of digital transformation. First, acquiring and organizing business architecture requirement data helps clarify the specific needs of the enterprise at the business level, providing a clear direction for subsequent application system and data asset design. Collecting and classifying existing application systems, as well as identifying and analyzing the interaction relationships between application systems, helps to comprehensively understand the dependencies and problems between systems, providing data support for optimized design and ensuring that the application architecture is efficient and scalable while meeting requirements. Second, optimized design based on the current application architecture data enables reasonable planning of enterprise-level and professional domain application groups, improving the flexibility and adaptability of the overall architecture, allowing it to better support the achievement of various business goals. The collection and classification of data assets, as well as the analysis of data flow, ensure that the data architecture, while meeting business needs, can comprehensively manage enterprise data in a structured manner, improving data utilization efficiency and security. Through conceptual entity extraction and structured design of the current data architecture data, the data architecture design can be ensured to have a high degree of clarity and standardization, while optimizing the organization and flow of data, thereby providing stronger data support capabilities for the enterprise and further improving the overall effectiveness of the architecture. These steps together promote the deep integration of business architecture and technical architecture, enabling power grid companies to respond more accurately to business needs, improve business efficiency, and ultimately achieve a more intelligent and digital enterprise management system.

[0034] Preferably, step S2 includes the following steps: Step S21: By surveying technical components and platforms and summarizing the data, obtain the current status data of the technical architecture; by evaluating security measures and security components and integrating the data, obtain the current status data of the security architecture. Step S22: Perform technology domain, primary technology classification, and secondary technology classification planning on the current technology architecture data, application architecture blueprint data, and data architecture blueprint data to obtain technology domain classification planning data; Step S23: Based on the technology domain classification planning data and application architecture blueprint data, design the mapping relationship between application modules and technology components to obtain application module technology mapping data; Step S24: Based on the technology domain classification planning data and data architecture blueprint data, design the mapping between conceptual entities and technical components to obtain conceptual entity technology mapping data; Step S25: Based on the application module technology mapping data and the concept entity technology mapping data, standard technology component relationships are collected to obtain technology component mapping relationship data; Step S26: Standardize the mapping relationship data of technical components and model the interaction relationship of components to obtain technical architecture blueprint data; Step S27: Perform security domain and security classification planning and design on the current security architecture data and technical architecture blueprint data to obtain security domain classification planning data; Step S28: Based on the security domain classification planning data and technical component mapping relationship data, perform the association design of security service and technical component protection requirements to obtain security service design data; Step S29: Perform security component deployment relationship planning and modeling on the security domain classification planning data and security service design data to obtain security architecture blueprint data.

[0035] In this embodiment of the invention, firstly, the deployment lists of each system server, operating system, middleware, and database management system are retrieved through the operation and maintenance management platform. The retrieval cycle is set to once every 7 days. A standardized XML interface is used for data collection, and firewall rules, intrusion detection policies, and endpoint protection system deployment status are simultaneously exported from the network security management platform. All data items are uniformly encoded and integrated using a field mapping method to obtain the current status data of the technical architecture and the current status data of the security architecture. Secondly, based on the current status data of the technical architecture, the application architecture blueprint data, and the data architecture blueprint data, the enterprise architecture management platform is used to classify infrastructure, application middleware, data storage and processing... The system is divided into four major categories: management, security, and protection. Each technical domain is further divided into at least three primary technical categories, and each primary category is subdivided into at least five secondary technical categories, forming technical domain classification planning data. Subsequently, based on the technical domain classification planning data and application architecture blueprint data, the association between application modules and technical components is established in the application-technology mapping analysis module. Each application module must correspond to at least two technical components, and these components must be assigned to a specific technical domain according to their actual deployment environment, forming application module technology mapping data. Simultaneously, based on the technical domain classification planning data and data architecture blueprint data, a physical association analysis method from conceptual entities to database instances is used. The process involves identifying the physical data storage component corresponding to each conceptual entity and categorizing it into specific technical domains according to data ownership logic, thus forming conceptual entity technology mapping data. Next, based on the application module technology mapping data and the conceptual entity technology mapping data, a hash comparison mechanism is used to verify the uniqueness of technical components, remove redundant component references, and uniformly collect technical component call relationships to obtain technical component mapping relationship data. Then, using the component layering specification in the architecture design management platform, the technical component mapping relationship data is used to establish the interaction relationships of the infrastructure layer, platform service layer, and application support layer. The component call order and data transmission path are defined, component interaction diagrams are drawn, and finally, technical architecture blueprint data is generated. In parallel, based on the current security architecture data and technical architecture blueprint data, and in accordance with the Southern Power Grid's security zoning and grading requirements, security domains are divided according to business importance and data sensitivity. Each security domain must contain no fewer than three systems. Security categories are also defined, refined to four types: access control, data encryption, identity authentication, and log auditing, resulting in security domain classification planning data. Then, based on the security domain classification planning data and technical component mapping relationship data, a security service requirement analysis tool is used to associate security control measures with each technical component according to the application system's protection level. Each key component is associated with at least two types of security services, such as a protection gateway and an encrypted communication module, forming security service design data.Finally, based on security domain classification planning data and security service design data, security architecture design tools were applied to deploy corresponding security components in different security domains. Each security domain was required to deploy four basic security components: endpoint protection, intrusion detection, log auditing, and data encryption. The component deployment topology adopted a star topology design to ensure that internal data exchange paths did not exceed three hops, ultimately generating security architecture blueprint data.

[0036] Through the implementation of the above steps, this invention enables more efficient and intelligent management and optimization of both technical and security architectures. First, by surveying technical components and platforms and evaluating security measures and components, the enterprise gains a comprehensive and clear understanding of the current technical and security status within the existing architecture, laying the foundation for subsequent architecture design and optimization. Integrating current technical and security architecture data helps identify potential technical bottlenecks and security vulnerabilities, thus providing guidance for future planning. Second, based on the classification and planning of the technical architecture, a more systematic and structured arrangement of technical domains, primary technical categories, and secondary technical categories can be achieved, supporting the hierarchical design of the architecture. Designing the mapping relationships between application modules and technical components, as well as the mapping relationships between conceptual entities and technical components, not only optimizes the collaboration between modules in the technical architecture but also ensures the deep integration of the data architecture and the technical architecture, enabling technical support to better serve business needs. Through the standardized design of technical component mapping relationships and the modeling of component interaction relationships, the technical architecture can be standardized and operated efficiently, improving the overall operability and maintainability of the system. Furthermore, the planning of security domains and security classifications within the security architecture, combined with the design of security services within the technical architecture, ensures comprehensive protection and response capabilities in both technology and security. Finally, the planning of security component deployment relationships based on data from the security service design effectively enhances the architecture's security and protection level, ensuring the security of each technical component during architecture implementation. Overall, this series of steps enables power grid companies to maintain the flexibility, security, and efficiency of their architecture in a rapidly changing business and technological environment, driving the intelligent and sustainable optimization of the enterprise architecture.

[0037] Preferably, step S3 includes the following steps: Step S31: Based on the current status data of application architecture, data architecture, technology architecture, and security architecture, classify the current status artifacts of each architecture to obtain aggregated architecture status data; Step S32: Extract each architectural element from the current architecture data and model the structure extracted from each architectural element to obtain the architecture asset library data. Step S33: Standardize and integrate the application architecture blueprint data, data architecture blueprint data, technical architecture blueprint data, and security architecture blueprint data to obtain integrated blueprint data; Step S34: Perform a blueprint content consistency, relevance, and completeness check on the integrated blueprint data, and optimize it based on the check results to obtain optimized blueprint data; Step S35: Based on the blueprint optimization data, perform unified archiving and version management of each architecture blueprint to obtain the architecture blueprint library data.

[0038] In this embodiment of the invention, firstly, application architecture status data, data architecture status data, technical architecture status data, and security architecture status data are imported through an enterprise-level architecture management platform. The data is initially categorized using architecture domain field tags. Application architecture status data is categorized by system modules, functional items, and process mappings; data architecture status data is categorized by data tables, fields, and primary key associations; technical architecture status data is categorized by server, platform component, and middleware version; and security architecture status data is categorized by security domain, protection measures, and security devices. SQL aggregation scripts are used during the classification process, with execution time controlled within 30 seconds, ultimately yielding aggregated architecture status data. Subsequently... Based on the current architecture data, relation extraction tools were used to extract various architecture elements according to the standard structure of "unique element identifier - set of element attributes - element association relationship". At least 1000 elements of each type were extracted. After extraction, data structure modeling was performed using an ER diagram. Model fields adhered to three basic constraints: mandatory uniqueness, non-nullability, and primary / foreign key integrity. This resulted in a complete architecture asset table and relation table, ultimately generating the architecture asset database. The asset database data was stored in a relational database using UTF-8 encoding, with a fixed table structure and no dynamic field expansion allowed. Next, the application architecture blueprint data, data architecture blueprint data, and technical architecture were analyzed. Blueprint data and security architecture blueprint data are unified and merged using a blueprint view merging tool. Following a unified naming convention (each view identifier consists of a domain name abbreviation and object number), naming conflicts are removed, and data field formats are standardized to complete the blueprint standardization and integration process, resulting in integrated blueprint data. Subsequently, consistency, correlation, and integrity checks are performed on the integrated blueprint data. The consistency check uses a hash verification algorithm to compare the attribute values ​​of elements with the same name, requiring a 100% consistency rate. The correlation check uses a depth-first traversal algorithm to check the connectivity of each element with upstream and downstream related paths, controlling the broken link rate to within 1%. The integrity check uses field coverage analysis... The analysis method ensures that the missing field rate does not exceed 2%. Based on the verification results, elements are added, redundancy is removed, and fields are standardized and adjusted to generate blueprint optimization data. Finally, based on the blueprint optimization data, version control management tools are used to archive each architecture blueprint in a unified manner according to the two-level version coding rules of timestamp and architecture domain. The archiving cycle is set to once per quarter. The blueprint data is saved in XML standard file format, and each archived document must include a metadata description list and version change record to complete unified archiving and version management, ultimately forming the architecture blueprint library data. All archived data is stored on a centralized architecture asset management platform, and a dual-machine hot standby architecture is enabled to ensure data security.

[0039] This invention, through the implementation of the above steps, enables more precise and efficient architecture management and optimization. First, based on the classification and aggregation of current status data for application architecture, data architecture, technical architecture, and security architecture, key data on the current status of each architecture can be comprehensively collected and organized, laying the foundation for further organization and management of architecture assets. Through the extraction and structural modeling of architecture elements, a detailed architecture asset library can be formed, providing systematic support and resource basis for enterprises in the process of architecture planning and optimization. Second, the standardized integration of various architecture blueprints ensures close connections between different architecture domains, enabling blueprints from different domains to achieve a high degree of coordination in terms of consistency, relevance, and completeness, avoiding possible duplication, omissions, or inconsistencies in architecture design. Through blueprint content verification and optimization, the quality of architecture blueprints can be improved, ensuring that they better support the actual needs and future development of enterprises, further enhancing the stability and flexibility of the architecture. Finally, unified archiving and version management based on blueprint optimization data provides a systematic management method for the long-term maintenance and updating of architecture blueprints, ensuring that architecture documents are always up-to-date and that historical versions can be traced at any time. The implementation of these steps has enabled power grid companies to have more comprehensive resource management capabilities, quality assurance mechanisms, and dynamic optimization capabilities in architecture management, thereby effectively supporting digital transformation and business innovation.

[0040] Preferably, step S4 includes the following steps: Step S41: Based on the data in the architecture asset library and the architecture blueprint library, compare the consistency between the current architecture status and the blueprint to obtain architecture compliance data; Step S42: Identify architectural element deviation issues in the architectural compliance analysis data to obtain architectural deviation identification data; Step S43: Evaluate the scope and severity of the impact of the architectural deviation identification data to obtain architectural deviation evaluation data; Step S44: Formulate an architecture domain monitoring plan based on the architecture deviation assessment data to obtain architecture monitoring strategy data; Step S45: Dynamically update the architecture compliance status of the architecture monitoring strategy data to obtain architecture management data.

[0041] In this embodiment of the invention, firstly, based on the architecture asset library data and the architecture blueprint library data, a consistency comparison tool is used to compare and verify the unique identifier, core attribute fields, and element relationships of each architecture element using a hash verification method. The hash value calculation uses the SHA-256 algorithm, and the difference threshold is set to not exceed 0.01%. The comparison scope covers four major areas: application, data, technology, and security. After comparison, a consistency comparison report is generated, forming architecture compliance data. Subsequently, based on the architecture compliance data, a difference extraction engine is used to extract and identify all cases with inconsistent attribute values, missing relationships, and redundant elements. Each deviation item records the element number, deviation description, and deviation type (addition, modification, and deletion), and is summarized according to architecture domain to obtain architecture deviation identification data. Next, based on the architecture deviation identification data, an impact analysis tool is used to quantitatively assess the impact scope according to the number of business processes associated with the deviation, the number of affected systems, and the number of associated data tables. The severity of the deviation is divided into high ( Architecture deviation assessment data is generated after deviations are classified into three levels: high (affecting 5 or more business processes), medium (affecting 2-4 business processes), and low (affecting 1 or no business process). Based on this assessment data, different monitoring frequencies are set in the monitoring strategy formulation system according to the risk levels: high-risk areas are monitored daily, medium-risk areas weekly, and low-risk areas monthly. Monitoring content covers four types of events: element additions, deletions, attribute changes, and relationship breaks. The monitoring method uniformly adopts an automatic detection mechanism based on a rule engine, forming architecture monitoring strategy data. Finally, based on the architecture monitoring strategy data, the architecture compliance dynamic monitoring platform uses a scheduled task mechanism (with Cron expressions specifying the execution time) to update the compliance status of each architecture element in real time. Each monitoring update synchronously generates a status change log and writes it to a unified log management system. The log format uses the standard JSON structure, and all update results form the latest architecture control data.

[0042] By implementing the above steps, this invention enables more efficient monitoring and optimization in architecture management, ensuring continuous compliance and efficient operation of the architecture. Firstly, by analyzing the current state of the architecture and... Figure 1Consistency comparison clearly identifies the gaps between the actual implementation of the architecture and the design blueprint, thus generating architecture compliance data and providing a foundation for subsequent optimization and adjustments. In-depth analysis of this data effectively identifies deviations in architectural elements, helping enterprises promptly discover potential design flaws or implementation deviations. Furthermore, by assessing the scope and severity of these deviations, enterprises can accurately determine which deviations significantly impact the overall architecture's operation and business objectives, ensuring optimization efforts focus on critical issues. Based on the deviation assessment results, a monitoring plan for the architecture domain can be developed, establishing a targeted and operational monitoring strategy to ensure the real-time and efficient operation of the architecture. Finally, through dynamic updates to the architecture compliance status, enterprises can monitor changes in compliance in real time and take timely adjustments and optimizations, ensuring the architecture always aligns with the enterprise's business needs, technological advancements, and security requirements. Overall, this series of steps not only enhances the intelligence level of architecture management but also strengthens the architecture's dynamic responsiveness, enabling enterprises to flexibly respond to rapidly changing technological environments and business needs, maintaining the stability and optimizability of the architecture.

[0043] Preferably, step S41 includes the following steps: Step S411: Extract each architectural element from the architectural asset library data and identify the relationships between each architectural element to obtain the current status of the architecture extracted data. Step S412: Extract each architectural element from the blueprint data in the architecture blueprint library and identify the structural relationships to obtain the architecture blueprint extraction data; Step S413: Based on the current architecture data and the architecture blueprint data, compare similar architecture elements to obtain the architecture element comparison result data. Step S414: Based on the comparison results of architectural elements, identify the differences to obtain architectural consistency difference data; Step S415: Perform a difference factor impact analysis based on the architecture consistency difference data to obtain architecture consistency analysis data; Step S416: Determine the overall architecture compliance status based on the architecture consistency analysis data to obtain architecture compliance data.

[0044] In this embodiment of the invention, firstly, the architecture asset database data is uniformly scheduled and processed through an enterprise architecture asset management platform. An element recognition algorithm is used to extract each architecture element according to four architecture domains: application, data, technology, and security. Extraction criteria include unique element codes, complete attribute records, and historical change records. Regular expressions are used to validate element naming conventions during the extraction process, and the element extraction accuracy is required to reach 100%. After extraction, an architecture element association table is constructed based on element association fields, such as application module calls, data entity associations, technical component deployments, and network security attribution, ultimately forming the current architecture status extraction data. Secondly, based on the architecture blueprint database data... In the unified blueprint management platform, a standardized extraction interface is called to extract various architectural elements from the blueprint view. Extraction fields include element identifier, architectural domain affiliation, blueprint version number, and view type. During extraction, a field deduplication mechanism is applied to ensure no duplicate element records. After extraction, the structural relationships of each architectural element are identified according to their hierarchical relationship in the blueprint, such as parent-child relationships, dependency calls, and business support relationships, outputting a standardized structural relationship table to obtain the extracted architectural blueprint data. Next, based on the extracted data from the current architectural status and the extracted architectural blueprint data, an element comparison engine performs one-to-one matching based on the element's unique identifier and key attribute values. The comparison method adopts... Using a string similarity algorithm (Levenshtein distance), a threshold of 90% similarity is set to determine inconsistency. The comparison results are categorized into three states: exact match, attribute deviation, and missing / added elements, forming architectural element comparison result data. Subsequently, based on the architectural element comparison result data, a difference identification module is applied to extract the difference fields for attribute deviation elements, classify missing elements as blueprint omissions or asset additions, and record the source and scope of impact for added elements, outputting a structured difference list to obtain architectural consistency difference data. Then, based on the architectural consistency difference data, a difference factor analysis tool is applied to analyze the number of business systems affected by the differences. The impact of the difference factors is quantified by three indicators: the number of impact data tables, the number of related technical components, etc. A weighted scoring method is used to calculate the comprehensive impact score, with a score range of 0-100 points. The impact level is divided into three levels: high (>70), medium (40-70), and low (<40), forming a difference impact level assessment table and generating architecture consistency analysis data. Finally, based on the architecture consistency analysis data, the architecture compliance status judgment module is used to comprehensively score the architecture compliance judgment results according to the proportion of high, medium, and low impact levels, combined with the overall architecture element consistency rate (≥95% is judged as compliant, <95% is judged as non-compliant), and output the final compliance judgment results, forming architecture compliance data.

[0045] This invention, by implementing the above steps, effectively enhances the ability to perform architectural consistency analysis and optimization, ensuring efficient integration between architectural design and actual implementation. First, by extracting and analyzing data from the architectural asset library and architectural blueprint library, enterprises can systematically identify architectural elements and their relationships, gaining a comprehensive understanding of the current architecture's composition and structure, providing comprehensive foundational data for subsequent comparison and analysis. The extraction of architectural elements and identification of structural relationships help ensure that various parts of the architectural design can form effective connections and provide a basis for architectural optimization and adjustment. Second, by comparing similar architectural elements, the differences between the current architecture and the blueprint can be deeply explored, clearly revealing deviations and inconsistencies between the actual architecture and the design blueprint. Further identification of discrepancies helps clarify which parts have problems, providing a clear direction for subsequent optimization. Analysis of the impact of discrepancy factors based on consistency discrepancy data can accurately assess the potential impact of these discrepancies on the overall operation of the architecture and business objectives, helping enterprises assess the priority of various discrepancies and ensuring that resources are focused on the most critical issues. Finally, the overall architectural compliance status determination based on consistency analysis data enables enterprises to clearly understand whether the current architecture meets expected requirements and provides necessary data support for subsequent optimization and adjustments. The implementation of this series of steps enables power grid companies to achieve precise, data-driven, and dynamic architecture management, ensuring continuous optimization and efficient operation of the architecture, thereby promoting the company's steady progress in technological development and business model innovation.

[0046] Preferably, step S44 includes the following steps: Step S441: Identify high-risk deviation architectures based on architecture deviation assessment data to obtain high-risk architecture domain data; Step S442: Extract monitoring requirements for each domain based on data from high-risk areas of the architecture, and formulate monitoring indicators based on the extraction results to obtain architecture monitoring requirement data; Step S443: Based on the architecture monitoring requirement data, plan and design the monitoring system to obtain the architecture monitoring solution data; Step S444: Standardize the monitoring elements based on the architecture monitoring solution data to obtain architecture monitoring strategy data.

[0047] In this embodiment of the invention, firstly, based on architecture deviation assessment data, a risk identification and analysis tool is used to screen high-risk deviations according to three dimensions: the scope of deviation impact, severity level, and business dependency. The screening criteria are set as follows: the number of affected business processes ≥ 5 and the deviation level is high, and the number of dependent systems ≥ 3. The screened deviations are included in the high-risk deviation architecture set, and high-risk architecture domain data is output. Subsequently, based on the high-risk architecture domain data, a monitoring requirement extraction tool is used to further extract the deviation types, change frequencies, and data consistency requirements in each domain. Deviation types include three categories: structural deviations, data item deviations, and call chain breaks. Change frequencies are defined as ≥ 2 changes per month as high frequency, and data consistency requirements are set as field consistency rate ≥ 98%. After extraction, monitoring indicators are formulated based on the extraction results. The monitoring indicators include element existence checks, attribute change detection, and association integrity verification. Each indicator uses a binary judgment. The rules (pass / fail) generate architecture monitoring requirement data. Next, based on this data, the monitoring system planning and design module refines the design in three aspects: element granularity, monitoring cycle, and alarm threshold. Monitoring granularity is based on a single architecture element; the monitoring cycle is set to once daily for high-risk areas; and the alarm threshold is set to trigger an alert after three consecutive failed monitoring attempts. This planning generates the architecture monitoring scheme data. Finally, based on the architecture monitoring scheme data, the standardization processing module uniformly encodes each monitoring element. Monitoring indicators are named using the "domain abbreviation-monitoring item number" standard, and each monitoring item is appended with a unique identifier (UUID format). Monitoring records use a unified log structure (fields include monitoring time, element identifier, detection result, and alarm status), and the logs are saved in JSON format, with an archiving cycle of once every 7 days. After standardization, the architecture monitoring strategy data is output.

[0048] This invention, through the implementation of the above steps, effectively identifies and manages high-risk areas in the architecture and establishes a sound monitoring system to ensure the stability and security of the architecture. First, by analyzing architecture deviation assessment data, high-risk areas in the architecture can be accurately identified, helping enterprises focus on key problem areas and avoid potential risks negatively impacting the overall operation of the architecture and the enterprise's business objectives. By extracting monitoring needs from high-risk areas and formulating corresponding monitoring indicators, enterprises can ensure that they can set up effective monitoring measures for these key areas, achieving precise monitoring of the architecture. Second, based on the analysis of architecture monitoring need data, a comprehensive monitoring system can be planned and designed, ensuring that the monitoring scheme covers all key areas of the architecture and has flexibility and scalability. Through standardized processing of monitoring scheme data, monitoring strategies can be unified, making them more consistent and operable during implementation, thereby improving the monitoring efficiency and responsiveness of the architecture. This ensures that enterprises can proactively identify and address potential risks in the architecture, improving the intelligence level of architecture management and risk response capabilities. Through a systematic monitoring scheme, power grid enterprises can achieve continuous monitoring and optimization of the architecture, ensuring that the architecture remains highly efficient and stable and can flexibly respond to rapidly changing technical and business needs.

[0049] Preferably, step S5, which involves designing and validating typical business transformation scenarios based on architecture asset library data and architecture management data, includes: Based on the architecture asset library data and architecture management data, the relationship between existing business processes and technical support is identified to obtain business change requirement data. Based on business transformation demand data, feasible business optimization solutions are designed to obtain business transformation candidate solution data. Conduct feasibility assessments on candidate business transformation solutions to obtain typical business transformation scenario data. Pilot application verification of data from typical business transformation scenarios to obtain business transformation verification data; By integrating and analyzing the verification results of business transformation verification data, we can obtain architecture application scenario data.

[0050] In this embodiment of the invention, firstly, based on architecture asset library data and architecture management data, a process-system mapping analysis tool is used to cross-reference business process numbers with application system support relationships, extracting process nodes, system functions, and data interaction points. The process node coverage rate is set to 100%, and based on the system support process step integrity detection rules (integrity standards are no omissions and no redundant support), support breakpoints, technical bottlenecks, and inefficient interactions are identified, forming a business process technical support mapping table and outputting business transformation requirement data. Subsequently, based on the business transformation requirement data, a business optimization solution generation engine is used to formulate optimization solutions around three directions: breaking down support breakpoints, simplifying processes, and integrating systems. Each optimization solution is required to include a clear objective description, implementation path, and resource requirements. The optimization solution design incorporates process duration compression rate (compression rate requirement ≥20%) and system integration rate (integration rate requirement ≥30%) as quantitative standards, forming business transformation candidate solution data. Next, the business transformation candidate solution data is applied to a solution evaluation engine, using a feasibility scoring model for comprehensive scoring. The scoring items include implementation cost. The evaluation process involves three key aspects: expected results, implementation cycle, and risk level. Individual scores range from 0 to 10 points, with the overall score calculated as a weighted average. The weighting is 40% for expected results, 30% for implementation costs, 20% for implementation cycle, and 10% for risk level. Solutions with an overall score ≥7 are included in the typical business transformation scenario data. Next, based on this data, representative application systems are selected for pilot deployment. Pilot deployments must cover at least two different business domains, and the pilot systems must operate in a defined environment for at least 30 days. Business process execution monitoring tools are used to record the execution time, error rate, and number of user operations for each process node in real time. Pilot operation log data is extracted to obtain business transformation verification data. Finally, based on this verification data, the verification results integration and analysis module statistically analyzes changes in business indicators during the pilot period. Indicators include average process execution time (required to decrease by ≥15%), system response time (required to decrease by ≥10%), and error rate changes (required to decrease by ≥5%). After analysis, typical scenarios that meet the optimization criteria are archived, outputting structured architecture application scenario data.

[0051] This invention, through the implementation of the above steps, enables more scientific and efficient optimization and verification during business transformation. First, based on the architecture asset library and architecture management data, identifying the relationship between existing business processes and technical support helps clarify the needs of business transformation, ensuring that the transformation addresses actual business pain points and technical bottlenecks, thus improving its relevance and effectiveness. Designing business transformation needs provides enterprises with multiple feasible business optimization solutions, offering diverse options for subsequent decision-making and implementation. Second, the feasibility assessment of candidate business transformation solutions helps enterprises select the most suitable solution from multiple options, ensuring the practical operability and sustainability of the transformation measures. Pilot application verification using typical scenario data allows for testing the effectiveness of business transformation in actual operation, providing reliable data support and practical experience for full-scale rollout. This verification process ensures the actual effectiveness of the transformation plan, allowing for timely identification and adjustment of potential shortcomings, guaranteeing the smooth implementation of the transformation. Finally, through the integrated analysis of verification data, power grid enterprises can obtain accurate architecture application scenario data, providing strong support for future architecture optimization and business model innovation. Overall, these steps enable companies to adopt more scientific, flexible, and effective strategies when facing business changes, ensuring that the implementation of changes can smoothly improve business efficiency, reduce risks, and drive continuous innovation and optimization in the process of digital transformation.

[0052] Preferably, step S5, which involves evaluating the effectiveness of architecture implementation based on architecture blueprint library data and architecture management data, and optimizing the effectiveness based on the evaluation results, includes: Based on the architecture blueprint library data and architecture management data, quantitative analysis of architecture implementation effectiveness indicators is conducted to obtain architecture effectiveness evaluation data. Based on the architecture performance evaluation data, the architecture deviation items and optimization requirements are classified and analyzed to obtain architecture optimization requirement data; Improvement measures are designed based on the architecture optimization requirements data to obtain architecture optimization scheme data; Simulation and verification of the architecture optimization scheme data are performed to obtain architecture optimization verification data; The architecture optimization verification data is archived and organized to obtain architecture optimization data.

[0053] In this embodiment of the invention, firstly, based on the architecture blueprint library data and architecture management data, the effect evaluation and analysis engine is invoked to quantitatively analyze the architecture implementation effect indicators according to three dimensions: blueprint implementation rate, architecture deviation rate, and problem response timeliness. The blueprint implementation rate is calculated as "number of completed blueprint elements / total number of elements in the blueprint library × 100%". The deviation rate is set with a baseline threshold of 5% for deviation elements, which is considered an over-standard deviation. The response timeliness requirement is to complete preliminary processing within 48 hours after the problem occurs. After extracting the scores of each indicator, unified architecture effect evaluation data is generated. Subsequently, based on the architecture effect evaluation data, the deviation items are categorized into four aspects: business impact, data consistency, technical performance, and security compliance through the deviation classification module. Each category is further subdivided according to the severity of impact: high severity is defined as affecting two or more critical business systems, medium severity is defined as affecting one business system, and low severity is defined as affecting a local module. At the same time, the corresponding optimization requirements are identified and summarized to generate architecture optimization requirement data. Next, based on the architecture optimization requirement data, within the improvement measure formulation platform, specific improvement measures are designed for each optimization requirement, focusing on the deviation category and severity. Each improvement measure is required to include the target optimization item, execution path, time node, and resource configuration, and each improvement measure is expected to reduce the deviation rate by at least 1%, forming architecture optimization scheme data. Then, based on the architecture optimization scheme data, a simulation verification engine is used to conduct simulation deployment in an isolated environment. The simulation test cycle is set to 7 consecutive days, and monitoring is performed every 24 hours during the simulation period, focusing on monitoring system stability, data flow accuracy (accuracy rate required ≥99%), and response latency changes (required to decrease by more than 5%). The simulation results are automatically collected and output as architecture optimization verification data. Finally, based on the architecture optimization verification data, it is uniformly archived and organized according to time order and the domain to which the optimization items belong. The archived files are stored in CSV format, with fields including optimization item number, optimization content, verification result, and application suggestions. The archiving cycle is once a month, ultimately generating architecture optimization data.

[0054] This invention, through the implementation of the above steps, can systematically evaluate and optimize the implementation effect of the architecture, ensuring its continuous optimization and adaptability. First, quantitative analysis based on the architecture blueprint library and architecture management data accurately assesses the implementation effect, providing objective data support for enterprises and helping to identify the advantages and disadvantages of the architecture in actual operation. Analysis of the architecture effect evaluation data effectively categorizes architecture deviations and optimization needs, providing a clear direction for further adjustments and improvements to the architecture. Furthermore, by designing improvement measures based on the architecture optimization need data, enterprises can formulate specific and executable optimization plans, ensuring that the architecture can better adapt to changes in business needs and the challenges of technological development. Simulation and verification of the architecture optimization plan, by simulating the implementation effect in a real environment, can identify potential problems and shortcomings in advance, providing sufficient assurance for the practical application of the optimization plan. Finally, by archiving and organizing the optimization verification data, power grid enterprises can establish comprehensive architecture optimization records, ensuring the transparency and traceability of the optimization process, providing important reference and support for future optimizations. Overall, this series of steps has helped power grid companies achieve continuous monitoring and dynamic optimization of their architecture, ensuring that the architecture can flexibly respond to ever-changing market demands and technological developments, thereby supporting continuous innovation and efficient operation during the digital transformation process.

[0055] Preferably, step S6 includes the following steps: Step S61: Extract typical application practice experience from the architecture application scenario data to obtain architecture application practice summary data; Step S62: Based on the architecture optimization data, perform optimization strategies, effectiveness analysis, and summarize improvement paths to obtain a summary of architecture optimization practices; Step S63: Extract best practice combination data based on the summary data of architecture application practices and the summary data of architecture optimization practices to obtain architecture knowledge element data; Step S64: Construct a knowledge system framework based on the architectural knowledge element data to obtain architectural knowledge system data; Step S65: Based on the architecture knowledge system data, perform various knowledge outcome document processing to obtain architecture knowledge outcome document data; Step S66: Based on the architecture knowledge outcome document data, perform version control and archiving management of the outcome content to obtain architecture knowledge outcome data.

[0056] In this embodiment of the invention, firstly, through an application scenario practice analysis platform, typical application practice experiences are extracted from the architecture application scenario data. The extraction rules require each application scenario to cover at least two business domains. The extracted content includes application objectives, supporting systems, data interaction methods, and effectiveness indicators. Effectiveness indicators include process reduction rate (≥20%), system integration rate (≥30%), and data consistency improvement rate (≥95%). After extraction, a summary of architecture application practices is generated. Subsequently, based on the architecture optimization data, optimization effectiveness analysis tools are used to summarize the optimization objects, optimization strategies, performance, and subsequent improvement directions. Optimization objects are categorized to cover four architecture domains: application, data, technology, and security. Optimization effectiveness is included based on an improvement rate of ≥10%. The summary results are output as architecture optimization practice summary data. Next, based on the architecture application practice summary data and the architecture optimization practice summary data, a best practice extraction engine is used to combine and extract application practices and optimization practices through a strategy combination method. The combination rules require that a best practice must include at least one application experience and one optimization measure, and both must point to the same business theme domain. After combination, the summary data is generated. A unique identifier is used to generate architecture knowledge element data. Then, based on this data, a knowledge framework is established in the knowledge system construction module according to four main categories: "Application Support - Data Governance - Technical Support - Security Protection." Each main category must contain at least five knowledge element nodes, organized in a parent-child hierarchical relationship. Node identifiers use UUID format, resulting in architecture knowledge system data. Next, based on this data, a document generation engine is used to document the knowledge deliverables. The document content structure is unified into five parts: "Background - Practical Content - Effectiveness Indicators - Application Conditions - Precautions." Each document is limited to 5000 words, in PDF format, and includes a metadata description file, outputting architecture knowledge deliverable document data. Finally, based on this data, a version control platform is used for version management and archiving. Version numbers use a combination of timestamp and incremental numbering, such as V20250427_001. The archiving cycle is set to quarterly. After archiving, the data is stored in the enterprise knowledge management system and saved using a dual-node distributed storage method, ultimately generating structured architecture knowledge deliverable data.

[0057] This invention, through the implementation of the above steps, can efficiently summarize and integrate practical experience in architecture optimization and application, establishing a systematic knowledge management system to provide continuous support for the long-term development of enterprises. First, by extracting typical application experience from architecture application scenario data, enterprises can summarize successful experiences and lessons learned in actual applications, thus providing valuable references for future architecture applications. Based on the optimization strategies and effectiveness analysis of architecture optimization data, the specific effects of architecture optimization can be clarified, and improvement paths can be revealed, providing guidance for further optimization. Next, by combining the summaries of architecture application practices and architecture optimization practices, best practices can be extracted, helping enterprises form a set of validated architecture optimization and application solutions, improving the maturity and practicality of architecture management. Based on these best practices, enterprises can build a systematic architecture knowledge framework, ensuring that architecture-related knowledge is effectively organized and stored, providing continuous support for future architecture design and optimization. Furthermore, through the documentation of architecture knowledge system data, enterprises can standardize knowledge outcomes and form standardized documents, facilitating knowledge dissemination and sharing. Finally, through version control and archiving management of the outcome content, the sustainable updating and inheritance of architecture knowledge within the enterprise can be ensured, preventing knowledge loss or obsolescence. Overall, this series of steps helps enterprises establish an efficient and sustainable architectural knowledge management system, improve the professionalism of architectural design and optimization, and promote the long-term success of enterprises in digital transformation.

[0058] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is not limited by the foregoing description. Thus, all changes falling within the meaning and scope of the equivalents of the application are intended to be included within the scope of the invention.

[0059] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for managing and controlling a digitalized power grid architecture, characterized in that, Comprising the following steps: Step S1: Obtain application architecture status data and data architecture status data; Based on the application architecture status data, the application group planning design is carried out, and the application architecture blueprint data is obtained; based on the data architecture status data, the data class, data sub-domain and concept entity structured design is carried out, and the data architecture blueprint data is obtained; Step S2: Obtain technology architecture status data and security architecture status data; based on the technology architecture status data, the application architecture blueprint data and the data architecture blueprint data, the technology component mapping relationship is constructed, and the technology architecture blueprint data is obtained; based on the security architecture status data and the technology architecture blueprint data, the security domain and security service design is carried out, and the security architecture blueprint data is obtained; Step S3: Based on the application architecture status data, the data architecture status data, the technology architecture status data and the security architecture status data, the architecture asset collection management is carried out, and the architecture asset library data is obtained; based on the application architecture blueprint data, the data architecture blueprint data, the technology architecture blueprint data and the security architecture blueprint data, the architecture blueprint integration is carried out, and the architecture blueprint library data is obtained; Step S4: Based on the architecture asset library data and the architecture blueprint library data, the architecture compliance review is carried out, and based on the architecture compliance review result, the monitoring is carried out, and the architecture management and control data is obtained; Step S5: The business change typical scene design is carried out on the architecture asset library data and the architecture management and control data, and the verification is carried out, and the architecture application scene data is obtained; the architecture implementation effect evaluation is carried out on the architecture blueprint library data and the architecture management and control data, and based on the evaluation result, the effect optimization is carried out, and the architecture optimization data is obtained; Step S6: The architecture knowledge achievement data is constructed by using the architecture application scene data and the architecture optimization data.

2. The method of claim 1, wherein, Step S1 comprises the following steps: Step S11: Obtain business architecture requirement data; Step S12: Based on the business architecture requirement data, the existing application system is collected, and is classified, and the application system hierarchical data is obtained; Step S13: The interaction relationship recognition and problem analysis are carried out on the application system hierarchical data, and the application architecture status data is obtained; Step S14: Based on the application architecture status data, the enterprise-level application group and the professional domain application group optimization design is carried out, and the application architecture blueprint data is obtained; Step S15: Based on the business architecture requirement data, the data asset collection is carried out, and is classified, and the data asset hierarchical data is obtained; Step S16: The concept entity extraction and data flow transfer analysis are carried out on the data asset hierarchical data, and the data architecture status data is obtained; Step S17: Based on the data architecture status data, the data class, data sub-domain and concept entity structured design is carried out, and the data architecture blueprint data is obtained.

3. The method of grid digitalization architecture of claim 1, wherein, Step S2 comprises the following steps: Step S21: The technology architecture status data is obtained by investigating the technology components and platforms and carrying out data induction; the security architecture status data is obtained by evaluating the security measures and security components and carrying out data integration; Step S22: The technology domain, primary technology classification and secondary technology classification planning data is obtained by carrying out the technology domain, primary technology classification and secondary technology classification planning on the technology architecture status data, the application architecture blueprint data and the data architecture blueprint data; Step S23: Design the mapping relationship between the application module and the technical component based on the technical domain classification planning data and the application architecture blueprint data, to obtain application module technical mapping data; Step S24: Perform concept entity and technical component mapping design based on the technical domain classification planning data and the data architecture blueprint data, to obtain concept entity technical mapping data; Step S25: Perform standard technical component relationship collection based on the application module technical mapping data and the concept entity technical mapping data, to obtain technical component mapping relationship data; Step S26: Perform standardized design and component interaction relationship modeling on the technical component mapping relationship data, to obtain technical architecture blueprint data; Step S27: Perform security domain and security classification planning design on the security architecture status data and the technical architecture blueprint data, to obtain security domain classification planning data; Step S28: Perform security service and technical component protection requirement association design based on the security domain classification planning data and the technical component mapping relationship data, to obtain security service design data; Step S29: Perform security component deployment relationship planning modeling on the security domain classification planning data and the security service design data, to obtain security architecture blueprint data.

4. The method of grid digitalization architecture of claim 1, wherein, Step S3 includes the following steps: Step S31: Perform each architecture status artifact classification based on the application architecture status data, the data architecture status data, the technical architecture status data, and the security architecture status data, to obtain architecture status collection data; Step S32: Extract each architecture element from the architecture status collection data, and perform modeling based on the extracted architecture element structure, to obtain architecture asset library data; Step S33: Perform standardized integration on the application architecture blueprint data, the data architecture blueprint data, the technical architecture blueprint data, and the security architecture blueprint data, to obtain blueprint integration data; Step S34: Perform blueprint content consistency, correlation, and integrity checking on the blueprint integration data, and perform optimization based on the checking results, to obtain blueprint optimization data; Step S35: Perform unified archiving and version management of each architecture blueprint based on the blueprint optimization data, to obtain architecture blueprint library data.

5. The method of grid digitalization architecture of claim 1, wherein, Step S4 includes the following steps: Step S41: Compare the consistency of the architecture status and the blueprint based on the architecture asset library data and the architecture blueprint library data, to obtain architecture compliance data; Step S42: Identify architecture element deviation problems from the architecture compliance analysis data, to obtain architecture deviation identification data; Step S43: Perform deviation impact range and severity assessment on the architecture deviation identification data, to obtain architecture deviation assessment data; Step S44: Develop an architecture field monitoring scheme based on the architecture deviation assessment data, to obtain architecture monitoring strategy data; Step S45: Perform dynamic updating of the architecture compliance state on the architecture monitoring strategy data, to obtain architecture management and control data.

6. The method of claim 5, wherein, Step S41 includes the following steps: Step S411: Extract each architecture element from the architecture asset library data, and identify the association relationship of each architecture element, to obtain architecture status extraction data; Step S412: Extract each architecture element from the architecture blueprint library data, and identify the structure relationship, to obtain architecture blueprint extraction data; Step S413: Compare the same type of architecture elements based on the architecture status extraction data and the architecture blueprint extraction data to obtain architecture element comparison result data; Step S414: Identify differences based on the architecture element comparison result data to obtain architecture consistency difference data; Step S415: Perform difference factor impact analysis based on the architecture consistency difference data to obtain architecture consistency analysis data; Step S416: Determine the overall architecture compliance state based on the architecture consistency analysis data to obtain architecture compliance data.

7. The method of grid digitalization architecture of claim 5, wherein, Step S44 includes the following steps: Step S441: Identify high-risk deviation architectures based on the architecture deviation evaluation data to obtain architecture high-risk field data; Step S442: Extract monitoring requirements for each field based on the architecture high-risk field data, and develop monitoring indicators based on the extraction results to obtain architecture monitoring requirement data; Step S443: Plan and design a monitoring system based on the architecture monitoring requirement data to obtain architecture monitoring scheme data; Step S444: Standardize each monitoring element based on the architecture monitoring scheme data to obtain architecture monitoring strategy data.

8. The method of grid digitalization architecture of claim 1, wherein, In step S5, business change typical scenario design is performed based on the architecture asset library data and the architecture control data, and verification includes: Identify the relationship between existing business processes and technical support based on the architecture asset library data and the architecture control data to obtain business change requirement data; Design a business optimization scheme based on the business change requirement data to obtain business change candidate scheme data; Evaluate the feasibility of the business change candidate scheme data to obtain business change typical scenario data; Verify the business change typical scenario data through pilot application to obtain business change verification data; Integrate and analyze the verification results of the business change verification data to obtain architecture application scenario data.

9. The method of grid digitalization framework of claim 1, wherein, In step S5, architecture implementation effect evaluation is performed based on the architecture blueprint library data and the architecture control data, and effect optimization is performed based on the evaluation results, including: Quantitative analyze the architecture implementation effect indicators based on the architecture blueprint library data and the architecture control data to obtain architecture effect evaluation data; Classify and analyze architecture deviation items and optimization requirements based on the architecture effect evaluation data to obtain architecture optimization requirement data; Design improvement measures for the architecture optimization requirement data to obtain architecture optimization scheme data; Simulate and verify the architecture optimization scheme data to obtain architecture optimization verification data; File and organize the architecture optimization verification data to obtain architecture optimization data.

10. The method of grid digitalization architecture of claim 1, wherein, Step S6 includes the following steps: Step S61: Extract typical application practice experience from the architecture application scenario data to obtain architecture application practice summary data; Step S62: Based on the architecture optimization data, perform optimization strategy, effectiveness analysis, and improvement path induction to obtain architecture optimization practice summary data; Step S63: Based on the architecture application practice summary data and the architecture optimization practice summary data, extract the best practice combination to obtain architecture knowledge element data; Step S64: Build a knowledge system framework based on the architecture knowledge element data to obtain architecture knowledge system data; Step S65: Based on the architecture knowledge system data, document various knowledge achievements to obtain architecture knowledge achievement document data; Step S66: Based on the architecture knowledge achievement document data, perform version control and archiving management on the achievement content to obtain architecture knowledge achievement data.

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