Power supply station service architecture optimization method based on digital power grid construction achievement

Abstract

The invention belongs to the technical field of power resource optimization, and particularly relates to a power supply station service architecture optimization method based on digital power grid construction achievements, which comprises the following steps: investigating power supply station service requirements, constructing a power supply station service architecture, and determining a power supply station application architecture; an intelligent Internet of Things terminal operation system based on an electric power gap base and a platformization solution are introduced into the application architecture, and a platformization and perception fused service operation system is constructed; core technologies such as an AI large model, a high-perception terminal, a security authentication mechanism and a 5G slicing network are introduced into asset management, production management, resource management and technical management, and an integrated service execution system with intelligent perception, efficient cooperation, security, credibility and standardized operation is constructed. According to the invention, the problems of scattered architecture, slow response, weak cooperative capability and the like in a traditional power grid system are solved, and reliable support and a technical path are provided for efficient, safe and intelligent development of a power grid.

Description

Technical Field

[0001] The present invention belongs to the technical field of power resource optimization, and in particular relates to a method for optimizing the business architecture of a power supply station based on the achievements of digital power grid construction. Background Art

[0002] Amid the rapid development of the internet, big data, and artificial intelligence, these emerging technologies are increasingly permeating various industries. For the power grid, a critical infrastructure of the national energy system, digital and intelligent operation and management have become key priorities for improving operational efficiency and ensuring energy security. Leveraging internet technology to connect and integrate data across power grid systems not only improves management efficiency and response speed, but also enables networked oversight of the entire power grid lifecycle, enhancing overall operational reliability and stability.

[0003] In recent years, the introduction of artificial intelligence (AI) technology has provided a new path for in-depth optimization of power grids. By applying deep learning and pattern recognition to grid operating data, functions such as fault prediction, anomaly detection, and intelligent scheduling can be implemented, thereby enhancing the security and stability of the grid. For example, the AI-enabled solution based on "Ascend AI + Industry Big Model" has demonstrated promising results in real-world applications, demonstrating the significant potential of AI in power grid scenarios.

[0004] However, the current digital transformation of power grids is still undergoing continuous exploration and improvement. While its goal is to improve operational efficiency and optimize the energy structure, the existing technology system still faces challenges in terms of flexible intelligent scheduling, real-time processing of massive data volumes, and intelligent system collaboration. Furthermore, a high degree of integration and collaboration between the perception layer, link layer, platform layer, and model service layer has yet to be achieved, hindering the overall intelligent effectiveness of the system.

[0005] Therefore, how to further develop a new power grid operation system that achieves high-precision perception of equipment status, intelligent collaboration across the entire supply chain, and dynamic optimization and dispatch of energy resources remains a key issue in current technological development. Driven by the goals of ensuring national energy security, independent control, and green, low-carbon development, power grid digitalization urgently requires new technical solutions to further overcome existing bottlenecks and promote its stable, intelligent, and efficient development. Summary of the Invention

[0006] In response to the above problems, the present invention provides a method for optimizing the business architecture of power supply stations based on the results of digital power grid construction. The method integrates key technologies such as the power Hongmeng operating system, Ascend AI big model, and 5G slicing communication, and constructs an integrated operation system with deep collaboration among perception, communication, platform and application layers. By realizing data connectivity and intelligent collaboration in core areas such as asset, production, resource and technology management, it improves the accuracy of equipment status perception, the real-time performance of data processing and the intelligence level of system operation, and effectively solves the problems of decentralized architecture, slow response and weak collaboration capabilities in traditional power grid systems, providing reliable support and technical paths for the efficient, safe and intelligent development of power grids.

[0007] The present invention provides a method for optimizing the business architecture of a power supply station based on the achievements of digital power grid construction, the method comprising:

[0008] Step 1: Conduct a survey on the business needs of the power supply station, build a business architecture for the power supply station based on the survey results, and determine the application architecture of the power supply station based on the business architecture of the power supply station;

[0009] Step 2: Introduce a smart IoT terminal operation system and platform solution based on the power Hongmeng operating system into the power station application architecture to form a business operation system;

[0010] Step 3: In the four areas of asset management, production management, resource management, and technology management, introduce large AI models, high-perception terminals, security authentication mechanisms, and 5G slicing networks to build a business execution system.

[0011] In a preferred implementation of the present application, further, step 2 includes:

[0012] Step 2.1: Introducing a smart IoT terminal operation system based on the power Hongmeng operating system. The steps include:

[0013] Build a Hongmeng operating system base that supports Zones I, II, and III to achieve the unification of terminal operating systems;

[0014] Enable the Hongmeng operating system base to adapt and access terminal devices including at least digital meters, IoT licensing devices, drones, robots, temperature and humidity sensors, and oil chromatography devices;

[0015] Deploy the terminal device in a resource scheduling scenario to collect resource location information, status information, and usage information, and upload the collected information to a resource management system;

[0016] Perform batch OTA upgrade management and full-link operation monitoring on the terminal devices;

[0017] Introducing industrial-grade security mechanisms into the terminal devices, including terminal intrusion detection, national secret encrypted communication, firewalls, forward and reverse isolation mechanisms, and situational awareness mechanisms, to achieve the construction of an integrated "end-chain-network" security architecture;

[0018] Step 2.2: Introduce a platform solution to build an integrated business support platform. The steps include:

[0019] Build a unified data platform to aggregate structured and semi-structured data from terminals;

[0020] Deploy an intelligent inference engine based on the Ascend AI chip and industry-leading models. This inference engine supports loading multi-source algorithm models, sample fine-tuning, task-driven deployment, and closed-loop iteration.

[0021] Build an automated business orchestration engine and business middle platform for scheduling and management of business processes;

[0022] Build an integrated process support module for asset management, production scheduling, resource allocation and technical standards promotion;

[0023] Build a master-slave switching mechanism, a dual-active mechanism in the same city, and a data disaster recovery architecture, combined with a microservice architecture and an exception monitoring mechanism to support the system's concurrent processing capabilities and fault-tolerant recovery capabilities.

[0024] In a preferred implementation of the present application, step 3 further includes:

[0025] Step 3.1: Introduce Ascend AI and industry big models into the asset management process to build an intelligent empowerment solution for asset management;

[0026] Step 3.2: During the production management process, a grid perception system is built based on the "Jimu" series of high-sensitivity sensors. A dedicated wireless substation network is constructed using the WAPI protocol, and an active-standby switching structure and a dual-active system structure are configured.

[0027] Step 3.3: During resource management, establish a terminal security authentication mechanism and data communication protocol standard system, and set up data transmission paths based on encryption algorithms that comply with national password management regulations;

[0028] Step 3.4: During the technical management process, use the 5G power slicing network to achieve isolated transmission of power data and build a management structure that includes a technical standards system, an operation analysis system, and a technical supervision system.

[0029] In a preferred implementation of the present application, further, in step 3.1, the process of forming an asset intelligence empowerment solution includes:

[0030] Build an asset management module based on the Ascend AI platform and industry big models;

[0031] In the asset management module, a multimodal analysis method combining AI semantic analysis, image recognition, and video processing is adopted to support the loading of multi-source algorithm models and the scheduling of AI inference clusters;

[0032] The industry big model is used for inspection tasks, analysis tasks and alarm tasks in the power industry;

[0033] Develop AI models through fine-tuning samples, model deployment, and iterative training;

[0034] Based on the model inference results, obtain asset status information and its distribution information.

[0035] In the preferred implementation of the present application, further, in step 3.2, the process of building a power grid perception capability based on the "Jimu" series of high-perception sensors includes: deploying multimodal intelligent sensors on key equipment in the power system, wherein the multimodal intelligent sensors integrate infrared detection modules, temperature and humidity detection modules, partial discharge detection modules, vibration detection modules, image acquisition modules and gas detection modules; connecting the multimodal intelligent sensors to an edge processing chip, and using the edge processing chip to preprocess the acquired raw perception data to form preprocessed data; uploading the preprocessed data to a power grid perception platform; comparing the preprocessed data with a historical operating condition model stored in the power grid perception platform, and using a time series prediction model to identify data trends; based on the comparison results and the recognition results, constructing a health assessment model that integrates multi-source data and multimodal recognition;

[0036] The process of building a WAPI-based substation wireless private network includes: constructing a dedicated wireless network system using the WAPI protocol; utilizing the WAPI+LTE-U+LoRa converged architecture to achieve unified broadband and narrowband access, as well as multi-band and multi-channel coverage; implementing secure communication between terminal devices and network devices through the WAPI two-way authentication mechanism combined with national cryptographic algorithms; connecting sensors and edge gateways to the network through the WAPI wireless network; and integrating the connected sensors and gateways into the terminal management platform for unified management.

[0037] The process of building a highly reliable system with active-standby switching and active-active architecture in the same city includes: deploying a primary center and a backup center in different physical locations; setting up a primary center to undertake business operation tasks, and setting up a backup center to maintain data synchronization with the primary center and have fault takeover capabilities; achieving two-way data synchronization between the primary and backup centers based on distributed storage and change data capture mechanisms; setting the recovery time objective RTO to less than 10 seconds and the recovery point objective RPO to zero; designing the system using a distributed microservice architecture to decouple each module and have module-level hot standby capabilities; configuring a service health check and heartbeat detection mechanism in the system; monitoring the service status in real time, and triggering an automatic switching mechanism when a fault is detected.

[0038] In a preferred implementation of the present application, further, in step 3.3, the process of establishing a terminal security authentication mechanism and a data communication protocol standard system includes:

[0039] Establish a terminal identity registration and authentication system, which uses the SM2 algorithm for two-way identity authentication and combines digital certificates to complete identity confirmation between the terminal and the platform;

[0040] Formulate and apply unified data communication protocol standards based on MQTT, HTTPS or DTLS, and combine national secret encryption algorithms for data communication processing;

[0041] Setting up an edge protocol conversion gateway, which implements data mapping and security filtering operations between multiple industrial protocols and standard protocols;

[0042] Constructing a data encryption transmission path that uses the SM2, SM3, and SM4 algorithms to encrypt and verify data integrity, enabling end-to-end secure communication between the terminal and the platform.

[0043] Configure access control policies and sensitive data labels, and implement multi-level permission management and data classification protection operations based on the policies and labels.

[0044] In a preferred implementation of the present application, further, in step 3.4, the process of constructing the technical standard system, the operation analysis system, and the technical supervision system includes:

[0045] Construct a technical standard system involving at least power communication, terminal access, safety specifications and system integration;

[0046] Collect equipment operation data from edge terminals and platform systems, apply clustering algorithms, classification algorithms, and time series prediction algorithms to the collected data for data modeling and risk analysis, and form information maps and evaluation data reflecting the equipment operation status to build an operation analysis system;

[0047] Supervise the access of new technologies or equipment, supervise the compliance of operation and maintenance activities, supervise the implementation of technical standards, and compare and analyze business logs and status data based on artificial intelligence rule engines to build a technical supervision system.

[0048] In the preferred implementation of the present application, further, in step 3.1, asset management includes asset management strategy, asset management plan, asset addition and retirement management and asset performance evaluation management, wherein the asset management strategy includes the overall strategy of the power supply station, asset development strategy, technology development strategy, asset investment strategy, asset retirement strategy, digital asset strategy, asset operation and maintenance strategy, asset maintenance strategy and carbon asset strategy; the asset management plan includes the medium- and long-term investment plan, annual investment plan and equipment retirement plan of the power supply station; asset addition and retirement management includes equipment access and quality control management, equipment acceptance management and equipment retirement management of the power supply station; asset performance evaluation management includes the asset management system and performance evaluation, power quality and reactive voltage management, reliability management, economic operation management and production indicator management of the power supply station.

[0049] In the preferred implementation of the present application, further, in step 3.2, production management includes equipment risk management, operation and maintenance management, production project management, environmental risk management and operational risk control, wherein equipment risk management includes equipment monitoring and early warning, equipment status assessment, equipment risk assessment, equipment defect management, equipment hidden danger management, equipment hidden danger management, equipment operation analysis, power grid risk control and network security management; operation and maintenance management includes operation plan management, duty management, work ticket management, operation ticket management, patrol maintenance management, anti-misoperation management, maintenance plan management, test management, maintenance management, emergency repair management, spare parts management and non-stop operation management; production project management includes production project implementation plan management, production project implementation process management, production project cost management, production project acceptance management, production project post-evaluation, relocation project management and production contractor management; environmental risk management includes operation environment visualization management, operating environment management, electricity-related public safety management and disaster prevention and reduction management; operational risk control includes operation plan, operation standard management, operation qualification and operation supervision.

[0050] In a preferred implementation of the present application, further, in step 3.3, resource management includes operational resource management, production team management, and digital resource management, wherein operational resource management includes tool management, production service vehicle management, emergency equipment management, and economic operation management; production team management includes team building, team evaluation, production personnel training, core skills management, core business management, production organization model management, and production management evaluation; digital resource management includes production data management, digital infrastructure management, algorithm and model management, knowledge base management, application platform management, and digital architecture management;

[0051] In step 3.4, technical management includes operation and maintenance management, new product and technology access management, technical standards management, equipment operation analysis, and technical supervision management. Among them, operation and maintenance management includes equipment category optimization management, equipment and Internet of Things standardization management, and equipment model review management; new product and technology access management includes the first set of major technical equipment assessment, new technology and new product network access management, and green and low-carbon management; technical standards management includes technical standard formulation management and technical standards system management; equipment operation analysis includes equipment delivery quality evaluation; technical supervision management includes countermeasure management and technical standards system management.

[0052] The beneficial effects of the present invention are:

[0053] First, the power supply station business architecture optimization method based on the digital power grid construction results of the present invention addresses the problems of insufficient flexibility in intelligent scheduling, poor real-time processing of massive data, and limited system collaborative intelligence in the existing power grid system. By constructing a multi-level business architecture oriented towards business needs, it integrates the power Hongmeng operating system base, AI big model, 5G slicing communication, security authentication mechanism and smart IoT terminals, and realizes the deep integration and collaboration of the perception layer, network layer, platform layer and application layer. This method not only improves the accuracy and response speed of equipment status perception, but also constructs an intelligent execution system that runs through the entire business domain of asset, production, resource and technology management, thereby improving the operating efficiency, system security and resource scheduling capabilities of the power supply station, and providing a unified and scalable technical path for the digital, intelligent and efficient development of the power grid, which has good promotion and application value.

[0054] Second, in the preferred implementation method, step 2 of the present invention builds an integrated power supply station business operation system with unified perception, centralized governance and intelligent decision-making capabilities by introducing a smart IoT terminal operation system and platform solution based on the power Hongmeng operating system base; at the perception layer, by supporting the deployment of Hongmeng operating systems in zones I, II, and III, unified adaptation and access of terminal devices are achieved, and combined with industrial-grade security mechanisms, OTA batch upgrades and full-link operation monitoring methods, the standardization, security and real-time performance of terminal management are improved; at the same time, the terminal has fast startup and edge computing capabilities, which improves the frequency and accuracy of data collection, effectively reduces data transmission delays, and enhances the real-time response capability of the perception layer to key state changes; at the platform layer, relying on Ascend AI and industry large models to deploy intelligent inference engines, the structured governance of multi-source data and model closed-loop iteration are integrated to achieve rapid data analysis and intelligent decision-making; combined with automated business orchestration and middle-office management mechanisms, it not only opens up the data processing chain between perception and application, but also supports rapid identification, real-time response and closed-loop disposal of abnormal events.

[0055] Third, in the preferred implementation, step 3 of the present invention systematically introduces key technologies such as Ascend AI and industry big models, "Jimu" series high-perception sensors, WAPI wireless private network, terminal security authentication mechanism and 5G power slicing network into the four core areas of asset management, production management, resource management and technology management, thereby building an integrated business execution system covering the entire chain of perception, communication, platform and execution. This solution effectively improves the intelligence level of asset status identification, the real-time perception capability of equipment operation, the security and reliability of resource scheduling, and the standardization and supervision capabilities of technology management.

[0056] Fourth, in the preferred implementation, step 3.1 of the present invention realizes high-precision identification and real-time perception of the power asset status by building an asset management module based on the Ascend AI platform and the industry big model, combining multimodal perception, AI reasoning and closed-loop development mechanism, thereby improving the efficiency and intelligence level of asset management; step 3.2 supports the second-level risk warning mechanism, effectively pre-fault judgment and risk intervention, builds a multi-source integrated health assessment system, and supports WAPI+LTE-U+LoRa integrated wireless private network and "active-standby switching+same-city dual-active" high-reliability system architecture to achieve wide coverage. Secure communications with integrated narrowband and coordinated frequency bands adopt a highly reliable system architecture of "active-standby switching + dual active in the same city". Through the distributed deployment of active-standby centers and a two-way data synchronization mechanism, the RTO is set to less than 10 seconds and the RPO is zero, achieving rapid recovery and uninterrupted business operation in disaster recovery scenarios. Furthermore, step 3.3 builds a terminal security authentication mechanism and a unified data communication protocol standard system to achieve trusted access and end-to-end encrypted transmission for multiple types of terminals. At the same time, step 3.4 realizes the institutionalization of technical specifications, pre-emptive control of operational risks, and the automatic closed loop of the supervision mechanism by establishing a complete system covering standard formulation, operation evaluation, and supervision management.

[0057] Fifth, in the preferred implementation mode, the present invention introduces a systematic and modular management system in the four major business areas of asset management, production management, resource management and technical management, refines the functional composition and management content of each module, and improves the power supply station's ability in full life cycle management, risk prevention and control, resource scheduling and technical governance; in terms of asset management, through full-chain strategies, plans, performance evaluation and decommissioning management, it realizes the refined operation and maintenance of assets and investment decision support; in terms of production management, through multi-dimensional equipment risk control and operation process management, it improves the safety and continuity of power supply business; in terms of resource management, it realizes the collaborative governance and dynamic configuration of three types of resources: people, objects and numbers, and enhances the resource guarantee capability; in terms of technical management, it constructs a closed-loop mechanism covering new technology access, standard system construction and operation supervision to ensure the advancement and compliance of the technical system. Overall, this application provides an integrated power grid management solution with comprehensive coverage, clear structure and strong executability, which promotes the intelligent, safe and efficient development of power supply stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 This is a flow chart of a method for optimizing a power supply station's business architecture based on digital power grid construction achievements according to an embodiment of the present invention;

[0059] Figure 2 Schematic diagram of the four-layer structure of the power supply station business architecture according to an embodiment of the present invention;

[0060] Figure 3 This is a schematic diagram of a perception layer security partitioning architecture according to an embodiment of the present invention;

[0061] Figure 4 This is a typical application scenario of the perception layer I area in the system architecture of the embodiment of the present invention;

[0062] Figure 5 This is a typical application scenario of the perception layer II area in the system architecture of the embodiment of the present invention;

[0063] Figure 6 This is a typical application scenario of the perception layer III zone in the system architecture of the embodiment of the present invention;

[0064] Figure 7 This is a three-zone mapping diagram of asset management and perception layer in an embodiment of the present invention;

[0065] Figure 8 This is a mapping relationship diagram between the production management and perception layer three zones of an embodiment of the present invention;

[0066] Figure 9 This is a diagram of the mapping relationship between resource management and the three zones of the perception layer according to an embodiment of the present invention;

[0067] Figure 10 This is a mapping relationship diagram between the technical management and perception layer three areas of an embodiment of the present invention. DETAILED DESCRIPTION

[0068] In order to enable those skilled in the art to better understand the technical solution of the present application, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0069] The terms "up", "down", "left", "right", "front", and "back" in this application are based on the positional relationships shown in the accompanying drawings. The corresponding positional relationships may vary depending on the drawings, and should not be construed as limiting the scope of protection.

[0070] In this application, the terms "installed," "connected," "connected," "connected," "fixed," etc. should be understood in a broad sense. For example, they can refer to fixed connection, detachable connection, integral connection, mechanical connection, electrical connection, or mutual communication. They can also be directly connected or indirectly connected through an intermediate medium. They can also refer to internal communication between two components or interaction between two components. For those skilled in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0071] Example

[0072] As the instruction manual Figure 1This embodiment provides a method for optimizing the business architecture of a power supply station based on the achievements of digital power grid construction, including the following steps:

[0073] Step 1: Conduct a survey on the business needs of the power supply station, build a business architecture for the power supply station based on the survey results, and determine the application architecture of the power supply station based on the business architecture of the power supply station.

[0074] The purpose of Step 1 is to comprehensively identify the core business needs of power stations in the context of digital transformation, thereby providing a clear and actionable basis for subsequent system planning, technology selection, and platform development. Through in-depth research and systematic analysis of the power station's business needs, a business-driven business architecture is constructed. Based on this, the application architecture is designed to achieve precise alignment between business goals and information technology, driving the power station's development towards intelligence, efficiency, and security.

[0075] Specifically, step 1 includes:

[0076] Step 1.1: Investigate the business needs of the power supply station.

[0077] The survey covered asset management, production management, resource management, and technical management. Asset management, in particular, can help power stations rationally allocate assets, regularly maintain and service equipment, and provide performance evaluations for decision-making. Production management can ensure the normal operation of power supply facilities and equipment, control power supply costs, and guarantee the continuity and reliability of power supply. Resource management helps the power supply bureau rationally allocate and utilize various resources, ensure the supply of key materials and equipment, guarantee the continuity of power supply, and meet social and economic needs. Technical management can ensure the effective implementation of daily operation and maintenance of power supply facilities, manage new technologies, and introduce new products.

[0078] Research methods include field research, questionnaires and interviews, and system data analysis. Field research involves visiting typical power supply stations to observe operations, maintenance, and management processes. Questionnaires and interviews gather insights on actual pain points and improvement suggestions across various functional positions. System data analysis involves data mining the operation logs of existing management systems (such as ERP and OMS) to identify bottlenecks.

[0079] It's important to note that ERP is an integrated management platform used by enterprises to consolidate core business processes, covering modules such as finance, supply chain, production, sales, and human resources, enabling resource coordination and data sharing. An OMS is an order management system that focuses on managing the entire order lifecycle, from order receipt and inventory lock-in to logistics distribution and after-sales service. It supports order aggregation and automated processing across multiple sales channels.

[0080] Step 1.2: Build the business architecture of the power supply station.

[0081] As the instruction manual Figure 2 , the business architecture of the power supply station is designed in layers, including the perception layer, network layer, platform layer, and application layer. Each layer is responsible for terminal data collection, secure communication, intelligent analysis, and business support. Among them, the perception layer is used to access terminal equipment, as well as on-site data collection and status perception, to build a data portal for the digital power grid. Terminal equipment includes but is not limited to digital meters, IoT licensing devices, drones, robots, temperature and humidity sensors, oil chromatography devices, and various status monitoring terminals installed at the substation site. In this application, the perception layer is based on the power Hongmeng operating system, and supports batch OTA (Over-The-Air, air upgrade technology) upgrades of terminals, full-link monitoring, and industrial-grade security protection. Its typical capabilities are to access more than 100,000 terminals, covering 29 types of equipment, and realize status monitoring, event reporting, and edge computing.

[0082] The network layer supports secure communication protocols such as WAPI and 5G slicing, ensuring smooth, secure, and low-latency data transmission between the perception layer and the platform layer. The network layer includes the WAPI protocol, LTE-U, and Lora converged IoT, a 5G power grid private network, and a dual-active network structure within the same city. In this application, the perception network layer is based on encrypted communication, forward and reverse isolation, firewalls, situational awareness, and national encryption authentication mechanisms. Its typical capabilities include "active / standby switching + dual-active within the same city + data disaster recovery," improving network reliability and information security.

[0083] The platform layer aggregates, processes, and analyzes data from the perception layer, supporting core capabilities such as AI, large models, and business rules. It includes the Ascend AI inference platform, a data center, a model management platform, and a unified protocol conversion platform. In this application, the platform layer supports multi-source algorithm model loading, sample fine-tuning, a microservices architecture, and service anomaly monitoring. Typical capabilities include intelligent support for business scenarios such as asset analysis, risk assessment, production scheduling, and fault warning.

[0084] The application layer provides various business support functions for power station personnel and serves as the entry point for digital operations. It encompasses asset management, production management, resource management, and technical management. Its typical capabilities include full-process, closed-loop business management, including one-stop operation and maintenance, remote monitoring, intelligent scheduling, and indicator evaluation.

[0085] Furthermore, business domains are constructed with services at the core. These domains include asset management, production management, resource management, and technical management. The asset management domain covers lifecycle management, performance evaluation, and asset retirement strategies. The production management domain includes equipment monitoring, maintenance management, operations management, and project control. The resource management domain includes operational resources, personnel management, digital resource governance, and security assurance. The technical management domain focuses on 5G slicing, technical standards, and operational data analysis.

[0086] It should be noted that the architectural layering is the technical carrier and support platform of the business domain, and the functional implementation of each business domain relies on the foundation of the four-layer architecture. The perception layer collects data required for the business (such as asset status and operating parameters), the network layer ensures the security of business communications through transmission, the platform layer performs AI analysis and data modeling, and the application layer provides specific functional interfaces and operational logic. The business domain defines the service objectives and content direction of the architectural layering, and the functional services carried by each layer are designed for business domains such as assets and production. For example, robots and sensors connected to the perception layer should be deployed according to the actual needs of asset management and operation management; the large AI model configured at the platform layer also needs to be trained for business goals such as equipment inspection and production scheduling.

[0087] For example, Table 1:

[0088]

[0089] All business domains are based on the four-layer logic of "terminal-network-platform-application", supporting data closed loop, model closed loop, and business closed loop, reflecting digitalization, automation, and intelligent capabilities.

[0090] Step 1.3: Build the power supply station application architecture based on the business requirements proposed by the power supply station business architecture.

[0091] It's important to note that the business architecture is the structure of a power station's core business areas, business processes, organizational roles, and business objectives. It focuses on "what the power station needs to do," such as the logical relationships and process design of business activities like equipment maintenance, resource scheduling, and job management. The application architecture, which supports the business architecture, encompasses the application systems and their functional modules, interactions, and integration methods deployed. It focuses on "what systems are used and how these businesses are implemented," including the composition and collaborative methods of asset management systems, job ticket systems, and AI analysis platforms.

[0092] Specifically, the business needs of power supply stations are management and service demands centered around the four core business areas of assets, production, resources, and technology, aiming to improve operational efficiency, safety, and intelligence. These requirements include asset management, production management, resource management, and technical management. Asset management requirements include: implementing full asset lifecycle management, including procurement, access, operation and maintenance, and decommissioning; improving asset status visualization and early warning capabilities; reducing operation and maintenance costs and improving asset utilization efficiency. Production management requirements include: strengthening equipment operating status monitoring and risk early warning; implementing efficient work planning, shift scheduling, and troubleshooting processes; and ensuring the safety, continuity, and controllability of production tasks. Resource management requirements include: optimizing the scheduling of human and material resources and improving emergency response capabilities; achieving unified governance and security protection of digital resources; and supporting intelligent decision-making for resource allocation. Technical management requirements include: promoting the rapid evaluation and access of new technologies and equipment; establishing a comprehensive technical standards system; and analyzing and monitoring equipment operating data to support preventive maintenance.

[0093] Step 2: Introduce a smart IoT terminal operation system and platform solution based on the power Hongmeng operating system base into the power station application architecture to form a business operation system.

[0094] The purpose of Step 2 is to transform the business architecture of the power supply station into an executable and scalable application architecture by introducing a smart IoT terminal operation system and platform solution based on the power Hongmeng operating system, achieve deep integration of business logic and digital technology, and build a technical support system for unified perception, unified management, and unified intelligent decision-making, thereby promoting the power supply station to move towards standardization, intelligence, and platformization, and meet the needs of efficient, safe, and replicable operations.

[0095] After completing the initial construction of the power supply station's business and application architecture, to ensure the effective implementation and actual operation of the architecture, Step 2 introduces two key capabilities at the perception and platform layers: a smart IoT terminal operation system and a business integration support platform, thereby transforming the architecture into an executable system. Step 2 includes:

[0096] Step 2.1: Introduce the smart IoT terminal operation system based on the power Hongmeng operating system.

[0097] The goal is to connect the perception and network layers of the application architecture, enabling comprehensive awareness and secure access to on-site operational status, and providing real-time data support for business activities such as assets, production, and resources. This system encompasses not only traditional perception terminals (such as meters and sensors), but also intelligent IoT devices for resource scheduling and security protection. This enables comprehensive visibility of power station operational status, unified terminal management, and controllable and preventable risks.

[0098] Specifically, the method of introducing the smart IoT terminal operation system based on the power Hongmeng operating system base in step 2.1 includes:

[0099] Step 2.1.1: Build the HarmonyOS operating system base and support deployment in Zones I, II, and III to meet the uniformity and isolation requirements of terminal operating systems in areas with different security levels, and achieve unified management and compatibility support at the terminal operating system level.

[0100] Based on the OpenHarmony kernel framework (an open source operating system project incubated and managed by the OpenAtom Foundation, which aims to build a distributed operating system framework for the full-scenario, fully connected, and fully intelligent era), and combined with the business scenario requirements of the power supply station, a lightweight embedded operating system version for perception-layer devices is tailored and customized to ensure compatibility with low-power, low-computing-power devices.

[0101] As the instruction manual Figure 3 The perception layer is designed based on the device security level zoning deployment architecture, divided into Zone I, Zone II, and Zone III. In Zone I (security isolation zone), high-level security policies (such as hardware trusted root + strong identity authentication) are deployed; in Zone II (control domain), standardized security communication modules are deployed; and in Zone III (business domain), universal perception access terminals are deployed. Communication isolation and protocol control are carried out between different areas through network gates or border security gateways. By unifying the SDK (Software Development Kit), driver adaptation layer, and firmware interface standards, the operating environment of various terminal devices under the Hongmeng base is unified, reducing the complexity of device integration.

[0102] As the instruction manual Figure 4-6 , Figure 4 It demonstrates the data flow path and access permission restriction mechanism between the network, platform and application layers of Zone I terminals, reflecting its high-security isolation deployment strategy. Figure 5 It demonstrates that the control terminals in Zone II communicate with the platform through an encrypted protocol, supporting the execution of SCADA (Supervisory Control and Data Acquisition) instructions and operating status monitoring functions. Figure 6 It demonstrates how common equipment in Zone III (such as meters and robots) can achieve flexible data connection with the platform through a protocol conversion gateway, supporting daily operations and maintenance and data visualization functions.

[0103] Step 2.1.2: Implement terminal adaptation and access, including but not limited to driver adaptation and protocol docking for digital meters, IoT licensing devices, drones, robots, temperature and humidity sensors, oil chromatography devices and other types of terminals, to achieve efficient access and data collection functions between the above terminals and the power supply station perception layer.

[0104] For terminals such as digital meters, drones, robots, temperature and humidity sensors, and oil chromatography devices, corresponding driver modules and communication protocol adaptation layers (such as DLT645, Modbus, and MQTT) are developed to implement standard data model mapping. A unified terminal registration mechanism is designed. When a terminal first goes online, registration, binding, and permission configuration are completed using the device's unique ID and digital certificate, and the mechanism is integrated into full lifecycle management. Terminal access proxy components are deployed on edge computing nodes, providing functions such as protocol conversion, data preprocessing, and access load balancing.

[0105] Step 2.1.3: Deploy IoT devices in the resource scheduling scenario to collect multi-dimensional data including resource location information, status information, and usage information, and transmit it back to the resource management system in real time through the communication link to achieve dynamic resource perception and data synchronization.

[0106] Integrate positioning modules (such as GPS / Beidou), status sensors (such as vibration and power), and communication modules into physical resources such as work vehicles and emergency equipment to create intelligent resource tags. Implement periodic or event-triggered reporting of resource data via cellular IoT (LTE-U / 5G) or LoRa low-power wide-area networks. Establish a resource scheduling platform interface to transmit device status, location, and operating condition information using a unified JSON structure, and synchronize with the resource management system in real time.

[0107] Step 2.1.4: Implement batch OTA (Over-The-Air) upgrade management and full-link operation status monitoring for perception layer terminal devices to improve system operation and maintenance efficiency and device controllability, and support unified configuration of terminal versions, remote upgrades, and continuous tracking of operation status.

[0108] Establish an OTA remote upgrade service, including device grouping strategies, grayscale release mechanisms, and upgrade rollback controls. Install a built-in operation monitoring probe on each terminal to collect CPU, memory, connection status, communication quality, and other metrics, and periodically report them to the platform. Build a visual operations and maintenance platform based on tools such as Grafana or ELK (Elasticsearch, Logstash, Kibana) to implement fault warnings, upgrade failure backtracking, and operation status heat maps.

[0109] Step 2.1.5: Introduce industrial-grade safety mechanisms and build an integrated “end-chain-network” intrinsically safe system.

[0110] Run a lightweight security agent on Hongmeng devices to monitor key file changes, illegal port access, abnormal behavior calls, etc. in real time. Use SM2 / SM3 / SM4 encryption and decryption algorithms to encrypt data links and key storage processes to meet the requirements of National Level Protection 2.0 / 3.0. Introduce one-way data transmission channels (such as optical gates) and forward and reverse firewall configurations, and cooperate with SD-WAN (Software-Defined Wide Area Network) technology to ensure that core data is not leaked. Build a local log centralized analysis system or connect to the provincial / municipal situational awareness platform to achieve centralized alerting, analysis and tracing of network attacks and abnormal behaviors.

[0111] The smart IoT terminal operation system implemented through the above method can complete unified access, cross-regional deployment, consistency management, security isolation and intelligent operation and maintenance of multiple types of terminals, providing basic support for the platform operation and business integration of power supply stations.

[0112] Step 2.2: Introduce platform solutions to build an integrated business support platform.

[0113] The goal is to connect the platform and application layers within the application architecture, integrate sensory data, and provide centralized support and intelligent empowerment for the four major business domains of the power station. As the core of the application architecture, the platform layer, through data integration and intelligent analysis, connects the collaborative chain between business domains and supports the refined operations of the power station.

[0114] Specifically, step 2.2 introduces a platform solution. The method of building a business integration support platform includes:

[0115] Step 2.2.1: Build a unified data platform to aggregate and manage structured and semi-structured data from terminals.

[0116] Build a centralized data aggregation module for power supply substation terminals. This module is used to receive structured data (such as operating parameters and indicator values) and semi-structured data (such as logs and image information) collected from perception layer devices. Set up a data standardization and governance module to unify the format, standardize labels, eliminate redundancy, and cleanse the incoming data to form a unified data resource pool.

[0117] Step 2.2.2: Deploy an intelligent inference engine based on Ascend AI and large industry models, supporting multi-source algorithm model loading, sample fine-tuning, task-driven deployment, and closed-loop iteration.

[0118] The process of deploying an intelligent inference engine includes: introducing an inference computing architecture based on the Ascend AI chip, loading large pre-trained industry models related to power scenarios, configuring a multi-source model management module for loading, switching, and scheduling different algorithm models, supporting algorithm combination and fusion reasoning, supporting a model sample fine-tuning mechanism, and optimizing model parameters through historical operation data of power stations to achieve scenario customization.

[0119] The process of building a closed-loop AI service mechanism includes: setting up a task-driven model deployment module, triggering model loading, inference and result output according to business needs, building a model evaluation and feedback mechanism, and reversely influencing business execution effects on model training to form a closed-loop process of training-deployment-feedback; the closed-loop mechanism is used to continuously optimize model performance and improve the accuracy and availability of inference results.

[0120] Step 2.2.3: Build an automated business orchestration engine and business middle platform to provide integrated support for asset management, production scheduling, resource allocation, and technical standards push.

[0121] The process of building an automated business orchestration engine and business middle platform includes: building a business process orchestration module, combining the process logic of each business domain (assets, production, resources, technology) to achieve automatic task triggering and node coordination; building a business middle platform system to provide a unified service interface, permission management and message bus, and support data interaction and module collaboration between multiple business systems; the orchestration engine and middle platform system realize the automated execution of business processes, information linkage and cross-domain sharing.

[0122] The process of achieving integrated business support includes: integrating functional modules for asset management, production scheduling, resource allocation, and technical standard push into the platform, running each module based on a unified data and service system, calling the intelligent engine output on demand, and realizing management decision assistance, task plan generation and execution monitoring; forming a closed-loop linkage process of "data access - intelligent analysis - business execution - feedback optimization".

[0123] Step 2.2.4: Build a master-slave switchover, active-active in the same city, and data disaster recovery architecture, combining the microservice architecture with an exception monitoring mechanism to achieve high concurrency processing, continuous operation, and disaster recovery capabilities of the system.

[0124] The process of building an architecture with active-standby switchover, active-active in the same city, and data disaster recovery involves adopting a three-tier disaster recovery architecture: active-standby switchover, active-active in the same city, and data disaster recovery. The active-standby switchover mechanism enables rapid failover to the backup node in the event of a system failure. Active-active deployment in the same city enables parallel online operation of two business centers, improving business continuity. The data disaster recovery module is configured with a remote backup mechanism to ensure system data integrity and recoverability.

[0125] The process of integrating microservice architecture and monitoring mechanism includes: the platform is deployed using microservice architecture, containerized and elastically deployed according to service modules, service exception monitoring module is configured, service operation status, call links and fault logs are collected in real time, and early warning strategies and self-recovery mechanisms are combined to achieve service degradation, load balancing and automatic fault tolerance.

[0126] Through the implementation of step 2, the power supply station can shift from architecture design to system operation, and from business logic to platform support, and build a closed-loop system of "terminal perception-secure transmission-platform intelligence-application service", providing a unified technical foundation and capability platform for the subsequent intelligent evolution of various business domains, and promoting the digital transformation of the power supply station from "business-driven" to "capability-driven".

[0127] Step 3: In the four areas of asset management, production management, resource management, and technology management, introduce large AI models, high-perception terminals, security authentication mechanisms, and 5G slicing networks to build a business execution system.

[0128] The purpose of Step 3 is to build a business execution system centered on intelligent perception, intelligent analysis, and intelligent decision-making, by focusing on intelligent optimization of specific business domains, centering around the four core areas of asset management, production management, resource management, and technical management, and systematically deploying next-generation digital technologies such as artificial intelligence, big data, the Internet of Things, and 5G. This will build a business execution system centered on intelligent perception, intelligent analysis, and intelligent decision-making. Through the integrated development of model-driven, process optimization, and security assurance, the power supply station business will move from "digital access" to "intelligent operation," achieving refined business management, proactive risk management, efficient resource allocation, and standardized technical execution, comprehensively improving the power supply station's operational efficiency, security capabilities, and technological leadership in the context of digital transformation.

[0129] Step 3 includes:

[0130] Step 3.1: Introduce Ascend AI and industry big models into the asset management process to build an intelligent empowerment solution for asset management.

[0131] Specifically, the process of forming an asset intelligence empowerment solution in step 3.1 includes:

[0132] Step 3.1.1: Build an asset management module based on the Ascend AI platform and industry big models.

[0133] Deploy edge servers or AI clusters based on Ascend AI chips (such as Ascend 310 / 910) as inference computing nodes. Adopt the CANN (Compute Architecture for Neural Networks) architecture to support the conversion and deployment of mainstream model formats such as Tensor (multi-dimensional array data structure), ONNX (Open Neural Network Exchange), and PyTorch (a combination of Python and Torch frameworks). Load large industry models through the MindSpore (full-scenario AI computing framework) deep learning framework to achieve unified model management, calling, and version control. The asset management module is embedded in the unified data platform or asset management system (such as EAM) of the power enterprise, forming a nested or microservice-style call.

[0134] As the instruction manual Figure 7 As shown, Figure 7 This diagram illustrates the collaborative architecture of terminals in three security zones within the perception layer of asset management. The perception layer is divided into Zones I, II, and III based on the security level of the equipment. Terminals in these three zones work together to perform different types of data collection and status perception tasks. Zone I terminals monitor the status of key assets, Zone II terminals provide operational control data, and Zone III terminals are responsible for daily inspections and data collection, providing multi-source perception support for asset lifecycle management. For example, Zone I focuses on the status monitoring and lifespan assessment of high-value core assets such as transformers and GIS cabinets, serving as a key data source for asset status assessment. Zone II deploys control terminals, whose operational status directly impacts the compliance of asset operation strategies and scheduling behaviors. Zone III deploys perception terminals such as inspection robots and digital meters, continuously collecting on-site operational data as "basic data" input for asset health records. The collaborative path of asset management, based on data from these three zones, forms a closed loop from status identification (Zone I) to control feedback (Zone II) to data support (Zone III), forming an intelligent asset management chain of "status perception - behavior judgment - health records."

[0135] Step 3.1.2: Multimodal analysis based on AI semantic analysis, image recognition, and video processing.

[0136] The input text data for semantic analysis comes from inspection records, alarm logs, and maintenance records. BERT (Bidirectional Encoder Representations from Transformers) industry-based pre-trained language models are used to identify asset anomaly keywords and fault description patterns, performing text classification and entity extraction.

[0137] Image recognition input sources include substation equipment images, infrared images, and drone aerial images. It uses models such as ResNet and YOLO to detect objects and identify defects, outputting equipment type, status label, and location box.

[0138] Video processing input sources include surveillance video streams and images sent back by inspection robots. 3D CNN (temporal convolution) or Transformer models are used to detect dynamic events such as smoke, oil leaks, and abnormal movements.

[0139] Multimodal features are uniformly represented through an attention mechanism or a fusion network to provide unified asset status assessment results.

[0140] Step 3.1.3: Support multi-source algorithm model loading and AI inference cluster scheduling.

[0141] A model registration center was established to support the classification and archiving of various algorithm models (classification, detection, and prediction) by function and device type. Furthermore, an AI inference scheduler was built to dynamically allocate workloads based on model size, inference time, and server load. The AI ​​inference scheduler supports flexible deployment via Kubernetes (a container orchestration system) or MindX Serving (a service-oriented framework designed for AI inference scenarios). Each model was configured and matched to specific asset scenarios (such as switchgear identification and thermal imaging analysis) to ensure optimal scheduling.

[0142] Step 3.1.4: Apply the industry big model to inspection, analysis and alarm tasks.

[0143] Inspection missions use drones or inspection robots to collect images and videos, then leverage AI models to identify equipment and determine its status (e.g., open / closed status, defective areas). This combines historical equipment data (e.g., load, current) with image data to conduct trend analysis and health assessments. AI automatically generates alerts when anomalies are detected, connects to the dispatch system or alarm platform, and automatically dispatches tasks based on pre-set rules.

[0144] Step 3.1.5: Closed-loop process of sample fine-tuning, model deployment, and iterative training.

[0145] New samples are acquired from patrol missions, annotated, and then entered into the model optimization process. The samples are fine-tuned, and the basic large model is retrained on a small sample using incremental learning techniques. The original model structure is maintained, and only parameter adjustments are made to certain layers to reduce the risk of overfitting. After verification, the model is uploaded to the model warehouse and distributed to each edge node through the inference service platform for deployment and updates. Deployment and updates support both grayscale releases and hot updates to ensure uninterrupted operation. Each prediction result is compared and evaluated with the actual processing result, forming a closed feedback loop. Decreased accuracy triggers the automatic transmission of data for retraining.

[0146] Step 3.1.6: Realize real-time perception and distribution identification of asset status based on model inference results.

[0147] Real-time asset status perception is performed, and model prediction output is structured into "asset ID + status value + confidence level + timestamp," which is then pushed to the asset management system front-end to display real-time status. Distribution identification is performed, and a GIS system or 3D visualization platform is used to generate map heat maps based on asset location coordinates and status codes. Areas with concentrated anomalies are automatically marked with high-risk levels to assist in operation and maintenance decision-making.

[0148] The asset management module of this application is formed based on the Ascend AI+ industry big model, which can solve the problem of a large number of assets and the difficulty of understanding the specific details and distribution of assets in real time when AI empowers them. Ascend AI+ industry big model provides comprehensive solutions for AI semantics, images, and videos, supports multi-source algorithm model loading and AI reasoning cluster scheduling, and applies big models to patrol, analysis, alarm and other scenarios in the power industry. It also forms a complete closed loop of AI development through sample fine-tuning, deployment, and iteration, improving the accuracy of industry scenario recognition and development efficiency under small samples, effectively solving the complex and fragmented scenarios in asset management and the problem that AI engineering is difficult to generalize and replicate, thereby optimizing the efficiency of asset management and improving the accuracy of asset monitoring and maintenance.

[0149] In this application, asset management includes asset management strategy, asset management plan, asset addition and retirement management and asset performance evaluation management. The asset management strategy includes the overall strategy of the power supply station, asset development strategy, technology development strategy, asset investment strategy, asset retirement strategy, digital asset strategy, asset operation and maintenance strategy, asset maintenance strategy and carbon asset strategy. Giving full play to the role of asset management can achieve asset full life cycle management and improve asset management efficiency and quality. The asset management plan includes the power supply station's medium- and long-term investment plan (project reserve pool), annual investment plan and equipment retirement plan. Giving full play to the role of asset management plan can ensure the consistency of corporate investment and strategy and improve decision-making quality. Asset addition and retirement management includes the power supply station's equipment access and quality control management, equipment acceptance management and equipment retirement management. Giving full play to the role of asset addition and retirement management can optimize resource allocation, improve asset utilization efficiency and quality, and reduce operating costs. Asset performance evaluation management includes the power supply station's asset management system and performance evaluation, power quality and reactive voltage management, reliability management, economic operation management and production indicator management. Giving full play to the role of asset performance evaluation management can improve operational efficiency and cost savings, optimize maintenance strategies and extend asset life.

[0150] Step 3.2: During the production management process, a power grid perception system is built based on the "Jimu" series of high-sensitivity sensors. A dedicated wireless substation network is constructed using the WAPI protocol, and a master-slave switching structure and a dual-active system structure in the same city are configured.

[0151] As the instruction manual Figure 8 As shown, Figure 8 A schematic diagram illustrates the functional division of three-zone terminals in the perception layer for production management. Within production management, perception layer terminals are deployed in three zones: Zones I, II, and III, based on functional importance and safety levels, forming a multi-level collaborative support system. Zone I terminals are used for operational risk warnings, Zone II terminals support operational command control, and Zone III terminals handle on-site inspections, planning tasks, and fault diagnosis. For example, Zone I terminals collect status information from key operating equipment (such as switchgear high-voltage terminals, arc fault detectors, and SF6 leak monitors) to implement fault warnings and operational status monitoring. Zone II terminals are responsible for issuing SCADA commands and responding to control operations, serving as key nodes for operational control strategy execution. Zone III terminals, including conventional sensing devices such as robots, sensors, and meters, are deployed for on-site inspections, fixed-point data collection, and fault diagnosis. Together, these three zones form a closed-loop production management process known as "prediction-execution-verification," providing full-chain support from on-site perception, fault monitoring, command execution, and result verification.

[0152] Specifically, step 3.2 includes:

[0153] Step 3.2.1: Build grid perception capabilities based on the "Jimu" series of high-perception sensors.

[0154] It should be noted that the "Jimu" series of multimodal smart sensors is a series of sensors or intelligent sensing devices with high perception capabilities. This name is commonly used in industries such as electricity, security, and transportation to enhance the system's real-time monitoring, identification, and early warning capabilities of environmental, equipment, and status factors. In this application, the "Jimu" series of multimodal smart sensors is used in the perception system of the power grid field.

[0155] First, the "Jimu" series of multi-modal smart sensors will be deployed on key equipment such as substations, transmission lines, switchgear, and relay protection devices. The "Jimu" series of multi-modal smart sensors includes: infrared thermal imaging sensors, temperature and humidity monitoring, partial discharge detection, vibration / noise sensing, visual image acquisition, and SF6 gas leak detection.

[0156] Each sensor uses an edge processing chip (such as Ascend 310) to pre-process the raw data, including filtering, feature extraction, and encoding. The pre-processed data is packaged and uploaded to the upper-level platform to reduce the pressure on network transmission bandwidth.

[0157] By comparing real-time sensor data with historical operating condition models, early identification of equipment abnormalities is achieved. Time series models such as LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) are used to predict and analyze sensor data, identifying trending risks. Faults or warning signals automatically trigger work order processes. A multi-source sensor fusion mechanism is established to synchronize and align image, temperature, and sound data. Multimodal recognition models are constructed using Transformer or Attention mechanisms to achieve a comprehensive assessment of equipment health.

[0158] Step 3.2.2: Build a substation wireless private network system based on the WAPI protocol.

[0159] First, a dedicated power wireless communication network based on the WAPI (WLAN Authentication and Privacy Infrastructure) protocol was established, and a WAPI+LTE-U+Lora fusion solution was introduced to achieve integrated broadband and narrowband access. Multi-channel AP (Access Point) devices in the 2.4GHz and 5GHz frequency bands were configured on demand to meet the coverage of the complex spatial structure of the substation.

[0160] The WAPI bidirectional authentication mechanism ensures the trustworthy identities of terminals and network devices. Data encryption employs national secret algorithms to ensure the confidentiality and integrity of data transmission. Dynamic key negotiation and AP isolation mechanisms are introduced to prevent unauthorized terminals from accessing the network. All JIMU sensors and edge gateways are wirelessly connected to the network via WAPI. Access devices are integrated into the terminal management platform, supporting terminal status monitoring, firmware OTA upgrades, intrusion detection, and other functions.

[0161] Step 3.2.3: Build a highly reliable system architecture with "active / standby switchover + active-active in the same city".

[0162] Deploy two independent systems, "primary center + backup center" in different physical locations, combined with the "active-active in the same city" redundant architecture. The primary center is responsible for daily business operations, while the backup center maintains data synchronization and can take over in seconds. By applying a two-way data synchronization mechanism (such as distributed storage + CDC change data capture), the application of a two-way data synchronization mechanism supports transaction-level log backup, snapshot recovery, and link encryption transmission. At the same time, all data supports the recovery target of RPO≈0 (no data loss) and RTO<10 seconds. Introduce a distributed microservice architecture, decouple services, support hot standby switching by module, set up health checks and heartbeat detection mechanisms, and automatically switch to the backup system when the primary system fails. At the same time, integrate service exception monitoring (such as Prometheus + Grafana) to observe the operation status in real time and issue fault alarms.

[0163] In this application, production management encompasses equipment risk management, operations and maintenance management, production project management, environmental risk management, and operational risk control. Leveraging production management can reduce failures and power outages, minimize unnecessary waste in the production process, and maximize cost-effectiveness.

[0164] Equipment risk management includes equipment monitoring and early warning, equipment status assessment, equipment risk assessment, equipment defect management, equipment hidden danger management, equipment hidden danger management, equipment operation analysis, power grid risk control and network security management. It is based on the super perception capability of the power grid created by the "Jimu" series of sensors. Sensors are used to support the substation operation support system to collect massive information data and conduct intelligent analysis. Through data relationships, the operation rules and potential risks of the power grid are discovered, so that the power grid has super perception capabilities, intelligent decision-making capabilities and rapid execution capabilities.

[0165] In addition, the "Jimu" series of sensors can also discover the operating patterns and potential risks of the power grid by collecting massive amounts of information data and performing intelligent analysis at the same time; in equipment risk management, these sensors can monitor equipment status in real time and collect key parameters such as voltage, current, temperature, etc. By analyzing this data, potential failures and risks of equipment can be predicted, so that preventive and maintenance measures can be taken in advance to reduce the probability of accidents; the sensors can also support rapid execution capabilities. Once a risk is discovered, it can respond quickly and implement necessary risk control measures, such as adjusting the power grid operation mode, isolating faulty equipment, etc., to minimize the impact of risks on power grid operations.

[0166] Operation and maintenance management includes operation plan management, duty management, work ticket management, operation ticket management, patrol maintenance management, anti-error operation management, maintenance plan management, test management, maintenance management, emergency repair management, spare parts management and non-stop operation management. Based on the domestic wireless communication protocol WAPI, a substation power wireless private network is built, and a WAPI+LTE-U+Lora broadband and narrowband integrated substation Internet of Things communication system is created, providing broadband, secure, ubiquitous and flexible wireless access methods for various new smart terminals in substations. This technology can better optimize operation and maintenance management.

[0167] It should be noted that WAPI is a wireless LAN security protocol, LTE-U is a Long Term Evolution Unlicensed long-term evolution unlicensed frequency band technology, and Lora is a long range technology.

[0168] WAPI technology, with its unique two-way identity authentication and key management mechanism, enhances the security of wireless communications, providing a safe and reliable network foundation for operation, maintenance and management, ensuring the security and integrity of data transmission and reducing potential security risks.

[0169] The highly reliable system is built based on "master-slave switching + dual active in the same city + data disaster recovery". At the platform layer, it builds a three-center layout in the same city. By building two sets of substation operation support systems, one master and one backup, it realizes dual active deployment of remote computer rooms and dual-node backup of full data. Then, it adopts cloud computing, microservices, service anomaly monitoring mechanism and other technologies to build a highly available, high-performance and high-concurrency system, which can realize real-time perception of system failures, automatic switching, and uninterrupted operation, and fully support the safe and continuous development of substation production business. This architectural design ensures that when the main system fails, it can quickly switch to the backup system to ensure business continuity and data loss, thereby improving system reliability and business continuity.

[0170] Production project management includes production project implementation plan management, production project implementation process management, production project cost management, production project acceptance management, production project post-evaluation, relocation project management and production contractor management. Fully utilizing production project management can ensure that projects in power supply stations are completed on time, according to quality and within budget, and achieve resource optimization and risk control.

[0171] Environmental risk management includes visual management of the operating environment, management of the working environment, management of electricity-related public safety, and disaster prevention and reduction management. Giving full play to the role of environmental risk management can achieve comprehensive monitoring and control of environmental risks and reduce the negative impact of power supply activities on the environment and employees.

[0172] Operational risk management includes operation planning, operation standard management, operation qualifications, and operation supervision. Fully utilizing operational risk management can ensure the safety and standardization of operations, enhance compliance, and protect the safety of operators.

[0173] Step 3.2 achieves intelligent production management and system-level high-reliability operation capabilities through three aspects: high-perception terminals create an on-site multi-modal real-time perception system with the "Jimu" series as the core; secure communication network builds a secure, flexible, and ubiquitous wireless private network based on the WAPI protocol; and the disaster recovery system architecture deploys a distributed high-availability operation platform with active-standby switching and dual active-active in the same city.

[0174] Step 3.3: In resource management, establish a terminal security authentication mechanism and a data communication protocol standard system, and use encryption algorithms that comply with national password management regulations to build data transmission paths.

[0175] As the instruction manual Figure 9 As shown, Figure 9A diagram illustrates the functional coordination of three-zone terminals in the perception layer of resource management. The resource management system, through the collaboration of terminals in Zones I, II, and III of the perception layer, implements closed-loop resource management, from authority allocation to location awareness. Zone I terminals are responsible for allocating security-level resources, Zone II terminals execute resource scheduling commands, and Zone III terminals provide on-site resource awareness. For example, Zone I terminals manage emergency supplies and safety control equipment, and are deployed in high-security zones. They require allocation authorization. Zone II terminals receive commands from the control center and execute operations such as switching power resources and activating backup power sources, serving as key nodes for resource control and execution. Zone III terminals deploy various operational assistance sensing devices, such as work vehicles, robots, and personnel tags, providing resource location awareness and task status feedback, supporting work order linkage and precise scheduling. Terminals in the three zones are deployed according to perception levels and control responsibilities, enabling a coordinated mechanism for secure resource allocation, dynamic awareness, and operational collaboration, enhancing the refinement and real-time nature of resource management.

[0176] Specifically, step 3.3 includes:

[0177] Step 3.3.1: Establishment of terminal security authentication mechanism.

[0178] All access terminals (such as sensors, cameras, and tool monitoring devices) must register their identities and be assigned a unique device ID upon initial access to complete device registration. Device-specific digital certificates are issued by the company's internal CA center or a third-party CA certified by the National Cryptography Administration, supporting SM2 / SM9 encryption systems. Asymmetric identity authentication based on the national encryption SM2 algorithm is used to establish a two-way authentication mechanism: the terminal verifies the platform's identity, and the platform verifies the terminal's authenticity. The authentication interface protocol uses the TLS+SM encryption communication protocol (e.g., GM / T0024-2014 National Encryption SSL) that complies with the Industrial Internet of Things security specifications to complete the authentication handshake.

[0179] Step 3.3.2: Construction of data communication protocol standard system.

[0180] Establish a unified object model standard, clarify the data format, attribute field, communication frequency and abnormal flag reported by the terminal, formulate a communication protocol system based on MQTT (lightweight transport protocol) + SM encrypted transmission, adapt to low-power terminals, and use HTTPS + national secret certificate encrypted channel or DTLS protocol transmission for high-data-intensive terminals (such as high-definition video streams), integrate national secret algorithms in the protocol stack (such as SM3 hash for integrity verification and SM4 symmetric encryption for data encryption) to achieve unified protocol standards.

[0181] For heterogeneous terminal devices, an edge-side protocol conversion gateway is set up to support data parsing and mapping between Modbus, IEC104, OPC UA and other protocols and internal enterprise standards. All communication data must complete security filtering, protocol adaptation and caching processing at the edge gateway to achieve unified gateway protocol conversion.

[0182] It should be noted that Modbus is an open industrial communication protocol, IEC104 is IEC 60870-5-104, a telecontrol communication protocol specifically for power systems, and OPC UA is the industrial process control object linking and embedding technology based on the OLE for Process Control Unified Architecture.

[0183] Step 3.3.3: Apply encryption algorithms that comply with national password management regulations.

[0184] It should be noted that among the encryption algorithms stipulated in the national password management regulations, SM2 is based on the elliptic curve cryptography system and is used for device identity authentication and digital signature; SM3 is used for data summary and integrity verification; SM4 is a national secret symmetric encryption algorithm, mainly used for data transmission encryption; SM9 (optional) is an identity-based cryptographic system, suitable for multi-terminal distributed identity management.

[0185] Build data transmission paths, establishing encrypted links from the terminal to the gateway and from the gateway to the platform to achieve end-to-end confidential transmission. Use SM2 for key exchange to generate symmetric encryption keys for subsequent communications, and use SM3 for message integrity verification to ensure that data has not been tampered with during transmission. Configure a data access whitelist in accordance with the network security level requirements of the production area (Zone III), label sensitive data (such as location and operating status), and implement hierarchical management and control through access control policies.

[0186] In this application, resource management includes operational resource management, production team management, and digital resource management. Giving full play to the role of resource management can effectively solve problems such as the difficulty in activating resources and an imperfect supply chain.

[0187] Digital resource management includes production data management, digital infrastructure management, algorithm and model management, knowledge base management, application platform management and digital architecture management. It is based on a terminal security and trusted authentication transmission system with security components and national secret encryption algorithms. It formulates unified terminal object model standards and unified protocol conversion and data transmission standards within the enterprise, conducts terminal intrusion detection, ensures on-site terminal network security, realizes industrial security protection, and ensures the security and integrity of digital resources.

[0188] All terminal equipment at the station adheres to unified substation security protection standards and is connected to Safety Production Zone III. Through encrypted communications, forward and reverse isolation, firewalls, and situational awareness, comprehensive monitoring and real-time prevention measures are implemented at all levels and throughout all links, enhancing system security and reliability. These measures, combined with digital resource management within resource management, enhance system security and reliability through comprehensive monitoring and real-time prevention measures such as encrypted communications, forward and reverse isolation, firewalls, and situational awareness, providing a solid foundation for the efficient management and safe use of digital resources.

[0189] Operational resource management includes tool management, production and service vehicle management, emergency equipment management, and economic operation management. Giving full play to the role of operational resource management can improve the working environment and ensure the personal safety of employees at work.

[0190] Production team management includes team building, team evaluation, production personnel training, core skills management, core business management, production organization model management and production management evaluation. Giving full play to the role of production team management can optimize employee team building and improve work enthusiasm.

[0191] Step 3.4: In technical management, implement isolated power data transmission based on the 5G power slicing network, and build a technical standard system, operation analysis system, and technical supervision system.

[0192] As the instruction manual Figure 10 As shown, Figure 10 A diagram shows the responsibilities of the three security zones within the perception layer within technical management. In the technical management scenario, the perception layer divides and deploys terminals into Zones I, II, and III based on device functions and application scenarios, enabling technical policy execution within different security domains. Zone I focuses on verifying key technical standards, Zone II ensures operational control compliance, and Zone III supports terminal access specifications and secure access mechanisms. These zones form the foundational structure of digital technical management in power supply stations. For example, Zone I terminals deploy technical standard verification equipment required for key equipment operational assurance, enabling core asset operation analysis, protection strategy simulation, and high-standard implementation monitoring. Zone II terminals primarily handle the implementation of SCADA and remote control operations, ensuring device behavior complies with standardized control policies and serve as key implementation terminals for operational procedures and codes of conduct. Zone III terminals, as the primary carriers of access devices, carry out functions such as protocol adaptation, secure access, and operational status upload. Their network access specifications and secure access protocols are crucial components of the technical standards system. The three zones collaborate to establish a closed-loop control mechanism covering the entire process, from technical standard formulation and code of conduct implementation to terminal access consistency. This creates a multi-layered technical management assurance system encompassing technical standard promotion, operational specification implementation, and access control.

[0193] Specifically, step 3.4 includes:

[0194] Step 3.4.1: Implement isolated power data transmission based on the 5G power slicing network.

[0195] Dedicated network slicing for power services is deployed in the 5G core network to provide dedicated network isolation capabilities for power applications (such as substation inspection and remote control). Each business scenario (such as relay protection, video surveillance, and SCADA data) is assigned an independent QoS (a mechanism for classifying and prioritizing different business traffic in the Quality of Service network) level and bandwidth guarantee to ensure real-time performance and security.

[0196] In terms of communication architecture: use physical or logical isolation to strictly separate power data from public network data, build edge MEC (Multi-access Edge Computing) nodes, and deploy them in the edge areas of power supply stations / substations to achieve localized low-latency data processing.

[0197] In terms of security mechanism: Slice data channels implement encryption and access control through dedicated APN configuration, IPsec (Internet Protocol Security) encrypted transmission and NSSA authentication mechanism, supporting device whitelist access, terminal identity authentication, slice-level traffic monitoring and anomaly detection.

[0198] Step 3.4.2: Build a technical standards system.

[0199] Clarify the standard specifications and interface definitions covering power communications, terminal access, safety specifications, system integration, etc., establish a unified technical standards platform, and realize the management, release, revision and sharing of standards.

[0200] The following core standards will be compiled and archived: communications standards (such as 5G slicing interface specifications and terminal access protocols); security standards (such as national encryption standards and firewall deployment specifications); and operations and maintenance standards (such as remote upgrade specifications and alarm level definitions). A standards management system will be established to support version control of technical standards, change approval processes, and automated push to various operations and maintenance systems.

[0201] Step 3.4.3: Build an operational analysis system.

[0202] By collecting, modeling, and analyzing equipment operation data, it provides capabilities such as predictive maintenance, energy efficiency optimization, and anomaly detection.

[0203] First, establish an operational data collection mechanism to collect operational data, including temperature, current, voltage, operating hours, and fault frequency, from edge terminals (such as power equipment and sensors) and platform systems (such as the OMS (Outage Management System) and the EMS (Energy Management System). AI algorithms are then used to identify operational patterns and conduct risk analysis. Using clustering, classification, and time series prediction algorithms, high-risk equipment and abnormal trends are identified, and equipment operation assessment reports are automatically generated, providing repair or replacement recommendations. Finally, operational status maps, health assessment radar charts, and event trajectory backtracking systems are constructed to assist in technical decision-making and enable a visual platform display.

[0204] Step 3.4.5: Establish a technical supervision system.

[0205] Implement comprehensive supervision and compliance inspections on the introduction, operation, and maintenance of various equipment and technologies.

[0206] The supervision objects include: access to new technologies / new equipment (such as network access test results, green and low-carbon standards), compliance of operation and maintenance activities (such as operation ticket management, anti-misoperation records), and implementation of technical standards (such as whether maintenance is carried out in accordance with technical documents).

[0207] By configuring an AI rules engine, we conduct real-time comparisons of logs, records, and status across various business systems. This intelligent monitoring system automatically generates alerts and technical audit reports upon detecting deviations from standard processes or abnormal equipment operation. We also establish a monitoring feedback mechanism. After each technical audit, we generate a monitoring log and a record of closed-loop rectification for subsequent audits.

[0208] In this application, technical management includes operation and maintenance management, new product and technology access management, technical standards management, equipment operation analysis, and technical supervision management. This module can solve problems such as shortage of technical personnel, insufficient information level, and aging equipment through the first domestically completed 5G power slicing architecture design and existing network deployment.

[0209] 5G technology enables power data to be isolated from the public and other industry services, achieving a high degree of physical isolation. 5G technology facilitates substation inspections, intelligent monitoring, and one-touch sequence control, among other substation services. The 5G power slicing architecture isolates power data from the public and other industry services. This high-strength, near-physical isolation enhances data transmission security.

[0210] Operation and maintenance management includes equipment category optimization management, equipment and Internet of Things standardization management, and equipment model review management. Giving full play to the role of operation and maintenance management can optimize equipment categories and ensure equipment performance.

[0211] The access management of new products and new technologies includes the assessment of the first set of major technical equipment, the network access management of new technologies and new products, and green and low-carbon management. Giving full play to the role of the access management of new products and new technologies can promote breakthroughs in major technologies and ensure the safety of new technologies entering the network.

[0212] Technical standards management includes technical standards formulation management and technical standards system management. Giving full play to the role of technical standards management will help power supply bureaus improve their management level, ensure that all work is carried out in accordance with established standards, and improve service capabilities and power supply stability.

[0213] Equipment operation analysis includes equipment delivery quality evaluation. Fully utilizing the role of equipment operation analysis can timely discover potential problems and take preventive maintenance measures, thereby improving equipment reliability and reducing the occurrence of failures.

[0214] Technical supervision management includes countermeasure management and technical standard system management. Giving full play to the role of technical supervision management can ensure that all power operation behaviors are placed under strict rules and systems, thereby improving the safety of equipment operation.

[0215] The power supply station business architecture optimization method based on the digital power grid construction results of the present invention integrates the power Hongmeng operating system, Ascend AI platform, industry big model, 5G slicing communication and security authentication mechanism to build an integrated business system with deep collaboration among perception layer, network layer, platform layer and application layer. By deploying smart IoT terminals and intelligent reasoning engines, it realizes the full life cycle management of the four core areas of assets, production, resources and technology, improves the accuracy of equipment status perception, resource scheduling efficiency and system operation security. This method has the advantages of clear architecture, complete functions and strong scalability, and provides reliable support and a unified technical path for the intelligent, safe and efficient operation of power stations.

[0216] The above is only an embodiment of the present invention, and common sense such as the specific structure and characteristics of the scheme are not described in detail here. For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes that fall within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any figure mark in the claims should not be regarded as limiting the claim involved.

Claims

1. A method for optimizing the business architecture of a power supply station based on the achievements of digital power grid construction, characterized in that: The method comprises: Step 1: Conduct a survey on the business needs of the power supply station, build a business architecture for the power supply station based on the survey results, and determine the application architecture of the power supply station based on the business architecture of the power supply station; Step 2: Introduce a smart IoT terminal operation system and platform solution based on the power Hongmeng operating system into the power station application architecture to form a business operation system; Step 3: In the four areas of asset management, production management, resource management, and technology management, introduce large AI models, high-perception terminals, security authentication mechanisms, and 5G slicing networks to build a business execution system.

2. The method for optimizing the business architecture of a power supply station based on the achievements of digital power grid construction according to claim 1 is characterized in that: Step 2 includes: Step 2.1: Introducing a smart IoT terminal operation system based on the power Hongmeng operating system. The steps include: Build a Hongmeng operating system base that supports deployment in Zones I, II, and III to achieve the unification of terminal operating systems; Enable the Hongmeng operating system base to adapt and access terminal devices including at least digital meters, IoT licensing devices, drones, robots, temperature and humidity sensors, and oil chromatography devices; Deploy the terminal device in a resource scheduling scenario to collect resource location information, status information, and usage information, and upload the collected information to a resource management system; Perform batch OTA upgrade management and full-link operation monitoring on the terminal devices; Introducing industrial-grade security mechanisms into the terminal devices, including terminal intrusion detection, national secret encrypted communication, firewalls, forward and reverse isolation mechanisms, and situational awareness mechanisms, to achieve the construction of an integrated "end-chain-network" security architecture; Step 2.2: Introduce a platform solution to build an integrated business support platform. The steps include: Build a unified data platform to aggregate structured and semi-structured data from terminals; Deploy an intelligent inference engine based on the Ascend AI chip and industry-leading models. This inference engine supports loading multi-source algorithm models, sample fine-tuning, task-driven deployment, and closed-loop iteration. Build an automated business orchestration engine and business middle platform for scheduling and management of business processes; Build an integrated process support module for asset management, production scheduling, resource allocation and technical standards promotion; Build a master-slave switching mechanism, a dual-active mechanism in the same city, and a data disaster recovery architecture, combined with a microservice architecture and an exception monitoring mechanism to support the system's concurrent processing capabilities and fault-tolerant recovery capabilities.

3. The method for optimizing the business architecture of a power supply station based on the achievements of digital power grid construction according to claim 1 is characterized in that: Step 3 includes: Step 3.1: Introduce Ascend AI and industry big models into the asset management process to build an intelligent empowerment solution for asset management; Step 3.2: During production management, a grid awareness system was built based on the "Jimu" series of high-sensitivity sensors. A dedicated wireless substation network was constructed using the WAPI protocol, and an active-standby switching structure and a dual-active system structure within the same city were configured. Step 3.3: During resource management, establish a terminal security authentication mechanism and data communication protocol standard system, and set up data transmission paths based on encryption algorithms that comply with national password management regulations; Step 3.4: During the technical management process, use the 5G power slicing network to achieve isolated transmission of power data and build a management structure that includes a technical standards system, an operation analysis system, and a technical supervision system.

4. The method for optimizing the business architecture of a power supply station based on the achievements of digital power grid construction according to claim 3 is characterized in that: In step 3.1, the process of forming an asset intelligence empowerment solution includes: Build an asset management module based on the Ascend AI platform and industry big models; In the asset management module, a multimodal analysis method combining AI semantic analysis, image recognition, and video processing is adopted to support the loading of multi-source algorithm models and the scheduling of AI inference clusters; The industry big model is used for inspection tasks, analysis tasks and alarm tasks in the power industry; Develop AI models through fine-tuning samples, model deployment, and iterative training; Based on the model inference results, obtain asset status information and its distribution information.

5. The method for optimizing the business architecture of a power supply station based on the achievements of digital power grid construction according to claim 3 is characterized in that: In step 3.2, the process of building grid perception capabilities based on the "Jimu" series of high-perception sensors includes: deploying multimodal intelligent sensors on key equipment in the power system, wherein the multimodal intelligent sensors integrate infrared detection modules, temperature and humidity detection modules, partial discharge detection modules, vibration detection modules, image acquisition modules, and gas detection modules; connecting the multimodal intelligent sensors to edge processing chips, and using the edge processing chips to preprocess the acquired raw perception data to form preprocessed data; uploading the preprocessed data to the grid perception platform; comparing the preprocessed data with the historical operating condition model stored in the grid perception platform, and using a time series prediction model to identify data trends; and constructing a health assessment model that integrates multi-source data and multimodal recognition based on the comparison and recognition results. The process of building a WAPI-based substation wireless private network includes: constructing a dedicated wireless network system using the WAPI protocol; utilizing the WAPI+LTE-U+LoRa converged architecture to achieve unified broadband and narrowband access, as well as multi-band and multi-channel coverage; implementing secure communication between terminal devices and network devices through the WAPI two-way authentication mechanism combined with national cryptographic algorithms; connecting sensors and edge gateways to the network through the WAPI wireless network; and integrating the connected sensors and gateways into the terminal management platform for unified management. The process of building a highly reliable system with active-standby switching and active-active architecture in the same city includes: deploying a primary center and a backup center in different physical locations; setting up a primary center to undertake business operation tasks, setting up a backup center to maintain data synchronization with the primary center and have fault takeover capabilities; achieving two-way data synchronization between the primary and backup centers based on distributed storage and change data capture mechanisms; setting the recovery time objective RTO to less than 10 seconds and the recovery point objective RPO to zero; designing the system using a distributed microservice architecture to decouple each module and have module-level hot standby capabilities; configuring a service health check and heartbeat detection mechanism in the system; monitoring the service status in real time, and triggering an automatic switching mechanism when a fault is detected.

6. The method for optimizing the business architecture of a power supply station based on the achievements of digital power grid construction according to claim 3 is characterized in that: In step 3.3, the process of establishing a terminal security authentication mechanism and data communication protocol standard system includes: Establish a terminal identity registration and authentication system, which uses the SM2 algorithm for two-way identity authentication and combines digital certificates to complete identity confirmation between the terminal and the platform; Formulate and apply unified data communication protocol standards based on MQTT, HTTPS or DTLS, and combine national secret encryption algorithms for data communication processing; Setting up an edge protocol conversion gateway, which implements data mapping and security filtering operations between multiple industrial protocols and standard protocols; Constructing a data encryption transmission path that uses the SM2, SM3, and SM4 algorithms to encrypt and verify data integrity, enabling end-to-end secure communication between the terminal and the platform. Configure access control policies and sensitive data labels, and implement multi-level permission management and data classification protection operations based on the policies and labels.

7. The method for optimizing the business architecture of a power supply station based on the achievements of digital power grid construction according to claim 3 is characterized in that: In step 3.4, the process of building the technical standards system, operation analysis system, and technical supervision system includes: Construct a technical standard system involving at least power communication, terminal access, safety specifications and system integration; Collect equipment operation data from edge terminals and platform systems, apply clustering algorithms, classification algorithms, and time series prediction algorithms to the collected data for data modeling and risk analysis, and form information maps and evaluation data reflecting the equipment operation status to build an operation analysis system; Supervise the access of new technologies or equipment, supervise the compliance of operation and maintenance activities, supervise the implementation of technical standards, and compare and analyze business logs and status data based on artificial intelligence rule engines to build a technical supervision system.

8. The method for optimizing the business architecture of a power supply station based on the achievements of digital power grid construction according to claim 3 is characterized in that: In step 3.1, asset management includes asset management strategy, asset management plan, asset addition and retirement management, and asset performance evaluation management. The asset management strategy includes the power supply station's overall strategy, asset development strategy, technology development strategy, asset investment strategy, asset retirement strategy, digital asset strategy, asset operation and maintenance strategy, asset maintenance strategy, and carbon asset strategy. The asset management plan includes the power supply station's medium- and long-term investment plan, annual investment plan, and equipment retirement plan. Asset addition and retirement management includes the power supply station's equipment access and quality control management, equipment acceptance management, and equipment retirement management. Asset performance evaluation management includes the power supply station's asset management system and performance evaluation, power quality and reactive voltage management, reliability management, economic operation management, and production indicator management.

9. The method for optimizing the business architecture of a power supply station based on the achievements of digital power grid construction according to claim 3 is characterized in that: In step 3.2, production management includes equipment risk management, operation and maintenance management, production project management, environmental risk management and operational risk control. Among them, equipment risk management includes equipment monitoring and early warning, equipment status assessment, equipment risk assessment, equipment defect management, equipment hidden danger management, equipment hidden danger management, equipment operation analysis, power grid risk control and network security management; operation and maintenance management includes operation plan management, duty management, work ticket management, operation ticket management, patrol maintenance management, anti-misoperation management, maintenance plan management, test management, maintenance management, emergency repair management, spare parts management and non-stop operation management; production project management includes production project implementation plan management, production project implementation process management, production project cost management, production project acceptance management, production project post-evaluation, relocation project management and production contractor management; environmental risk management includes operation environment visualization management, operation environment management, electricity-related public safety management and disaster prevention and reduction management; operational risk control includes operation plan, operation standard management, operation qualification and operation supervision.

10. The method for optimizing the business architecture of a power supply station based on the achievements of digital power grid construction according to claim 3, characterized in that: In step 3.3, resource management includes operational resource management, production team management, and digital resource management. Operational resource management includes tool management, production service vehicle management, emergency equipment management, and economic operation management. Production team management includes team building, team evaluation, production personnel training, core skills management, core business management, production organization model management, and production management evaluation. Digital resource management includes production data management, digital infrastructure management, algorithm and model management, knowledge base management, application platform management, and digital architecture management. In step 3.4, technical management includes operation and maintenance management, new product and technology access management, technical standards management, equipment operation analysis, and technical supervision management. Among them, operation and maintenance management includes equipment category optimization management, equipment and Internet of Things standardization management, and equipment model review management; new product and technology access management includes the first set of major technical equipment assessment, new technology and new product network access management, and green and low-carbon management; technical standards management includes technical standard formulation management and technical standards system management; equipment operation analysis includes equipment delivery quality evaluation; technical supervision management includes countermeasure management and technical standards system management.

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